A New Job Shop Scheduling Method for Remanufacturing Systems Using Extended Artificial Bee Colony Algorithm

With the emergence of the remanufacturing industry, scheduling problems related to remanufacturing systems have drawn considerable interest from scholars. There are usually two execution modes of remanufacturing for reprocessing components, i.e., replacement mode and repair mode. However, few studies have focused on the job shop scheduling problem that considers the trade-off between the two execution modes. Thus, a new job shop scheduling method with job families (JSS-JF) is proposed, which can handle the selection of appropriate execution modes for reprocessing components with different damaged conditions. It decomposes the scheduling problem into three sub-problems: (1) execution mode selection; (2) replacement job assignment and sequencing; and (3) repair machine assignment and operation sequencing. The scheduling objective is to minimize the total completion time and total cost of job families. To solve the proposed JSS-JF model, an extended artificial bee colony algorithm with a new three-dimensional encoding scheme is presented to find a near-optimal solution of the proposed JSS-JF model. The crossover and mutation operators, local search, and elite replacement strategy are also integrated in the proposed algorithm. The experiments are conducted for verifying the practicality and effectiveness of the presented algorithm by comparing with six baseline algorithms.

Automated summary

With the emergence of the remanufacturing industry, scholars have shown considerable interest in scheduling problems related to remanufacturing systems. A new job shop scheduling method with job families (JSS-JF) has been proposed to handle the selection of appropriate execution modes for reprocessing components with different damaged conditions, aiming to minimize the total completion time and total cost of job families. The proposed method uses an extended artificial bee colony algorithm with a new three-dimensional encoding scheme and integrates crossover and mutation operators, local search, and elite replacement strategy to find a near-optimal solution.