Modeling Supply Chain Facility Location Problem and Its Solution Using a Genetic Algorithm

Wang, Bing; Fu, Xiaokang; Chen, Tinggui; Zhou, Guanglan

doi:10.4304/jsw.9.9.2335-2341

Cited by 4 publications

(3 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Article [3] concentrates on facility location problem, focusing on the linear programming model and a genetic algorithm in the location problem analysis and analytical method. In the linear programming model, because the given complex table calculation method is too complicated and the workload is very large, the Excel software is proposed to solve the location problem, which can greatly improve the efficiency of enterprise facility location problem.…”

Section: Facility Location Problems Solved In Excel Environmentmentioning

confidence: 99%

Optimization of the Multi-Facility Location Problem Using Widely Available Office Software

Němec

Stodola

2021

Algorithms

View full text Add to dashboard Cite

Multi-facility location problem is a type of task often solved (not only) in logistics. It consists in finding the optimal location of the required number of centers for a given number of points. One of the possible solutions is to use the principle of the genetic algorithm. The Solver add-in, which uses the evolutionary method, is available in the Excel office software. It was used to solve the benchmark in 4 levels of difficulty (from 5 centers for 25 points to 20 centers for 100 points), and one task from practice. The obtained results were compared with the results obtained by the metaheuristic simulated annealing method. It was found that the results obtained by the evolutionary method are sufficiently accurate. Their accuracy depends on the complexity of the task and the performance of the HW used. The advantage of the proposed solution is easy availability and minimal requirements for user knowledge.

show abstract

Section: Facility Location Problems Solved In Excel Environmentmentioning

confidence: 99%

Optimization of the Multi-Facility Location Problem Using Widely Available Office Software

Němec

Stodola

2021

Algorithms

View full text Add to dashboard Cite

show abstract

“…In addition to vehicle routing and logistics network , there are other factors concerning order fulfillment like replenishment policies optimization with different number of warehouse and retailers [94],facility location [92], [71]and man power shift plan using GA [65]etc.…”

Section: Other Issuesmentioning

confidence: 99%

Optimization Algorithm in Supply Chain Management

Bandekar*¹,

C*²

2019

IJITEE

View full text Add to dashboard Cite

 Abstract: Optimization in the field of Operations Research has applications in various industries, be it medicine, business, analytics or education. Likewise Supply Chain Management (SCM) is required in every industry and with the need comes various challenges to get the optimized and best quality solution. There are stochastic, analytical models working on attaining optimization in various sub events involved in SCM. Supply Chain is a network at global level used for delivering of products and services from unprocessed materials to consumers through well-structured and planned flow of information, physical distribution and money. The process of managing this supply chain is Supply Chain Management. A major work on the previous research done using various mathematical models, be it mixed integer linear, nonlinear programming or evolutionary have been depicted in this paper. The aim is to get the best result and comparative approach is focused. This article provides a detailed study on various techniques, algorithms and mathematical models in optimization of SCM and in particular it focuses on Genetic Algorithm (GA) in SCM.

show abstract

“…In GA, a group of strings is created and grown with the general objective of maximizing the fitness of the whole population or minimizing the population-related costs. Holland, the first who did valuable works in this regard in the 60s and 70s, made this algorithm popular (Aickelin, 2002), but, finally, Goldberg, one of his students, collected them (Wang et al, 2014). Holland's earlier works had been already compiled, printed, and published in his book and he was the first who tried to provide a theoretical foundation for genetic algorithms through stating the theory of patterns.…”

Section: Introductionmentioning

confidence: 99%

Locating distribution/service centers based on multi objective decision making using set covering and proximity to stock market

Dabibi

Moghaddam²,

Kazemi³

2016

10.5267/j.ijiec

View full text Add to dashboard Cite

In the present competitive world, facility location is an important aspect of the supply chain (sc) optimization. It involves selecting specific locations for facility construction and allocation of the distribution channel among different SC levels. In fact, it is a strategic issue which directly affects many operational/tactical decisions. Besides the accessibility, which results in customer satisfaction, the present paper optimizes the establishment costs of a number of distribution channels by considering their proximity to the stock market of the goods they distribute, and proposes mathematical models for two objective functions using the set covering problem. Then, two objective functions are proposed into one through the ε-constraint method and solved by the metaheuristic Genetic Algorithm (GA). To test the resulted model, a smaller scale problem is solved. Results from running the algorithm with different ε-values show that, on average, a 10% increase in ε, which increases the value of the second objective function -distance covered by customers will cause a 2% decrease in the value of the first objective function including the costs of establishing distribution centers). The repeatability and solution convergence of the twoobjective model presented by the GA are other results obtained in this study.

show abstract

Modeling Supply Chain Facility Location Problem and Its Solution Using a Genetic Algorithm

Cited by 4 publications

References 27 publications

Optimization of the Multi-Facility Location Problem Using Widely Available Office Software

Optimization of the Multi-Facility Location Problem Using Widely Available Office Software

Optimization Algorithm in Supply Chain Management

Locating distribution/service centers based on multi objective decision making using set covering and proximity to stock market

Contact Info

Product

Resources

About