Global optimization using a genetic algorithm with hierarchically structured population

This paper applies a genetic algorithm with hierarchically structured population to solve unconstrained optimization problems. The population has individuals distributed in several overlapping clusters, each one with a leader and a variable number of support individuals. The hierarchy establishes that leaders must be fitter than its supporters with the topological organization of the clusters following a tree. Computational tests evaluate different population structures, population sizes and crossover operators for better algorithm performance. A set of known benchmark test problems is solved and the results found are compared with those obtained from other methods described in the literature, namely, two genetic algorithms, a simulated annealing, a differential evolution and a particle swarm optimization. The results indicate that the method employed is capable of achieving better performance than the previous approaches in regard as the two criteria usually employed for comparisons: the number of function evaluations and rate of success. The method also has a superior performance if the number of problems solved is taken into account. (C) 2013 Elsevier B.V. All rights reserved.26134135

A genetic algorithm/mathematical programming approach to solve a two-level soft drink production problem

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)This study applies a genetic algorithm embedded with mathematical programming techniques to solve a synchronized and integrated two-level lot sizing and scheduling problem motivated by a real-world problem that arises in soft drink production. The problem considers a production process compounded by raw material preparation/storage and soft drink bottling. The lot sizing and scheduling decisions should be made simultaneously for raw material preparation/storage in tanks and soft drink bottling in several production lines minimizing inventory, shortage and setup costs. The literature provides mixed-integer programming models for this problem, as well as solution methods based on evolutionary algorithms and relax-and-fix approaches. The method applied by this paper uses a new approach which combines a genetic algorithm (GA) with mathematical programming techniques. The GA deals with sequencing decisions for production lots, so that an exact method can solve a simplified linear programming model, responsible for lot sizing decisions. The computational results show that this evolutionary/mathematical programming approach outperforms the literature methods in terms of production costs and run times when applied to a set of real-world problem instances provided by a soft drink company. (C) 2014 Elsevier Ltd. All rights reserved.484052Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)CNPq [483474/2013-4]FAPESP [2010/10133-0

Solving the integrated production and imperfect preventive maintenance planning problem

This paper considers the integrated production and imperfect preventive maintenance planning problem. The article provides more details on how Relax-and-Fix/Fix-and-Optimize as well as Dantzig-Wolfe Decomposition and Lagrangian Relaxation techniques were applied and implemented for solving the integrated production and imperfect preventive maintenance planning problem. More experiments were also carried out. The objective of this planning problem is to determine an optimal integrated production and preventive maintenance plan that concurrently minimizes production as well as preventive maintenance costs during a given finite planning horizon. Three solution approaches were investigated and applied to the reformulated version of the problem, and their performances are compared and discussed. The Relax-and-Fix/Fix-and-Optimize method (RFFO) determines first an initial feasible solution, generated by the relax-and-fix heuristic step, which is further improved in the fix-and-optimize step. Dantzig-Wolfe Decomposition (DWD) and Lagrangian Relaxation (LR) techniques are also applied to the same reformulation of the problem and the results of these three approaches are compared in terms of the solution quality as well as CPU time. The computational results obtained for different instances of the integrated production planning and imperfect preventive maintenance planning problem, show that the RFFO method is very efficient and is competitive in term of the solutions quality. It provides quite good solutions to the tested instances with a noticeable improvement in computational time. Dantzig-Wolfe Decomposition (DWD) and Lagrangian Relaxation (LR) methods, on the other hand, exhibit a good enhancement in terms of computational time especially for large instances, however, the quality of solution still requires some more improvements