Solving a multi-objective manufacturing cell scheduling problem with the consideration of warehouses using a simulated annealing based procedure

Abstract

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.

A study of a kanban based assembly line feeding system through integration of simulation and particle swarm optimization

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Optimizing a multiproduct manufacturing system with delayed differentiation, outsourcing, expedited rate, and rework strategies

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Upstream logistic transport planning in the oil-industry: a case study

“…In real-life problems, where large-dimension search spaces and/or a variety of hard constraints are included, classical exact solution methods can be highly time-consuming (Nesmachnow, 2014;Toncovich et al, 2019). Therefore, designing heuristics can be a valuable approach for constructing fast feasible solutions.…”