Knapsack problems: algorithms and computer implementations
Knapsack problems: algorithms and computer implementations
Probabilistic analysis of the generalised assignment problem
Mathematical Programming: Series A and B
A class of greedy algorithms for the generalized assignment problem
Discrete Applied Mathematics
A probabilitic analyis of the multi-period single-sourcing problem
Discrete Applied Mathematics - Special issue on the combinatorial optimization symposium
Models and Methods for Merge-in-Transit Operations
Transportation Science
Generating Experimental Data for the Generalized Assignment Problem
Operations Research
Greedy approaches for a class of nonlinear Generalized Assignment Problems
Discrete Applied Mathematics
Survey: Facility location dynamics: An overview of classifications and applications
Computers and Industrial Engineering
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The multi-period single-sourcing problem that we address in this paper can be used as a tactical tool for evaluating logistics network designs in a dynamic environment. In particular, our objective is to find an assignment of customers to facilities, as well as the location, timing and size of production and inventory levels, that minimizes total assignment, production, and inventory costs. We propose a greedy heuristic, and prove that this greedy heuristic is asymptotically optimal in a probabilistic sense for the subclass of problems where the assignment of customers to facilities is allowed to vary over time. In addition, we prove a similar result for the subclass of problems where each customer needs to be assigned to the same facility over the planning horizon, and where the demand for each customer exhibits the same seasonality pattern. We illustrate the behavior of the greedy heuristic, as well as some improvements where the greedy heuristic is used as the starting point of a local interchange procedure, on a set of randomly generated test problems. These results suggest that the greedy heuristic may be asymptotically optimal even for the cases that we were unable to analyze theoretically.