A static semi-kitting strategy system of JIT material distribution scheduling for mixed-flow assembly lines

Abstract

The green scheduling plays an increasingly important role in many manufacturing enterprises and trading off the Just-in-Time (JIT) and energy efficiency is a complex optimization problem. A static semi-kitting strategy is proposed to deal with a bi-objective JIT material distribution scheduling problem considering energy consumption (BJMDSP-EC) for mixed-flow assembly lines. A mathematical model that aims to minimize the total energy consumption and the total line-side inventory is established, which meets the request of energy saving and JIT. Due to the NP-hard nature of the proposed problem, a Memory-based Bi-objective Chaotic Gravitational Search Algorithm (MBCGSA) is developed to solve the BJMDSP-EC, which hybridizes the Gravitational Search Algorithm (GSA) and the non-dominated sorting genetic algorithm-II (NSGA-II). The memory-based search strategy and the chaotic gravity constant are applied to the bi-objective optimization algorithm to balance exploration and exploitation. Moreover, clustering and Cauchy deviates are utilized to initialize the population, and two local search optimization operators are designed to optimize two objectives. Finally, computational experiments are performed to evaluate the performance of the MBCGSA by being compared with two other bi-objective optimization algorithms, the multi-objective particle swarm optimization (MOPSO) and NSGA-II, and the results reveal the effectiveness and efficiency of the MBCGSA when solving BJMDSP-EC.