Stochastic dominance and expected utility: survey and analysis
Management Science
A genetic algorithm for the generalised assignment problem
Computers and Operations Research
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Operations Research: An Introduction (8th Edition)
Operations Research: An Introduction (8th Edition)
The Respective Roles of Risk and Decision Analyses in Decision Support
Decision Analysis
Assignment Problems
Exact and Heuristic Algorithms for the Weapon-Target Assignment Problem
Operations Research
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We consider resource allocation problems in which agents are assigned to tasks with the aim of (1) minimizing the costs of assigning the agents and (2) maximizing the overall value resulting from the completion of tasks. Often, such assignment problems are challenging, because it may not be known to what extent the agents can complete tasks or what the value of either full or partial task completion is. Furthermore, it may be difficult to determine how important the tasks are relative to each other. In this paper, we therefore develop an optimization framework that helps determine for a range of levels of resource expenditure (1) which combinations of agents are cost-effective and (2) to which tasks these agents should be assigned. The parameters for the optimization problem can be derived, for instance, by eliciting evaluation judgments from experts. We also provide tools for analyzing which combinations of agents outperform others in view of the judgments of all experts, and which ones are cost-ineffective based on the judgments of some or all experts. A computational algorithm is presented, and the framework is illustrated by reporting a real application in military planning.