Logistics planning and inventory optimization using swarm intelligence: a third party perspective

Abstract

The economic and competitive pressures have made it imperative for organizations to focus on third party or outsourcing to reduce costs and improve operating efficiencies. The thrust of global economy drives the organizations to outsource process, parts, and labor, virtually anywhere in the world and get the desired combination of low cost and high quality. However, ineffective utilization of shipment practices in supply chain prevents to achieve the anticipated outsourcing benefits. In this research, a three-stage inventory model is developed to address the outsourcing issues with different shipment policies between manufacturer, exporter, and assembly point for any manufacturing industry. The model provides a measure for establishing an optimal balance between two conflicting objectives viz. net costs involved and the transportation discounts with consideration of holding cost at all the points in the supply chain. Owing to inherent computational complexities of the problem with higher dimensions, various deterministic approaches practically fail. Proposed work, therefore, utilizes a nature-inspired evolutionary algorithm, namely particle swarm optimization, to solve the problem. This paper applies enhanced particle swarm optimization, a variant of particle swarm optimization for solution purpose. Furthermore, the practical benefits and implications of the proposed model are demonstrated. The results obtained delineate efficacy to handle the fluctuations in the possible shipment options and simultaneously deciding the optimal shipment policies.