Random assignment method based on genetic algorithms and its application in resource allocation

Authors:
Fachao Li;Li Da Xu;Chenxia Jin;Hong Wang
Affiliations:
School of Economics and Management, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, China;Department of Information Technology and Decision Science, Old Dominion University, Norfolk, VA 23529, USA;School of Economics and Management, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, China;School of Business and Economics, North Carolina A&T State University, Greensboro, NC 27411, USA
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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Abstract

Assignment problem is considered a well-known optimization problem in manufacturing and management processes in which a decision maker's point of view is merged into a decision process and a valid solution is established. In this study, taking the complementary relations between expected value and variance in decision making and the synthesizing effect of random variables into consideration, a new model for random assignment problems is proposed; in which the characteristic of assignment problems are considered to present a concrete scheme based on genetic algorithms (denoted by SE @? GA-SAF, for short). We study the model's convergence using the Markov chain theory, and analyze its performance through simulation. All of these indicate that this solution model can effectively aid decision making in the assignment process, and that it possesses the desirable features such as interpretability and computational efficiency, as such it can be widely used in many aspects including manufacturing, operations, logistics, etc.