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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A multi-phase algorithm for a joint lot-sizing and pricing problem with stochastic demands. / Jenny Li, Hongyan; Thorstenson, Anders.

In: International Journal of Production Research, 2014, p. 1-18.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

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@article{07ec940ef4cb4619b54ccf8d498112a2,
title = "A multi-phase algorithm for a joint lot-sizing and pricing problem with stochastic demands",
abstract = "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.",
keywords = "Capacitated lot sizing, Pricing, Dynamic programming, Decomposition",
author = "{Jenny Li}, Hongyan and Anders Thorstenson",
note = "Campus adgang til artiklen / Campus access to the article",
year = "2014",
doi = "10.1080/00207543.2013.864053",
language = "English",
pages = "1--18",
journal = "International Journal of Production Research",
issn = "0020-7543",
publisher = "Taylor & Francis",

}

RIS

TY - JOUR

T1 - A multi-phase algorithm for a joint lot-sizing and pricing problem with stochastic demands

AU - Jenny Li, Hongyan

AU - Thorstenson, Anders

N1 - Campus adgang til artiklen / Campus access to the article

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

KW - Capacitated lot sizing

KW - Pricing

KW - Dynamic programming

KW - Decomposition

U2 - 10.1080/00207543.2013.864053

DO - 10.1080/00207543.2013.864053

M3 - Journal article

SP - 1

EP - 18

JO - International Journal of Production Research

JF - International Journal of Production Research

SN - 0020-7543

ER -