Using a metaheuristic algorithm for solving a home health care routing and scheduling problem

This paper aims to develop a mathematical modeling for Home Health Care Routing and Scheduling Problem and to solve it by means of Simulated Annealing (SA) algorithm considering real condition (staff vehicle traveling, conditions of patients and so forth). | Using a metaheuristic algorithm for solving a home health care routing and scheduling problem Journal of Project Management 5 2020 27 40 Contents lists available at GrowingScience Journal of Project Management homepage Using a metaheuristic algorithm for solving a home health care routing and scheduling problem Neda Manavizadeha Hamed Farrokhi-Aslb and Parya Beiraghdarc a Department of Industrial Engineering KHATAM University Tehran Iran b School of Industrial Engineering Iran University of Science amp Technology Tehran Iran c School of Civil Engineering College of Engineering University of Manitoba Winnipeg Canada CHRONICLE ABSTRACT Article history The Health Care system is changing from the hospitalization to the home care and the World Received May 25 2019 Health Organization has announced that the rate of care-dependent elderly people in Europe Received in revised format June will considerably increase within the next decades. Thus scientific planning for this area is 21 2019 an essential factor to improve the community health. This paper aims to develop a mathe- Accepted July 21 2019 Available online matical modeling for Home Health Care Routing and Scheduling Problem and to solve it by July 22 2019 means of Simulated Annealing SA algorithm considering real condition staff vehicle trav- Keywords eling conditions of patients and so forth . We permit interdependent services for patients in Home Health Care which they can order as many services as they want with any relation between them Multi- Routing ple Services and supposed time window for each service. The mathematical formulation of Scheduling the problem is coded in GMAS software which is a well-known commercial software for Simulated Annealing solving optimization problems. In addition for large-scale problems where GAMS is unable Interdependent Services to solve SA algorithm is applied to tackle the problems. Finally sensitivity analysis on the most important parameters number of

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