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

Abstract

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.

A Hybrid Genetic Algorithm for the Simple Assembly Line Balancing Problem with a Fixed Number of Workstations

“…The interval of cycle times is also explored using the lower bound method. The indirect solution representation is also used in [20][21][22].…”

Pure and mixed lexicographic-paretian many-objective optimization: state of the art

“…Among the others, (Castro-Gutie ´rrez et al 2009) suggests to weakly lexicographically bias the search of a particle swarm optimizer so as to produce a Pareto front closer to the priorities manifested by the decision maker. Meng et al (2020) implements a lexicographic whale optimization algorithm to design balanced assembly lines integrating knowledge about preemptive maintenance scenarios. This choice, which allows one to more easily bypass unavailable workstations, is realized by two objectives prioritized as follows: first the productivity under regular operation scenario is optimized; then, the production continuity under preventive maintenance scenario is guaranteed as much as possible.…”

“…Lexicographic multi-/many-objective (LMO) optimization represents a well-grounded and very active research field. The literature, especially the most recent one, is full of its concrete application to everyday problems such as pricing optimization (Zhong et al 2021) and allocation tasks (Meng et al 2020). Even if at first sight the strict preference ordering may seem to be a too strict assumption, evidences testify this is not the case.…”

A Demand-Driven Model for Reallocating Workers in Assembly Lines

“…In this way, we would be automating the fine-tuning process and avoiding the timeconsuming activity of systematically testing values. Recently, in the ALB literature, innovative optimization algorithms are being adapted and/or improved to solve specific assembly line balancing problems; for example: lexicographical whale optimization algorithm for the type-II ALBP considering preventive maintenance [24]; water-flow like algorithm for solving U-shaped ALBPs [25]; and multi-objective genetic flatworm algorithm for solving stochastic, mixed-model, two-sided disassembly lines [23]; to mention a few.…”