Exploiting Knowledge About Future Demands for Real-Time Vehicle Dispatching
Transportation Science
Computers and Industrial Engineering
Computers and Operations Research
Optimal Baggage-Limit Policy: Airline Passenger and Cargo Allocation
Transportation Science
Computers and Operations Research
Computers and Operations Research
TECHNICAL NOTE---The Adaptive Knapsack Problem with Stochastic Rewards
Operations Research
A two-stage bid-price control for make-to-order revenue management
Computers and Operations Research
Yield management of workforce for IT service providers
Decision Support Systems
A PTAS for the chance-constrained knapsack problem with random item sizes
Operations Research Letters
Planning in logistics: a survey
Proceedings of the 10th Performance Metrics for Intelligent Systems Workshop
Proceedings of Programming Models and Applications on Multicores and Manycores
An improved firefly algorithm for solving dynamic multidimensional knapsack problems
Expert Systems with Applications: An International Journal
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A resource allocation problem, called the dynamic and stochastic knapsack problem (DSKP), is studied. A known quantity of resource is available, and demands for the resource arrive randomly over time. Each demand requires an amount of resource and has an associated reward. The resource requirements and rewards are unknown before arrival and become known at the time of the demand's arrival. Demands can be either accepted or rejected. If a demand is accepted, the associated reward is received; if a demand is rejected, a penalty is incurred. The problem can be stopped at any time, at which time a terminal value is received that depends on the quantity of resource remaining. A holding cost that depends on the amount of resource allocated is incurred until the process is stopped. The objective is to determine an optimal policy for accepting demands and for stopping that maximizes the expected value (rewards minus costs) accumulated. The DSKP is analyzed for both the infinite horizon and the finite horizon cases. It is shown that the DSKP has an optimal policy that consists of an easily computed threshold acceptance rule and an optimal stopping rule. A number of monotonicity and convexity properties are studied. This problem is motivated by the issues facing a manager of an LTL transportation operation regarding the acceptance of loads and the dispatching of a vehicle. It also has applications in many other areas, such as the scheduling of batch processors, the selling of assets, the selection of investment projects, and yield management.