The competition manufacturing companies face has driven the development of novel and efficient methods that enhance the decision making process. In this work, a specific flow shop scheduling problem of practical interest in the industry is presented and formalized using a mathematical programming model. The problem considers a manufacturing system arranged as a work cell that takes into account the transport operations of raw material and final products between the manufacturing cell and warehouses. For solving this problem, we present a multiobjective metaheuristic strategy based on simulated annealing, the Pareto Archived Simulated Annealing (PASA). We tested this strategy on two kinds of benchmark problem sets proposed by the authors. The first group is composed by small-sized problems. On these tests, PASA was able to obtain optimal or near-optimal solutions in significantly short computing times. In order to complete the analysis, we compared these results to the exact Pareto front of the instances obtained with augmented ε-constraint method. Then, we also tested the algorithm in a set of larger problems to evaluate its performance in more extensive search spaces. We performed this assessment through an analysis of the hypervolume metric. Both sets of tests showed the competitiveness of the Pareto Archived Simulated Annealing to efficiently solve this problem and obtain good quality solutions while using reasonable computational resources.

In this paper, we present a new decision-making tool aimed at improving the efficiency of the operational planning of pick-up processes in logistic distribution centers. It is based on a memetic algorithm (MA) solving both the Order Batching Problem (OBP) and the Order Picking Problem (OPP). The result yields a sequence of simultaneous pick up operations of lots for different clients in a storing facility, satisfying a previously defined distribution plan. The objective is the minimization of the operational cost of the entire process, which is directly proportional to the time spent on different activities involved. The failure to satisfy the conditions, either leads to overstocking, delays in delivery or creates inefficiency costs. The analysis of the results obtained with our algorithmic tool indicates that it has a good performance in comparison with other known algorithms used to solve this kind of problem.