A genetic algorithm for the mixed flow shop problem

In this thesis we present a new interesting version of the mixed flow shop se-quencing problem, which at the same time is a version of the classic flow shop,a very common topic on operations research.We propose a genetic algorithm to solve it that we will compare at the endwith a simple initial genetic-based algorithm previously design. For that wefirst focus on the crossover operator as we consider it the most challenging parton a sequencing problem. We study and compare 5 different crossover operatorsand we choose the one that performs better. Finally we calibrate the populationsize, the weight of mutation and crossover operators on the algorithm and alsothe mutations operator itself.The goal of the thesis is to better understand the specific mixed flow shopproblem version presented and design a genetic algorithm that clearly improvesthe performance of the initial algorith

A review and classification of heuristics for permutation flow-shop scheduling with makespan objective

Makespan minimization in permutation flow-shop scheduling is an operations research topic that has been intensively addressed during the last 40 years. Since the problem is known to be NP-hard for more than two machines, most of the research effort has been devoted to the development of heuristic procedures in order to provide good approximate solutions to the problem. However, little attention has been devoted to establish a common framework for these heuristics so that they can be effectively combined or extended. In this paper, we review and classify the main contributions regarding this topic and discuss future research issues.Ministerio de Ciencia y Tecnología DPI-2001-311

DISCRETE PARTICLE SWARM OPTIMIZATION FOR THE ORIENTEERING PROBLEM

Discrete particle swarm optimization (DPSO) is gaining popularity in the area of combinatorial optimization in the recent past due to its simplicity in coding and consistency in performance.  A DPSO algorithm has been developed for orienteering problem (OP) which has been shown to have many practical applications.  It uses reduced variable neighborhood search as a local search tool.  The DPSO algorithm was compared with ten heuristic models from the literature using benchmark problems.  The results show that the DPSO algorithm is a robust algorithm that can optimally solve the well known OP test problems

A Multi-Restart Iterated Local Search Algorithm for the Permutation Flow Shop Problem Minimizing Total Flow Time

A variety of metaheuristics have been developed to solve the permutation flow shop problem minimizing total flow time. Iterated local search (ILS) is a simple but powerful metaheuristic used to solve this problem. Fundamentally, ILS is a procedure that needs to be restarted from another solution when it is trapped in a local optimum. A new solution is often generated by only slightly perturbing the best known solution, narrowing the search space and leading to a stagnant state. In this paper, a strategy is proposed to allow the restart solution to be generated from a group of solutions drawn from local optima. This allows an extension of the search space, while maintaining the quality of the restart solution. A multi-restart ILS (MRSILS) is proposed, with the performance evaluated on a set of benchmark instances and compared with six state of the art metaheuristics. The results show that the easily implementable MRSILS is significantly better than five of the other metaheuristics and comparable to or slightly better than the remaining one. © 2012 Elsevier Ltd. All rights reserved