Solving multi-objective energy-saving flexible job shop scheduling problem by hybrid search genetic algorithm

Abstract

For the issue of energy consumption in the multi-objective flexible job shop scheduling problem (MOFJSP), balancing machine load is significant for enhancing environmental sustainability and cost efficiency in intelligent manufacturing. Most studies overlook the critical role of machine load in energy consumption. Therefore, this paper establishes a multi-objective energy-saving flexible job shop scheduling model with the objectives of minimizing the maximum and total machine load, makespan, then proposes a hybrid search genetic algorithm (HSGA) to solve this problem. Firstly, for enhancing population diversity, this paper proposes a cluster-based initial solution selection strategy, preventing the issue of limited search range caused by high similarity among initial solutions. Secondly, to broaden the search scope for multi-objective optimal solutions, this paper proposes a population selection strategy that considers individual neighborhood density and designed multiple neighborhood operators for variable neighborhood search (VNS). Finally, this paper proposes an adaptive strategy to dynamically adjust crossover and mutation probabilities for genetic operators, achieving a balance between global and local search. Experimental results show that the HSGA exhibits superior performance.