Dynamic Priority Rule Selection for Solving Multi-objective Job Shop Scheduling Problems

Teymourifar, Aydin; Bahadir, Ozan; Öztürk, Gürkan

doi:10.13189/ujibm.2018.060102

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…Since the run times of these algorithms are non-deterministic polynomial, scheduling problems are called NP-hard. Because of the high complexity, the usage of heuristics to solve especially real-life and large-scale scheduling problems has become popular [4][5][6][7]. One category of robust heuristics is related to dispatching rules (DRs), which transform the solution time into the polynomial.…”

Section: Introductionmentioning

confidence: 99%

An Open-Source Simulation Model for Solving Scheduling Problems

Teymourifar¹,

Li²,

Li³

et al. 2022

ujam

Self Cite

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In this study, an open-source simulation model is presented for solving scheduling problems. The model is capable of solving different benchmarks. The methods involved in the simulation are mainly based on generating dispatching rules or using them to solve problems, but there are other heuristics as well. Dispatching rules in an evolutionary process are generated using Gene Expression Programming. For this aim, a coding method, which has not been described in the literature before, is explained. Along with the explanation of the properties of the source code, information about deterministic, dynamic models, buffer states, machine breakdown states, and the methods used to deal with them is presented. Concepts are explained with visual examples. In addition, a subject that has not been investigated in the literature before is analyzed by using the simulation model. This topic is to examine the results of solving machine assignment and operation sequencing sub-problems in flexible job shop scheduling problems with different rules. Moreover, objective functions that the source code can handle are discussed. Unlike many studies in the literature, the codes are presented to the readers as open source. Also, it is open to development and can be easily modified by users to solve other types of problems. Finally, in the study, experimental results are presented on the basis of some benchmarks available in the literature, and the limits of the study and source code are explained.

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Section: Introductionmentioning

confidence: 99%

An Open-Source Simulation Model for Solving Scheduling Problems

Teymourifar¹,

Li²,

Li³

et al. 2022

ujam

Self Cite

View full text Add to dashboard Cite

show abstract

“…For example, Ho et al have defined the production process of the printed circuit board (PCB) in the electronic manufacturing service (EMS) as a FJSSP [19]. Because of the complex structure of this combinatorial problem, it is difficult to solve it with the non-hybrid and exact methods [45]. So, several hybrid methods, which integrate the population-based meta-heuristics (P-metaheuristics), single-based meta-heuristics (S-metaheuristics), heuristics, mathematical programming, constraint programming, machine learning and graph-based methods are suggested for solving this problem [1] [36] [39] [41] [42] [43] [44] [47] [50].…”

Section: Introductionmentioning

confidence: 99%

A Comparison between Two Modified NSGA-II Algorithms for Solving the Multi-objective Flexible Job Shop Scheduling Problem

Teymourifar¹,

Öztürk²,

Bahadir³

2018

ujam

Self Cite

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Many evolutionary algorithms have been used to solve multi-objective scheduling problems. NSGA-II is one of them that is based on the Pareto optimality concept and generally obtains good results. However, it is possible to improve its performance with some modifications. In this paper, two modified NSGA-II algorithms have been suggested for solving the multi-objective flexible job shop scheduling problem. The neighborhood structures defined for the problem are integrated into the algorithms to create better generations during the iterations. Also, their initial populations are created with an effective heuristic. In the first modified NSGA-II, after the creation of the offspring population, a neighbor of each individual in the parent population is constructed, and then one of them is selected according to the domination state of the solutions. Then the populations are merged to create a new population. In the second modified NSGA-II, only the solutions on the first and second fronts of the parent population and also their neighbors are merged with the offspring population. Other operators of the algorithms like the non-dominated sorting and calculating the crowding distances are as the classic NSGA-II. A comparison is done with a classic NSGA-II based on two metrics. The results show that as it is in the first modified NSGA-II, including neighbors of more individuals of the population provides better results because it increases diversity and intensity of the search. The performance of the second modified NSGA-II is almost similar to the NSGA-II. So, it can be concluded that although integrating the neighborhood structures can improve the performance of search, it is better to define that the structures should be applied to how many and which solutions, in otherwise the quality of search may not increase.

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A Comparison Between Linear and Non-linear Combinations of Priority Rules for Solving Flexible Job Shop Scheduling Problem

Teymourifar¹,

et al. 2023

Lecture Notes in Management and Industrial Engineering

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Dynamic Priority Rule Selection for Solving Multi-objective Job Shop Scheduling Problems

Cited by 5 publications

References 18 publications

An Open-Source Simulation Model for Solving Scheduling Problems

An Open-Source Simulation Model for Solving Scheduling Problems

A Comparison between Two Modified NSGA-II Algorithms for Solving the Multi-objective Flexible Job Shop Scheduling Problem

A Comparison Between Linear and Non-linear Combinations of Priority Rules for Solving Flexible Job Shop Scheduling Problem

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