Multi-Objective Artificial Bee Colony for Assembly Flexible Job Shop with Transportation and Setup Times

Abstract

In this article, a multi-objective artificial bee colony algorithm with dynamic neighborhood search (MOABC) is employed to address the two-stage assembly flexible job scheduling problem (AFJSP) with transportation and setup times. In the considered problem, there are two stages as follows: 1) in the first stage, the classic flexible job shop scheduling problem (FJSP) with transportation and setup times is considered, and 2) each product is assembled in the second stage, where the setup times between products is embedded. To address the problem, first, a mixed integer linear programming model is developed, wherein makespan and total energy consumption are optimized simultaneously. Second, an effective initialization strategy is designed to generate an initial population with high performance. Next, in the decoding phase, two types of neighborhood knowledge based on the problem characteristics are extracted. Subsequently, to enhance the local search capabilities, a dynamic neighborhood search (DNS) heuristic with five different neighborhood structures in the onlooker stage is proposed. Finally, comprehensive computational comparisons and statistical analysis with state-of-the-art algorithms verified the effectiveness of the proposed algorithm.