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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Bibliographic Details
Published in:Journal of heuristics 2010-04, Vol.16 (2), p.189-209
Main Authors: Herbots, Jade, Herroelen, Willy, Leus, Roel
Format: Article
Language:English
Subjects:
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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Summary: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.
ISSN:1381-1231
1572-9397
DOI:10.1007/s10732-008-9096-9