NDLI: Research and application of multiple constrained hot strip mill scheduling problem based on HPSA

Content Provider	Springer Nature Link
Author	Liu, Lilan Liu, Chao Liu, Xuewei Wang, Sen Zhou, Wei Zhang, Zhenyou
Copyright Year	2015
Abstract	This paper proposes a new algorithm combining particle swarm optimization (PSO) and simulated annealing (SA) to solve hot strip mill scheduling problem which is formulated as a multi-objective optimization problem with multi-constraints which process schedule constraints, production means constraints, and energy consumption constraints into account. The constraints in hot mill processes are analyzed in three aspects—process schedule, product means, and energy consumption. According to actual experience, Hybrid Vehicle Routing Problem (HVRP) model is established to solve the scheduling problem which is a combination of variable fleet vehicle routing problem, prize collection vehicle routing, and capacitated vehicle routing problem. In this new algorithm, the PSO algorithm is redefined and modified by introducing metropolis criterion of SA twice, which is used to update two extremes of particle swarm, including the individual optimal solution and the global optimal solution to improve the convergence precision and the convergence rate. The proposed method has been applied in a hot mill line to verify its availability and feasibility by comparing the manual scheduling result.
Starting Page	1817
Ending Page	1829
Page Count	13
File Format	PDF
ISSN	02683768
Journal	The International Journal of Advanced Manufacturing Technology
Volume Number	81
Issue Number	9-12
e-ISSN	14333015
Language	English
Publisher	Springer London
Publisher Date	2015-05-28
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Hot strip mill Scheduling Hybrid vehicle routing problem Particle swarm optimization Industrial and Production Engineering Production Mechanical Engineering Computer-Aided Engineering (CAD, CAE) and Design
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Control and Systems Engineering Mechanical Engineering Computer Science Applications Software

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