# Reactive Scheduling by Intelligent DSS

Yumin He, Yaohu Lin, Hongbo Liu, Mengpeng Guo

► To cite this version:

Yumin He, Yaohu Lin, Hongbo Liu, Mengpeng Guo. Reactive Scheduling by Intelligent DSS. IFIP International Conference on Advances in Production Management Systems (APMS), Sep 2021, Nantes, France. pp.267-274, 10.1007/978-3-030-85874-2_28. hal-04030359

HAL Id: hal-04030359

https://inria.hal.science/hal-04030359

Submitted on 15 Mar 2023

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

ifip

International Federation for Information Processing

This document is the original author manuscript of a paper submitted to an IFIP
conference proceedings or other IFIP publication by Springer Nature. As such, there
may be some differences in the official published version of the paper. Such
differences, if any, are usually due to reformatting during preparation for publication or
minor corrections made by the author(s) during final proofreading of the publication
manuscript.
Reactive Scheduling by Intelligent DSS

Yumin He¹*, Yaohu Lin¹, Hongbo Liu², and Mengpeng Guo³

¹ Beihang University, Beijing 100191, P. R. China

² Avic Chengdu Civil Aircraft Co., Ltd., Chengdu 610000, Sichuan, P. R. China

³ Beijing Shuguang Aviation Electric Co., Ltd., Beijing 101300, P. R. China
heyumin@buaa.edu.cn

**Abstract.** Agile manufacturing is in practice by many companies. In agile manufacturing environments, it is important for companies to make quick response to the changes in the environments. This paper proposes an intelligent decision support system (DSS) for reactive scheduling to handle disturbances in agile manufacturing environments. The intelligent decision support system integrates a knowledge-based system for intelligent and multiple criteria decision-making. The intelligent DSS includes three basic modules, the database module, the model base module, and the interface module. The framework of the intelligent DSS is presented. The objective-oriented data model, the knowledge-based rules, and rule induction are designed. The reactive scheduling algorithm is developed. Radio frequency identification and knowledge acquisition tools are applied by the intelligent DSS. The intelligent DSS can be implemented by applying contemporary information technology and can provide an approach to make reactive production scheduling decisions quickly to handle disturbances for manufacturing firms to obtain competitive advantage and agility.

**Keywords:** Reactive Scheduling, Multi-criteria Decision Making, Decision Support System, Inductive Learning, Knowledge-based System, Agile Manufacturing.

# 1 Introduction

Agile manufacturing provides a new way for new challenges and wants companies to react quickly to customer demands and market changes [1]. In manufacturing and supply chain environments, disturbances may occur, such as a machine failure, job priority changes, unavailable materials, and so forth [2,3]. Therefore, companies have to react quickly to environment changes to obtain competitive advantage and agility.

This paper considers a production scheduling problem with disturbances in agile manufacturing environments. The shop floor contains multiple resources to produce orders. The suitability and availability of the resources are not guaranteed. Disturbances occur in the shop floor and reactive scheduling is applied. An intelligent DSS is proposed. The approach applies the technology of database and knowledge base.
Researchers have studied reactive scheduling. For example, Du and Chiou [2] proposed a reactive scheduling architecture based on objective-oriented database technology. They applied version management of an object-oriented database, demonstrated the different types strategies for reactive scheduling, and considered two types of unexpected events.

Sauer [4] studied vertical data integration that used data from a shop floor. The vertical data integration was made for scheduling decisions on higher levels so as to support reactive scheduling in supply chains.

Paprocka and Skolud [3] proposed a hybrid multi-objective immune algorithm for predictive and reactive scheduling. Their approach applied heuristics to minimize the impact of disrupted operations on scheduling. Their approach considered and predicted time of failure and used maintenance work into a schedule.

## 2 Intelligent DSS for Reactive Scheduling

Agile manufacturing is in practice by many companies. Information technology is suggested for agile manufacturing practices to obtain competitive advantage and agility [5].

The decision support system (DSS) is part of information technology/information system and mainly contains a database management system, a model base system, and a user interface system [6]. An decision support system can support decision-making in many situations such as production planning, inventory control, and so forth [6].

The knowledge-based system (KBS) has been employed in computer-based decision-making. Two primary approaches utilizing KBSs are the use of KBSs directly as a type of DSSs and the integration of KBSs with conventional DSSs [7].

In this paper, an intelligent decision support system is proposed for reactive scheduling in agile manufacturing environments. The intelligent DSS integrates a knowledge-based system to make intelligent decisions. Customers and the shop floor are in the Internet environments. The intelligent DSS includes mainly three modules, the database module, the model base module, and the interface module.

The architecture of the intelligent DSS is illustrated in Fig. 1. RFID technology is the significant advance in managing dynamic systems, which contains the components of tags attached to the objects to be identified [8]. Tools in the DSS includes knowledge acquisition tools, RFID tag identification tools, FRID information processing tools, and other tools. Services include web servers, DNS servers, ONS servers, database servers, and other servers.

## 3 Components in Intelligent DSS

### 3.1 Object-oriented Data Model

Object-oriented database technology has been applied in reactive scheduling [2]. An object-oriented data model is developed for the database model of the intelligent DSS.
Fig. 2 shows the object-oriented data model. In the figure, primary and foreign keys of an entity are expressed by underlines and stars, respectively. A crow's foot is used to express one to many relationships. Optional and mandatory relationships are expressed by circles and bars, respectively [9].

<figure><img src="image_3.png" /><figcaption>Fig. 1. Architecture of the intelligent DSS.</figcaption></figure>

## 3.2 Reactive Rules

The rule base, fact base, and inference engine are designed for the model management module. Researchers have considered disturbances in shop floor such as machine breakdowns, lack of materials, and so forth [2,3]. The disturbances considered are newly added machines, machine breakdowns, and rushed orders. Knowledge-based rules are developed to handle disturbances.

The production scheduling problem is a multiple criteria decision-making (MCDM) problem. A additive utility function is applied [10]. Notation used to describe the rules is listed in Table 1. The rules developed in the rule base include the following.
<figure><img src="image_4.png" /><figcaption>Fig. 2. Object-oriented data model.</figcaption></figure>

<table><caption>Table 1. Notation for rules.</caption><thead><tr><th>Symbol</th><th>Description</th></tr></thead><tbody><tr><td>d</td><td>Disturbance type, d ∈ {added, broken, rushed}</td></tr><tr><td>t<sub>n</sub></td><td>Total disturbances</td></tr><tr><td>f<sub>o</sub></td><td>Total orders finished</td></tr><tr><td>s</td><td>System status, s ∈ {no, new}</td></tr><tr><td>m</td><td>Manager, m ∈ {no, new}</td></tr><tr><td>o</td><td>order, o ∈ {0,1}</td></tr><tr><td>r</td><td>Resource, r ∈ {0,1}</td></tr><tr><td>c</td><td>Weight choice, c ∈ {user, system}</td></tr><tr><td>U</td><td>Additive utility function</td></tr><tr><td>u<sub>1</sub>(x<sub>1</sub>)</td><td>Utility function 1</td></tr><tr><td>u<sub>2</sub>(x<sub>2</sub>)</td><td>Utility function 2</td></tr><tr><td>a<sub>1</sub></td><td>Weight for u<sub>1</sub>(x<sub>1</sub>)</td></tr><tr><td>a<sub>2</sub></td><td>Weight for u<sub>2</sub>(x<sub>2</sub>)</td></tr></tbody></table>
Rule 1. If $m = \{no\} \to s = \{no\}$;

Rule 2. If $t_n \ge 4$ and $m = \{new\} \to s = \{new\}$;

Rule 3. If $f_o \ge 90\%$ and $m = \{new\} \to s = \{new\}$;

Rule 4. If $t_n \ge 4 \to s = \{no\}$;

Rule 5. If $f_o \ge 90\% \to s = \{no\}$;

Rule 6. If $t_n < 4$ and $f_o < 90\% \to s = \{new\}$;

Rule 7. If $d = \{added\} \to r = \{1\}$;

Rule 8. If $d = \{broken\} \to r = \{0\}$;

Rule 9. If $d = \{rushed\} \to o = \{1\}$;

Rule 10. If $c = \{user\} \to$ obtain $a_1$ and $a_2$ from a user;

Rule 11. If $a_1 \neq 0$ and $a_2 \neq 0 \to U = a_1u_1(x_1) + a_2u_2(x_2)$;

Rule 12. If $a_1 \neq 0$ and $a_2 = 0 \to U = a_1u_1(x_1)$;

Rule 13. If $a_1 = 0$ and $a_2 \neq 0 \to U = a_2u_2(x_2)$.

The system status and manager can be one parameter in the set of ‘no’ and ‘new’. The ‘no’ means not to change the current schedule. The ‘new’ means to change the current schedule to a new schedule. For example, if $f_o \ge 90\%$ and $m = \{new\}$ in Rule 3. The rule gives the status of the shop floor with the total orders finished are larger than 90% and the manager decides to change to a new schedule. This rule results in the formulation of a new schedule by the system when the status occurs. When a disturbance occurs, the status of the shop floor is monitored and checked. The DSS can apply the rules to result in appropriate decisions.

### 3.3 Rule Induction

Rule induction is conducted in handling disturbances in the inference engine. Inductive learning ability is designed. The mechanism of rule induction is illustrated in Fig. 3. First, the fact base and the rule base are formed. The next step is the reasoning recurrence of resolving facts to obtain sub-facts, matching facts to obtain candidate rules, and matching rules to obtain candidate facts. If there are disturbances in the shop floor, the fact base is automatically updated. After the reasoning recurrence is accomplished, the rule base is automatically updated.

## 4 Reactive Scheduling Algorithm

The production scheduling problem is an MCDM problem. The additive utility function is applied to the problem as stated before. The objective of the problem is described in the following. Symbols used are provided in Table 1.

$$ \begin{array}{l} \text{Min} \quad U = a_1u_1(x_1) + a_2u_2(x_2) \hfill (1) \\ \qquad 0 \le a_1 \le 1, \ 0 \le a_2 \le 1, \ a_1 + a_2 = 1. \end{array} $$
<figure><img src="image_5.png" /><figcaption>Fig. 3. Mechanism of rule induction.</figcaption></figure>

The reactive scheduling algorithm is developed. Resources are used to perform a group of tasks which belong to manufacturing orders having their processing routings, processing times, and due dates. Symbols used in the algorithm are described in Table 2. The pseudo code of the algorithm is presented in the following.

<table><caption>Table 2. Symbol for algorithm.</caption><thead><tr><td>Symbol</td><td>Description</td></tr></thead><tbody><tr><td><i>i</i>'</td><td>Resource type</td></tr><tr><td><i>I</i>'</td><td>Total resource types</td></tr><tr><td><i>k</i></td><td>Operation</td></tr><tr><td><i>K</i></td><td>Total operations</td></tr><tr><td><i>j</i></td><td>Order</td></tr><tr><td><i>i</i></td><td>Resource</td></tr><tr><td>Ø</td><td>Empty set</td></tr><tr><td><i>t</i><sub>m</sub></td><td>Temporary storage</td></tr><tr><td><i>t</i><sub>r</sub></td><td>Temporary storage</td></tr></tbody></table>
Reactive Scheduling Algorithm

$i' \leftarrow 1;$

$k \leftarrow 1;$

while $k < K$ do

$t_m \leftarrow$ orders of operation $k$;

while $i' < I$ do

$t_r \leftarrow$ orders $\in t_m$ and to be assigned to resource type $i'$;

while $t_r \neq \emptyset$ do

$j \leftarrow$ orders $\in t_r$, and having minimal $U$ by Equation (1);

$i \leftarrow$ resource having the same type of $i'$ and having the earliest
available time;

assign $j$ to $i$;

remove $j$ from $t_r$;

remove $j$ from $t_m$;

end

$i' = i' + 1;$

end

$k = k + 1;$

end

The algorithm is to make new schedules immediately and continuously according
to current situations of the shop floor. The information such as disturbance occurring
times and other relevant data can be obtained through a monitoring system. The DSS
processes these information and other information to analyze the status of the shop
floor. The monitoring system can utilize information technology such as RFID that is
the significant advance in managing dynamic systems as described before.

In the intelligent DSS, the algorithm, the knowledge-based rules, the inference
engine work together to make intelligent decisions for handling disturbances. The
reactive rules are applied by the DSS to make decisions on if a new schedule needs to
be developed or not. The rules are updated after disturbances occur. Once the DSS
makes a decision of formulating a new schedule, this reactive algorithm is applied to
make a new schedule.

5 Conclusion

This paper proposes an intelligent DSS for reactive scheduling to handle disturbances
in agile manufacturing environments. The proposed approach applies information
technology, which is different from many traditional production scheduling
approaches. The objective-oriented data model, the knowledge-based rules, and the
rule induction mechanism are designed. The reactive scheduling algorithm is
developed.

In agile manufacturing environments, it is important for manufacturing firms to
react quickly in the changing environments for agility. The intelligent DSS can be
implemented by applying contemporary information technology. It could provide an
approach for reactive scheduling to handle disturbances for manufacturing firms to
obtain competitive advantage and agility.

Acknowledgment

The authors would like to thank the session chair and the referees for the efforts and
valuable comments.

References

1. Singh, P.L., Sindhwani R., Dua, N.K., Jamwal A., Aggarwal, A., Iqbal, A., Gautam, N.: Evaluation of common barriers to the combined lean-green-agile manufacturing system by two-way assessment method. K. Shanker et al. (eds.), Advances in Industrial and Production Engineering, Lecture Notes in Mechanical Engineering. 653-672 (2019).

2. Du, T.C., Chiou, R.J.: Applying version management of object-oriented database technology in reactive scheduling. International Journal of Production Research 38 1183-1200 (2000).

3. Paprocka, I., Skołud, B.: A hybrid multi-objective immune algorithm for predictive and reactive scheduling. Journal of Scheduling 20 165-182 (2017).

4. Sauer, J.: Vertical data integration for reactive scheduling. Künstl Intell 24 123-129 (2010).

5. Rao, J.J., Kumar V.: Technology adoption in the SME sector for promoting agile manufacturing practices. S. C. Satapathy et al. (eds.), Smart Intelligent Computing and Applications, Smart Innovation, Systems and Technologies 105 659-665 (2019).

6. Vargas, A., Boza, A., Patel, S., Patel, D., Cuenca, L., Ortiz, A.: Inter-enterprise architecture as a tool to empower decision-making in hierarchical collaborative production planning. Data & Knowledge Engineering 105 5-22 (2016).

7. Ho, T.B.: Rule induction in constructing knowledge-based decision support. Decision Support Systems for Sustainable Development, a Source Book of Methods and Applications, International Development Research Center and Kluwer Academic Publishers 263-270 (2000).

8. Dolgui, A., Proth, J.-M.: Special section on radio frequency identification, IEEE Transactions on Industrial Informatics 8 688 (2012).

9. Simsion, G.C.: Data Modeling Essentials, Analysis, Design, and Innovation, the Coriolis Groups (2001).

10. Ballestero, E., Romero, C.; Multiple Criteria Decision Making and Its Applications to Economic Problems, Kluwer Academic Publishers (1998).