Solving Flexible Multi-objective JSP Problem Using A Improved Genetic Algorithm

Li, Meng; Xu, Tao; Peng, Ling

doi:10.4304/jsw.5.10.1107-1113

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…Based on the traditional multi-technique, multi-process FJSP, this paper probes into the optimization of the equal-size lot-splitting JSP involving numerous jobs. The research findings have great theoretical and practical significance for largescale multi-technique and multi-process manufacturing enterprises [1][2][3][4].…”

Section: Introductionmentioning

confidence: 86%

Modified Binary Particle Swarm Optimization Algorithm in Lot-Splitting Scheduling Involving Multiple Techniques

Zhang¹,

Ye²,

Yang³

et al. 2018

Int. j. simul. model.

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This paper aims to strike a balance between cost, time and quality of multi-technique, multi-process flexible job-shop scheduling problem (FJSP) and thus improve the overall performance of the FJSP model. For this purpose, a bi-objective planning model was established for multi-technique, multiprocess FJSP according to the multi-objective planning method in operational theory. Then, the structure of solution was designed for the established model, and the binary particle swarm optimization (BPSO) algorithm was modified to simulate the proposed method. Through the simulation, the following conclusions were put forward: (1) The established bi-objective planning model for multi-technique, multi-process FJSP meet the general requirements of manufacturing enterprises, laying the basis for further modification and derivation; (2) The modified BPSO (MBPSO) algorithm can solve the said model in a stable, fast and accurate manner. The research findings shed theoretical new light on the solution and application of real-world multi-technique, multi-process FJSP and provide practical guidance for the flow shop enterprises to improve their scheduling plans.

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Section: Introductionmentioning

confidence: 86%

Modified Binary Particle Swarm Optimization Algorithm in Lot-Splitting Scheduling Involving Multiple Techniques

Zhang¹,

Ye²,

Yang³

et al. 2018

Int. j. simul. model.

View full text Add to dashboard Cite

show abstract

“…Lan et al [14] introduced an improved GA to solve the complex multi-objective problem of flexible job-shop scheduling, and demonstrated the effectiveness of the algorithm through experiments. Considering the sequence of task points and its impact on trajectory distance, Baizid et al [15] proposed a GA-based method to optimize the sequence of visiting task points, and experimentally proved the validity of the method.…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Optimization Algorithm for Multi-objective Job-Shop Scheduling Problem Considering Job Transport

Ren¹,

Ye²,

Li³

2020

JESA

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This paper solves the job-shop scheduling problem (JSP) considering job transport, with the aim to minimize the maximum makespan, tardiness, and energy consumption. In the first stage, the improved fast elitist nondominated sorting genetic algorithm II (INSGA-II) was combined with N5 neighborhood structure and the local search strategy of nondominant relationship to generate new neighborhood solutions by exchanging the operations on the key paths. In the second stage, the ant colony algorithm based on reinforcement learning (RL-ACA) was designed to optimize the job transport task, abstract the task into polar coordinates, and further optimizes the task. The proposed two-stage algorithm was tested on small, medium, and large-scale examples. The results show that our algorithm is superior to other algorithms in solving similar problems.

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A research survey: review of flexible job shop scheduling techniques

Chaudhry

Khan

2015

Int Trans Operational Res

354

138

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In the last 25 years, extensive research has been carried out addressing the flexible job shop scheduling (JSS) problem. A variety of techniques ranging from exact methods to hybrid techniques have been used in this research. The paper aims at presenting the development of flexible JSS and a consolidated survey of various techniques that have been employed since 1990 for problem resolution. The paper comprises evaluation of publications and research methods used in various research papers. Finally, conclusions are drawn based on performed survey results.

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Solving Flexible Multi-objective JSP Problem Using A Improved Genetic Algorithm

Abstract: Genetic algorithm is a combinatorial optimization problem solving in the field of search algorithm, because of its versatility and robustness, it has been widely used in various fields of science. However, there are some defects in traditional genetic algorithm Show more

Cited by 4 publications

References 11 publications

Modified Binary Particle Swarm Optimization Algorithm in Lot-Splitting Scheduling Involving Multiple Techniques

Modified Binary Particle Swarm Optimization Algorithm in Lot-Splitting Scheduling Involving Multiple Techniques

A Two-Stage Optimization Algorithm for Multi-objective Job-Shop Scheduling Problem Considering Job Transport

A research survey: review of flexible job shop scheduling techniques

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