考虑分布式仓库的两级供应链生产与运输协同调度问题研究

发布时间：2018-04-18 20:02

本文选题：批调度 + 生产与运输协同调度　；参考：《合肥工业大学》2017年硕士论文

【摘要】：目前,在经济全球化的大环境下,企业之间的竞争越来越激烈,为了增强企业的竞争力,许多企业越来越重视通过与供应链成员企业之间的合作,以求降低企业的生产成本,提高企业对市场需求的响应速度。物联网技术的发展,使得企业可以实时获得产品的生产和运输信息,这些信息可以有效减轻不确定因素对供应链整体的影响,保证供应链企业之间在调度层面上的协同,从而使得供应链企业间得以正常运作。但是,如何将这些信息转化为经济效益和社会效益,是供应链调度领域内面临的一个关键性问题。本文针对这一问题,以钢铁行业为背景,结合批调度和生产与运输协同调度等相关理论,研究了由一个制造商和一个客户组成的两级供应链的生产与运输协同调度问题,其中制造商拥有多个分布在不同地理位置的仓库。本文分两种情况进行研究:一、在单机环境下,以实现最大完工时间的最小化为优化目标,根据问题特性设计启发式规则,利用加入交叉和变异算子的改进离散粒子群算法(MBPSO)求解工件分批、批次加工顺序和批次运输顺序问题,并设计不同规模的算例进行仿真实验比较,分析了算法的满意性;二、在平行机环境下,以实现制造跨度的最小化为目标函数,求解对加工时间不同的差异工件进行分批、批次分配和批次运输的调度问题,阐述了问题的性质,并结合批次分配的启发式规则提出了一种算法(P-MBPSO),通过对比不同规模算例的仿真试验结果,证实了算法的有效性和满意性。
[Abstract]:At present, under the environment of economic globalization, the competition among enterprises is becoming more and more fierce. In order to enhance the competitiveness of enterprises, many enterprises pay more and more attention to the cooperation with supply chain members in order to reduce the production costs of enterprises.Improve the response speed of enterprises to market demand.With the development of Internet of things (IoT) technology, enterprises can obtain production and transportation information of products in real time. These information can effectively reduce the influence of uncertain factors on the whole supply chain and ensure the coordination between supply chain enterprises at the scheduling level.As a result, supply chain enterprises can operate normally.However, how to transform these information into economic and social benefits is a key problem in the field of supply chain scheduling.In this paper, based on the background of iron and steel industry, combined with batch scheduling and production and transportation cooperative scheduling theory, this paper studies the production and transportation cooperative scheduling problem in a two-level supply chain composed of a manufacturer and a customer.The manufacturer has multiple warehouses located in different geographical locations.This paper is divided into two situations: first, in a single machine environment, with the aim of minimizing the maximum completion time, a heuristic rule is designed according to the characteristics of the problem.The improved discrete particle swarm optimization (MBPSO) algorithm with crossover and mutation operators is used to solve the batch, batch processing and batch transportation order problems. The simulation results of different scale examples are compared, and the satisfaction of the algorithm is analyzed.In parallel machine environment, taking the minimization of manufacturing span as the objective function, the problem of batch distribution, batch distribution and batch transportation for different workpieces with different processing time is solved, and the properties of the problem are expounded.In combination with the heuristic rules of batch allocation, an algorithm called P-MBPSOO is proposed. The effectiveness and satisfaction of the algorithm are verified by comparing the simulation results of different scale examples.
【学位授予单位】：合肥工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F274;F426.31

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