a review of some methods for ranking fuzzy subsets this paper deals with the problem of ranking n fuzzy subsets of the unit interval a number of methods suggested in the literature is reviewed and tested on a group of selected examples where the fuzzy sets can be nonnormal and or nonconvex the ranking is obtained from i the index of strict preference defined by watson ii three indexes proposed by yager iii the algorithm used by chang iv three versions of the a preference index suggested by adamo v the index defined by baas and kwakernaak vi three modified versions used by baldwin and guild vii the method proposed by kerre viii three forms of the index suggested by jain ix the four grades of dominance studied by dubois and prade in simple cases the results are good for all the methods with some exceptions in questionable cases where the decision must be probably modelled in accordance with the context in which it is imbedded the best indexes seem to be the dominances suggested by dubois and prade these indexes do not force any particular choice but clearly describe the situation hence allowing the decision maker himself to make his best choice

ranking fuzzy numbers with integral value abstract ranking fuzzy numbers is important in decision making since very often the alternatives are evaluated by fuzzy numbers in a vague environment a comparison between these fuzzy numbers is indeed a comparison between alternatives this paper proposes a method of ranking fuzzy numbers with integral value the method which is independent of the type of membership functions used and the normality of the functions can rank more than two fuzzy numbers simultaneously it is relatively simple in computation especially in ranking triangular and trapezoidal fuzzy numbers further an index of optimism is used to reflect the decision maker s optimistic attitude discussion on comparative advantages is included

search with evolutionary ruin and stochastic rebuild a theoretic framework and a case study on exam timetabling this paper presents a state transition based formal framework for a new search method called evolutionary ruin and stochastic recreate which tries to learn and adapt to the changing environments during the search process it improves the performance of the original ruin and recreate principle by embedding an additional phase of evolutionary ruin to mimic the survival of the fittest mechanism within single solutions this method executes a cycle of solution decomposition evolutionary ruin stochastic recreate and solution acceptance until a certain stopping condition is met the solution decomposition phase first uses some problem specific knowledge to decompose a complete solution into its components and assigns a score to each component the evolutionary ruin phase then employs two evolutionary operators namely selection and mutation to destroy a certain fraction of the solution and the next stochastic recreate phase repairs the broken solution last the solution acceptance phase selects a specific strategy to determine the probability of accepting the newly generated solution hence optimisation is achieved by an iterative process of component evaluation solution disruption and stochastic constructive repair from the state transitions point of view this paper presents a probabilistic model and implements a markov chain analysis on some theoretical properties of the approach unlike the theoretical work on genetic algorithm and simulated annealing which are based on state transitions within the space of complete assignments our model is based on state transitions within the space of partial assignments the exam timetabling problems are used to test the performance in solving real world hard problems

remarks on quenching on considere le probleme aux limites parabolique non lineaire suivant u t u f u t 0 x u 0 t 0 n u 0 x u 0 x 0 x on demontre un phenomene de trempe pour des non linearites non convexes dans un espace de dimension arbitraire

a discrete artificial bee colony algorithm for the total flowtime minimization in permutation flow shops obtaining an optimal solution for a permutation flowshop scheduling problem with the total flowtime criterion in a reasonable computational timeframe using traditional approaches and optimization tools has been a challenge this paper presents a discrete artificial bee colony algorithm hybridized with a variant of iterated greedy algorithms to find the permutation that gives the smallest total flowtime iterated greedy algorithms are comprised of local search procedures based on insertion and swap neighborhood structures in the same context we also consider a discrete differential evolution algorithm from our previous work the performance of the proposed algorithms is tested on the well known benchmark suite of taillard the highly effective performance of the discrete artificial bee colony and hybrid differential evolution algorithms is compared against the best performing algorithms from the existing literature in terms of both solution quality and cpu times ultimately 44 out of the 90 best known solutions provided very recently by the best performing estimation of distribution and genetic local search algorithms are further improved by the proposed algorithms with short term searches the solutions known to be the best to date are reported for the benchmark suite of taillard with long term searches as well

cooperative metaheuristics for the permutation flowshop scheduling problem in this work we propose cooperative metaheuristic methods for the permutation flowshop scheduling problem considering two objectives separately total tardiness and makespan we use the island model where each island runs an instance of the algorithm and communications start when the islands have reached certain level of evolution that is communication is not allowed from the beginning of the execution subsequent ones occur when new better solutions are found we carry out an exhaustive comparison of the cooperative methods against the sequential counterparts running in completely comparable scenarios results have been carefully analysed by means of statistical procedures and we can conclude that the cooperative methods yield much better results than the sequential algorithms and state of the art methods running in the same number of processors but without communications the proposed cooperative schemes are easy to apply to other algorithms and problems

the permutation flow shop with buffers a tabu search approach the paper deals with the criteria of makespan minimisation for the permutation flow shop problem with buffers of limited capacity located between successive machines as the problem is np hard an approximation algorithm is proposed which has been designed using a non trivial generalisation of the block elimination properties known for the classic flow shop problem this algorithm is able to achieve excellent results for problems up to 200 jobs and 20 machines as it exploits the structural properties defined in the problem and combines these with a local search technique controlled by a tabu search strategy

non linear elliptic and parabolic equations involving measure data in this paper we prove the existence of solutions for equations of the type div a du f in a bounded open set u 0 on where a is a possibly non linear function satisfying some coerciveness and monotonicity assumptions and f is a bounded measure we also consider the equation div a du g u f in u 0 on with f l1 or f m u u 0 and the parabolic equivalent of the first elliptic equation

a comprehensive review and evaluation of permutation flowshop heuristics in this work we present a review and comparative evaluation of heuristics and metaheuristics for the well known permutation flowshop problem with the makespan criterion a number of reviews and evaluations have already been proposed however the evaluations do not include the latest heuristics available and there is still no comparison of metaheuristics furthermore since no common benchmarks and computing platforms are used the results cannot be generalised we propose a comparison of 25 methods ranging from the classical johnson s algorithm or dispatching rules to the most recent metaheuristics including tabu search simulated annealing genetic algorithms iterated local search and hybrid techniques for the evaluation we use the standard test of taillard eur j operation res 64 1993 278 composed of 120 instances of different sizes in the evaluations we use the experimental design approach to obtain valid conclusions on the effectiveness and efficiency of the different methods tested

heuristics for a flowshop scheduling problem with stepwise job objective function abstract in this work we introduce the flowshop scheduling problem with delivery dates and cumulative payoffs this problem is a variation of the flowshop scheduling problem with job release dates that maximizes the total payoff with a stepwise job objective function this paper contributes towards proposing a mathematical formulation for this new problem and an original constructive heuristic additionally we develop a new benchmark of 300 hard instances which are available in a public repository besides we provide primal and dual bounds for them extensive computational experiments were carried out taking into account classical constructive heuristics and hybrid local searches results show the merit of the ff heuristic when compared to other classical heuristics for flowshop scheduling problems additionally we compare an iterated local search ils an iterated greedy search igs and a biased random key genetic algorithm brkga both customized for the studied problem and a commercial mixed integer programming solver the comparison between these methods showed that the brkga starting with a solution from ff heuristic is able to find the best solutions in a very short period of time iterated local search and the commercial solver presented significantly worse results than the aforementioned methods

sampling based adaptive bounding evolutionary algorithm for continuous optimization problems abstract this paper proposes a novel sampling based adaptive bounding evolutionary algorithm termed sabea that is capable of dynamically updating the search space during the evolution process for continuous optimization problems the proposed sabea adopts two bounding strategies namely fitness based bounding and probabilistic sampling based bounding to select a set of individuals over multiple generations and leverage the value information from these individuals to update the search space of a given problem for improving the solution accuracy and search efficiency to evaluate the performance of this method sabea is applied on top of the classic differential evolution de algorithm and a de variant and sabea is compared to a state of the art distribution based adaptive bounding genetic algorithm dabga on a set of 27 selected benchmark functions the results show that sabea can be used as a complementary strategy for further enhancing the performance of existing evolutionary algorithms and it also outperforms dabga finally a practical problem namely the model calibration for an agent based simulation is used to further evaluate sabea the results show sabea s applicability to diverse problems and its advantages over the traditional genetic algorithm based calibration method and dabga

robust counterpart optimization for the redundancy allocation problem in series parallel systems with component mixing under uncertainty in this paper a robust optimization approach is used to solve the redundancy allocation problem rap in series parallel systems with component mixing where uncertainty exists in components reliabilities in real world the reliabilities of components are imprecisely estimated or the reliability of some components may vary due to some realistic factors therefore we may deal with a system where there are many components with uncertain values of reliabilities to deal with this problem for the first time a robust optimization approach is applied to rap with component mixing to produce a robust solution which is relatively insensitive with respect to uncertainty in reliability of components in addition the advantages of the proposed robust technique are illustrated by considering a series parallel system and finding the suitable redundancy levels and then monte carlo simulation is implemented to examine the quality of the robust solutions the results indicate that applying the proposed robust rap can be more reliable to determine system reliability in the designing phase of systems

tabu enhanced iterated greedy algorithm a case study in the quadratic multiple knapsack problem abstract iterated greedy search is a simple and effective metaheuristic for combinatorial problems its flexibility enables the incorporation of components from other metaheuristics with the aim of obtaining effective and powerful hybrid approaches we propose a tabu enhanced destruction mechanism for iterated greedy search that records the last removed objects and avoids removing them again in subsequent iterations the aim is to provide a more diversified and successful search process with regards to the standard destruction mechanism which selects the solution components for removal completely at random we have considered the quadratic multiple knapsack problem as the application domain for which we also propose a novel local search procedure and have developed experiments in order to assess the benefits of the proposal the results show that the tabu enhanced iterated greedy approach in conjunction with the new local search operator effectively exploits the problem knowledge associated with the requirements of the problem considered attaining competitive results with regard to the corresponding state of the art algorithms

nonuniqueness in the quenching problem this paper deals with nonnegative solutions of for u t delta u u q chi u 0 qquad rm in omega times 0 infty qquad qquad rm q with q in 0 1 and prescribed continuous dirichlet data b b x on it is proved that for n 6 there is a critical parameter q c in 0 1 with the following property if q q c then there exist at least two continuous weak solutions emanating from some explicitly known stationary solution w one that coincides with w and another one that satisfies u w but u not equiv w for n 6 and q q c or n 7 however such a second solution above w is impossible moreover it is shown that for n 6 q q c and any sufficiently small nonnegative boundary data b there exist initial values admitting at least two continuous weak solutions of q the final result asserts that for any n and q nonuniqueness for q holds at least for some boundary and initial data

a fast tabu search algorithm for the permutation flow shop problem abstract a fast and easily implementable approximation algorithm for the problem of finding a minimum makespan in the permutation flow shop is presented the algorithm is based on a tabu search technique with a specific neighborhood definition which employs a block of jobs notion computational experiments up to 500 jobs and 20 machines show its excellent numerical properties

approaching the rank aggregation problem by local search based metaheuristics abstract encouraged by the success of applying metaheuristics algorithms to other ranking based problems kemeny ranking problem and parameter estimation for mallows distributions in this paper we deal with the rank aggregation problem rap which can be viewed as a generalization of the kemeny problem to arbitrary rankings while in the kemeny problem the input is a set of permutations the rap consists in obtaining the consensus permutation for a sample of arbitrary rankings this is an np hard problem which can be approached by using greedy heuristic algorithms e g borda such algorithms are fast but the solutions so obtained are far to be optimal in this paper we propose the use of more complex search processes to deal with the rap in particular we perform a comparative study among some local based search metaheuristics hill climbing hc iterated local search ils variable neighborhood search vns and greedy randomized adaptive search procedure grasp we provide a complete analysis of the experimental study regarding accuracy and number of iterations required to reach the best solution from the results we can conclude that the selection of a suitable neighborhood plays a key role and that depending on the available resources cpu time a different algorithm vns ils or grasp could be the proper choice

the weighted independent domination problem integer linear programming models and metaheuristic approaches abstract this work deals with the so called weighted independent domination problem which is an np hard combinatorial optimization problem in graphs in contrast to previous work this paper considers the problem from a non theoretical perspective the first contribution consists in the development of three integer linear programming models second two greedy heuristics are proposed finally the last contribution is a population based iterated greedy metaheuristic which is applied in two different ways 1 the metaheuristic is applied directly to each problem instance and 2 the metaheuristic is applied at each iteration of a higher level framework known as construct merge solve and adapt to sub instances of the tackled problem instances the results of the considered algorithmic approaches show that integer linear programming approaches can only compete with the developed metaheuristics in the context of graphs with up to 100 nodes when larger graphs are concerned the application of the populated based iterated greedy algorithm within the higher level framework works generally best the experimental evaluation considers graphs of different types sizes densities and ways of generating the node and edge weights

complete quenching for singular parabolic problems we prove finite time extinction of the solution of the equation ut u u 0 u f u 0 in 0 with boundary data u x t 0 on 0 and initial condition u x 0 u0 x in where rn is a bounded smooth domain 0 0 is a parameter for every small enough 0 there exists a time t0 0 such that the solution is identically equal to zero

fuzzy multi objective mathematical programming on reliability optimization model abstract this paper considers multi objective reliability optimization problem for system reliability where reliability enhancement is involved with several mutually conflicting objectives it is conflicting to reduce the cost of the system and improve the reliability of the same system simultaneously a new fuzzy multi objective optimization method is introduced and it is used for the optimization decision making of the series and complex system reliability with two objectives in general cost of the components of the system s objective goals has not been stated clearly this imprecise reliability optimization model is solved through the fuzzy multi objective optimization method examples are shown to illustrate the method

an ensemble of discrete differential evolution algorithms for solving the generalized traveling salesman problem in this paper an ensemble of discrete differential evolution algorithms with parallel populations is presented in a single populated discrete differential evolution dde algorithm the destruction and construction dc procedure is employed to generate the mutant population whereas the trial population is obtained through a crossover operator the performance of the dde algorithm is substantially affected by the parameters of dc procedure as well as the choice of crossover operator in order to enable the dde algorithm to make use of different parameter values and crossover operators simultaneously we propose an ensemble of dde edde algorithms where each parameter set and crossover operator is assigned to one of the parallel populations each parallel parent population does not only compete with offspring population generated by its own population but also the offspring populations generated by all other parallel populations which use different parameter settings and crossover operators as an application area the well known generalized traveling salesman problem gtsp is chosen where the set of nodes is divided into clusters so that the objective is to find a tour with minimum cost passing through exactly one node from each cluster the experimental results show that none of the single populated variants was effective in solving all the gtsp instances whereas the edde performed substantially better than the single populated variants on a set of problem instances furthermore through the experimental analysis of results the performance of the edde algorithm is also compared against the best performing algorithms from the literature ultimately all of the best known averaged solutions for larger instances are further improved by the edde algorithm

a variable iterated greedy algorithm for the traveling salesman problem with time windows this paper presents a variable iterated greedy algorithm for solving the traveling salesman problem with time windows tsptw to identify a tour minimizing the total travel cost or the makespan separately the tsptw has several practical applications in both production scheduling and logistic operations the proposed algorithm basically relies on a greedy algorithm generating an increasing number of neighboring solutions through the use of the idea of neighborhood change in variable neighborhood search vns algorithms in other words neighboring solutions are generated by destructing a solution component and re constructing the solution again with variable destruction sizes in addition the proposed algorithm is hybridized with a vns algorithm employing backward and forward 1 opt local searches to further enhance the solution quality the performance of the proposed algorithm was tested on several benchmark suites from the literature experimental results confirm that the proposed algorithm is either competitive to or even better than the best performing algorithms from the literature ultimately new best known solutions are obtained for 38 out of 125 problem instances of the recently proposed benchmark suite whereas 15 out of 31 problem instances are also further improved for the makespan criterion

modelling redundancy allocation for a fuzzy random parallel series system due to subjective judgment imprecise human knowledge and perception in capturing statistical data the real data of lifetimes in many systems are both random and fuzzy in nature based on the fuzzy random variables that are used to characterize the lifetimes this paper studies the redundancy allocation problems to a fuzzy random parallel series system two fuzzy random redundancy allocation models fr ram are developed through reliability maximization and cost minimization respectively some properties of the fr ram are obtained in which an analytical formula of reliability with convex lifetimes is derived and the sensitivity of the reliability is discussed to solve the fr rams we first address the computation of reliability a random simulation method based on the derived analytical formula is proposed to compute the reliability with convex lifetimes as for the reliability with nonconvex lifetimes the technique of fuzzy random simulation together with the discretization method of fuzzy random variable is employed to compute the reliability and a convergence theorem of the fuzzy random simulation is proved subsequently we integrate the computation approaches of the reliability and genetic algorithm ga to search for the approximately optimal redundancy allocation of the models finally some numerical examples are provided to illustrate the feasibility of the solution algorithm and quantify its effectiveness

a simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem over the last decade many metaheuristics have been applied to the flowshop scheduling problem ranging from simulated annealing or tabu search to complex hybrid techniques some of these methods provide excellent effectiveness and efficiency at the expense of being utterly complicated in fact several published methods require substantial implementation efforts exploit problem specific speed up techniques that cannot be applied to slight variations of the original problem and often re implementations of these methods by other researchers produce results that are quite different from the original ones in this work we present a new iterated greedy algorithm that applies two phases iteratively named destruction were some jobs are eliminated from the incumbent solution and construction where the eliminated jobs are reinserted into the sequence using the well known neh construction heuristic optionally a local search can be applied after the construction phase our iterated greedy algorithm is both very simple to implement and as shown by experimental results highly effective when compared to state of the art methods

the extinction versus the blow up global and non global existence of solutions of source types of degenerate parabolic equations with a singular absorption abstract we consider nonnegative solutions of degenerate parabolic equations with a singular absorption term and a source nonlinear term t u u x p 2 u x x u u 0 f u x t in i 0 t with the homogeneous zero boundary condition on i x 1 x 2 an open bounded interval in r through this paper we assume that p 2 and 0 1 to show the local existence result we prove first a sharp pointwise estimate for u x one of our main goals is to analyze conditions on which local solutions can be extended to the whole time interval t 0 the so called global solutions or by the contrary a finite time blow up 0 0 arises such that lim t 0 u t l i moreover we prove that any global solution must vanish identically after a finite time if provided that either the initial data or the source term is small enough finally we show that the condition f 0 x t 0 x t i 0 is a necessary and sufficient condition for the existence of solution of equations of this type

multi objective sequence dependent setup times permutation flowshop a new algorithm and a comprehensive study abstract the permutation flowshop scheduling problem has been thoroughly studied in recent decades both from single objective as well as from multi objective perspectives to the best of our knowledge little has been done regarding the multi objective flowshop with pareto approach when sequence dependent setup times are considered as setup times and multi criteria problems are important in industry we must focus on this area we propose a simple yet powerful algorithm for the sequence dependent setup times flowshop problem with several criteria the presented method is referred to as restarted iterated pareto greedy or ripg and is compared against the best performing approaches from the relevant literature comprehensive computational and statistical analyses are carried out in order to demonstrate that the proposed ripg method clearly outperforms all other algorithms and as a consequence it is a state of art method for this important and practical scheduling problem

an iterated greedy heuristic for the sequence dependent setup times flowshop problem with makespan and weighted tardiness objectives iterated greedy ig algorithms are based on a very simple principle are easy to implement and can show excellent performance in this paper we propose two new ig algorithms for a complex flowshop problem that results from the consideration of sequence dependent setup times on machines a characteristic that is often found in industrial settings the first ig algorithm is a straightforward adaption of the ig principle while the second incorporates a simple descent local search furthermore we consider two different optimization objectives the minimization of the maximum completion time or makespan and the minimization of the total weighted tardiness extensive experiments and statistical analyses demonstrate that despite their simplicity the ig algorithms are new state of the art methods for both objectives

a new heuristic method for the flow shop sequencing problem abstract a new heuristic method is presented for solving the m machine n job flow shop scheduling problem this method named spirit is composed of two phases the first finds an initial sequence using an analogy with the travelling salesman problem and the second tries to improve this solution using taboo search techniques the results of the heuristic are compared with those from other well known methods

an iterated greedy heuristic for a market segmentation problem with multiple attributes this research has been supported by the mexican national council for science and technology conacyt through grants cb2005 01 48499y and cb2011 01 166397 and a scholarship for graduate studies and by the universidad autonoma de nuevo leon through its scientific and technological research support program paicyt grants ca1478 07 ce012 09 it511 10 and ce331 15 ruben ruiz is partially supported by the spanish ministry of economy and competitiveness under the project scheyard optimization of scheduling problems in container yards no dpi2015 65895 r financed by feder funds we would like to thank rafael frinhani richard fuchshuber and their corresponding research teams for providing us the source code of their algorithms to carry out the corresponding tests furthermore we are grateful to the editor and the four anonymous reviewers for their careful reading of our manuscript and their constructive comments and suggestions which helped us improve its quality

local search methods for the flowshop scheduling problem with flowtime minimization abstract flowshop scheduling is a very active research area this problem still attracts a considerable amount of interest despite the sheer amount of available results total flowtime minimization of a flowshop has been actively studied and many effective algorithms have been proposed in the last few years new best solutions have been found for common benchmarks at a rapid pace however these improvements many times come at the cost of sophisticated algorithms complex methods hinder potential applications and are difficult to extend to small problem variations replicability of results is also a challenge in this paper we examine simple and easy to implement methods that at the same time result in state of the art performance the first two proposed methods are based on the well known iterated local search ils and iterated greedy ig frameworks which have been applied with great success to other flowshop problems additionally we present extensions of these methods that work over populations something that we refer to as population based ils pils and population based ig piga respectively we calibrate the presented algorithms by means of the design of experiments doe approach extensive comparative evaluations are carried out against the most recent techniques for the considered problem in the literature the results of a comprehensive computational and statistical analysis show that the presented algorithms are very effective furthermore we show that despite their simplicity the presented methods are able to improve 12 out of 120 best known solutions of taillard s flowshop benchmark with total flowtime criterion

accelerated tabu search for no wait flowshop scheduling problem with maximum lateness criterion abstract for the no wait flowshop scheduling problem with maximum lateness criterion properties are developed to speed up three kinds of basic operations generating candidate solutions i e the insertion of a new job into a partial sequence and the insertion and exchange neighborhood moves the properties reduce the time to evaluate a candidate from o nm to o 1 and simplify the implementation of the heuristics based on the basic operations by evaluating candidates before their generation the properties also reduce from o n 3 m to o n 2 the time complexity of well known neh heuristic and the complete evaluation of the insertion and exchange neighborhoods tabu search ts is applied to the considered problem since ts tries to find the best neighbor of the current solution in each iteration and therefore can much benefit from the speedups three different ways to use insertion and exchange neighborhoods are compared in ts computational experiments show that the speedups are more helpful as job number increases and all proposed ts algorithms are more effective and robust than the existing algorithms although two and single neighborhood ts algorithms are not significantly different two neighborhood ts algorithms are more preferable

quenching rate for a nonlocal problem arising in the micro electro mechanical system abstract in this paper we study the quenching rate of the solution for a nonlocal parabolic problem which arises in the study of the micro electro mechanical system this question is equivalent to the stabilization of the solution to the transformed problem in self similar variables first some a priori estimates are provided in order to construct a lyapunov function due to the lack of time monotonicity property we then derive some very useful and challenging estimates by a delicate analysis finally with this lyapunov function we prove that the quenching rate is self similar which is the same as the problem without the nonlocal term except the constant limit depends on the solution itself

shifting representation search for hybrid flexible flowline problems this paper considers the hybrid flexible flowline scheduling problem with a set of additional restrictions and generalizations that are common in practice these include precedence constraints sequence dependent setup times time lags machine eligibility and release times there are many potential solution representations for this problem ranging from simple and compact to more complex and complete typically when choosing the degree of detail of the solution representation a tradeoff can be found between efficiency of the algorithm and the size of the search space several adaptations of existing methods are introduced memetic algorithm iterated local search iterated greedy as well as a novel algorithm called shifting representation search srs this new method starts with an iterated greedy algorithm applied to a permutation version of the problem and at a given time switches to an iterated local search on the full search space as far as we know this shift of the solution representation is new in the scheduling literature experimental results and statistical tests clearly prove the superiority of srs compared with classical and existing methods

iterated greedy for the maximum diversity problem abstract the problem of choosing a subset of elements with maximum diversity from a given set is known as the maximum diversity problem many algorithms and methods have been proposed for this hard combinatorial problem including several highly sophisticated procedures by contrast in this paper we present a simple iterated greedy metaheuristic that generates a sequence of solutions by iterating over a greedy construction heuristic using destruction and construction phases extensive computational experiments reveal that the proposed algorithm is highly effective as compared to the best so far metaheuristics for the problem under consideration

solving the flowshop scheduling problem with sequence dependent setup times using advanced metaheuristics abstract this paper deals with the permutation flowshop scheduling problem in which there are sequence dependent setup times on each machine commonly known as the sdst flowshop the optimisation criteria considered is the minimisation of the makespan or cmax genetic algorithms have been successfully applied to regular flowshops before and the objective of this paper is to assess their effectiveness in a more realistic and complex environment we present two advanced genetic algorithms as well as several adaptations of existing advanced metaheuristics that have shown superior performance when applied to regular flowshops we show a calibration of the genetic algorithm s parameters and operators by means of a design of experiments doe approach for evaluating the proposed algorithms we have coded several if not all known sdst flowshop specific algorithms all methods are tested against an augmented benchmark based on the instances of taillard the results show a clear superiority of the algorithms proposed especially for the genetic algorithms regardless of instance type and size

a scatter search algorithm for the distributed permutation flowshop scheduling problem abstract the distributed permutation flowshop problem has been recently proposed as a generalization of the regular flowshop setting where more than one factory is available to process jobs distributed manufacturing is a common situation for large enterprises that compete in a globalized market the problem has two dimensions assigning jobs to factories and scheduling the jobs assigned to each factory despite being recently introduced this interesting scheduling problem has attracted attention and several heuristic and metaheuristic methods have been proposed in the literature in this paper we present a scatter search ss method for this problem to optimize makespan ss has seldom been explored for flowshop settings in the proposed algorithm we employ some advanced techniques like a reference set made up of complete and partial solutions along with other features like restarts and local search a comprehensive computational campaign including 10 existing algorithms together with statistical analyses shows that the proposed scatter search algorithm produces better results than existing algorithms by a significant margin moreover all 720 known best solutions for this problem are improved

a hybrid differential evolution algorithm to vehicle routing problem with fuzzy demands in this paper the vehicle routing problem with fuzzy demands vrpfd is considered and a fuzzy chance constrained program model is designed based on fuzzy credibility theory then stochastic simulation and differential evolution algorithm are integrated to design a hybrid intelligent algorithm to solve the fuzzy chance constrained program model moreover the influence of the dispatcher preference index on the final objective of the problem is discussed using stochastic simulation and the best value of the dispatcher preference index is obtained

an effective hybrid discrete differential evolution algorithm for the flow shop scheduling with intermediate buffers in this paper an effective hybrid discrete differential evolution hdde algorithm is proposed to minimize the maximum completion time makespan for a flow shop scheduling problem with intermediate buffers located between two consecutive machines different from traditional differential evolution algorithms the proposed hdde algorithm adopted job permutation to represent individuals and applies job permutation based mutation and crossover operations to generate new candidate solutions moreover a one to one selection scheme with probabilistic jumping is used to determine whether the candidates will become members of the target population in next generation in addition an efficient local search algorithm based on both insert and swap neighborhood structures is presented and embedded in the hdde algorithm to enhance the algorithm s local searching ability computational simulations and comparisons based on the well known benchmark instances are provided it shows that the proposed hdde algorithm is not only capable to generate better results than the existing hybrid genetic algorithm and hybrid particle swarm optimization algorithm but outperforms two recently proposed discrete differential evolution dde algorithms as well especially the hdde algorithm is able to achieve excellent results for large scale problems with up to 500 jobs and 20 machines

metaheuristics for the linear ordering problem with cumulative costs the linear ordering problem with cumulative costs lopcc is a variant of the well known linear ordering problem in which a cumulative propagation makes the objective function highly non linear the lopcc has been recently introduced in the context of mobile phone telecommunications in this paper we propose two metaheuristic methods for this np hard problem the first one is based on the grasp methodology while the second one implements an iterated greedy strategic oscillation procedure we also propose a post processing based on path relinking to obtain improved outcomes we compare our methods with the state of the art procedures on a set of 218 previously reported instances the comparison favors the iterated greedy strategic oscillation with the path relinking post processing which is able to identify 87 new best objective function values

some efficient heuristic methods for the flow shop sequencing problem abstract in this paper the best heuristic methods known up to now are compared to solve the flow shop sequencing problem and we improve the complexity of the best one next this problem is applied to taboo search a new technique to solve combinatorial optimization problems and computational experiments are reported finally a parallel taboo search algorithm is presented and experimental results show that this heuristic allows very good speed up

a gradient estimate to a degenerate parabolic equation with a singular absorption term the global quenching phenomena we prove global existence of nonnegative solutions to the one dimensional degenerate parabolic problems containing a singular term we also show the global quenching phenomena for l1 initial datums moreover the free boundary problem is considered in this paper

iterated greedy local search methods for unrelated parallel machine scheduling abstract this work deals with the parallel machine scheduling problem which consists in the assignment of n jobs on m parallel machines the most general variant of this problem is when the processing time depends on the machine to which each job is assigned to this case is known as the unrelated parallel machine problem similarly to most of the literature this paper deals with the minimization of the maximum completion time of the jobs commonly referred to as makespan c max many algorithms and methods have been proposed for this hard combinatorial problem including several highly sophisticated procedures by contrast in this paper we propose a set of simple iterated greedy local search based metaheuristics that produce solutions of very good quality in a very short amount of time extensive computational campaigns show that these solutions are most of the time better than the current state of the art methodologies by a statistically significant margin

scheduling flow shops using differential evolution algorithm this paper describes a novel optimization method based on a differential evolution exploration algorithm and its applications to solving non linear programming problems containing integer and discrete variables the techniques for handling discrete variables are described as well as the techniques needed to handle boundary constraints in particular the application of differential evolution algorithm to minimization of makespan flowtime and tardiness in a flow shop manufacturing system is given in order to illustrate the capabilities and the practical use of the method experiments were carried out to compare results from the differential evolution algorithm and the genetic algorithm which has a reputation for being very powerful the results obtained have proven satisfactory in solution quality when compared with genetic algorithm the novel method requires few control variables is relatively easy to implement and use effective and efficient which makes it an attractive and widely applicable approach for solving practical engineering problems future directions in terms of research and applications are given

complete quenching for a quasilinear parabolic equation abstract we study the homogeneous dirichlet problem for the quasilinear parabolic equation with the singular absorption term t u p u 1 u 0 u f x u in q t 0 t here r d d 1 is a bounded domain p u div u p 2 u is the p laplace operator and 0 1 is a given parameter it is assumed that the initial datum satisfies the conditions u 0 w 0 1 p l u 0 0 a e in the right hand side f r 0 is a caratheodory function satisfying the power growth conditions 0 f x s s q 1 c with positive constants c and q 1 we establish conditions of local and global in time existence of nonnegative solutions and show that if q p and and c are sufficiently small then every global solution vanishes in a finite time a e in