We present here a hybrid algorithm for the Flexible Job-Shop Scheduling Problem (FJSSP). This problem involves the optimal use of resources in a flexible production environment in which each operation can be carried out by more than a single machine. Our algorithm allocates, in a first step, the machines to operations and in a second stage it sequences them by integrating a Multi-Objective Evolutionary Algorithm (MOEA) and a path-dependent search algorithm (Multi-Objective Simulated Annealing), which is enacted at the genetic phase of the procedure. The joint interaction of those two components yields a very efficient procedure for solving the FJSSP. An important step in the development of the algorithm was the selection of the right MOEA. Candidates were tested on problems of low, medium and high complexity. Further analyses showed the relevance of the search algorithm in the hybrid structure. Finally, comparisons with other algorithms in the literature indicate that the performance of our alternative is good.

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.

This paper analyzes the integration of two combinatorial problems that frequently arise in production and distribution systems. One is the Bin Packing Problem (BPP) problem, which involves finding an ordering of some objects of different volumes to be packed into the minimal number of containers of the same or different size. An optimal solution to this NP-Hard problem can be approximated by means of meta-heuristic methods. On the other hand, we consider the Capacitated Vehicle Routing Problem with Time Windows (CVRPTW), which is a variant of the Travelling Salesman Problem (again a NP-Hard problem) with extra constraints. Here we model those two problems in a single framework and use an evolutionary meta-heuristics to solve them jointly. Furthermore, we use data from a real world company as a test-bed for the method introduced here.