# Rail Multi Vehicle Scheduling Method for Intermediate Depot of Steel Plant Based on Improved Genetic Algorithm

Authors: Bo Fang; Ming Feng; Cheng Qiu; Ximing Zhang
Journal: Scientific Programming
 (2022)

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## Abstract

Appropriate transportation network plays a significant role in the economic development of country. The rising transport demand increases the congestion in railway networks, and thus, they become more interdependent and more complex to operate. A ground railway system is established in the reservoir area of steel plants to ensure smooth running of multi vehicle logistics transportation. In recent years, the development of the ground rail multi vehicle logistics system in the reservoir area of steel plants has been increased. However, such logistic systems need an intelligent dispatching control system to avoid the possibility of safety hazards, to follow optimal tracks and to effectively manage logistics operation. In this paper, an improved hybrid genetic algorithm is proposed to realize the decision making and control of multi vehicle scheduling. Task assignment to multiple vehicles and multi-stage subparent vehicle scheduling is performed based on the information obtained from the concerned subsection. The self-learning hybrid algorithm works on the data extracted from an improved population and suggests an optimized solution. The individual behavior and optimization process are updated by self-learning, which ensures the effectiveness of iterative evolution. The selection, cross mutation, and self-learning expert base operation methods of the hybrid genetic algorithm are optimized. The proposed system is evaluated by taking the multi vehicle logistics system of cold rolling intermediate steel depot as a case study. The improved algorithm is implemented in MATLAB and is compared with the traditional genetic and particle swarm optimization algorithms. Results of the analysis prove that the hybrid genetic algorithm of self-learning knowledge expert base is effective in solving the multi vehicle logistics scheduling optimization problem. With the inclusion of the self-learning knowledge expert base genetic algorithm, the convergence trend of the proposed algorithm is enhanced; the traditional genetic algorithm converges after 140 iterations, while in the proposed algorithm, it is reduced to 100 iterations. The evaluation reveals that the proposed algorithm is speedier than the traditional genetic algorithm (GA) and particle swam optimization (PSO); the average solution time of the proposed algorithm is 119.1 while that of GA, PSO is 137.4, 131.4, respectively. The proposed algorithm is applicable to improve the operation and efficiency of the logistics transportation system. The approach is beneficial in intelligent control of metallurgical production. The proposed algorithm has practical significance to be followed in the development of intelligent metallurgical production and logistics transportation.


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## Body

## 1. Introduction

With the rise and implementation of “industry 4.0,” in recent years, focus has been shifted to the domestic iron and steel manufacturing industry. Especially, the important development direction of iron and steel logistics mentioned in “made in China 2025” is as follows: intelligent logistics, networked logistics, Internet of things + iron, and steel logistics. As a typical production logistics system in the middle reservoir area of the steel plant, the overhead crane transportation system has always had the disadvantages of high-altitude and heavy-duty hoisting for many years. Under the concept of green intelligence, a safer ground rail type multi vehicle network transportation system has gradually emerged. The system solves the shortcomings and potential safety hazards of overhead crane operation. Moreover, such systems are safer and efficient. It can effectively improve the productivity of workshop production logistics in modern iron and steel enterprises. However, at present, there is a cross relationship of multi -mode, multi -path, multi equipment, and multi disturbance in the multi vehicle transportation process section of the middle reservoir area of the steel plant. Its logistics control is no longer a fixed programmed repetitive control but needs to carry out intelligent dispatching control with the help of a dispatching decision-making system with the independent intelligent algorithm [1] The algorithm is required to meet the needs of modern steel production logistics operation under different process conditions and complex working conditions. Therefore, it can be said that the multi vehicle logistics scheduling algorithm in the intermediate warehouse of steel plant is the brain and soul of the whole intermediate warehouse transportation system. As it is also a kind of problem to be solved urgently in the control optimization of the multi vehicle logistics system in the intermediate warehouse of steel plant [2], it is becoming a core research area. In this paper, an intelligent scheduling algorithm of rail multi vehicle logistics system in the intermediate warehouse of metallurgical steel plant is studied and explored. The characteristics of the multi vehicle scheduling problem in the intermediate warehouse of steel plant in accordance with the basic parameters of hybrid flow shop scheduling model are studied. Therefore, the multi vehicle scheduling problem in intermediate warehouse is actually an NP problem of hybrid flow shop scheduling.This research study proposes an expert knowledge-based system with self-learning mechanism for effective scheduling of the multi-stage child parent vehicle logistics system. Intelligent scheduling is performed on the basis of the improved genetic algorithm. A segmented coding method is designed for multi vehicle task allocation. The genetic algorithm is enhanced by improved population and optimized compromise solution function. With the self-learning feature, the compromise solution of expert base is updated to ensure effectiveness of the iterative evolution. The proposed approach is applicable in the construction of green workshop, parallel flow shop scheduling, and workload management.


## 2. Literature Review

In intelligent systems, scheduling problem is usually solved by the genetic algorithms [3, 4]. Some researchers preferred to follow flow shop scheduling [5]. A heuristic algorithm is proposed in [6] for parallel flow shop scheduling. The algorithm proposed in [7] considers human error along with preventive maintenance in an open shop scheduling problem. Nouri et al. tried to establish a hybrid heuristic multi-objective model to solve the job shop scheduling problem [8]. Lei and Zhang analyzed the multi machine multi request AGV system from the perspective of flexible manufacturing system and proposed a scheduling model based on the hybrid genetic algorithm with minimum path and highest priority [9]. Dong et al. tried to establish a mathematical model of flexible production job scheduling with cache constraints, namely, AGV job integration [10]. Zhang et al. used the improved genetic algorithm to solve the time window logistics scheduling problem of semiclosed flow shop [11]. Zhang et al. established a modular improved genetic algorithm in a mobile robot system to deal with the robot mobile cooperative allocation and scheduling problem [12]. Mousavi et al. studied the multi-objective algorithm based on self-learning and time window to solve the hybrid flow shop scheduling problem [13]. Kim and Lee studied multi-objective flow shop scheduling with superimposed waiting time constraints [14].Most of the methods discussed suffer from four integral issues: (1) low search convergence speed, (2) poor compromise solution, (3) local optimization instead of global optimization, and (4) fixed mutation probability. In order to overcome these issues in optimization and scheduling, this study presents an intelligent self-learning-based system with compromise solution function.


## 3. Methodology

In the current era of economic globalization, a manufacturing industry, particularly steel manufacturing industry, is facing exceptional competition. To increase productivity and to ensure smooth running of multi vehicle logistics transportation, the ground railway system is established in the reservoir area of steel plants. However, an intelligent real-time dispatching control system is required to avoid the possibility of safety hazards and to optimally manage multi vehicle railway tracks and to avoid congestion over the tracks. In recent years, with the wide use of computer software programs, optimization and scheduling problems are performed by intelligent algorithms. Some of the widely used algorithms are the ant colony algorithm, genetic algorithm, simulated annealing algorithm, tabu search algorithm, neural network algorithm, and particle swarm optimization algorithm [15]. The hybrid or improved algorithms are generated by the combination of two or more of the algorithms. These algorithms combine the advantages of three mainstream algorithms: intelligent algorithm, accurate algorithm, and heuristic algorithm [16]. The heuristic intelligent algorithm is used to solve the multi vehicle transportation flow scheduling model iteratively, which can better overcome the local optimization and obtain a global optimal solution. Therefore, the algorithm can overcome the problem of solving different types of flow scheduling models.There are various types of intermediate warehouses in the production enterprise, which are referred to as the intermediate warehouse for short. The coil logistics of cold rolling intermediate warehouse is also synchronized with the production process. It is a real-time dynamic logistics activity from the export of cold rolling main line equipment to the redistribution of coil storage and transportation scheduling to downstream rewinding equipment and packaging equipment [17]. The hybrid flow shop scheduling (FJSSP) model is based on the combination of multiple subvehicles and parent vehicles in the ground multi vehicle logistics system in the middle reservoir area of the steel plant. It is necessary to consider not only the differentiation and multi batch of task allocation of subvehicles and parent vehicles but also the adaptability and solution steps of the model algorithm for adjustment according to the constraints formulated by the operation rules of the whole logistics transportation system [18, 19]. Therefore, a model should be designed according to the characteristics of the system, the working schedule, and the boundary agreement conditions of multi vehicle logistics network system in the reservoir area of steel plant. The model should reflect multi vehicle, multi-stage scheduling information besides the other rules. A scheduling algorithm satisfying these conditions will be suitable and need to be designed. Furthermore, a model can be further enhanced by the integration of the classical heuristic intelligent algorithm [20].With the advantages of simple operation and easy implementation, the genetic algorithm is not only applied to mathematical decision making such as multi-modal combinatorial function and multi-objective problem optimization but also widely used in the application level, such as machine learning, production scheduling, image recognition, and data mining [16]. Aiming at the ground multi-vehicle logistics transportation scheduling of steel plant intermediate warehouse, which is a flexible flow shop iterative principle model, the standard and structure of the genetic algorithm are used as the technical support to optimize the design and working of the scheduling model. Using the genetic algorithm in a model has the following advantages in solving the flow shop scheduling problem:(i)
Genetic algorithm does not have much mathematical structure requirements for the optimization problem. Because it has evolutionary attributes, it is not necessary to define the internal attributes of the problem in the search process. Therefore, it can solve the problem of discrete or continuous objective function obeying all the conditions and constraints.(ii)
Genetic algorithm takes the value of objective function as the search convergence condition rather than using the derivation of objective function. This simplifies the search direction and range of the algorithm and improves the efficiency of the algorithm. When dealing with complex problems, its operator has good scalability and is easy to combine with other complementary algorithms to produce a hybrid algorithm with higher fitness.(iii)
Genetic algorithm can provide a more flexible structure combination for various application problems and construct a heuristic process with different constraints so as to ensure the effectiveness of the algorithm.Owing to the above-stated reasons, the genetic algorithm is selected for the proposed research work. The algorithm is optimized by adding expert knowledge base and self-learning functionality to improve the accuracy and response speed of the algorithm and make up for the lack of convergence of the algorithm [21].


## 4. Optimization Design Based on Genetic Algorithm

Although the genetic algorithm has its advantages when it is widely used in various optimization problems, it has its own shortcomings. This paper attempts to optimize the scheduling algorithm from three aspects: convergence range, fitness selection, and population iteration. By that way, efficiency and accuracy of the algorithm are improved.


### 4.1. Search Scope Convergence Optimization

For the optimization algorithm to solve the logistics transportation scheduling problem in this paper, the coding content and expression method of the scheduling algorithm used are on the basis of search and optimization of the algorithm. The population individuals of the optimization problem must determine the search range of all individuals again during each iteration, which determines the accuracy and search efficiency of individual solutions to a certain extent [22]. According to the multi vehicle logistics, a scheduling model is established according to the flow shop operation principle. For the multi vehicle task allocation of single or joint operation and for the allocation and selection of child and parent vehicles in different operation stages, we try to use discrete integer piecewise coding. This is to improve directed search, accuracy, and efficiency of the whole iterative process and speed up the search convergence as well. The model appropriately solves the problem of slow convergence and local convergence of the traditional genetic algorithm. The coding form of the optimal scheduling algorithm is kept as discrete integer coding to represent the scheduling characteristics of the system model in segments. Specific operation examples are given as follows:The operation status and task content of all child vehicles and parent vehicles are encoded in the logistics transportation system to form the chromosome of each individual. All genes are expressed by two digit integers. The coding form is as follows:(1)X11W11Y11Z11,X21W21Y21Z21…XK1WK1YK1ZK1…XN1WN1YN1ZM1,X12W12Y12Z12,X22W22Y22Z22…XK2WK2YK2ZK2…XN2WN2YN2ZM2,…XKJWKJYKJZKJ….The code includes the system job task type and execution sequence, the task allocation of child and parent vehicles, and the scheduling information of child and parent vehicles in each stage. WhereinXK1 represents the target roadway address of the parent vehicle K for the coil storage task in the first scheduling batch, WK1 represents the cargo location saddle address of the subvehicle K in the first scheduling batch for unloading the steel coil in the buffer zone channel, and YK1 represents the starting roadway address of the parent vehicle K for the coil taking task in the first scheduling batch. ZK1 represents the location saddle address of the first batch of neutron truck K loading steel coils in the buffer zone channel. XK2 represents the target roadway address of the volume storage task executed in the second scheduling batch. YK2 represents the starting roadway address of the volume retrieval task executed in the second scheduling batch. There are j batches of scheduling, and so on. An example of its coding chromosome gene elements is shown in Table 1.Table 1
Example of chromosome gene elements encoded by scheduling model.
Storage task parent vehicle part (X)Storage task subvehicle part (W)Take task parent vehicle part (Y)Take task subvehicle part (Z)1169127121416……………………For example, the coding chromosomes are as follows: 11, 6, 9, 12; 7, 12, 14, 16; 0, 0, 0, 0. It means that the subparent car 1 is assigned to perform the joint operation task. First, it goes to the saddle 6 of roadway 11 for coil storage, and then goes to the saddle 12 of roadway 9 for coil taking. The subparent car 2 is assigned to perform the coil storage of saddle 12 of roadway 7. The coil taking operation is on saddle 16 of roadway 14, and the values of parent car 3 andX, W, Y, and Z are zero. It means that the volume access task is not assigned. It is in the waiting state; that is, the first batch scheduling is completed. In order to ensure the uniqueness of the system code, the number of main channel lanes is sorted from 1 according to the logistics trend from the main entrance, that is, 1 ≤ XKJ ≤ the total number of lane and 1 ≤ YKJ ≤ the total number of lane. The storage saddle of the buffer channel is sorted from 1 in the lowest direction of the logistics area, and 1 ≤ WN1 ≤ the largest saddle number; 1 ≤ ZM1 ≤ the maximum saddle number; each lane and cargo space saddle address is unique.


### 4.2. Initial Population Generation by Introducing Knowledge Expert Base

The initial population generation of the traditional genetic algorithm is random. When applied to practical engineering, due to the increase of uncertain factors and constraints, the population may fall into local optimization lose. Thus, the optimal solution or premature search will suffer from unsatisfactory diversity. Adopting a random generation method will also produce more unavailable solutions, resulting in algorithm failure. In such condition, the method will be unable to generate effective scheduling solutions.Therefore, in this study, a knowledge expert base system is introduced to construct the initialization population by using the empirical value, to establish a self-learning mechanism, and to store in the knowledge expert base of the optimal solution generated by the last iteration. In the proposed method, some chromosomes from the expert base will be called to generate initialization population in a scheduling optimization iteration. By this way, global scale and diversity in the initial population are ensured. To improve the efficiency of evolution, several subpopulations exchange the best individuals and evolve synchronously. The initial population size is generally 10 to 100. For the same initial population size of steel coil conveying scheduling in the intermediate warehouse of the steel plant, 30 chromosomes are generated according to the mentioned coding format and operation tasks to accomplish the scheduling cycle. The specific method of forming each chromosome is as follows:We assume that the volume storage task isA1, A2, …, AK, AK+J, …We fetch that the coil tasks areB1, B2, …, BK, BK+J, …Then, the volume storage operation will be randomly assignedAK to X11, W11 and AK+J to X21, W21; The coiling operation will be randomly assigned BK to Y11, Z11 and BK+J to Y21, Z21. And so on, until the chromosome of a scheduling cycle population is formed.


### 4.3. Cross Mutation with Adaptive Function

In the traditional genetic algorithm, the crossover probabilityPC and mutation probability PM are generally fixed values. Therefore, the iterative search of the algorithm is easy to produce fatigue premature solution, resulting in local optimization rather than global optimization [17]. In this paper, the dynamic operation of changing the values of crossover probability PC and mutation probability PM with fitness is adopted to improve the responsiveness and convergence of the scheduling algorithm, model evolution, and diversity of population. The calculation formulas of crossover probability PC and mutation probability PM are as follows:WhenF1 ≥ FA, crossover probability is(2)PC=PCMAX−PCMAX−PCMIN⋅F1−FAFM−FA.WhenFA ≥ F1, crossover probability is(3)PC=PCMAX.WhenF2 ≥ FA, variation probability is(4)PM=PMMAX−PMMAX−PMMIN⋅F2−FAFM−FA.WhenFA ≥ F2, variation probability is(5)PM=PMMAX.In the above formula,Pcmax is the maximum fixed crossover probability and Pcmin is the minimum crossover probability, while Pmmax is the maximum fixed mutation probability and Pmmin is the minimum mutation probability, and FM is the maximum fitness function value of the population. FA is the fitness function value of the no generation population, and F1 is the larger fitness function value of the two individuals to be crossed. F2 is the fitness function value of the individual to be mutated.It can be seen from the above formula that when the fitness value of an individual is lower than the average fitness value, the values ofPC and PM are the largest. It is ensured that bad individuals can cross and mutate as much as possible. When the fitness value of individuals is higher than the average fitness value, the values of PC and PM are lower, and the best individuals are retained into the next generation without crossover and variation. When the difference of individual fitness values is small and tends to be consistent, the values of PC and PM tend to increase. When the difference of individual fitness values is large and tends to be scattered, the values of PC and PM tend to decrease so that the algorithm search has convergence characteristics and can ensure the diversity of the population.The crossover operator performs the random multipoint crossover operation of adaptive dynamic crossover probabilityPC for the population individual genes of the coil storage task and the coil retrieval task, that is, XKJ and XK+IJ exchanging positions with each other, WKJ and WK+IJ exchanging positions with each other, YKJ and YK+IJ exchanging positions with each other, and ZKJ and ZK+IJ exchanging positions with each other. If XKJ = YKJ = 0, this chromosome gene is invalid. Accessing coil task can be randomly selected from the current set of scheduling tasks to generate new chromosome genes.Crossover operation only ensures that iterative evolution maintains the excellent genes. It is the exchange of the combination order of the original solution set. There are still a few genes, which cannot jump out of the local optimization. Mutation operation is more conducive to the algorithm to find the global optimal solution. Therefore, the mutation operator is used to carry out the random single point mutation operation of adaptive dynamic mutation probabilityPM for the individual genes of the inventory operation task and the take-up operation task. The mutation probability PM is used to change a gene value of an individual chromosome of one population to the value of the allele of an individual chromosome in another population. For XKJ and XKJ+I, WKJ and WKJ+I, YKJ and YKJ+I, ZKJ and ZKJ+I mutation and replacement of the alleles with population individuals in different scheduling batches to ensure the globality and diversity of the algorithm.


### 4.4. Fuzzy Selection of Fitness Function of Compromise Solution

Fitness is a direct index to judge the individual quality of the population in the iterative process of the algorithm. In view of the disadvantage, the genetic algorithm has a large burden and is not easy to get out of trouble and jump out of the local when calculating the fitness of the model with a certain complexity. The logistics transportation scheduling model mainly solves the multi-objective optimization problem with the minimum transportation operation time and the minimum congestion delay. The compromise method is used to solve the fuzzy problem. Its advantage is that it has high computational efficiency in the evolution process and can quickly approach to the solution goal. It is not easy to fall into local optimization and improves the search ability of the algorithm. At the same time, the effectiveness of the fitness function will directly determine the search direction and evolution results of the genetic algorithm. In practical engineering applications, it is usually difficult to attain an ideal solution [23]. In this paper, the compromise method is used to construct the fitness function for fuzzy solution, which can well replace the ideal solution to evaluate the best solution. The specific method of compromise solution is to standardize the mean value with the variance of population characteristics based on the comparison of mean value and variance. Next comparison among different classes is performed. If the mean value deviates far, the weight of this population increases. If the deviation is very small, the weight of this population decreases. Its advantage is that in the process of evolution, the calculation efficiency of fitness function is high, it can quickly approach the solution goal, it is not easy to fall into local optimization, and the search ability of the algorithm is improved. Specific operation examples are as follows:If the population number isS and the characteristic number of a chromosome individual is F, the importance index of characteristic R to each kind of population can be obtained. The characteristic importance index matrix R is expressed as follows:(6)R=R11R12⋯R1FR21R22⋯R2F⋅⋅⋅⋅RS1RS2⋯RSF.Among them, feature importance is(7)R=A−BT.The characteristic variance is(8)T=Ax1+Bx2.Let functionsA and B be the set of solutions of two different dimensional characteristics of the logistics scheduling system. A is the optimization objective function of subparent vehicle operation rule scheduling of the intermediate warehouse logistics system, B is the total conflict time of congestion delay of the logistics scheduling system, and X1 and X2 are the corresponding number of samples, respectively.The average value of the feature importance index can be calculated from the feature importance matrixR:(9)RA=∑i=1S∑j=1FRijS∗F.The sum ofRS is greater than the average value RA of characteristic importance index:(10)RS=∑i=1S∑j=1FRij,Rij≥RA.The fitness function established is(11)K=RS∗RA2.In the above formula, the average value of characteristic importance RA is treated by power function to increase the analogy degree of fitness function value, which can better reflect that the greater the average value of population characteristic importance index, the stronger the analogy degree of selected characteristics. Meanwhile, the sum of the characteristic importance indices RS above the average value of the features can also reflect the importance of the selected features. Therefore, the fitness function K expression constructed by a compromise solution based on the mean variance ratio can well compare the ability to distinguish individual characteristics of a population.


### 4.5. Establishment of Self-Learning Knowledge Expert Base by Population Iterative Process

In order to overcome the shortcomings of insensitive local search, premature global search, and loss of optimal solution of the traditional genetic algorithm, the evolutionary iteration mechanism of adaptive learning expert base is established by using the machine learning mechanism in the process of population iteration. The timely self-learning function of knowledge expert base is used to continuously accumulate past experience and update and enrich the knowledge base so as to improve the direction and speed of iterative evolution [24].According to the mechanism of the genetic algorithm, the population individuals generated in the last iterative evolution are optimal, by classifying and storing the individuals generated in the last iteration after the completion of each iteration into the population module of knowledge expert base. In the next iterative calculation, a part of the chromosomes of the optimal individuals is called from the population module as part of the initial individual chromosomes so as to reduce the number of iterative evolution. At the same time, the optimal individuals in the knowledge expert base are synchronously or regularly called for multiple subpopulations. By this way, the speed and optimality of population iterative evolution are improved as a whole [23]. In the multi vehicle logistics scheduling of intermediate depot, the number of subvehicles, parent vehicles, the number and location of storage/uncoiling tasks, operation process constraints, and requirements are basically determined. There are many repetitive scheduling tasks of subvehicles and parent vehicles which are needed to be performed accordingly. Therefore, the solution of each fitness function optimization is stored in the empirical value module of the knowledge expert base. When there are scheduling tasks of the same class, the empirical data in the knowledge expert base are called directly. This saves the time of optimal scheduling. Meanwhile, according to the adaptive learning mechanism, the solution of the fitness function is compared and updated with the same module data in the expert knowledge base to ensure the optimal and up-to-date data of the expert knowledge base.The specific implementation of the self-learning update mechanism is compared with the results generated by each calculation of the fitness function value of the scheduling model. Moreover, comparison is also performed with the population individuals with similar constraints and scheduling requirements in the knowledge expert base of the scheduling system. If the fitness value is better than the fitness value of the existing population individuals in the knowledge expert base, it will be replaced to complete the update of the scheduling strategy data of the expert base [23]. At the same time, in order to ensure the optimal knowledge content structure of the expert base, the search efficiency of knowledge data is effectively improved, and the huge transition of the knowledge database is alleviated. The same type of data information in the knowledge expert base is arranged in descending order according to the system call frequency.The knowledge base is based on the principles of job scheduling and location management. Empirical knowledge about logistics scheduling obtained from experts is stored and updated in the knowledge base. The knowledge expert base is composed of multiple data modules, mainly including the important information generated during the operation of the scheduling system. The information includes the description of the scheduling problem, intermediate results, algorithm solution process, and other information. Some related information such as modifying or adding knowledge as needed and the resulting data changes are also stored so as to ensure the integrity and optimality of the knowledge expert base. The initial constraint rules of the knowledge expert base are based on the scheduling principles of steel coil logistics transportation in the intermediate warehouse and the principles of cargo location management, mainly including the following sections:


#### 4.5.1. Priority

Some tasks are more important than other; therefore, priority be given to a meriting task. While processing the tasks, sorting is performed according to the priorities of the tasks. Precedence should be paid to the task with the highest priority. Moreover, priority should be given to the task that arrives first, regardless of task type, priority, and other factors. This applies to populations with similar tasks or priorities that have little impact on the system.


#### 4.5.2. Processing Time

The completion time of each access volume task is different. First, the task application is processed with short completion time to reduce the occupation of logistics channels. This principle is mainly aimed at populations with small tasks, batches, and multiple types.


#### 4.5.3. Joint Operation

When there are multiple volume saving and volume taking operation tasks, the two operation tasks shall be optimized and combined appropriately. The qualified volume access tasks shall be combined into joint operation to shorten the access cycle and improve the access efficiency [25].


#### 4.5.4. Random Rule

An individual can be randomly extracted from the population queue when there are no requirements for the priority of the incoming and outgoing inventory take-up task, the joint job, and the execution time of the job task. This mainly takes into account the balance and integrity of sampling for multi type task population.


#### 4.5.5. Classification Principle

As far as possible, goods of the same specification shall be evenly distributed in different tunnels for storage. This principle will make all sub cars to run efficiently at the same time. This will also prevent a sub car from being damaged or a roadway from blocking and unable to access volumes, resulting in production logistics blocking [24].


#### 4.5.6. Proximity Principle

The location of steel coil goods with different frequencies and weights in the cache location is also different. According to the proximity principle, when storing coils, the uncoiled location is looked closest to the entrance in the corresponding area. Similarly, the coiled location is looked closest to the exit when taking coils. These proximity principles are followed to minimize the access time and achieve the purpose of improving efficiency.


## 4.1. Search Scope Convergence Optimization

For the optimization algorithm to solve the logistics transportation scheduling problem in this paper, the coding content and expression method of the scheduling algorithm used are on the basis of search and optimization of the algorithm. The population individuals of the optimization problem must determine the search range of all individuals again during each iteration, which determines the accuracy and search efficiency of individual solutions to a certain extent [22]. According to the multi vehicle logistics, a scheduling model is established according to the flow shop operation principle. For the multi vehicle task allocation of single or joint operation and for the allocation and selection of child and parent vehicles in different operation stages, we try to use discrete integer piecewise coding. This is to improve directed search, accuracy, and efficiency of the whole iterative process and speed up the search convergence as well. The model appropriately solves the problem of slow convergence and local convergence of the traditional genetic algorithm. The coding form of the optimal scheduling algorithm is kept as discrete integer coding to represent the scheduling characteristics of the system model in segments. Specific operation examples are given as follows:The operation status and task content of all child vehicles and parent vehicles are encoded in the logistics transportation system to form the chromosome of each individual. All genes are expressed by two digit integers. The coding form is as follows:(1)X11W11Y11Z11,X21W21Y21Z21…XK1WK1YK1ZK1…XN1WN1YN1ZM1,X12W12Y12Z12,X22W22Y22Z22…XK2WK2YK2ZK2…XN2WN2YN2ZM2,…XKJWKJYKJZKJ….The code includes the system job task type and execution sequence, the task allocation of child and parent vehicles, and the scheduling information of child and parent vehicles in each stage. WhereinXK1 represents the target roadway address of the parent vehicle K for the coil storage task in the first scheduling batch, WK1 represents the cargo location saddle address of the subvehicle K in the first scheduling batch for unloading the steel coil in the buffer zone channel, and YK1 represents the starting roadway address of the parent vehicle K for the coil taking task in the first scheduling batch. ZK1 represents the location saddle address of the first batch of neutron truck K loading steel coils in the buffer zone channel. XK2 represents the target roadway address of the volume storage task executed in the second scheduling batch. YK2 represents the starting roadway address of the volume retrieval task executed in the second scheduling batch. There are j batches of scheduling, and so on. An example of its coding chromosome gene elements is shown in Table 1.Table 1
Example of chromosome gene elements encoded by scheduling model.
Storage task parent vehicle part (X)Storage task subvehicle part (W)Take task parent vehicle part (Y)Take task subvehicle part (Z)1169127121416……………………For example, the coding chromosomes are as follows: 11, 6, 9, 12; 7, 12, 14, 16; 0, 0, 0, 0. It means that the subparent car 1 is assigned to perform the joint operation task. First, it goes to the saddle 6 of roadway 11 for coil storage, and then goes to the saddle 12 of roadway 9 for coil taking. The subparent car 2 is assigned to perform the coil storage of saddle 12 of roadway 7. The coil taking operation is on saddle 16 of roadway 14, and the values of parent car 3 andX, W, Y, and Z are zero. It means that the volume access task is not assigned. It is in the waiting state; that is, the first batch scheduling is completed. In order to ensure the uniqueness of the system code, the number of main channel lanes is sorted from 1 according to the logistics trend from the main entrance, that is, 1 ≤ XKJ ≤ the total number of lane and 1 ≤ YKJ ≤ the total number of lane. The storage saddle of the buffer channel is sorted from 1 in the lowest direction of the logistics area, and 1 ≤ WN1 ≤ the largest saddle number; 1 ≤ ZM1 ≤ the maximum saddle number; each lane and cargo space saddle address is unique.


## 4.2. Initial Population Generation by Introducing Knowledge Expert Base

The initial population generation of the traditional genetic algorithm is random. When applied to practical engineering, due to the increase of uncertain factors and constraints, the population may fall into local optimization lose. Thus, the optimal solution or premature search will suffer from unsatisfactory diversity. Adopting a random generation method will also produce more unavailable solutions, resulting in algorithm failure. In such condition, the method will be unable to generate effective scheduling solutions.Therefore, in this study, a knowledge expert base system is introduced to construct the initialization population by using the empirical value, to establish a self-learning mechanism, and to store in the knowledge expert base of the optimal solution generated by the last iteration. In the proposed method, some chromosomes from the expert base will be called to generate initialization population in a scheduling optimization iteration. By this way, global scale and diversity in the initial population are ensured. To improve the efficiency of evolution, several subpopulations exchange the best individuals and evolve synchronously. The initial population size is generally 10 to 100. For the same initial population size of steel coil conveying scheduling in the intermediate warehouse of the steel plant, 30 chromosomes are generated according to the mentioned coding format and operation tasks to accomplish the scheduling cycle. The specific method of forming each chromosome is as follows:We assume that the volume storage task isA1, A2, …, AK, AK+J, …We fetch that the coil tasks areB1, B2, …, BK, BK+J, …Then, the volume storage operation will be randomly assignedAK to X11, W11 and AK+J to X21, W21; The coiling operation will be randomly assigned BK to Y11, Z11 and BK+J to Y21, Z21. And so on, until the chromosome of a scheduling cycle population is formed.


## 4.3. Cross Mutation with Adaptive Function

In the traditional genetic algorithm, the crossover probabilityPC and mutation probability PM are generally fixed values. Therefore, the iterative search of the algorithm is easy to produce fatigue premature solution, resulting in local optimization rather than global optimization [17]. In this paper, the dynamic operation of changing the values of crossover probability PC and mutation probability PM with fitness is adopted to improve the responsiveness and convergence of the scheduling algorithm, model evolution, and diversity of population. The calculation formulas of crossover probability PC and mutation probability PM are as follows:WhenF1 ≥ FA, crossover probability is(2)PC=PCMAX−PCMAX−PCMIN⋅F1−FAFM−FA.WhenFA ≥ F1, crossover probability is(3)PC=PCMAX.WhenF2 ≥ FA, variation probability is(4)PM=PMMAX−PMMAX−PMMIN⋅F2−FAFM−FA.WhenFA ≥ F2, variation probability is(5)PM=PMMAX.In the above formula,Pcmax is the maximum fixed crossover probability and Pcmin is the minimum crossover probability, while Pmmax is the maximum fixed mutation probability and Pmmin is the minimum mutation probability, and FM is the maximum fitness function value of the population. FA is the fitness function value of the no generation population, and F1 is the larger fitness function value of the two individuals to be crossed. F2 is the fitness function value of the individual to be mutated.It can be seen from the above formula that when the fitness value of an individual is lower than the average fitness value, the values ofPC and PM are the largest. It is ensured that bad individuals can cross and mutate as much as possible. When the fitness value of individuals is higher than the average fitness value, the values of PC and PM are lower, and the best individuals are retained into the next generation without crossover and variation. When the difference of individual fitness values is small and tends to be consistent, the values of PC and PM tend to increase. When the difference of individual fitness values is large and tends to be scattered, the values of PC and PM tend to decrease so that the algorithm search has convergence characteristics and can ensure the diversity of the population.The crossover operator performs the random multipoint crossover operation of adaptive dynamic crossover probabilityPC for the population individual genes of the coil storage task and the coil retrieval task, that is, XKJ and XK+IJ exchanging positions with each other, WKJ and WK+IJ exchanging positions with each other, YKJ and YK+IJ exchanging positions with each other, and ZKJ and ZK+IJ exchanging positions with each other. If XKJ = YKJ = 0, this chromosome gene is invalid. Accessing coil task can be randomly selected from the current set of scheduling tasks to generate new chromosome genes.Crossover operation only ensures that iterative evolution maintains the excellent genes. It is the exchange of the combination order of the original solution set. There are still a few genes, which cannot jump out of the local optimization. Mutation operation is more conducive to the algorithm to find the global optimal solution. Therefore, the mutation operator is used to carry out the random single point mutation operation of adaptive dynamic mutation probabilityPM for the individual genes of the inventory operation task and the take-up operation task. The mutation probability PM is used to change a gene value of an individual chromosome of one population to the value of the allele of an individual chromosome in another population. For XKJ and XKJ+I, WKJ and WKJ+I, YKJ and YKJ+I, ZKJ and ZKJ+I mutation and replacement of the alleles with population individuals in different scheduling batches to ensure the globality and diversity of the algorithm.


## 4.4. Fuzzy Selection of Fitness Function of Compromise Solution

Fitness is a direct index to judge the individual quality of the population in the iterative process of the algorithm. In view of the disadvantage, the genetic algorithm has a large burden and is not easy to get out of trouble and jump out of the local when calculating the fitness of the model with a certain complexity. The logistics transportation scheduling model mainly solves the multi-objective optimization problem with the minimum transportation operation time and the minimum congestion delay. The compromise method is used to solve the fuzzy problem. Its advantage is that it has high computational efficiency in the evolution process and can quickly approach to the solution goal. It is not easy to fall into local optimization and improves the search ability of the algorithm. At the same time, the effectiveness of the fitness function will directly determine the search direction and evolution results of the genetic algorithm. In practical engineering applications, it is usually difficult to attain an ideal solution [23]. In this paper, the compromise method is used to construct the fitness function for fuzzy solution, which can well replace the ideal solution to evaluate the best solution. The specific method of compromise solution is to standardize the mean value with the variance of population characteristics based on the comparison of mean value and variance. Next comparison among different classes is performed. If the mean value deviates far, the weight of this population increases. If the deviation is very small, the weight of this population decreases. Its advantage is that in the process of evolution, the calculation efficiency of fitness function is high, it can quickly approach the solution goal, it is not easy to fall into local optimization, and the search ability of the algorithm is improved. Specific operation examples are as follows:If the population number isS and the characteristic number of a chromosome individual is F, the importance index of characteristic R to each kind of population can be obtained. The characteristic importance index matrix R is expressed as follows:(6)R=R11R12⋯R1FR21R22⋯R2F⋅⋅⋅⋅RS1RS2⋯RSF.Among them, feature importance is(7)R=A−BT.The characteristic variance is(8)T=Ax1+Bx2.Let functionsA and B be the set of solutions of two different dimensional characteristics of the logistics scheduling system. A is the optimization objective function of subparent vehicle operation rule scheduling of the intermediate warehouse logistics system, B is the total conflict time of congestion delay of the logistics scheduling system, and X1 and X2 are the corresponding number of samples, respectively.The average value of the feature importance index can be calculated from the feature importance matrixR:(9)RA=∑i=1S∑j=1FRijS∗F.The sum ofRS is greater than the average value RA of characteristic importance index:(10)RS=∑i=1S∑j=1FRij,Rij≥RA.The fitness function established is(11)K=RS∗RA2.In the above formula, the average value of characteristic importance RA is treated by power function to increase the analogy degree of fitness function value, which can better reflect that the greater the average value of population characteristic importance index, the stronger the analogy degree of selected characteristics. Meanwhile, the sum of the characteristic importance indices RS above the average value of the features can also reflect the importance of the selected features. Therefore, the fitness function K expression constructed by a compromise solution based on the mean variance ratio can well compare the ability to distinguish individual characteristics of a population.


## 4.5. Establishment of Self-Learning Knowledge Expert Base by Population Iterative Process

In order to overcome the shortcomings of insensitive local search, premature global search, and loss of optimal solution of the traditional genetic algorithm, the evolutionary iteration mechanism of adaptive learning expert base is established by using the machine learning mechanism in the process of population iteration. The timely self-learning function of knowledge expert base is used to continuously accumulate past experience and update and enrich the knowledge base so as to improve the direction and speed of iterative evolution [24].According to the mechanism of the genetic algorithm, the population individuals generated in the last iterative evolution are optimal, by classifying and storing the individuals generated in the last iteration after the completion of each iteration into the population module of knowledge expert base. In the next iterative calculation, a part of the chromosomes of the optimal individuals is called from the population module as part of the initial individual chromosomes so as to reduce the number of iterative evolution. At the same time, the optimal individuals in the knowledge expert base are synchronously or regularly called for multiple subpopulations. By this way, the speed and optimality of population iterative evolution are improved as a whole [23]. In the multi vehicle logistics scheduling of intermediate depot, the number of subvehicles, parent vehicles, the number and location of storage/uncoiling tasks, operation process constraints, and requirements are basically determined. There are many repetitive scheduling tasks of subvehicles and parent vehicles which are needed to be performed accordingly. Therefore, the solution of each fitness function optimization is stored in the empirical value module of the knowledge expert base. When there are scheduling tasks of the same class, the empirical data in the knowledge expert base are called directly. This saves the time of optimal scheduling. Meanwhile, according to the adaptive learning mechanism, the solution of the fitness function is compared and updated with the same module data in the expert knowledge base to ensure the optimal and up-to-date data of the expert knowledge base.The specific implementation of the self-learning update mechanism is compared with the results generated by each calculation of the fitness function value of the scheduling model. Moreover, comparison is also performed with the population individuals with similar constraints and scheduling requirements in the knowledge expert base of the scheduling system. If the fitness value is better than the fitness value of the existing population individuals in the knowledge expert base, it will be replaced to complete the update of the scheduling strategy data of the expert base [23]. At the same time, in order to ensure the optimal knowledge content structure of the expert base, the search efficiency of knowledge data is effectively improved, and the huge transition of the knowledge database is alleviated. The same type of data information in the knowledge expert base is arranged in descending order according to the system call frequency.The knowledge base is based on the principles of job scheduling and location management. Empirical knowledge about logistics scheduling obtained from experts is stored and updated in the knowledge base. The knowledge expert base is composed of multiple data modules, mainly including the important information generated during the operation of the scheduling system. The information includes the description of the scheduling problem, intermediate results, algorithm solution process, and other information. Some related information such as modifying or adding knowledge as needed and the resulting data changes are also stored so as to ensure the integrity and optimality of the knowledge expert base. The initial constraint rules of the knowledge expert base are based on the scheduling principles of steel coil logistics transportation in the intermediate warehouse and the principles of cargo location management, mainly including the following sections:


### 4.5.1. Priority

Some tasks are more important than other; therefore, priority be given to a meriting task. While processing the tasks, sorting is performed according to the priorities of the tasks. Precedence should be paid to the task with the highest priority. Moreover, priority should be given to the task that arrives first, regardless of task type, priority, and other factors. This applies to populations with similar tasks or priorities that have little impact on the system.


### 4.5.2. Processing Time

The completion time of each access volume task is different. First, the task application is processed with short completion time to reduce the occupation of logistics channels. This principle is mainly aimed at populations with small tasks, batches, and multiple types.


### 4.5.3. Joint Operation

When there are multiple volume saving and volume taking operation tasks, the two operation tasks shall be optimized and combined appropriately. The qualified volume access tasks shall be combined into joint operation to shorten the access cycle and improve the access efficiency [25].


### 4.5.4. Random Rule

An individual can be randomly extracted from the population queue when there are no requirements for the priority of the incoming and outgoing inventory take-up task, the joint job, and the execution time of the job task. This mainly takes into account the balance and integrity of sampling for multi type task population.


### 4.5.5. Classification Principle

As far as possible, goods of the same specification shall be evenly distributed in different tunnels for storage. This principle will make all sub cars to run efficiently at the same time. This will also prevent a sub car from being damaged or a roadway from blocking and unable to access volumes, resulting in production logistics blocking [24].


### 4.5.6. Proximity Principle

The location of steel coil goods with different frequencies and weights in the cache location is also different. According to the proximity principle, when storing coils, the uncoiled location is looked closest to the entrance in the corresponding area. Similarly, the coiled location is looked closest to the exit when taking coils. These proximity principles are followed to minimize the access time and achieve the purpose of improving efficiency.


## 4.5.1. Priority

Some tasks are more important than other; therefore, priority be given to a meriting task. While processing the tasks, sorting is performed according to the priorities of the tasks. Precedence should be paid to the task with the highest priority. Moreover, priority should be given to the task that arrives first, regardless of task type, priority, and other factors. This applies to populations with similar tasks or priorities that have little impact on the system.


## 4.5.2. Processing Time

The completion time of each access volume task is different. First, the task application is processed with short completion time to reduce the occupation of logistics channels. This principle is mainly aimed at populations with small tasks, batches, and multiple types.


## 4.5.3. Joint Operation

When there are multiple volume saving and volume taking operation tasks, the two operation tasks shall be optimized and combined appropriately. The qualified volume access tasks shall be combined into joint operation to shorten the access cycle and improve the access efficiency [25].


## 4.5.4. Random Rule

An individual can be randomly extracted from the population queue when there are no requirements for the priority of the incoming and outgoing inventory take-up task, the joint job, and the execution time of the job task. This mainly takes into account the balance and integrity of sampling for multi type task population.


## 4.5.5. Classification Principle

As far as possible, goods of the same specification shall be evenly distributed in different tunnels for storage. This principle will make all sub cars to run efficiently at the same time. This will also prevent a sub car from being damaged or a roadway from blocking and unable to access volumes, resulting in production logistics blocking [24].


## 4.5.6. Proximity Principle

The location of steel coil goods with different frequencies and weights in the cache location is also different. According to the proximity principle, when storing coils, the uncoiled location is looked closest to the entrance in the corresponding area. Similarly, the coiled location is looked closest to the exit when taking coils. These proximity principles are followed to minimize the access time and achieve the purpose of improving efficiency.


## 5. Operation Flow of Scheduling Algorithm

After completing the above optimization design of the key links of the scheduling model algorithm of the intermediate warehouse logistics transportation system, based on the static expert system, knowledge base and dynamic optimization algorithm are collectively utilized to summarize the key technologies such as real-time event scheduling, sudden knowledge, and logistics scheduling system based on event scheduling. The logistics scheduling algorithm is based on the unit scheduling rules and the stacking rules of the warehouse yard. By triggering the scheduling by on-site events, a logistics scheduling instructions will be automatically generated. Similarly,reservation warehouse and execution command are generated [26, 27]. The main execution steps of the improved genetic algorithm with self-learning mechanism of knowledge expert base are as follows:Step 1: Search and compare the compromise solution in the data module of the knowledge expert base according to the real-time operation status and access volume task information and real-time working conditions of the subparent vehicle of the intermediate warehouse logistics transportation system. If a compromise solution matching the actual situation is found in the database, the current work task will directly call the knowledge expert base to output the corresponding compromise solution.Step 2: Select a part of chromosomes from the same type of population individuals in the knowledge expert base to generate a part of the initial population, and then select a part of chromosomes from the random population to generate other parts of the initial population.Step 3: Encode the chromosome of the initial population according to the coding form of the scheduling algorithm, and bring the individual coding of the initial population into the feature importanceR and feature variance T for sorting and calculation.Step 4: Calculate the fitness value according to the fitness functionK = RS ∗ RA2 of the compromise solution, sort the solutions from small to large, and find the optimal solution individual. Finally, judge whether the convergence condition of evolutionary iteration is satisfied. If so, directly jump to Step 10.Step 5: Through the combination of selection operator, classical roulette, and elite strategy, select the best individual in the population and copy it to other subpopulations, replace the worst individual in the current population with the best individual in the previous generation population to retain the best individual, and copy the best individual from other populations at the same time.Step 6: According to the previously designed adaptive crossover probabilityPC formula, the crossover operator is operated on the population individuals to make the algorithm evolve to obtain wide and diverse optimal individuals.Step 7: Through the previously designed adaptive crossover probabilityPM formula, the mutation operator is operated on the population individuals to prevent falling into the local optimization of iterative evolution, retain the best individuals, and update the population data module of the knowledge expert base.Step 8: When the number of iteration cycles of the algorithm reaches the preset population evolution algebra, the operation of the algorithm is terminated and the optimal calculation result is output. Otherwise, return to Step 3 and continue to repeat the operation.Step 9: Store the newly generated population compromise solution in the knowledge expert base data module, update the knowledge data, and output the final compromise solution.Step 10: According to the output result of the compromise solution, schedule multiple vehicles of the logistics system to cooperate to access the volume.


## 6. Case Solving and Verification

This paper takes the ground multi vehicle logistics transportation system in the middle cold rolling reservoir area of a steel plant as an example. In order to more intuitively and effectively simulate and analyze the improved genetic algorithm based on knowledge expert base self-learning, the scale of the algorithm research object is simplified. The intermediate warehouse logistics system with four main channels and five buffer areas is simplified to the research object of multi child vehicle, the parent vehicle scheduling algorithm with two main channels and two buffer areas. In this system, each main channel is equipped with 3 parent cars and each buffer area is equipped with 4 sub cars. As a whole, there are 6 parent cars and 8 sub cars. There is one I/O port at both ends of each main channel; that is, there are four I/O ports in total. The number of cache lanes of the system is 36, and each cache lane has 12 cargo space saddles. The speed of the sub car and the parent car is 1m/s, and the influence of acceleration is ignored.According to the main hardware parameters of the algorithm research object, as defined above, address and code the relative position relationship of each logistics hardware of the system. Address location information of cache location saddle includes cache area, roadway number, and location saddle number. The location of each access volume location saddle can be uniquely determined. The location saddle coding format is (lane number, saddle number). Cache sub car runs vertically on the cache channel, and its parking position is consistent with the cache storage saddle, so the location coding format of sub car is also (lane number, saddle number). The horizontal operation parking position of the parent vehicle is at the interface position of the intersection of the main channel and the cache roadway, so the location information of the parent vehicle in the system is channel number and roadway number, and its location coding format is (channel number, roadway number). The I/O interface is the starting point of the volume saving task and the end point of the volume taking task, respectively. The addressing coding format of the location information of I/O interface is (I/O, ID number). In the verification example, the system has 36 columns (from 1 to 36) and 24 rows (from 1 to 24), and there is an I/O inlet and outlet at both ends of the two main channels.Figure1 shows the intermediate library logistics dispatch algorithm and the instance object local sample diagram. The local area of the algorithm validation instance object is defined and labeled. The horizontal direction of the area range is from lane number 6 to 21, and the vertical direction is from cached saddle number 4 to 18. It contains the logistics parameter characteristics of all the algorithm instance objects. From the diagram, it can be seen that the addressing location of each cached location saddle is composed of unique lane number and cache number, which guarantees the uniqueness of the address coding of the shelf saddle and the consistency with the coding of the scheduling model algorithm. In order to meet the needs of transporting goods across the channel, temporary cache channels are reserved in the cache area according to the needs of scheduling, and no coils are stored in all the shelf saddles in the channel, which ensures that the cached sub car can be loaded. Coils are transported through a caching channel; goods coils with different specifications and different branches are stored centrally in a caching channel according to the scheduling model location management principle. For each caching channel in each caching area, 12 slot saddles are divided into upper and lower parts and 6 slot saddles are accessed from the main channel next to each other. It is ensured that all cargo coil caching access entries and exits are always open for executable tasks.Figure 1
Partial example of intermediate warehouse logistics scheduling algorithm object.In order to verify the effectiveness of the self-learning knowledge expert base genetic algorithm proposed in this paper for solving the steel coil conveying scheduling, the performance of the same batch of tasks is compared with the traditional genetic algorithm and particle swarm optimization algorithm. The randomly generated set of access task queues is given as(12)T=9,16,0,0;10,1,0,0;13,15,0,0;17,14,0,0;0,0,20,4;0,0,11,6;0,0,9,12;0,0,7,12.The initial positions of six parent cars are(13)Cn=9,1;4,1;14,1;8,2;12,2;18,2.The random initial positions of eight sub cars are(14)Cm=3,6;4,8;12,5;17,2;11,12;14,4;6,2;7,10.Three algorithms are used to solve the scheduling model at the same time to find the optimal solution and to minimize the time of task queue. The parameters of the three algorithms are set as follows:The initial population size is 60 and the maximum number of iterations is 150; GA:Pc1 = 0.9, Pc2 = 0.5, Pm1 = 0.1, Pm2 = 0.02; PSO: w = 0.7, C1 = C2 = 2; R1 and R2 are the random numbers from 0 to 1. The GAFS self-learning knowledge expert base genetic algorithm parameters are the same as GA. The three algorithms are simulated in MATLAB according to the above parameters. The examples of the three algorithms in Figure 2 are obtained to solve the average convergence curve.Figure 2
Iterative convergence trend of GA, PSO, and GAFS.It can be seen that the traditional genetic algorithm converges only after 140 iterations, and because of the existence of crossover and mutation factors, the convergence is relatively slow. The convergence of PSO is stepped, which is easy to fall into local convergence and is not easy to obtain the optimal solution. The self-learning knowledge expert base genetic algorithm GAFS tends to converge after 100 iterations. Due to the introduction of the knowledge expert base with self-learning mechanism, it not only improves the search efficiency and diversity but also avoids falling into local optimization. Compared with the particle swarm optimization algorithm, it is easier to jump out of local convergence and get a better solution.The average value, standard deviation, and other data of the optimal search solution of the three algorithms are extracted for statistics. The key performance indexes of the three algorithms are compared. The outcomes are shown in Figure3. It is clear from the figure that under the same population size and iteration times, the three algorithms are effective for solving the multi child vehicle and multi parent vehicle scheduling mathematical model of the intermediate warehouse logistics transportation system. The traditional genetic algorithm GA has slow convergence speed, and the average value of the optimal solution is not ideal. Because particle swarm PSO tends to local optimization, the standard deviation of the solution is large and the volatility of searching the optimal solution is also large. The self-learning knowledge expert base genetic algorithm GAFS makes up for the accuracy defect of the algorithm and improves the search efficiency by continuously learning, updating, and calling the knowledge data of the expert base in the iteration.Figure 3
Comparison of key performance indexes of three algorithms.With the increase of the number of iterations, the optimal compromise solution is gradually obtained through self-learning, and the system operation reaches the maximum stability. Its search solution is the best 110 s among the three algorithms, the standard deviation of 7.21% is also small, and the later convergence speed is better than the first two algorithms.Based on the above comparison of algorithm performance parameters for example solution, the expert base improved genetic algorithm GAFS with self-learning mechanism proposed in this paper has fast search convergence speed, small standard deviation of compromise solution, and good accuracy. It can search more extensive and diverse optimal solutions than the other two algorithms to avoid falling into local optimization and has good comprehensive performance. Therefore, the mathematical model for multi-objective optimal scheduling is practical and effective, and can provide good algorithm support for the solution of the mixed flow shop scheduling model of multiple sub vehicles and parent vehicles in the intermediate warehouse logistics transportation system.


## 7. Conclusion

According to the scheduling requirements of rail multi vehicle logistics transportation system in cold rolling intermediate depot of steel plant, this paper selects and optimizes the scheduling algorithm. The research study proposes to add an expert database system with self-learning mechanism on the basis of the improved genetic algorithm, specifically designs a segmented coding method for multi vehicle task allocation and multi-stage child parent vehicle scheduling information. An expert base genetic algorithm with improved population and optimized compromise solution function with knowledge self-learning is proposed. For its individual behavior and optimization process, self-learning is used to update the compromise solution of the expert base to ensure the effectiveness of iterative evolution. Finally, an example of the improved scheduling algorithm is given to verify the effectiveness of the optimization algorithm for solving this kind of flexible flow shop logistics scheduling problem. At the same time, it is verified that the algorithm can effectively improve the operation efficiency of the rail multi vehicle coil conveying system in the intermediate warehouse of a steel plant. The approach has practical significance for realizing the artificial intelligent scheduling of logistics links in the intermediate reservoir area in the field of metallurgical production and the construction of green workshop. In parallel flow shop scheduling, unbalanced workload is a critical problem. As our future work, we are planning to utilize the proposed method in solving the imbalance workload and delay time completion problems to achieve a promising optimization.



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*Source: 4971638-2022-04-22.xml*