Implementation of Bacterial Foraging Optimization Algorithm in Leaf Spring Cutting Stock Problem

Although the latest enterprise resource planning software provides efficient scheduling of production processes, they have less impact on material marking process, since they focus on the utilization of materials and machines with respect to time. The classical optimization procedures are found to be inefficient in finding the optimal material usage, when the real world complex problems become exponential. Recent publications of many researchers prove the successful implementation of the bio inspired optimization algorithms in such domains. In this article, the chief mechanism of bacterial foraging optimization algorithm and their search parameters are exploited to provide optimal solutions for the cutting stock problem faced by the leaf spring assembly manufacturing industries. Minimization of the trim loss during the cutting of twelve leaves in batches is considered for optimization in order to maximize the material utilization. It is found that the optimal values evolved out of the BFOA are superior when compared with Tabu Search Method. The search characteristics of the E.coli bacteria under consideration is found to be excel in producing the optimal results closer to null values for trim loss. This work also highlights the easy implementation and adoptability of the developed optimization procedure to similar kind of domains.