Abstract
The strategic supply chain design problem which allows capacity shifts and budget limitations can be formulated as a linear program. Since facilities are allowed to be opened or shut down during the planning horizon, this problem is in fact a mixed integer problem. Choosing the optimal set of facilities to serve the customer demands is an NP-hard combinatorial optimization problem. We present a hybrid method combining an evolutionary algorithm and LP based solvers for solving large-scale supply chain problems, which takes its power from filtering out infeasible solutions. The EA incorporating these filters is shown to be faster than the MIP solver ILOG CPLEX in most of the considered instances. For the remaining instances it finds feasible solutions much faster than the MIP solver.
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Wolf, S., Merz, P. (2007). A Hybrid Method for Solving Large-Scale Supply Chain Problems. In: Cotta, C., van Hemert, J. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2007. Lecture Notes in Computer Science, vol 4446. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71615-0_20
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DOI: https://doi.org/10.1007/978-3-540-71615-0_20
Publisher Name: Springer, Berlin, Heidelberg
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