A comparison between genetic algorithms and the RAND method for solving the joint replenishment problem

The purpose of this paper is to compare the performance of genetic algorith ms (GAs) and the best available heuristic, known as the RAND, for solving t he joint replenishment problem (JRP). An important feature of the JRP which makes it suitable for GAs is that it can be formulated as a problem having one continuous decision variable and a number of integer decision variable s equal to the number of products being produced or ordered. Experiments on randomly generated problems indicate that GAs can provide be tter solutions to the JRP than the RAND for some problems, and at worst can almost match the performance of the RAND from a practical point of view fo r the rest of the problems. GAs never converged to solution with a total co st of more than 0.08% of the total cost of the RAND for 1600 randomly gener ated problems. In addition, GAs have the advantages of : (i) being easy to implement (e.g. less than 200 lines of code); (ii) having a code which is e asy to understand and modify; and ( iii) dealing easily with constrained JR Ps which are neglected by most of the available methods including the RAND, in spite of their importance in practice.