Robust multi-objective job-shop scheduling for make-to-order manufacturing

用于按单生产的稳健多目标车间调度

• 批准号: 521778-2017
• 负责人: KazemiZanjani, Masoumeh
• 金额: $ 1.82万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=639430
• 关键词: Robust; multi; objective; job; shop

Effective order sequencing and task scheduling has become a necessity for survival in today's competitivemarkets. Scheduling in a manufacturing environment concerns with translating released orders into jobs withassociated due dates and allocating them to designated workstations. Job-shop scheduling is a multi-objectivecombinatorial optimization problem. Nonetheless, the majority of scheduling algorithms are usually efficientunder a given objective function rather than a combination of them. Furthermore, unforeseen events on theshop floor, such as machine break-downs along with rush/canceled orders may have a significant impact on theschedule. In order to reduce the need for major changes in the initial schedule, robustness criteria should beincorporated into scheduling models.In this project, the applicant will team up with Festo Didactic Ltd. in order to overcome the challenges thecompany has been recently confronted in scheduling the continuously increasing sales. The main goal is todevelop a library of algorithmic procedures for robust multi-objective job-shop scheduling. The first steptowards achieving this goal would be the characterization of the specific scheduling problem via investigationof manufacturing processes corresponding to different product families. Identification of variousdecision-making criteria with corresponding priorities in addition to the most pertinent sources of disruptionswill be also an essential step in the project. The new scheduling engine that is expected to be integrated intotheir current production planning software will incorporate a set of basic and composite dispatching rules alongwith local search and metaheuristic algorithms to generate alternative schedules. Developing a discrete-eventsimulation model for the purpose of assessing the performance of generated schedules would be also within therealm of this research project.The results of this project will provide essential benefits to the company in terms of manufacturing lead-timeand work-in-process inventory reduction. Furthermore, the developed scheduling engine can be incorporated

有效的订单排序和任务调度已成为在当今竞争激烈的市场中生存的必要条件。在制造业环境中，排程是指将已发布的订单转换成具有相关到期日的作业，并将其分配到指定的工作站。作业车间调度是一个多目标组合优化问题。然而，大多数调度算法通常是在给定的目标函数下有效的，而不是它们的组合。此外，车间发生的不可预见事件，如机器故障沿着紧急/取消订单，可能会对计划产生重大影响。为了减少对初始计划的重大更改，应将稳健性标准纳入计划模型中。在本项目中，申请人将与Festo Didactic Ltd.合作，以克服公司最近在计划持续增长的销售方面所面临的挑战。主要目标是开发一个库的算法程序的鲁棒多目标车间作业调度。实现这一目标的第一步是通过对不同产品系列的制造过程进行研究，来描述特定的调度问题。除了最相关的干扰源之外，确定各种决策标准以及相应的优先级也是项目中的一个重要步骤。新的调度引擎，预计将被集成到他们目前的生产计划软件将纳入一套基本和复合调度规则以及本地搜索和元启发式算法，以产生替代计划。开发一个离散事件仿真模型的目的，评估所产生的时间表的性能也将在reerealm的这个研究项目。这个项目的结果将提供必要的好处，该公司在制造提前期和在制品库存减少。此外，所开发的调度引擎可以被并入