A hybrid genetic algorithm model for transshipment management decisions

Transshipment is a critical area of supply chain management that may lead to cost reductions and improved services for companies to make greater profits and to become more competitive. In this study, we present a transshipment planning model for the petroleum refinery industry. The main objective of the model is to minimize the total transshipment cost, maximize production, satisfy storage requirements at depots and meet the demand for oil in these sales areas. To accommodate imprecision, the goals are defined in a fuzzy sense and a fuzzy goal programming (FGP) model is developed. To provide flexibility to the decision-maker, we integrate a genetic algorithm (GA) within the FGP framework in such a way that it can find solutions for different sets of target and tolerance values of the goals in a single run. A case example is presented to demonstrate the usefulness of the integrated technique.