scholarly journals An Application of an Unequal-Area Facilities Layout Problem with Fixed-Shape Facilities

Algorithms ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 306
Author(s):  
Alan McKendall ◽  
Artak Hakobyan
Keyword(s):  
Genetic Algorithm ◽  
Computational Time ◽  
Test Problems ◽  
Layout Problem ◽  
Solution Quality ◽  
Dual Simplex Method ◽  
Facilities Layout ◽  
Fixed Dimension ◽  
Continuous Plant ◽  
Production Area

The unequal-area facility layout problem (UA-FLP) is the problem of locating rectangular facilities on a rectangular floor space such that facilities do not overlap while optimizing some objective. The objective considered in this paper is minimizing the total distance materials travel between facilities. The UA-FLP considered in this paper considers facilities with fixed dimension and was motivated by the investigation of layout options for a production area at the Toyota Motor Manufacturing West Virginia (TMMWV) plant in Buffalo, WV, USA. This paper presents a mathematical model and a genetic algorithm for locating facilities on a continuous plant floor. More specifically, a genetic algorithm, which consists of a boundary search heuristic (BSH), a linear program, and a dual simplex method, is developed for an UA-FLP. To test the performance of the proposed technique, several test problems taken from the literature are used in the analysis. The results show that the proposed heuristic performs well with respect to solution quality and computational time.

scholarly journals An Enhanced Genetic Algorithm for the Generalized Traveling Salesman Problem

2017 ◽  
Vol 7 (6) ◽  
pp. 2260-2265 ◽  
Author(s):  
H. Jafarzadeh ◽  
N. Moradinasab ◽  
M. Elyasi
Keyword(s):  
Genetic Algorithm ◽  
Traveling Salesman Problem ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Nearest Neighbor Search ◽  
Traveling Salesman ◽  
Computational Time ◽  
Test Problems ◽  
Solution Quality ◽  
Generalized Traveling Salesman Problem

The generalized traveling salesman problem (GTSP) deals with finding the minimum-cost tour in a clustered set of cities. In this problem, the traveler is interested in finding the best path that goes through all clusters. As this problem is NP-hard, implementing a metaheuristic algorithm to solve the large scale problems is inevitable. The performance of these algorithms can be intensively promoted by other heuristic algorithms. In this study, a search method is developed that improves the quality of the solutions and competition time considerably in comparison with Genetic Algorithm. In the proposed algorithm, the genetic algorithms with the Nearest Neighbor Search (NNS) are combined and a heuristic mutation operator is applied. According to the experimental results on a set of standard test problems with symmetric distances, the proposed algorithm finds the best solutions in most cases with the least computational time. The proposed algorithm is highly competitive with the published until now algorithms in both solution quality and running time.

A solution to the unequal area facilities layout problem by genetic algorithm

2005 ◽  
Vol 56 (2) ◽  
pp. 207-220 ◽  
Author(s):  
Ming-Jaan Wang ◽  
Michael H. Hu ◽  
Meei-Yuh Ku
Keyword(s):  
Genetic Algorithm ◽  
Layout Problem ◽  
Facilities Layout

A HYBRID GREEDY RANDOMIZED ADAPTIVE SEARCH HEURISTIC TO SOLVE THE DIAL-A-RIDE PROBLEM

2013 ◽  
Vol 30 (01) ◽  
pp. 1250046 ◽  
Author(s):  
FRANCESCA GUERRIERO ◽  
MARIA ELENA BRUNI ◽  
FRANCESCA GRECO
Keyword(s):  
Hybrid Approach ◽  
Terms Of Trade ◽  
Computational Time ◽  
Test Problems ◽  
Fixed Costs ◽  
Computational Results ◽  
Adaptive Search ◽  
Solution Quality ◽  
Search Heuristic ◽  
Number Of Customers

This paper presents a hybrid metaheuristic for solving the static dial-a-ride problem with heterogeneous vehicles and fixed costs. The hybridization combines a reactive greedy randomized adaptive search, used as outer scheme, with a tabu search heuristic in the local search phase. The algorithm is evaluated on well-known instances taken from the literature and on a set of randomly generated test problems, varying in the number of customers. Extensive computational results show the effectiveness of the hybrid approach in terms of trade-off between solution quality and computational time.

A Non-Dominant Sorting Genetic Algorithm for Optimization of a Product Design and Selection of Its Suppliers

2020 ◽  
Vol 19 (01) ◽  
pp. 167-188
Author(s):  
Oulfa Labbi ◽  
Abdeslam Ahmadi ◽  
Latifa Ouzizi ◽  
Mohammed Douimi
Keyword(s):  
Genetic Algorithm ◽  
Product Design ◽  
Computational Time ◽  
Optimization Approach ◽  
Nsga Ii ◽  
Solution Quality ◽  
Holding Costs ◽  
Weighted Sum Method ◽  
The Impact ◽  
Selection Of

The aim of this paper is to address the problem of supplier selection in a context of an integrated product design. Indeed, the product specificities and the suppliers’ constraints are both integrated into product design phase. We consider the case of improving the design of an existing product and study the selection of its suppliers adopting a bi-objective optimization approach. Considering multi-products, multi-suppliers and multi-periods, the mathematical model proposed aims to minimize supplying, transport and holding costs of product components as well as quality rejected items. To solve the bi-objective problem, an evolutionary algorithm namely, non-dominant sorting genetic algorithm (NSGA-II) is employed. The algorithm provides a set of Pareto front solutions optimizing the two objective functions at once. Since parameters values of genetic algorithms have a significant impact on their efficiency, we have proposed to study the impact of each parameter on the fitness functions in order to determine the optimal combination of these parameters. Thus, a number of simulations evaluating the effects of crossover rate, mutation rate and number of generations on Pareto fronts are presented. To evaluate performance of the algorithm, results are compared to those obtained by the weighted sum method through a numerical experiment. According to the computational results, the non-dominant sorting genetic algorithm outperforms the CPLEX MIP solver in both solution quality and computational time.

A genetic algorithm and queuing theory based methodology for facilities layout problem

2009 ◽  
Vol 47 (20) ◽  
pp. 5611-5635 ◽  
Author(s):  
Dhamodharan Raman ◽  
Sev V. Nagalingam ◽  
Bruce W. Gurd
Keyword(s):  
Genetic Algorithm ◽  
Queuing Theory ◽  
Layout Problem ◽  
Facilities Layout

scholarly journals A Genetic Algorithm for the Double Row Layout Problem

2020 ◽  
Vol 22 (2) ◽  
pp. 85-92
Author(s):  
Achmad Pratama Rifai ◽  
Setyo Tri Windras Mara ◽  
Putri Adriani Kusumastuti ◽  
Rakyan Galuh Wiraningrum
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Programming ◽  
Material Handling ◽  
Programming Model ◽  
Computational Time ◽  
Mathematical Programming Model ◽  
Layout Problem ◽  
Double Row ◽  
Handling Cost ◽  
Material Handling Cost

The double row layout problem (DRLP) is an NP-hard and has many applications in the industry. The problem concerns on arranging the position of  machines on the two rows so that the material handling cost is minimized. Although several mathematical programming models and local heuristics have been previously proposed, there is still a requirement to develop an approach that can solve the problem efficiently. Here, a genetic algorithm is proposed, which is aimed to solve the DRLP in a reasonable and applicable time. The performances of the proposed method, both its obtained objective values and computational time, are evaluated by comparing it with the existing mathematical programming model. The results demonstrate that the proposed GA can find relatively high-quality solutions in much shorter time than the mathematical programming model, especially in the problem with large number of machines.

Optimizing Bi-Criteria Permutation Flow Shop Scheduling Problem by Improved NSGA III

2018 ◽  
Author(s):  
Ronghua Meng ◽  
Yunqing Rao ◽  
Qiang Luo
Keyword(s):  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Computational Time ◽  
Test Problems ◽  
Scheduling Problem ◽  
Nsga Ii ◽  
Solution Quality ◽  
Shop Scheduling ◽  
Permutation Flow Shop Scheduling ◽  
Permutation Flow Shop

This paper addresses a bi-objective distribution permutation flow shop scheduling problem (FSP) with setup times aiming to minimize the makespan and the total tardiness. It is very difficult to obtain an optimal solution by using traditional approaches in reasonable computational time. This paper presents an appropriate non-dominated sorting Genetic Algorithm III based on the reference point. The NEH strategy is applied into the generation of the initial solution set. To validate the performance of the NEH strategy improved NSGA III (NNSGA III) on solution quality and diversity level, various test problems are carried out. Three algorithms, including NSGA II, NEH strategy improved NSGA II(NNSGA II) and NNSGA III are utilized to solve this FSP. Experimental results suggest that the proposed NNSGA III outperforms the other algorithms on the Inverse Generation Distance metric, and the distribution of Pareto solutions are improved excellently.

COMPARISON OF SCHEDULING EFFICIENCY IN TWO/THREE-MACHINE NO-WAIT FLOW SHOP PROBLEM USING SIMULATED ANNEALING AND GENETIC ALGORITHM

2006 ◽  
Vol 23 (01) ◽  
pp. 41-59 ◽  
Author(s):  
TAI-YUE WANG ◽  
YIH-HWANG YANG ◽  
HERN-JIANG LIN
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Flow Shop ◽  
Optimization Problems ◽  
Flow Shop Scheduling ◽  
Computational Time ◽  
Scheduling Problems ◽  
Solution Quality ◽  
Computation Efficiency ◽  
No Wait

This paper considers two/three-machine no-wait flow shop scheduling problems with makespan minimization. Inherited with the NP-hard problem nature, this scheduling problem is solved by using a Simulated Annealing (SA) and Genetic Algorithm (GA) instead of mathematical programming. These two well-known search algorithms are often used to solve complex combinational optimization problems. In order to compare the performance of the two algorithms, we use solution quality (under the same computational time) and computation efficiency (under the same solution quality) as the measuring criteria. From the example problem, we found that SA is superior to GA in both solution quality and computation efficiency under identical terminating conditions. The performance of SA and GA decreased upon increasing the number of jobs. Our main contributions are to compare SA and GA under limited resources, which would be more consistent with the real world.

scholarly journals The Ordered Clustered Travelling Salesman Problem: A Hybrid Genetic Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Zakir Hussain Ahmed
Keyword(s):  
Genetic Algorithm ◽  
Travelling Salesman Problem ◽  
Hybrid Genetic Algorithm ◽  
Computational Time ◽  
Computational Results ◽  
Travelling Salesman ◽  
Solution Quality ◽  
Heuristic Solution ◽  
Present Solution ◽  
Sequencing Problems

The ordered clustered travelling salesman problem is a variation of the usual travelling salesman problem in which a set of vertices (except the starting vertex) of the network is divided into some prespecified clusters. The objective is to find the least cost Hamiltonian tour in which vertices of any cluster are visited contiguously and the clusters are visited in the prespecified order. The problem is NP-hard, and it arises in practical transportation and sequencing problems. This paper develops a hybrid genetic algorithm using sequential constructive crossover, 2-opt search, and a local search for obtaining heuristic solution to the problem. The efficiency of the algorithm has been examined against two existing algorithms for some asymmetric and symmetric TSPLIB instances of various sizes. The computational results show that the proposed algorithm is very effective in terms of solution quality and computational time. Finally, we present solution to some more symmetric TSPLIB instances.

Enhancements to the Localized Genetic Algorithm for Large Scale Capacitated Vehicle Routing Problems

2013 ◽  
Vol 4 (1) ◽  
pp. 17-38 ◽  
Author(s):  
Ziauddin Ursani ◽  
Daryl Essam ◽  
David Cornforth ◽  
Robert Stocker
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Large Scale ◽  
Computational Time ◽  
Small Scale ◽  
Problem Decomposition ◽  
Vehicle Routing Problems ◽  
Solution Quality ◽  
Routing Problems ◽  
Capacitated Vehicle

This paper is a continuation of two previous papers where the authors used Genetic Algorithm with automated problem decomposition strategy for small scale capacitated vehicle routing problems (CVRP) and vehicle routing problem with time windows (VRPTW). In this paper they have extended their scheme to large scale capacitated vehicle routing problems by introducing selective search version of the automated problem decomposition strategy, a faster genotype to phenotype translation scheme, and various search reduction techniques. The authors have shown that genetic algorithm used with automated problem decomposition strategy outperforms the GAs applied on the problem as a whole not only in terms of solution quality but also in terms of computational time on the large scale problems.

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