Optimisation of supply chain logistics network using random search techniques

Fierce market competition is making companies move from their traditional business strategies towards integrated strategic alliances. In order to integrate and manage their business processes like procurement, inventory, manufacturing, logistics and sales, a new technological and quantitative tool is needed. In this paper, a supply chain logistics network model is developed with the objective of minimising the total cost of production and distribution. The Genetic Algorithm and Particle Swarm search techniques are proposed for optimising the supply chain logistics network. The computational results of these algorithms are validated with the results obtained using Excel's Solver Optimizer.