Mitsuo Gen

Genetic algorithms and engineering optimization

Foundations of Genetic Algorithms. Combinatorial Optimization Problems. Multiobjective Optimization Problems. Fuzzy Optimization Problems. Reliability Design Problems. Scheduling Problems. Advanced Transportation Problems. Network Design and Routing. Manufacturing Cell Design. References. Index.

A tutorial survey of job-shop scheduling problems using genetic algorithms—I: representation

Abstract Job-shop scheduling problem (abbreviated to JSP) is one of the well-known hardest combinatorial optimization problems. During the last three decades, the problem has captured the interest of a significant number of researchers and a lot of literature has been published, but no efficient solution algorithm has been found yet for solving it to optimality in polynomial time. This has led to recent interest in using genetic algorithms (GAs) to address it. The purpose of this paper and its companion (Part II: Hybrid Genetic Search Strategies) is to give a tutorial survey of recent works on solving classical JSP using genetic algorithms. In Part I, we devote our attention to the representation schemes proposed for JSP. In Part II, we will discuss various hybrid approaches of genetic algorithms and conventional heuristics. The research works on GA/JSP provide very rich experiences for the constrained combinatorial optimization problems. All of the techniques developed for JSP may be useful for other scheduling problems in modern flexible manufacturing systems and other combinatorial optimization problems.

A genetic algorithm approach for multi-objective optimization of supply chain networks

Supply chain network (SCN) design is to provide an optimal platform for efficient and effective supply chain management. It is an important and strategic operations management problem in supply chain management, and usually involves multiple and conflicting objectives such as cost, service level, resource utilization, etc. This paper proposes a new solution procedure based on genetic algorithms to find the set of Pareto-optimal solutions for multi-objective SCN design problem. To deal with multi-objective and enable the decision maker for evaluating a greater number of alternative solutions, two different weight approaches are implemented in the proposed solution procedure. An experimental study using actual data from a company, which is a producer of plastic products in Turkey, is carried out into two stages. While the effects of weight approaches on the performance of proposed solution procedure are investigated in the first stage, the proposed solution procedure and simulated annealing are compared according to quality of Pareto-optimal solutions in the second stage.

A hybrid genetic and variable neighborhood descent algorithm for flexible job shop scheduling problems

This paper addresses the flexible job shop scheduling problem (fJSP) with three objectives: min makespan, min maximal machine workload and min total workload. We developed a hybrid genetic algorithm (GA) for the problem. The GA uses two vectors to represent solutions. Advanced crossover and mutation operators are used to adapt to the special chromosome structure and the characteristics of the problem. In order to strengthen the search ability, individuals of GA are first improved by a variable neighborhood descent (VND), which involves two local search procedures: local search of moving one operation and local search of moving two operations. Moving an operation is to delete the operation, find an assignable time interval for it, and allocate it in the assignable interval. We developed an efficient method to find assignable time intervals for the deleted operations based on the concept of earliest and latest event time. The local optima of moving one operation are further improved by moving two operations simultaneously. An extensive computational study on 181 benchmark problems shows the performance of our approach.

A tutorial survey of job-shop scheduling problems using genetic algorithms, part II: hybrid genetic search strategies

Abstract Job-shop scheduling problem is one of the well-known hardest combinatorial optimization problems. During the last three decades, this problem has captured the interest of a significant number of researchers. A lot of literature has been published, but no efficient solution algorithm has been found yet for solving it to optimality in polynomial time. This has led to recent interest in using genetic algorithms to address the problem. How to adapt genetic algorithms to the job-shop scheduling problems is very challenging but frustrating. Many efforts have been made in order to give an efficient implementation of genetic algorithms to the problem. During the past decade, two important issues have been extensively studied. One is how to encode a solution of the problem into a chromosome so as to ensure that a chromosome will correspond to a feasible solution. The other issue is how to enhance the performance of genetic search by incorporating traditional heuristic methods. Because the genetic algorithms are not well suited for fine-tuning of solutions around optima, various methods of hybridization have been suggested to compensate for this shortcoming. The purpose of the paper is to give a tutorial survey of recent works on various hybrid approaches in genetic job-shop scheduling practices. The research on how to adapt the genetic algorithms to the job-shop scheduling problem provide very rich experiences for the constrained combinatorial optimization problems. All of the techniques developed for the problem are very useful for other scheduling problems in modern flexible manufacturing systems and other difficult-to-solve combinatorial optimization problems.

Study on multi-stage logistic chain network: a spanning tree-based genetic algorithm approach

In recent years, many of the developments in logistics are connected to the need of information of efficient supply chain flow. An important issue in the logistics system is to find the network strategy that can give the least cost of the physical distribution flow. In this paper, we consider the logistic chain network problem formulated by 0-1 mixed integer linear programming model. The design tasks of this problem involve the choice of the facilities (plants and distribution centers) to be opened and the distribution network design to satisfy the demand with minimum cost. As the solution method, we propose the spanning tree-based genetic algorithm by using Prufer number representation. We design the feasibility criteria and develop the repairing procedure for the infeasible Prufer number, so that it can work for relatively large size problems. The efficacy and the efficiency of this method are demonstrated by comparing its numerical experiment results with those of traditional matrix-based genetic algorithm and professional software package LINDO.

A steady-state genetic algorithm for multi-product supply chain network design

Supply chain network (SCN) design is to provide an optimal platform for efficient and effective supply chain management (SCM). The problem is often an important and strategic operations management problem in SCM. The design task involves the choice of facilities (plants and distribution centers (DCs)) to be opened and the distribution network design to satisfy the customer demand with minimum cost. This paper presents a solution procedure based on steady-state genetic algorithms (ssGA) with a new encoding structure for the design of a single-source, multi-product, multi-stage SCN. The effectiveness of the ssGA has been investigated by comparing its results with those obtained by CPLEX, Lagrangean heuristic, hyrid GA and simulated annealing on a set of SCN design problems with different sizes.

Genetic algorithm for non-linear mixed integer programming problems and its applications

In this paper we propose a method for solving non-linear mixed integer programming (NMIP) problems using genetic algorithm (GAs) to get an optimal or near optimal solution. The penalty function method was used to evaluate those infeasible chromosomes generated from genetic reproduction. Also, we apply the method for solving several optimization problems of system reliability which belong to non-linear integer programming (NIP) or (NMIP) problems, using the proposed method. Numerical experiments and comparisons with previous works are illustrated to demonstrate the efficiency of the proposed method.

Soft computing approach for reliability optimization: State-of-the-art survey

In the broadest sense, reliability is a measure of performance of systems. As systems have grown more complex, the consequences of their unreliable behavior have become severe in terms of cost, effort, lives, etc., and the interest in assessing system reliability and the need for improving the reliability of products and systems have become very important. Most solution methods for reliability optimization assume that systems have redundancy components in series and/or parallel systems and alternative designs are available. Reliability optimization problems concentrate on optimal allocation of redundancy components and optimal selection of alternative designs to meet system requirement. In the past two decades, numerous reliability optimization techniques have been proposed. Generally, these techniques can be classified as linear programming, dynamic programming, integer programming, geometric programming, heuristic method, Lagrangean multiplier method and so on. A Genetic Algorithm (GA), as a soft computing approach, is a powerful tool for solving various reliability optimization problems. In this paper, we briefly survey GA-based approach for various reliability optimization problems, such as reliability optimization of redundant system, reliability optimization with alternative design, reliability optimization with time-dependent reliability, reliability optimization with interval coefficients, bicriteria reliability optimization, and reliability optimization with fuzzy goals. We also introduce the hybrid approaches for combining GA with fuzzy logic, neural network and other conventional search techniques. Finally, we have some experiments with an example of various reliability optimization problems using hybrid GA approach.

The balanced allocation of customers to multiple distribution centers in the supply chain network: a genetic algorithm approach

In a typical location-allocation problem, customer demand data are often aggregated according to some arbitrary spatial points (e.g. population centers) or boundaries (e.g. census districts). Since such points or boundaries do not represent true sources of customer demands, allocation of aggregated customers to distribution centers can lead to underutilization of distribution centers and deterioration of customer services. In an effort to design a supply chain network that maintains the best balance of transportation cost and customer service, this paper proposes a new model based on naive balanced star spanning forest formulation. This model goes beyond traditional mathematical programming by incorporating a genetic algorithm that is proven to be effective in dealing with the NP-hard problem.