Department of Economics and Business Economics

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

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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.
Original languageEnglish
JournalInternational Journal of Production Research
Pages (from-to)1-18
Number of pages19
Publication statusPublished - 2014

Bibliographical note

Campus adgang til artiklen / Campus access to the article

    Research areas

  • Capacitated lot sizing, Pricing, Dynamic programming, Decomposition

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