A Heuristic Approach to the Multi-Period Single-Sourcing Problem with Production and Inventory Capacities and Perishability Constraints

Authors:
Ravindra K. Ahuja;Wei Huang;H. Edwin Romeijn;Dolores Romero Morales
Affiliations:
Department of Industrial and Systems Engineering, University of Florida, P.O. Box 116595, Gainesville, Florida 32611-6595, USA;Innovative Scheduling, Gainesville Technology Enterprise Center (GTEC), 2153 Hawthorne Road Suite 128, Gainesville, Florida 32641, USA;Department of Industrial and Systems Engineering, University of Florida, P.O. Box 116595, Gainesville, Florida 32611-6595, USA;Saïd Business School, University of Oxford, Park End Street, Oxford OX1 1HP, United Kingdom
Venue:
INFORMS Journal on Computing
Year:
2007

Citing 8
Cited 2

Knapsack problems: algorithms and computer implementations

Knapsack problems: algorithms and computer implementations
Network flows: theory, algorithms, and applications

Network flows: theory, algorithms, and applications
Cyclic transfer algorithms for multivehicle routing and scheduling problems

Operations Research
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
A survey of very large-scale neighborhood search techniques

Discrete Applied Mathematics
Asymptotic Analysis of a Greedy Heuristic for the Multi-Period Single-Sourcing Problem: The Acyclic Case

Journal of Heuristics
A composite very large-scale neighborhood structure for the capacitated minimum spanning tree problem

Operations Research Letters

Greedy approaches for a class of nonlinear Generalized Assignment Problems

Discrete Applied Mathematics
A facility neighborhood search heuristic for capacitated facility location with single-source constraints and flexible demand

Journal of Heuristics

Quantified Score

Hi-index	0.00

Visualization

Abstract

The multi-period single-sourcing problem that we address in this paper can be used as a tool for evaluating logistics network designs in a dynamic environment. We consider the assignment of retailers to facilities, taking into account the timing, location, and size of production and inventories, in the presence of various types of constraints. We formulate the problem as a nonlinear assignment problem, and develop efficient algorithms for solving the capacitated lot-sizing subproblems that form the objective function of this formulation. We propose a greedy heuristic, and prove that this heuristic is asymptotically optimal in a probabilistic sense when retailer demands share a common seasonality pattern. In addition, we develop an efficient implementation of the very-large-scale-neighborhood-search method that can be used to improve the greedy solution. We perform extensive tests on a set of randomly generated problem instances, and conclude that our approach produces very high quality solutions in limited time.