Integrated green scheduling optimization of flexible job shop and crane transportation considering comprehensive energy consumption

摘要

Since the energy waste of manufacturing industries exacerbates pollution emissions and directly affects the environment, the energy efficiency optimization of the production process is an effective way to promote cleaner production. The transportation process of a workpiece is non-negligible during the entire workshop production process. Particularly in traditional heavy-duty industrial manufacturing enterprises, the energy consumption of the transportation process accounts for a significant proportion of the total workshop energy consumption. To reduce the comprehensive energy consumption of the machining process and transportation process in an actual manufacturing environment, this paper addresses a novel integrated green scheduling problem of flexible job shop and crane transportation (IGSP-FJS&CT). First, a mixed integer programming (MIP) model is formulated to minimize the total cost of the comprehensive energy consumption and makespan in the IGSP-EIS&CT. In the MIP model, a comprehensive energy consumption model is built to optimize the comprehensive energy efficiency of the crane's complete operating cycle and machining process. Then, an integrated GA-GSO-GTHS algorithm is presented to solve the proposed MIP model. In GA-GSO-GTHS, a genetic algorithm (GA) is employed to perform the global search and a glowworm swarm optimization (GSO) algorithm is applied to perform the local search. The green transport heuristic strategy (GTHS) is proposed to guide the search direction of the GA-GSO algorithm. Finally, the performance and effectiveness of the proposed method are demonstrated in a case study. In China's traditional heavy industry manufacturing enterprises, the workshop production is mainly scheduled by manual dispatcher. Consequently, the proposed method has a wide application background in improving energy waste of workshop production, which provides a guiding significance on promoting cleaner production of traditional heavy industry manufacturing. (C) 2018 Elsevier Ltd. All rights reserved.