Using simulated annealing to solve routing and location problems. (English) Zbl 0593.90054

Summary: In recent papers by S. Kirkpatrick, C. D. Gelatt and M. P. Vecchi [“Optimization by simulated annealing”, IBM T. J. Watson Research Centre, Yorktown Hights, NY (1982); Science 220, 671–680 (1983; Zbl 1225.90162)] an analogy between the statistical mechanics of large multivariate physical systems and combinatorial optimization has been presented and used to develop a general strategy for solving discrete optimization problems. The method relies on probabilistically accepting intermediate increases in the objective function through a set of user-controlled parameters. It is argued that by taking such controlled uphill steps, from time to time, a high quality solution can eventually be found in a moderate amount of computer time. In this paper, we implement this idea, apply it to the traveling salesman problem and the p-median location problem, and test the approach extensively.


90C10 Integer programming
65K05 Numerical mathematical programming methods
68Q25 Analysis of algorithms and problem complexity
90B05 Inventory, storage, reservoirs


Zbl 1225.90162
Full Text: DOI


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