Genetic subgradient method for solving location-allocation problems
Applied Soft Computing
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The multisource Weber problem is to locate simultaneously p facilities in the Euclidean plane in order to minimize the total transportation cost for satisfying the demand of n fixed users, each supplied from its closest facility. Many heuristics have been proposed for this problem, as well as a few exact algorithms. Heuristics are needed to solve quickly large problems and to provide good initial solutions for exact algorithms. We compare various heuristics, i.e., alternative location- allocation, projection, Tabu search, p-Median plus Weber, Genetic Search and several versions of Variable Neighbourhood Search. It appears that most traditional and some recent heuristics give pour results when the number of facilities to locate is large and that Variable Neighbourhood Search gives consistently best results on average, in moderate computing time.