检索结果-内蒙古大学图书馆

A multi-stage stochastic integer programming approach for a multi-echelon lot-sizing problem with returns and lost sales

引用

COMPUTERS & OPERATIONS RESEARCH 2020年 116卷 104865-000页

作者： Quezada, Franco Gicquel, Celine Kedad-Sidhoum, Safia Dong Quan Vu Sorbonne Univ CNRS Lab Informat Paris 6 F-75005 Paris France Univ Paris Saclay LRI F-91190 Gif Sur Yvette France CEDRIC CNAM F-75003 Paris France Nokia Paris Saclay Nokia Bell Labs Route Villejust F-91620 Nozay France

We consider an uncapacitated multi-item multi-echelon lot-sizing problem within a remanufacturing system involving three production echelons: disassembly, refurbishing and reassembly. We seek to plan the production activities on this system over a multi-period horizon. We consider a stochastic environment, in which the input data of the optimization problem are subject to uncertainty. We propose a multi-stage stochastic integer programming approach relying on scenario trees to represent the uncertain information structure and develop a branch-and-cut algorithm in order to solve the resulting mixed-integer linear program to optimality. This algorithm relies on a new set of tree inequalities obtained by combining valid inequalities previously known for each individual scenario of the scenario tree. These inequalities are used within a cutting-plane generation procedure based on a heuristic resolution of the corresponding separation problem. Computational experiments carried out on randomly generated instances show that the proposed branch-and-cut algorithm performs well as compared to the use of a stand-alone mathematical solver. Finally, rolling horizon simulations are carried out to assess the practical performance of the multi-stage stochastic planning model with respect to a deterministic model and a two-stage stochastic planning model. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： stochastic lot-sizing Remanufacturing system Lost sales multi-stage stochastic integer programming Scenario tree Valid inequalities Branch-and-cut algorithm

来源：评论

学校读者我要写书评

暂无评论

An approximate dynamic programming approach for solving aircraft fleet engine maintenance problem: Methodology and a case study

引用

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2025年第3期321卷 958-973页

作者： Zhang, Miao Yang, Jingyuan Zhang, Chuwen He, Simai Liu, Huikang Wang, Jinshen Wang, Zizhuo Shanghai Univ Finance & Econ Sch Informat Management & Engn Shanghai 200433 Peoples R China Univ Chicago Booth Sch Business Chicago IL 60637 USA Shanghai Jiao Tong Univ Antai Coll Econ & Management Shanghai 200240 Peoples R China China Southern Airlines Co Ltd Guangzhou 510403 Peoples R China Chinese Univ Hong Kong Sch Data Sci Shenzhen 518172 Guangdong Peoples R China Cardinal Operat Co Ltd Beijing 100102 Peoples R China

We consider a long-term engine maintenance planning problem for an aircraft fleet. The objective is to guarantee sufficient on-wing engines to reach service levels while effectively organizing shop visits for engines. However, complexity arises from intricate maintenance policies and uncertainty in engine deterioration. To address this problem, we propose a graph-based approach representing high-dimensional engine statuses and transitions. We then formulate the problem as a multi-stage stochastic integer program with endogenous uncertainty. We develop an approximate dynamic programming algorithm enhanced by dynamic graph generation and policy-sifting techniques so as to reduce the computational overhead in large problems. We demonstrate the efficacy of our method, compared with other popular methods, in terms of running time and solution quality. In the case study, we present an implementation in a real-world decision system in China Southern Airlines, in which the proposed method works seamlessly with other supporting modules and significantly improves the efficiency of engine maintenance management.

关键词： OR in airlines Engine maintenance Fleet management multi-stage stochastic integer programming Approximate dynamic programming

来源：评论

学校读者我要写书评

暂无评论

A multi-stage stochastic programming approach for network capacity expansion with multiple sources of capacity

引用

NAVAL RESEARCH LOGISTICS 2016年第8期63卷 600-614页

作者： Taghavi, Majid Huang, Kai Dalhousie Univ Sch Hlth Adm Fac Hlth Profess Halifax NS Canada McMaster Univ DeGroote Sch Business Hamilton ON Canada

In networks, there are often more than one sources of capacity. The capacities can be permanently or temporarily owned by the decision maker. Depending on the nature of sources, we identify the permanent capacity, spot market capacity, and contract capacity. We use a scenario tree to model the uncertainty, and build a multi-stage stochastic integer program that can incorporate multiple sources and multiple types of capacities in a general network. We propose two solution methodologies for the problem. Firstly, we design an asymptotically convergent approximation algorithm. Secondly, we design a cutting plane algorithm based on Benders decomposition to find tight bounds for the problem. The numerical experiments show superb performance of the proposed algorithms compared with commercial software. (C) 2016 Wiley Periodicals, Inc.

关键词： multi-stage stochastic integer programming scenario tree capacity expansion spot market contract approximation algorithm Benders decomposition

来源：评论

学校读者我要写书评

暂无评论

A multi-stage stochastic programming approach for supply chain risk mitigation via product substitution

引用

COMPUTERS & INDUSTRIAL ENGINEERING 2020年 149卷 106786-106786页

作者： Khalilabadi, Seyed Mahdi Ghorashi Zegordi, Seyed Hessameddin Nikbakhsh, Ehsan Tarbiat Modares Univ Fac Ind & Syst Engn Post Box 14117-13116 Tehran Iran

Trends like globalization, shorter product life-cycles, and cost reduction strategies in the global business environment have exposed many supply chains to various risks. Disruptions are one of the supply chain risks that can interrupt product flow, delay customer deliveries, and reduce supply chain revenues considerably. Prior planning for disruptions could greatly alleviate these consequences. A method to cope with disruptions is to use product substitution in the case of a product shortage. In this research, the supply chain of a livestock-drug distribution company in Iran, facing demand disruptions, has been chosen as a case study. For this purpose, a multi-stage stochastic integer programming model is proposed and solved using a customized progressive hedging algorithm. Moreover, the effect of uncertainty on the supply chain performance is measured using the value of the stochastic solution (VSS) and the expected value of perfect information (EVPI) metrics. Based on the different instances of the problem solved, the VSS metric shows that modeling and solving the proposed stochastic model could enhance the company profit by about 3.27 percent on average. In addition, the EVPI metric demonstrates that planning and investing in proactive demand management could enhance the profit up to 9.42 percent. Finally, analyses indicate that when dealing with increased demand uncertainty levels, the importance of using the proposed method increases as the profitability of the company decreases.

关键词： Supply chain Disruption multi-stage stochastic integer programming Progressive hedging algorithm

来源：评论

学校读者我要写书评

暂无评论

A stochastic dual dynamic integer programming based approach for remanufacturing planning under uncertainty

引用

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2023年第17期61卷 5992-6012页

作者： Quezada, Franco Gicquel, Celine Kedad-Sidhoum, Safia Univ Santiago Chile USACH Fac Engn Ind Engn Dept Santiago 8320000 Chile Univ Santiago Chile USACH Fac Engn Program Dev Sustainable Prod Syst PDSPS Santiago 8320000 Chile Univ Paris Saclay LISN Gif Sur Yvette France CNAM CEDRIC Paris France

We seek to optimize the production planning of a three-echelon remanufacturing system under uncertain input data. We consider a multi-stage stochastic integer programming approach and use scenario trees to represent the uncertain information structure. We introduce a new dynamic programming formulation that relies on a partial nested decomposition of the scenario tree. We then propose a new approximate stochastic dual dynamic integer programming algorithm based on this partial decomposition. Our numerical results show that the proposed solution approach is able to provide near-optimal solutions for large-size instances with a reasonable computational effort.

关键词： Production planning lot-sizing remanufacturing multi-stage stochastic integer programming stochastic dual dynamic programming

来源：评论

学校读者我要写书评

暂无评论

A Branch-and-Cut algorithm for the stochastic uncapacitated lot-sizing problem

引用

MATHEMATICAL programming 2006年第1期105卷 55-84页

作者： Guan, YP Ahmed, S Nemhauser, GL Miller, AJ Georgia Inst Technol Sch Ind & Syst Engn Atlanta GA 30332 USA Univ Wisconsin Dept Ind Engn Madison WI 53706 USA

This paper addresses a multi-stage stochastic integer programming formulation of the uncapacitated lot-sizing problem under uncertainty. We show that the classical (l, S) inequalities for the deterministic lot-sizing polytope are also valid for the stochastic lot-sizing polytope. We then extend the (l, S) inequalities to a general class of valid inequalities, called the ( Q, S-Q) inequalities, and we establish necessary and sufficient conditions which guarantee that the ( Q, S-Q) inequalities are facet-defining. A separation heuristic for ( Q, S-Q) inequalities is developed and incorporated into a branch-and-cut algorithm. A computational study verifies the usefulness of the ( Q, S-Q) inequalities as cuts.

关键词： stochastic lot-sizing multi-stage stochastic integer programming polyhedral study Branch-and-Cut

来源：评论

学校读者我要写书评

暂无评论

Condition-based maintenance for multi-component systems: Modeling, structural properties, and algorithms

引用

IISE TRANSACTIONS 2021年第1期53卷 88-100页

作者： Zhu, Zhicheng Xiang, Yisha Texas Tech Univ Dept Ind Mfg & Syst Engn Lubbock TX 79409 USA

Condition-Based Maintenance (CBM) is an effective maintenance strategy to improve system performance while lowering operating and maintenance costs. Real-world systems typically consist of a large number of components with various interactions among components. However, existing studies on CBM mainly focus on single-component systems. multi-component CBM, which joins the components' stochastic degradation processes and the combinatorial maintenance grouping problem, remains an open issue in the literature. In this article, we study the CBM optimization problem for multi-component systems. We first develop a multi-stage stochastic integer model with the objective of minimizing the total maintenance cost over a finite planning horizon. We then investigate the structural properties of a two-stage model. Based on the structural properties, two efficient algorithms are designed to solve the two-stage model. Algorithm 1 solves the problem to its optimality and Algorithm 2 heuristically searches for high-quality solutions based on Algorithm 1. Our computational studies show that Algorithm 1 obtains optimal solutions in a reasonable amount of time and Algorithm 2 can find high-quality solutions quickly. The multi-stage problem is solved using a rolling horizon approach based on the algorithms for the two-stage problem. are available for this article. Go to the publisher's online edition of IISE Transaction, datasets, additional tables, detailed proofs, etc.

关键词： Condition-based maintenance multi-component systems multi-stage stochastic integer programming endogenous uncertainty

来源：评论

学校读者我要写书评

暂无评论

stochastic capacity expansion with multiple sources of capacity

引用

OPERATIONS RESEARCH LETTERS 2014年第4期42卷 263-267页

作者： Taghavi, Majid Huang, Kai McMaster Univ DeGroote Sch Business Hamilton ON L8S 4M4 Canada

In this paper, we consider the multi-period single resource stochastic capacity expansion problem with three sources of capacity: permanent, contract, and spot market. The problem is modeled as a multi-stage stochastic integer program. We show that the problem has the totally unimodular property and develop polynomial-time primal and dual algorithms to solve the problem. Crown Copyright (C) 2014 Published by Elsevier B.V.

关键词： Capacity expansion multi-stage stochastic integer programming Spot market Contract

来源：评论

学校读者我要写书评

暂无评论

A Lagrangian relaxation approach for stochastic network capacity expansion with budget constraints

引用

ANNALS OF OPERATIONS RESEARCH 2020年第2期284卷 605-621页

作者： Taghavi, Majid Huang, Kai Dalhousie Univ Fac Hlth Sch Hlth Adm Halifax NS B3H 4R2 Canada McMaster Univ DeGroote Sch Business Hamilton ON L8S 4M4 Canada

In this paper, we consider capacity expansion for network models subject to uncertainty and budget constraints. We use a scenario tree approach to handle the uncertainty and construct a multi-stage stochastic mixed-integer programming model for the network capacity expansion problem. We assume that permanent capacity and spot market capacity are available, which can be used permanently or temporarily by the decision maker respectively. By relaxing the budget constraints, we propose a heuristic Lagrangian relaxation method to solve the problem. Two algorithms are developed to find tight upper bounds for the Lagrangian relaxation procedure. The experimental results show superior performance of the proposed Lagrangian relaxation method compared with CPLEX.

关键词： Capacity expansion Scenario tree multi-stage stochastic integer programming Lagrangian relaxation Network flow Spot market Permanent capacity

来源：评论

学校读者我要写书评

暂无评论

Undominated Valid Inequalities for a stochastic Capacitated Discrete Lot-sizing Problem with Lead Times, Cancellation and Postponement 8th

Undominated Valid Inequalities for a Stochastic Capacitated ...

引用

8th International Conference on Operations Research and Enterprise Systems (ICORES)

作者： Testuri, Carlos E. Cancela, Hector Albornoz, Victor M. Univ Republica Fac Ingn Inst Comp Dept Invest Operat J Herrera y Reissig 565 Montevideo 11300 Uruguay Univ Tecn Federico Santa Maria Dept Ind Campus Santiago VitacuraAvda Santa Maria 6400 Santiago Chile

ISBN: (纸本)9789897583520

The problem addresses the expected cost minimization of meeting the uncertain demand of a product during a discrete time planning horizon. The product is supplied by selecting fixed quantity shipments that have lead times. Due to the uncertainty of demand, corrective actions, such as shipment cancellations and postponements, must be taken with associated costs and delays. The problem is modeled as an extension of the discrete lot-sizing problem with different capacities and uncertain demand, which belongs to the NP-hard class. To improve the resolution of the problem by tightening its formulation, valid inequalities based on the (l, S) inequalities approach are used. Given that the inequalities are highly dominated for most experimental instances, a scheme is established to determine undominated ones. Computational experiments are performed on the resolution of the model and variants that include subsets of undominated and representative valid inequalities for instances of several information structures of uncertainty. The experimental results allow to conclude that the inclusion of undominated and representative derived (l, S) valid inequalities enable a more efficient resolution of the model.

关键词： stochastic Lot-sizing multi-stage stochastic integer programming Valid Inequalities

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：