• Journal of the military operations research society of Korea
• /
• v.24 no.2
• /
• pp.117-129
• /
• 1998

This study is concerned with developing a heuristic for a web-based vehicle routing problem using mixed genetic algorithm(VRPMGA) which determines each vehicle route in order to minimize the transportation costs, subject to meeting the demands of all delivery points. VRP is known to be NP-hard, and it needs a lot of computing time to get the optimal solution, so that heuristics are more frequently developed than optimal algorithms. This study aims to develop a mixed genetic algorithm by partitioned strategy which can give a good solution in comparatively brief time. The good features of the VRPMGA are, fristly, the ability of early convergence and, secondly, the capability of producing multiple, alternative, and near-optimal solutions. The VRPMGA is a useful algorithm that can be appliable to VRP and TSP. Finally, the computational test were performed using the benchmark problems and the proposed heuristic is compared with the other existing algorithms (COSA). The result of computational tests shows that proposed heuristic gives good solutions, in much shorter time, which are same as the best known solutions in the pervious research.

• Geomechanics and Engineering
• /
• v.12 no.2
• /
• pp.197-210
• /
• 2017

This paper focuses on the regularization of structural configurations by employing meta-heuristic optimization algorithms such as Particle Swarm Optimization (PSO) and Biogeography-Based Optimization (BBO). The regularization of structural configuration means obtaining a structure whose members have equal or almost equal lengths, or whose member's lengths are based on a specific pattern; which in this case, by changing the length of these elements and reducing the number of different profiles of needed members, the construction of the considered structure can be made easier. In this article, two different objective functions have been used to minimize the difference between member lengths with a specific pattern. It is found that by using a small number of iterations in these optimization methods, a structure made of equal-length members can be obtained.

• Industrial Engineering and Management Systems
• /
• v.3 no.1
• /
• pp.22-37
• /
• 2004

An integrated heuristic approach based on genetic algorithms (GAs) is proposed for solving the container selection and loading problems. The GA for container selection solves a two-dimensional knapsack problem, determining a set of containers to minimize the transportation or shipment cost. The GA for container loading solves for the weighted coefficients in the evaluation functions that are applied in selecting loading positions and boxes to be loaded, so that the volume utilization is maximized. Several loading constraints such as box orientation, stack priority, stack stability, and container stability are also incorporated into the algorithm. In general, our computational results based on randomly generated data and problems from the literature suggest that the proposed heuristic provides a good solution in a reasonable amount of computational time.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.23 no.56
• /
• pp.9-18
• /
• 2000

This paper considers the problem of two-machine, M-parallel flow shop scheduling to minimize makespan, and proposes a series of heuristic algorithms and a branch and bound algorithm. Two processing times of each job at two machines on each line are identical on any line. Since each flow-shop line consists of two machines, Johnson's sequence is optimal for each flow-shop line. Heuristic algorithms are developed in this paper by combining a "list scheduling" method and a "local search with global evaluation" method. Numerical experiments show that the proposed heuristics can efficiently give optimal or near-optimal schedules with high accuracy. with high accuracy.

• Structural Engineering and Mechanics
• /
• v.60 no.6
• /
• pp.1093-1117
• /
• 2016

Global sensitivity analysis (GSA) has been widely used to investigate the sensitivity of the model output with respect to its input parameters. In this paper a new single-solution search optimization algorithm is developed based on the GSA, and applied to the size optimization of truss structures. In this method the search space of the optimization is determined using the sensitivity indicator of variables. Unlike the common meta-heuristic algorithms, where all the variables are simultaneously changed in the optimization process, in this approach the sensitive variables of solution are iteratively changed more rapidly than the less sensitive ones in the search space. Comparisons of the present results with those of some previous population-based meta-heuristic algorithms demonstrate its capability, especially for decreasing the number of fitness functions evaluations, in solving the presented benchmark problems.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.9 no.13
• /
• pp.79-86
• /
• 1986

This paper presents a heuristic algorithm for a crew scheduling problem with dead head flights. This paper modifies and improves saving method for finding the Multiple Salesman tours in the graph. The results show that the computing time from this algorithm is implemented very much than those from general crew scheduling algorithms by set covering models.

• Journal of Korean Institute of Industrial Engineers
• /
• v.34 no.4
• /
• pp.481-488
• /
• 2008

This paper deals with a scheduling problem for two-machine flow shop, in which the preceding machine is a batch processing machine that can process a number of jobs simultaneously. To minimize makespan of the system, we present a mixed integer linear programming formulation for the problem, and using this formulation, it is shown that an optimal solution for small problem can be obtained by a commercial optimization software. However, since the problem is NP-hard and the size of a real problem is very large, we propose a number of heuristic algorithms including genetic algorithm to solve practical big-sized problems in a reasonable computational time. To verify performances of the algorithms, we compare them with lower bound for the problem. From the results of these computational experiments, some of the heuristic algorithms show very good performances for the problem.

• Advances in Computational Design
• /
• v.7 no.4
• /
• pp.297-319
• /
• 2022

Due to their natural and social revelation, also their ease and flexibility, human collective behavior and teamwork sports are inspired to introduce optimization algorithms to solve various engineering and scientific problems. Nowadays, meta-heuristic algorithms are becoming some striking methods for solving complex real-world problems. In that respect in the present study, the authors propose a novel meta-innovative algorithm based on soccer teamwork sport, suitable for optimization problems. The method may be referred to as the Soccer League Optimization-based Championship Algorithm, inspired by the Soccer league. This method consists of two main steps, including: 1. Qualifying competitions and 2. Main competitions. To evaluate the robustness of the proposed method, six different benchmark mathematical functions, and two engineering design problem was performed for optimization to assess its efficiency in achieving optimal solutions to various problems. The results show that the proposed algorithm may well explore better performance than some well-known algorithms in various aspects such as consistency through runs and a fast and steep convergence in all problems towards the global optimal fitness value.

• Journal of the Korea Safety Management & Science
• /
• v.11 no.3
• /
• pp.157-166
• /
• 2009

In this study, we developed a heuristic algorithm to get better efficiency of clustering than conventional algorithms. Conventional clustering algorithm had lower efficiency of clustering as there were no solid method for selecting initial center of cluster and as they had difficulty in search solution for clustering. EMC(Expanded Moving Center) heuristic algorithm was suggested to clear the problem of low efficiency in clustering. We developed algorithm to select initial center of cluster and search solution systematically in clustering. Experiments of clustering are performed to evaluate performance of EMC heuristic algorithm. Squared-error of EMC heuristic algorithm showed better performance for real case study and improved greatly with increase of cluster number than the other ones.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.22 no.51
• /
• pp.21-28
• /
• 1999

The TSP(Traveling Salesman Problem) is one of the most widely studied problems in combinatorial optimization. The most common interpretation of TSP is finding a shortest Hamiltonian tour of all cities. The objective of this paper proposes a new heuristic algorithm MCH(Multi-Convex hulls Heuristic). MCH is a algorithm for finding good approximate solutions to practical TSP. The MCH algorithm is using the characteristics of the optimal tour. The performance results of MCH algorithm are superior to others algorithms (NNH, CCA) in CPU time.