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A profit-maximization location-capacity model for designing a service system with risk of service interruptions
Institution:1. Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany;2. Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA;3. Department of Biomedical Sciences and Morphological and Functional Imaging, University Hospital Messina, Messina, Italy;1. Department of Urology, Tokyo Medical and Dental University Graduate School, Tokyo, Japan;2. Department of Pathology, Tokyo Medical and Dental University Graduate School, Tokyo, Japan;3. Department of Urology, Tokyo Metropolitan Cancer and Infectious Diseases Center, Komagome Hospital, Tokyo, Japan;1. Institute for Neuroscience and Physiology, Department of Physiology, Sahlgrenska Academy, University of Gothenburg, 40530 Gothenburg, Sweden;2. Institute of Tendon and Bone Regeneration, Paracelsus Medical University, Spinal Cord Injury and Tissue Regeneration Center Salzburg, Salzburg, Austria;3. Department of Traumatology and Sports Injuries, Paracelsus Medical University Salzburg, Austria;4. Austrian Cluster for Tissue Regeneration Vienna, Austria;5. Wyoming Pregnancy and Life Course Health Center, University of Wyoming, Laramie, WY 82071, United States;1. Department of Neuropathology, University of Marburg, Germany;2. Department of Neuropathology, Edinger Institute (Institute of Neurology), Goethe University, Frankfurt/Main, Germany;3. Department of Neuropathology, Ruprecht-Karls-University of Heidelberg, Germany;4. Department of Pathology, Klinikum Stuttgart, Germany;5. Department of Neuropathology, Institute of Pathology and Neuropathology, Eberhard-Karls-University of Tuebingen, Germany;1. Barcelona Supercomputing Center (BSC), Barcelona, Spain;2. Computer Architecture and Operating Systems Department, University Autonoma of Barcelona, Spain
Abstract:This paper considers the design of an immobile service system in which each facility’s service process is subject to the risk of interruptions. The location-capacity decisions and allocations are simultaneously made to maximize the difference between the service provider’s profit and the sum of customers’ transportation and waiting costs. An efficient Lagrangian-based solution algorithm is developed, which solves large-sized instances with up to 50 service facilities and 500 customers in a few seconds. Several sensitivity analyses and managerial insights are presented. The model is also applied to a case study on a logistics network design problem in the zinc mining industry.
Keywords:Location-allocation problem  Service system design  Congested network  Queue systems with service interruptions  Mixed-integer nonlinear optimization  Lagrangian relaxation
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