Bi-Population Balancing Multi-Objective Algorithm for Fuzzy Flexible Job Shop With Energy and Transportation

摘要

Flexible job shop scheduling problem (FJSP) is one of the challenging issues in industrial systems. In this study, we propose a bi-population balancing multi-objective evolutionary algorithm, to solve the distributed FJSPs from a steelmaking system, with considering the fuzzy processing time and crane transportation processes. Two objectives are considered simultaneously, including minimization of the maximum fuzzy completion time and the energy consumption during machine processing and crane transportation. Firstly, the mathematical model is formulated for the considered problem. Then, an efficient problem-specific initialization heuristic is developed. To balance the convergence and diversity abilities, a novel crossover operator and two cooperative population environmental selection mechanisms are developed. In addition, an efficient population size adaptive adjustment mechanism is designed. Then, an enhanced local search heuristic is developed to further improve the searching abilities. Finally, a set of randomly generated instances based on realistic industrial processes are tested, and through comprehensive computational comparison and statistical analysis, the highly effective performance of the proposed algorithm is favorably compared against several presented algorithms. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In practical manufacturing processes, the processing times for each job should not be considered as deterministic values because of the disruption events, such as machine breakdown, resource limitation, and machine maintenance. Therefore, the fuzzy scheduling should be considered in many industrial procedures. This study considered multi-objective optimization flexible job shop with energy and robotic transportations, where the fuzzy makespan and energy consumptions are minimized simultaneously. Two populations balancing the convergence and diversity abilities are developed. Efficient problem-specific heuristics are designed to enhance the searching performance. The proposed methods can be generalized and applied to many applications considering both the realistic constraints and objectives.