离散流水线车间内交货期与鲁棒性及能耗成本的集成优化  

Integrated Optimization of Makespan,Robustness and Energy Cost for the Flow Shop in Manufacturing Plant

作　　者：崔维伟 蒋诚仁 刘新波 CUI Weiwei;JIANG Chengren;LIU Xinbo(School of Management,Shanghai University,Shanghai 200444,China)

机构地区：[1]上海大学管理学院,上海200444

出　　处：《运筹与管理》2024年第8期58-64,共7页Operations Research and Management Science

基　　金：国家自然科学基金青年基金项目(71801147)。

摘　　要：当今车间管理者不仅需要在最小化制造工期的同时尽可能应对设备随机故障等因素引起的不稳定性,更需要节省分时电价模式下的能耗成本以提高产品价格竞争力。本研究以考虑设备随机故障的流水线车间为研究对象,以工件交货期、系统鲁棒性、能耗成本为目标,建立了涉及生产调度、设备维护、能量分配三个维度的模型。首先,以代理指标方法结合蒙特卡洛仿真验证解决了不确定环境下的可行解评估问题;其次以NSGA-II框架为基础结合启发式解码方法以搜索此多目标问题的帕累托曲线。数值实验验证了所提目标评估方法的有效性以及集成方案相较于传统规则的优越性;在各工序间插入合适的缓冲时间,既可以吸收故障冲击以提高系统鲁棒性,又可以调整设备开机时段以降低总能耗成本。Managers within manufacturing plants confront increasingly intricate scenarios,necessitating efforts to minimize manufacturing lead times amidst the destabilizing impact of random failures.Concurrently,they must also endeavor to curtail energy costs within time-of-use tariffs,thereby bolstering the price competitiveness of products.Focusing on the discrete flow shop,this study incorporates considerations of energy consumption costs within the framework of TOU tariff policies and the stochastic nature of equipment failures.Through the integration of production scheduling and equipment maintenance,this study aims to devise a cohesive modeling approach that enables comprehensive planning for both activities.The devised integrated optimization scheme outlined in this paper is poised to significantly aid enterprises in achieving peak shaving and valley filling,alongside cost reduction and efficiency enhancements under time-sharing tariff policies.Furthermore,it offers valuable insights for workshop managers seeking to formulate judicious and effective production plans within complex and uncertain production environments.This study focuses on multiple interrelated dimensions within the manufacturing shop,establishing a mathematical model that encompasses three decision dimensions:production scheduling,equipment maintenance,and energy allocation.A two-layer algorithm is devised to tackle this model effectively.Firstly,a method based on surrogate measures is designed to evaluate the performance of solutions.Then,a metaheuristic algorithm is designed combining the NSGA-II framework and the constructive-heuristic rules to search the Pareto curve of this multi-objective problem.Data are acquired through Monte Carlo simulation.Under the assumption that random faults follow an exponential distribution,random numbers are generated by sampling iteratively to simulate the system,followed by conducting several tests.In the algorithm’s validation phase,Monte Carlo sampling simulation is employed to compute the expected value of

关 键 词：流水线 生产调度 设备维护 能耗需求 多目标优化 

分 类 号：TK-9[动力工程及工程热物理] C931.1[经济管理—管理学]