Multi-objective differential evolution and its application to enterprise planning

Agility is important to modern enterprises. The effective coordination of large numbers of potential suppliers and manufacturer, demands a scientific methodology rather than just practical experience to make decisions on supply manufacturing planning problems. Particularly in cases where multiple decision objectives are important to process planning, empirical decisions are insufficient. This paper introduces formal methods to solve such multi-objective decision problems involved in general supply manufacturing planning, and specifically describes the extension of differential evolution methods to discrete problem domains. An enterprise planning problem with two objectives-cycle time and cost is used as a principal example. Such multi-objective optimization problems usually are very large and nonlinear. In this paper, the concept of differential evolution, which is well-known in single-objective continuous domain for its fast convergence and adaptive parameter setting, is extended to the discrete domain by introducing greedy probability, mutation probability, and crossover probability. Moreover, this concept is extended to discrete multi-objective optimization problem. The proposed discrete multi-objective differential evolution, or D-MODE algorithm is applied to obtain Pareto solutions of this general planning problem. A practical example in the electronics industry is used as an illustrative example to demonstrate the effectiveness of the proposed D-MODE.