A multi-phase algorithm for a joint lot-sizing and pricing problem with stochastic demands

Stochastic lot-sizing problems have been addressed quite extensively, but relatively few studies also consider marketing factors, such as pricing. In this paper, we address a joint stochastic lot-sizing and pricing problem with capacity constraints and backlogging for a firm that produces a single i...

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Bibliographic Details
Published in:International journal of production research 2014-04, Vol.52 (8), p.2345-2362
Main Authors: Li, Hongyan, Thorstenson, Anders
Format: Article
Language:English
Subjects:
capacitated lot sizing
Complexity
Decision making
Decomposition
Demand
dynamic programming
Heuristic
Inventory management
Marketing
Mathematical models
Mathematical problems
Mathematical programming
Pricing
Programming
stochastic model
Stochastic models
Stochasticity
Studies
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Summary:Stochastic lot-sizing problems have been addressed quite extensively, but relatively few studies also consider marketing factors, such as pricing. In this paper, we address a joint stochastic lot-sizing and pricing problem with capacity constraints and backlogging for a firm that produces a single item over a finite multi-period planning horizon. Thece-dependent demands. The stochastic demand is captured by the scenario analysis approach, and this leads to a multiple-stage stochastic programming problem. Given the complexity of the stochastic programming problem, it is hard to determine optimal prices and lot sizes simultaneously. Therefore, we decompose the joint lot-sizing and pricing problem with stochastic demands and capacity constraints into a multi-phase decision process. In each phase, we solve the associated sub-problem to optimality. The decomposed decision process corresponds to a practically viable approach to decision-making. In addition to incorporating market uncertainty and pricing decisions in the traditional production and inventory planning process, our approach also accommodates the complexity of time-varying cost and capacity constraints. Finally, our numerical results show that the multi-phase heuristic algorithm solves the example problems effectively.
ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2013.864053