Bi-Objective Optimal Scheduling With Raw Material's Shelf-Life Constraints in Unrelated Parallel Machines Production

This paper studies a challenging optimal scheduling problem considering the raw material with shelf-life constraints in unrelated parallel machines production. The aim is to optimize the assignment and sequencing of jobs to achieve tradeoffs between minimizing total completion time and the minimizat...

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Published in:IEEE transactions on systems, man, and cybernetics. Systems Vol. 50; no. 11; pp. 4598 - 4610
Main Authors: Wang, Ming-Zheng, Zhang, Ling-Ling, Choi, Tsan-Ming
Format: Journal Article
Language:English
Published: New York IEEE 01.11.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Bi-objective optimization
Completion time
discrete particle swarm algorithm
Evolutionary algorithms
Integer programming
Job shop scheduling
Job shops
Optimal scheduling
Parallel machines
Particle swarm optimization
Performance measurement
Raw materials
raw material’s shelf life
Scheduling
Shelf life
unrelated parallel machines scheduling
ISSN:2168-2216, 2168-2232
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Abstract This paper studies a challenging optimal scheduling problem considering the raw material with shelf-life constraints in unrelated parallel machines production. The aim is to optimize the assignment and sequencing of jobs to achieve tradeoffs between minimizing total completion time and the minimization of raw material costs. We formulate a bi-objective nonlinear 0-1 integer programming model for it. To solve the bi-objective problem which is NP-hard, we propose an evolutionary discrete particle swarm optimization algorithm (EDPSO) with a hybrid-greedy method (for generating the initial population), a new particle updating strategy, and an SPT-local search method (which has been proven for improving solutions' quality theoretically and practically), and the Pareto archive updating strategy for storing good solutions. Computational experiments verify the effectiveness of EDPSO and show that it can obtain better solutions compared to other competing algorithms based on four important performance metrics.
AbstractList This paper studies a challenging optimal scheduling problem considering the raw material with shelf-life constraints in unrelated parallel machines production. The aim is to optimize the assignment and sequencing of jobs to achieve tradeoffs between minimizing total completion time and the minimization of raw material costs. We formulate a bi-objective nonlinear 0-1 integer programming model for it. To solve the bi-objective problem which is NP-hard, we propose an evolutionary discrete particle swarm optimization algorithm (EDPSO) with a hybrid-greedy method (for generating the initial population), a new particle updating strategy, and an SPT-local search method (which has been proven for improving solutions' quality theoretically and practically), and the Pareto archive updating strategy for storing good solutions. Computational experiments verify the effectiveness of EDPSO and show that it can obtain better solutions compared to other competing algorithms based on four important performance metrics.
Author Wang, Ming-Zheng
Zhang, Ling-Ling
Choi, Tsan-Ming
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  givenname: Tsan-Ming
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SubjectTerms Bi-objective optimization
Completion time
discrete particle swarm algorithm
Evolutionary algorithms
Integer programming
Job shop scheduling
Job shops
Optimal scheduling
Parallel machines
Particle swarm optimization
Performance measurement
Raw materials
raw material’s shelf life
Scheduling
Shelf life
unrelated parallel machines scheduling
Title Bi-Objective Optimal Scheduling With Raw Material's Shelf-Life Constraints in Unrelated Parallel Machines Production
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Volume 50
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