Multi-Factory Remanufacturing Process Optimization with Discrete Battle Royale Optimizer

Abstract

In the context of advancing transportation and communication infrastructures, companies are increasingly establishing factories across the globe to support their international expansion strategies. They achieve information sharing through networks, forming a distributed multi-factory working environment. This work introduces and addresses a multi-factory remanufacturing process optimization problem by considering hybrid disassembly line balancing and multi-skilled worker allocation. It has three phases: disassembly factory selection, disassembly task scheduling, and manufacturing factory selection. A linear programming model is established with the objective of optimizing profitability. This work proposes to use a discrete Battle Royale optimizer to solve the problem with a newly proposed encoding structure. The experimental results are benchmarked against CPLEX, confirming the validity and efficiency of the proposed method. The results of its comparisons with genetic algorithm, discrete migratory bird optimizer, fruit fly optimizer, and dingo optimizer further validate its superiority over its peers.