In this paper, we generalize conventional P-median location problems by considering the unreliability of facilities. The unreliable location problem is defined by introducing the probability that a facility may become inactive. We proposed efficient solution methods to determine locations of these facilities in the unreliable location model. Space-filling curve-based algorithms are developed to determine initial locations of these facilities. The unreliable P-median location problem is then decomposed to P 1-median location problems; each problem is solved to the optimum. A bounding procedure is used to monitor the iterative search, and to provide a consistent basis for termination. Extensive computational tests have indicated that the heuristics are efficient and effective for solving unreliable location problems. Scope and purpose This paper addresses an important class of location problems, where p unreliable facilities are to be located on the plane, so as to minimize the expected travel distance or related transportation cost between the customers and their nearest available facilities. The unreliable location problem is defined by introducing the probability that a facility may become inactive. Potential application of the unreliable location problem is found in numerous areas. The facilities to be located can be fire station or emergency shelter, where it fails to provide service during some time window, due to the capacity or resource constraints. Alternatively, the facilities can be telecommunication posts or logistic/distribution centers, where the service is unavailable due to breakdown, repair, shutdown of unknown causes. In this paper, we prescribed heuristic procedures to determine the location of new facilities in the unreliable location problems. The numerical study of 2800 randomly generated instances has shown that these solution procedures are both efficient and effective, in terms of computational time and solution quality.
All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Modelling and Simulation
- Management Science and Operations Research