Single-pass and approximate dynamic-programming algorithms for order acceptance and capacity planning

This paper investigates dynamic order acceptance and capacity planning under limited regular and non-regular resources. Our goal is to maximize the profits of the accepted projects within a finite planning horizon. The way in which the projects are planned affects their payout time and, as a consequ...

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Vydáno v:	Journal of heuristics Ročník 16; číslo 2; s. 189 - 209
Hlavní autoři:	Herbots, Jade, Herroelen, Willy, Leus, Roel
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Boston Springer US 01.04.2010 Springer Science + Business Media Springer Nature B.V
Témata:	Algorithms Artificial Intelligence Calculus of Variations and Optimal Control; Optimization Dynamic programming Interest rates Management Science Mathematics Mathematics and Statistics Operations Research Operations Research/Decision Theory Planning Simulation Studies Workloads Approximate dynamic programming Order acceptance Simulation Multi-project Capacity planning
ISSN:	1381-1231, 1572-9397
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Abstract	This paper investigates dynamic order acceptance and capacity planning under limited regular and non-regular resources. Our goal is to maximize the profits of the accepted projects within a finite planning horizon. The way in which the projects are planned affects their payout time and, as a consequence, the reinvestment revenues as well as the available capacity for future arriving projects. In general, project proposals arise dynamically to the organization, and their actual characteristics are only revealed upon arrival. Dynamic solution approaches are therefore most likely to obtain good results. Although the problem can theoretically be solved to optimality as a stochastic dynamic program, real-life problem instances are too difficult to be solved exactly within a reasonable amount of time. Efficient and effective heuristics are thus required that supply a response without delay. For this reason, this paper considers both ‘single-pass’ algorithms as well as approximate dynamic-programming algorithms and investigates their suitability to solve the problem. Simulation experiments compare the performance of our procedures to a first-come, first-served policy that is commonly used in practice.
AbstractList	This paper investigates dynamic order acceptance and capacity planning under limited regular and non-regular resources. Our goal is to maximize the profits of the accepted projects within a finite planning horizon. The way in which the projects are planned affects their payout time and, as a consequence, the reinvestment revenues as well as the available capacity for future arriving projects. In general, project proposals arise dynamically to the organization, and their actual characteristics are only revealed upon arrival. Dynamic solution approaches are therefore most likely to obtain good results. Although the problem can theoretically be solved to optimality as a stochastic dynamic program, real-life problem instances are too difficult to be solved exactly within a reasonable amount of time. Efficient and effective heuristics are thus required that supply a response without delay. For this reason, this paper considers both ‘single-pass’ algorithms as well as approximate dynamic-programming algorithms and investigates their suitability to solve the problem. Simulation experiments compare the performance of our procedures to a first-come, first-served policy that is commonly used in practice. This paper investigates dynamic order acceptance and capacity planning under limited regular and non-regular resources. Our goal is to maximize the profits of the accepted projects within a finite planning horizon. The way in which the projects are planned affects their payout time and, as a consequence, the reinvestment revenues as well as the available capacity for future arriving projects. In general, project proposals arise dynamically to the organization, and their actual characteristics are only revealed upon arrival. Dynamic solution approaches are therefore most likely to obtain good results. Although the problem can theoretically be solved to optimality as a stochastic dynamic program, real-life problem instances are too difficult to be solved exactly within a reasonable amount of time. Efficient and effective heuristics are thus required that supply a response without delay. For this reason, this paper considers both 'single-pass' algorithms as well as approximate dynamic-programming algorithms and investigates their suitability to solve the problem. Simulation experiments compare the performance of our procedures to a first-come, first-served policy that is commonly used in practice.[PUBLICATION ABSTRACT]
Author	Herbots, Jade Leus, Roel Herroelen, Willy
Author_xml	– sequence: 1 givenname: Jade surname: Herbots fullname: Herbots, Jade organization: Department of Decision Sciences and Information Management, Katholieke Universiteit Leuven – sequence: 2 givenname: Willy surname: Herroelen fullname: Herroelen, Willy organization: Department of Decision Sciences and Information Management, Katholieke Universiteit Leuven – sequence: 3 givenname: Roel surname: Leus fullname: Leus, Roel email: Roel.Leus@econ.kuleuven.be organization: Department of Decision Sciences and Information Management, Katholieke Universiteit Leuven
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Cites_doi	10.1016/S0377-2217(96)00112-9 10.1287/mnsc.48.10.1227.275 10.1023/A:1009634810396 10.1007/s10479-005-2060-2 10.1007/s10107-004-0551-6 10.1002/nav.20259 10.1287/opre.40.5.831 10.1016/S0377-2217(96)00021-5 10.1007/978-3-662-22341-3 10.2139/ssrn.968623 10.1007/s00291-004-0171-9
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Keywords	Approximate dynamic programming Order acceptance Simulation Multi-project Capacity planning
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SubjectTerms	Algorithms Artificial Intelligence Calculus of Variations and Optimal Control; Optimization Dynamic programming Interest rates Management Science Mathematics Mathematics and Statistics Operations Research Operations Research/Decision Theory Planning Simulation Studies Workloads
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Title	Single-pass and approximate dynamic-programming algorithms for order acceptance and capacity planning
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