An Improved Lexicographical Whale Optimization Algorithm for the Type-II Assembly Line Balancing Problem Considering Preventive Maintenance Scenarios

In the traditional assembly line balancing, all the workstations are assumed available and hence the unavailability of any workstation brings about the stoppage of the whole line and the waste of the production capacity in the rest workstations. Considering the planning characteristic of preventive...

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Bibliographic Details
Main Authors: Kai Meng, Qiuhua Tang, Zikai Zhang, Xinbo Qian
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/8988259/
Description
Summary:In the traditional assembly line balancing, all the workstations are assumed available and hence the unavailability of any workstation brings about the stoppage of the whole line and the waste of the production capacity in the rest workstations. Considering the planning characteristic of preventive maintenance, this paper proposes a novel methodology of integrating the preventive maintenance scenarios into assembly line balancing problems to bypass the unavailable workstation. A lexicographic model is formulated to generate multiple task assignment plans that ensure primarily the high productivity under regular operation scenario and guarantee secondarily the production continuity under preventive maintenance scenarios. And, an improved whale optimization algorithm (IWOA) with three modifications is proposed to solve this problem. Specifically, a combined crossover operator enhances better combination in exploration; three best search agents promote the exploitation; partial regeneration avoids being trapped in local optima. More than five thousand experiments demonstrate that the joint of three modifications endows the IWOA significant superiority over six variants and other six well-known algorithms. Moreover, integrating preventive maintenance scenarios into the assembly line balancing problem increases the production efficiency by 1% at the cost of small production adjustment.
ISSN:2169-3536