A genetic algorithm approach to the balanced allocation of customers to multiple warehouses with varying capacities

With the increasing importance of seamless supply chain integration to business success, the role of warehouses has become more of flow-through transshipment facilities intended for timely order fulfilment than inventory stocking points. As the role of warehouses changes, their strategic position is often decided by their ability to service as many customers as possible in a timely manner without incurring additional costs. One of the most effective ways of enhancing the strategic position of warehouses is the balanced allocation of customers to nearby warehouses. To solve the balanced allocation problem, this paper presents an integer programming model to formulate the balanced allocation problem with capacity constraints and then develops a tree-based genetic algorithm (GA) to solve it through its equivalent formulation of a capacitated balanced star-spanning forest. Unlike traditional heuristics, the proposed GA allows the decision-maker to consider many practical alternatives by generating multiple “satisficing” solutions. Tests of a new heuristic algorithm with real data show its usefulness and accuracy.