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The Impact of Demographic Changes on Human Resources Management in European Supply Chains-Selected Aspects Read chapter Read first chapter

2014 | OriginalPaper | Chapter

A Pareto-Archived Differential Evolution Algorithm for Multi-Objective Flexible Job Shop Scheduling Problems

Authors : Warisa Wisittipanich, Voratas Kachitvichyanukul

Published in: Logistics Operations, Supply Chain Management and Sustainability

Publisher: Springer International Publishing

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Abstract

This chapter presents an efficient evolutionary algorithm, called multi-objective differential evolution algorithm (MODE) to find a Pareto front for multi-objective flexible job shop scheduling problems. The objective is to simultaneously minimize makespan and total tardiness of jobs. The MODE framework adopts the idea of the Elite group to store solutions and utilizes those solutions as the guidance of the vectors. Five mutation strategies with different search behaviors are proposed in MODE algorithms in order to search for the good quality Pareto front. The performances of the MODE algorithms with different mutation strategies are evaluated on a set of benchmark problems and compared with results obtained from an existing algorithm. The experimental results demonstrated that the MODE algorithms are highly competitive approaches which are capable of providing a set of diverse and high-quality non-dominated solutions.

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previous chapter Optimal Reutilization of the Leased Products in a Closed Loop Supply Chain
next chapter Sugarcane Harvest Scheduling to Maximize Total Sugar Yield with Consideration of Equity in Quality Among the Growers
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Metadata
Title
A Pareto-Archived Differential Evolution Algorithm for Multi-Objective Flexible Job Shop Scheduling Problems
Authors
Warisa Wisittipanich
Voratas Kachitvichyanukul
Copyright Year
2014
Book
Logistics Operations, Supply Chain Management and Sustainability

Print ISBN: 978-3-319-07286-9

Electronic ISBN: 978-3-319-07287-6

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-3-319-07287-6_23