Exact and approximate solution methods for batch scheduling problems

批量调度问题的精确和近似求解方法

• 批准号: RGPIN-2019-05691
• 负责人: Ozturk, Onur
• 金额: $ 1.89万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=715745
• 关键词: Exact; approximate; solution; methods; batch

In production and service industries, the scheduling and sequencing of activities play a crucial role in the efficient allocation of tasks to resources. Batch scheduling is the type of scheduling in which multiple jobs are grouped and processed together. Some examples where batch scheduling is encountered are semiconductor manufacturing, furniture manufacturing, chemical production, metal industry, textile industry, transportation, etc. Vis-à-vis the Canadian economy, two very important sectors where batch scheduling is encountered are steel and aluminum production. In 2017, these industries employed more than a total of 33,000 people and contributed around $9 billion to Canada's gross domestic product. Decision making in production planning is a dynamic process and the application of a general (daily, weekly, etc.) policy is unlikely to be determined. Analytical models can be used to take optimal (or close to optimal) batching decisions. Most of the time, these models are helpful to solve only small size problems because of their mathematical complexity. Our aim is to develop efficient solution methods for a generic model that incorporates as many of the more crucial realistic aspects of the batch scheduling problem as we can. While the batch scheduling literature is rich in heuristic and metaheuristic solution methods, mathematical decomposition methods and exact algorithms are developed for a few problems with simplified hypotheses. With regards to approximation algorithms, most of the existing work has focused on makespan minimization. The proposed research program seeks to develop novel solution techniques by grouping real-life hypotheses such as parallel machines, job release dates, due dates, job families, job dimensions, etc. At first, we will focus on mathematical decomposition methods based on time indexed column generation models (also coupled with row generation depending on the problem type) capable of representing real-life hypotheses. Then we will continue with another setting in which problem data is not fully known in advance. For that setting, we will develop online approximation algorithms to take real-time batching decisions. It is also crucial to test how optimizing the batching step effects the efficiency of the overall system. For that purpose, we will build a generic simulation model for steel production and integrate previously developed batching algorithms as scheduling decisions in that simulation model. The proposed research targets primarily the Operations Research (OR) community, but it also intends to allow practitioners to take optimum decisions in production planning and scheduling. I also expect the outcome of this research to generate new research ideas for the OR community and help to improve the notion of applying OR techniques in production industries.

在生产和服务行业中，活动的调度和测序在批处理计划的效率中起着至关重要的作用纺织工业，运输等。阿纳德经济，批量计划的两个非常重要的是钢铝的生产。 生产计划中的决策是一个动态的过程，可以使用一般（每周等）模型的应用来采取最佳（或接近最佳）批处理决策，这些模型只能解决他们的尺寸问题。通用模型的数学复杂性。 尽管批处理文献富含启发式和元尿素解决方案方法，但对于一些Sith而言，Algori Thms却在近似算法方面开发出来。 ，工作释放日期，作业，J amilies，工作维度等。首先，我们将基于时间索引列的生成模型（也与座位相结合），重点介绍数学分解方法。在哪个问题数据中并不完全知道。设置，我们将在线近似算法进行实时批次决定。 THAATS模拟模型。 拟议的人针对运营研究（OR）社区，但它也打算采取最佳决策II n生产计划和调度在生产行业中应用或技术的概念。