Improved Heuristic Kalman Algorithm for Solving Multi-Objective Flexible Job Shop Scheduling Problem

The Flexible Job Shop Scheduling Problem (FJSSP), as a typical NP-hard optimization problem, has a significant value in manufacturing environment. This paper presents an improved estimation method of Multi-Objective Heuristic Kalman Algorithm (MOHKA) for solving Multi-Objective Flexible Job Shop Sch...

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Published inProcedia manufacturing Vol. 17; pp. 895 - 902
Main Authors Robert, Ojstersek, Hankun, Zhang, Shifeng, Liu, Borut, Buchmeister
Format Journal Article
LanguageEnglish
Published Elsevier B.V 2018
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ISSN2351-9789
2351-9789
DOI10.1016/j.promfg.2018.10.142

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Abstract The Flexible Job Shop Scheduling Problem (FJSSP), as a typical NP-hard optimization problem, has a significant value in manufacturing environment. This paper presents an improved estimation method of Multi-Objective Heuristic Kalman Algorithm (MOHKA) for solving Multi-Objective Flexible Job Shop Scheduling Problem (MOFJSSP). The optimization results of improved MOHKA for the MOFJSSP were implemented in the five Kacem and ten Brendimarte benchmarks. First, an improved mathematical model of MOHKA was proposed and implemented in MATLAB. Then we applied MOHKA to solve MOFJSSP with an improved real number encoding system, optimized for three benchmark optimization parameters, the maximum completion time of on all jobs (makespan), the total workload on all machine, the workload of the critical machine (the maximum workload among the machines). The results presented in the paper show that the improved method of MOHKA for solving MOFJSSP can optimize multi-objective parameters especially for some of these selected cases in which our algorithm gives us high-quality results.
AbstractList The Flexible Job Shop Scheduling Problem (FJSSP), as a typical NP-hard optimization problem, has a significant value in manufacturing environment. This paper presents an improved estimation method of Multi-Objective Heuristic Kalman Algorithm (MOHKA) for solving Multi-Objective Flexible Job Shop Scheduling Problem (MOFJSSP). The optimization results of improved MOHKA for the MOFJSSP were implemented in the five Kacem and ten Brendimarte benchmarks. First, an improved mathematical model of MOHKA was proposed and implemented in MATLAB. Then we applied MOHKA to solve MOFJSSP with an improved real number encoding system, optimized for three benchmark optimization parameters, the maximum completion time of on all jobs (makespan), the total workload on all machine, the workload of the critical machine (the maximum workload among the machines). The results presented in the paper show that the improved method of MOHKA for solving MOFJSSP can optimize multi-objective parameters especially for some of these selected cases in which our algorithm gives us high-quality results.
Author Robert, Ojstersek
Borut, Buchmeister
Hankun, Zhang
Shifeng, Liu
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Keywords Flexible Job Shop Scheduling Problem
Improved Heuristic Kalman Algorithm
Multi-Objective Optimization
Language English
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  article-title: A Fuzzy Hybrid GA-PSO Algorithm for Multi-Objective AGV Scheduling in FMS
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Snippet The Flexible Job Shop Scheduling Problem (FJSSP), as a typical NP-hard optimization problem, has a significant value in manufacturing environment. This paper...
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SubjectTerms Flexible Job Shop Scheduling Problem
Improved Heuristic Kalman Algorithm
Multi-Objective Optimization
Title Improved Heuristic Kalman Algorithm for Solving Multi-Objective Flexible Job Shop Scheduling Problem
URI https://dx.doi.org/10.1016/j.promfg.2018.10.142
https://doi.org/10.1016/j.promfg.2018.10.142
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