Finite Horizon Stochastic Knapsacks with Applications to Yield Management

The finite horizon stochastic knapsack combines a secretary problem with an integer knapsack problem. It is useful for optimizing sales of perishable commodities with low marginal costs to impatient customers. Applications include yield management for airlines, hotels/motels, broadcasting advertisements, and car rentals. In these problems,K types of customers arrive stochastically. Customer type,k, has an integer weightw k , a valueb k , and an arrival rate? k( t) (which depends on time). We consider arrivals over a continuous time horizon [0;T] to a "knapsack" with capacityW. For each arrival that fits in the remaining knapsack capacity, we may (1) accept it, receivingb k , while giving up capacityw k ; or (2) reject it, forgoing the value and not losing capacity. The choice must be immediate; a customer not accepted on arrival is lost. We model the problem using continuous time, discrete state, finite horizon, dynamic programming. We characterize the optimal return function and the optimal acceptance strategy for this problem, and we give solution methods. We generalize to multidimensional knapsack problems. We also consider the special case wherew k= 1 for allk. This is the classic airline yield problem. Finally, we formulate and solve a new version of the secretary problem.

[1]  Thomas S. Ferguson,et al.  Who Solved the Secretary Problem , 1989 .

[2]  Samuel E. Bodily,et al.  A Taxonomy and Research Overview of Perishable-Asset Revenue Management: Yield Management, Overbooking, and Pricing , 1992, Oper. Res..

[3]  Janakiram Subramanian,et al.  Airline Yield Management with Overbooking, Cancellations, and No-Shows , 1999, Transp. Sci..

[4]  J. C. Burkill,et al.  Ordinary Differential Equations , 1964 .

[5]  Jeffrey I. McGill,et al.  Airline Seat Allocation with Multiple Nested Fare Classes , 1993, Oper. Res..

[6]  E. Coddington,et al.  Theory of Ordinary Differential Equations , 1955 .

[7]  Sheldon M. Ross,et al.  Stochastic Processes , 2018, Gauge Integral Structures for Stochastic Calculus and Quantum Electrodynamics.

[8]  Lawrence W. Robinson,et al.  Optimal and Approximate Control Policies for Airline Booking with Sequential Nonmonotonic Fare Classes , 1995, Oper. Res..

[9]  D. V. Lindley,et al.  An Introduction to Probability Theory and Its Applications. Volume II , 1967, The Mathematical Gazette.

[10]  Jeffrey I. McGill,et al.  Revenue Management: Research Overview and Prospects , 1999, Transp. Sci..

[11]  Renwick E. Curry,et al.  Optimal Airline Seat Allocation with Fare Classes Nested by Origins and Destinations , 1990, Transp. Sci..

[12]  Keith W. Ross,et al.  The stochastic knapsack problem , 1989, IEEE Trans. Commun..

[13]  William Feller,et al.  An Introduction to Probability Theory and Its Applications , 1951 .

[14]  Yigao Liang,et al.  Solution to the Continuous Time Dynamic Yield Management Model , 1999, Transp. Sci..

[15]  Frank E. Grubbs,et al.  An Introduction to Probability Theory and Its Applications , 1951 .

[16]  Richard D. Wollmer,et al.  An Airline Seat Management Model for a Single Leg Route When Lower Fare Classes Book First , 1992, Oper. Res..

[17]  Anton J. Kleywegt,et al.  The Dynamic and Stochastic Knapsack Problem , 1998, Oper. Res..

[18]  P. Freeman The Secretary Problem and its Extensions: A Review , 1983 .

[19]  William H. Press,et al.  Numerical Recipes in C, 2nd Edition , 1992 .

[20]  William H. Press,et al.  Numerical recipes in C (2nd ed.): the art of scientific computing , 1992 .

[21]  F. Mosteller,et al.  Recognizing the Maximum of a Sequence , 1966 .

[22]  Anton J. Kleywegt,et al.  Dynamic and stochastic models with freight distribution applications , 1996 .

[23]  Peter Belobaba,et al.  OR Practice - Application of a Probabilistic Decision Model to Airline Seat Inventory Control , 1989, Oper. Res..

[24]  Marvin Hersh,et al.  A Model for Dynamic Airline Seat Inventory Control with Multiple Seat Bookings , 1993, Transp. Sci..