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A revenue management model for make-to-order bidding

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  1. Abstract
  2. Introduction
  3. Literature review
  4. Revenue management bidding model
    1. Decision periods and probability of customer arrivals
    2. Bidding decision rules
  5. Simulation results and analysis
    1. Parameters settings
    2. Cases of experiments
  6. Discussion on simulation results
    1. Performance of RM in the short length of planning horizon
    2. Performance of RM under different patterns of time-sensitive customer arrival
    3. Interactions of arrival rates and arrival patterns
  7. Conclusions
  8. Appendix
  9. References

This paper extends the single-period price and due date setting model for contingent Make-to-Order (MTO) firm into a finite-period stochastic system. Based on the revenue management framework (viewing every single available timeslot of production capacity as a perishable product), MTO firms can dynamically set price and due date to achieve highest average profitability per timeslot given a certain distribution pattern of future demand within a finite horizon. Heuristically, given a state of production system, admission control policy is then proposed to bid at the shadow price of the required timeslots or higher. An experimental simulation was performed and reported with insightful discussion. In the context of Make-to-order bidding, after quoting the bid, the firm has to wait for customer's decision (to deny or award the job). The contingency of the decision affects the planned capacity allocation. Reserving available timeslots for contingent bids is equivalent to have overbooked capacity in a hotel or airline services. As suggested by [1] and [2], MTO firms can utilize the Markovian's state transition scheme to effectively manage the overbooking scheme that balances the penalty cost of tardiness and opportunity loss of subsequent bids.

[...] In each case, the simulation experiments on the single-period bidding Model (A.1) and on the proposed RM bidding model were performed for 20 replicates and the final experimental results were measured by averaging all runs in terms of total revenues (summation of winning bid prices), total penalty charges for tardiness, total profits (total revenue total penalty), and total number of winning bids. Their values were accumulated through the whole planning horizon. We also collected other information for analysis purposes, for example, the total number of requests and the total number of requests to bid at adjusted prices. [...]


[...] For a system with longer than 15 planning periods, RM bidding model should be purely equipped with FCFS sequencing rule, for it can better compromise between computational time and quality of solution Conclusions Applying the RM approach to the MTO bidding system, we incorporated prospective future demands pattern into the bidding model that dynamically determines the bidding price and the due date for each new order arrival. A dynamic programming algorithm was deployed to estimate the shadow price (also referred as marginal value) of required timeslots for the current state of production system, given the contingent orders in the bidding system of marketing department. [...]


[...] Plambeck, E. L Optimal leadtime differentiation via diffusion approximations. Operations Research , 213?228. Maglaras, C. and Meissner J Dynamic pricing strategies for multi-product revenue management problems, Manufacturing & Service Operations Management, forthcoming. Maglaras, C Revenue management for a multi-class single-server queue via a fluid model analysis, Operations Research, Forthcoming. Keskinocak P. and Tayur S ?Due-Date Management Policies?, in Handbook of Quantitative Supply Chain Analysis: Modeling in the E-Business Era, edited by David Simchi-Levi, S. David Wu, and Z. Max Shen, [...]

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