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In the Capacitated Clustering Problem, a given set of customers with distinct demands must be partitioned into p clusters with limited capacities. The objective is to find p customers, called medians, from which the sum of the distances to all other customers in the cluster is minimized. In this article, a new adaptive tabu search approach is applied to solve the problem. Initial solutions are obtained by four constructive heuristics that use weights and distances as optimization criteria. Two neighborhood generation mechanisms are used by the local search heuristic: pairwise interchange and insertion. Computational results from 20 instances found in the literature indicate that the proposed method outperforms alternative metaheuristics developed for solving this problem.

This work presents an optimization model to support decisions in the production planning and control of the electrofused grain industry. A case study was carried out in a Brazilian company with the aim of helping to increase productivity and improve customer service concerning meeting deadlines. A mixed integer linear programming model combining known models of process selection and single-stage lot sizing were applied to the production scheduling of electrofused grains. Optimizing this scheduling is not a simple task mainly because of the scale of the equipment setup times, the diversity of the products and the deadlines of the order due dates. A constructive heuristic is also proposed as an alternative solution method, particularly for large-sized instances. The results show that the model and the heuristic can produce better solutions than the ones currently used by the company.