Dynamic admission control for two customer classes with stochastic demands and strict due dates. Issue 20 (17th October 2016)

• Record Type: Journal Article
• Authors: Mlinar, Tanja; Chevalier, Philippe
• Is Part Of: International journal of production research. Volume 54:Issue 20(2016)
• Page Start: 6156
• Page End: 6173
• Publication Date: 2016-10-17
• Subjects: demand and revenue management -- admission control -- Markov decision process -- heuristics -- due dates; Factory management -- Periodicals; 658.57
• DOI: 10.1080/00207543.2015.1055846
• Languages: English
• ISSNs: 0020-7543

Abstract

Abstract : We study a dynamic capacity allocation problem with admission control decisions of a company that caters for two demand classes with random arrivals, capacity requirements and strict due dates. We formulate the problem as a Markov decision process (MDP) in order to find the optimal admission control policy that maximises the expected profit of the company. Such a formulation suffers a state-space explosion. Moreover, it involves an additional dimension arising from the multiple possible order sizes that customers can request which further increases the complexity of the problem. To reduce the cardinality of possible policies, and, thus, the computational requirements, we propose a threshold-based policy. We formulate an MDP to generate such a policy. To deal with the curse of dimensionality, we develop threshold-based approximate algorithms based on the state-reduction heuristics with aggregation proposed previously. Our results reveal that for the majority of instances considered the optimal policy has a threshold structure. We then demonstrate the superiority of the proposed threshold-based approximate algorithms over two benchmark policies in terms of the generated profits and the robustness of the solutions to changes in operational conditions. Finally, we show that our proposed policies are also robust to changes in actual demand from its estimation.