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Facility Location decisions are part of the company’s strategy which are important due to significant investment and decision is usually irreversible. Facility locations are important because (a) it requires large investment that cannot be recovered, (b) decisions affect the competitiveness of the company, and (c) decisions affect not only costs but the company’s income (d) Customer satisfaction and trade off of decision based on service level. In a supply chain network design, facility locations (for example, warehouse, distributor, manufacturing, cross dock, or retailers locations) play an important role in driving efficient distribution planning and satisfying customer service level. The problem is to identify optimal set of facilities that can serve all the customer’s demand with given service level at minimal cost. This problem becomes complex when possible locations are not known (Green field problem) and cost is the major driver for selection of facilities. This paper addresses this problem in two parts (a) first, improving the existing methodology (clustering) to achieve optimum clustering solution. With improved clustering outcome, we get better set of facility locations to be installed for Greenfield scenario. (b) Second, extend solution towards mathematical optimization based on cost, demand, service level, priority of facilities and business-based constraints (Brown filed Problem). This solution will help in selecting the best set of available facility location respecting business constraint so that customer demand is met in cost. Clustering algorithm, improved Meta Heuristic and GLPK (Open Source package to solve LP/MILP Problems) based mathematical optimization has been developed in Java. Currently we are pursuing further research in this direction to improve network and facility decisions.
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- Multi-stage Greenfield and Brownfield Network Optimization with Improved Meta Heuristics
- Springer Singapore