An evolutionary algorithm to solve the joint replenishment problem using direct grouping

Authors:
Anne L. Olsen
Affiliations:
Department of Computer Science and Quantitative Methods, College of Business Administration, Winthrop University, 126 Thurmond Building, Rock Hill, SC
Venue:
Computers and Industrial Engineering
Year:
2005

Citing 5
Cited 1

Periodic versus `can-order' policies for coordinated multi-item inventory systems

Management Science
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)

Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
How to solve it: modern heuristics

How to solve it: modern heuristics
Genetic Algorithms and Grouping Problems

Genetic Algorithms and Grouping Problems
An evolutionary algorithm for the joint replenishment problem

An evolutionary algorithm for the joint replenishment problem

A genetic algorithm for joint replenishment based on the exact inventory cost

Computers and Operations Research

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Abstract

There has been much work in establishing a joint replenishment policy to minimize the total cost of inventory replenishment. Most of this work uses the indirect grouping method. Little research has been done with direct grouping methods. In this paper we develop an evolutionary algorithm (EA) that uses direct grouping to solve the joint replenishment problem (JRP). We test the EA and compare these results with results with the best available algorithm. The EA is shown to find a replenishment policy that incurs a lower total cost than the best available algorithm for some problem parameters.