2008•Unpublished venueRequires access

Order Acceptance and Capacity Allocation Policies Based on Revenue Management

Lifan Fan, Xu Chen

Open publisher page 3 citations

Abstract

Selective order acceptance and capacity allocation policies are vital to the success of make-to-order (MTO) manufactures. The importance of these two policies has been acknowledged both in manufacturing industry and academic research. After the huge success of revenue management in retail and service industries, there are more and more MTO manufactures introducing revenue management techniques into MTO manufacturing industry. In this paper, we consider the application of revenue management into a make-to-order system. Based on the maximizing revenue policy, we use the stochastic dynamic programming to model the MTO revenue problem, and solve it with the certainty equivalence approach, then the optimal order acceptance and capacity allocation polices are proposed-accept a new coming order as long as its revenue is greater than or equal to the minimum of the shadow revenue, and if it is accepted, allocate it a machine which with the minimum shadow revenue. In the end, the further research directions are presented.

About this research paper

What this paper is about

Selective order acceptance and capacity allocation policies are vital to the success of make-to-order (MTO) manufactures. The importance of these two policies has been acknowledged both in manufacturing industry and academic research. After the huge success of revenue management in retail and service industries, there are more and more MTO manufactures introducing revenue management techniques into MTO manufacturing industry. In this paper, we consider the application of revenue management into a make-to-order system. Based on the maximizing revenue policy, we use the stochastic dynamic programming to model the MTO revenue problem, and solve it with the certainty equivalence approach, then the optimal order acceptance and capacity allocation polices are proposed-accept a new coming order as long as its revenue is greater than or equal to the minimum of the shadow revenue, and if it is accepted, allocate it a machine which with the minimum shadow revenue. In the end, the further research directions are presented.

Why it matters

OpenAlex reports 3 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

Selective order acceptance and capacity allocation policies are vital to the success of make-to-order (MTO) manufactures. The importance of these two policies has been acknowledged both in manufacturing industry and academic research. After the huge success of revenue management in retail and service industries, there are more and more MTO manufactures introducing revenue management techniques into MTO manufacturing industry. In this paper, we consider the application of revenue management into a make-to-order system. Based on the maximizing revenue policy, we use the stochastic dynamic programming to model the MTO revenue problem, and solve it with the certainty equivalence approach, then the optimal order acceptance and capacity allocation polices are proposed-accept a new coming order as long as its revenue is greater than or equal to the minimum of the shadow revenue, and if it is accepted, allocate it a machine which with the minimum shadow revenue. In the end, the further research directions are presented.

Key concepts: Revenue management, Revenue, Revenue assurance, Order (exchange), Yield management, Build to order, Revenue model, Shadow (psychology)

Related papers

Back to paper searchBrowse research topicsOriginal source
Order Acceptance and Capacity Allocation Policies Based on Revenue Management — Research Paper | ScholarLens