运筹与管理 ›› 2018, Vol. 27 ›› Issue (10): 17-22.DOI: 10.12005/orms.2018.0225

• 理论分析与方法探讨 • 上一篇    下一篇

考虑生产计划的多周期切割问题优化研究

马宁1,2, 周支立1,2, 刘雅1,2   

  1. 1.西安交通大学管理学院工业工程系,陕西 西安 710049;
    2.西安交通大学机械制造系统工程国家重点实验室,陕西 西安 710049
  • 收稿日期:2015-09-20 出版日期:2018-10-25
  • 作者简介:马宁(1990-),男,河南人,博士研究生,研究方向:优化调度;周支立(1960-),男,浙江人,教授,博士,研究方向:物流管理;刘雅(1982-),女,河北人,副教授,博士,研究方向:物流与生产调度。
  • 基金资助:
    国家自然科学基金项目(71390333,71301127);陕西省自然科学基金项目(2015JM7369)

Research on the Multi-period Cutting Stock Problem Coordinating with Production Planning

MA Ning1,2, ZHOU Zhi-li1,2, LIU Ya1,2   

  1. 1.Department of Industrial Engineering, School of Management, Xi’an Jiaotong University, Xi’an 710049, China;
    2.State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, China
  • Received:2015-09-20 Online:2018-10-25

摘要: 切割生产广泛存在于工业企业,是原材料加工的重要环节。已有文献主要关注单周期切割问题,但是切割计划也是生产计划的一部分,切割计划和生产计划应该协调优化,达到全局最优。本文研究考虑生产计划的多周期切割问题,目标是最小化运营成本,包括准备成本、切割成本、库存成本以及母材消耗成本。首先建立混合整数规划模型;提出动态规划启发式算法;最后对算例在多种情境下测试,分析成本因子变化对最优结果的影响。算法结果与CPLEX最优结果比较,平均误差为1.85%,表明算法是有效的。

关键词: 切割问题, 协调调度, 动态规划, 启发式算法

Abstract: Cutting stock production is the key stage of raw materials processing, which widely spreads in industrial companies. There is much literature concentrating on single period cutting stock problem. However, cutting stage is part of production plan. An advanced manufacturing plan consists of not only cutting plans but also multi-period production plans. This paper investigates a multi-period cutting stock problem coordinating with production planning. The objective is minimizing total operational cost, including production setup cost, cutting patterns change cost, inventory cost and input rods consumed cost. We first formulate a mixed integer mathematical model, and propose a dynamic based heuristic programming to solve it. Finally we test random instances based on different scenarios, analyzing the effect of cost factor. The average cost is 1.85%, compared with optimal solutions obtained by CPLEX. This indicates the algorithm can obtain near-optimal solutions efficiently.

Key words: utting stock, coordinating scheduling, dynamic programming, heuristic

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