Simulation based swarm intelligence optimization to develop manufacturing distribution plan

Integrated manufacturing-distribution planning increases profitability of business and reduces the cost incurred in the management of any supply chain. This kind of planning is very much essential for various divisions that operate in different parts of the world in order to satisfy customer demand. Therefore, it is a vital part of supply chain management. The present research was carried out to investigate an integrated manufacturing-distribution planning problem under stochastic demand scenario. Uncertainty in demand is a universal problem in all types of businesses. In this paper, a simulation-based heuristic discrete particle swarm intelligence method is used to develop manufacturing-distribution plan taking into account of regular time manufacturing strategy, overtime manufacturing strategy and outsourced manufacturing costs including backlog, inventory carrying, recruiting/dismissing and distribution expenses. The obtained result is also benchmarked with that of the solution of simulation based heuristic binary coded genetic algorithm. The quality cost under stochastic demand scenario is considered in this research work for the first time. The mixed integer linear programming model is implemented for a popular bearing-production company situated in India. Near optimum solutions for the stochastic demand case is obtained by the simulation-based optimization approach. This research ensures the entire lots of Near optimum solutions for the stochastic demand case is obtained by the simulation-based optimization approach.parts delivered are of good quality.