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A robust optimization approach for the road network daily maintenance routing problem with uncertain service time
Institution:1. CIRRELT – Interuniversity Research Center on Enterprise Networks, Logistics and Transportation, École de Technologie Supérieure de Montréal, Canada;2. CIRRELT – Interuniversity Research Center on Enterprise Networks, Logistics and Transportation, and Département de mathématiques et génie industriel, École Polytechnique de Montréal, Canada;3. CIRRELT – Interuniversity Research Center on Enterprise Networks, Logistics and Transportation, and Département de management et de technologie, Université du Québec à Montréal, Canada;4. GERAD – Group for Research in Decision Analysis, and Département de mathématiques et génie industriel, École Polytechnique de Montréal, Canada;5. Département de génie de la construction, École de Technologie Supérieure de Montréal, Canada;1. Department of Management Sciences, Tippie College of Business, University of Iowa, Iowa City, IA, 52242, United States;2. Information Systems and Operations Management, Quinlan School of Business, Loyola University Chicago, Chicago, IL, 60611, United States;3. Department of Management Science and Engineering, International Business School, Beijing Foreign Studies University, Beijing, 100089, China;1. College of Management of Technology, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland;2. Department of Industrial and Systems Engineering, National University of Singapore, Singapore
Abstract:This paper studies the robust optimization approach for the routing problem encountered in daily maintenance operations of a road network. The uncertainty of service time is considered. The robust optimization approach yields routes that minimize total cost while being less sensitive to substantial deviations of service times. A robust optimization model is developed and solved by the branch-and-cut method. In computational experiments, the behavior of the robust solutions and their performance are analyzed using Monte Carlo simulation. The robust optimization model is also compared with a classic chance-constrained programming model. The experimental analysis provides managerial insights for decision makers to determine an appropriate routing strategy.
Keywords:Arc routing problem  Robust optimization  Uncertainty  Chance-constrained programming  Branch-and-cut  Monte Carlo simulation
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