Multi-source facility location–allocation and inventory problem

Yao, Zhishuang; Lee, Loo Hay; Jaruphongsa, Wikrom; Tan, Vicky L.M.; Hui, Chen Fei

doi:10.1016/j.ejor.2010.06.006

Cited by 70 publications

(25 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…12 Yao et al (2010) Location-allocation problem of facilities and stocks, allowing the use of multiple sources of warehouses (2015) Determine a subset of enabled facilities to be opened in order to meet the demands of customers so that the costs are minimized.…”

Section: Reference Problem Heuristic Approachmentioning

confidence: 99%

Otimização da rede de uma cadeia de suprimentos com a utilização de uma heurística baseada em Busca Tabu

2016

View full text Add to dashboard Cite

Resumo: Este artigo discute a implementação e avaliação de uma heurística baseada em Busca Tabu para otimizar uma rede de cadeia de suprimentos. Para tanto, o modelo single-source proposto por Farias & Borenstein (2012) foi implementado. O problema foi resolvido por uma adaptação do método de Lee & Kwon (2010), substituindo centros de distribuição (CDs) e arcos a fim de encontrar o menor custo para uma rede de cadeia de suprimentos. Foram resolvidas as 22 instâncias propostas por Farias & Borenstein (2012) e os resultados indicam que, para esses cenários, o método aplicado teve um bom desempenho computacional, obtendo resultados com uma redução de 81,03% no tempo médio de processamento. Contudo, houve um aumento de 4,98% no custo médio das soluções obtido pelo método heurístico quando comparado com os resultados ótimos. Por fim, o problema foi resolvido para outras quatro instâncias com características reais, comprovando a eficiência da heurística para problemas de grande escala, visto que todas as soluções foram obtidas em menos de 2 minutos de processamento.Abstract: This paper discusses the implementation and evaluation of a heuristic based on Tabu Search to optimize a supply chain network. To this end, a single-source model proposed by Farias & Borenstein (2012) was implemented. The problem was solved by adapting the Lee & Kwon method (2010), exchanging distribution centers (DCs) and arcs to find the lowest cost for a supply chain network. Twenty-two instances proposed by Farias & Borenstein (2012) were solved and the results indicate that, for the scenarios, the method applied presented good computational performance, obtaining results with 81.03% reduction of the average processing time. However, there was an increase of 4.98% in the average cost of the solutions obtained through the heuristic method when compared with the optimal results. Finally, the problem was solved for four other instances with real features, proving the efficiency of this heuristic for large-scale problems, considering that all solutions were obtained in less than 2 minutes of processing.

show abstract

Section: Reference Problem Heuristic Approachmentioning

confidence: 99%

Otimização da rede de uma cadeia de suprimentos com a utilização de uma heurística baseada em Busca Tabu

2016

View full text Add to dashboard Cite

show abstract

“…Expressions (2) and (3) are replaced by constraints (17) and (18). The final binary and integer variables are defined by constraints (19) and (20), respectively.…”

Section: The Mathematical Modelmentioning

confidence: 99%

“…Authors considered the objective function based on minimal electricity generation costs and investigated financial effects of increasing upper limits of the capacities of the power plants. A joint facility LA and inventory problem with stochastic demands was proposed by Yao et al [19]. The problem was to find locations of warehouses, allocation of customers and inventory levels of warehouses.…”

Section: Introductionmentioning

confidence: 99%

A capacitated location-allocation problem with stochastic demands using sub-sources: An empirical study

Alizadeh

Mahdavi

Mahdavi–Amiri

et al. 2015

Applied Soft Computing

View full text Add to dashboard Cite

“…Error ratio up to some extent may be determined by the initial setup cost of the resource. The size of a resource is determined by the number of resources [5]. For some resources it is preferable to have large number of smaller resources as compared to few large resources as in case of hospitals while on the other hand, resources like amusement parks need to be very large and have high ambience thus increasing the setup cost despite their small number.…”

Section: Continuous Resourse Locationmentioning

confidence: 99%

“…For some resources it is preferable to have large number of smaller resources as compared to few large resources as in case of hospitals while on the other hand, resources like amusement parks need to be very large and have high ambience thus increasing the setup cost despite their small number. In [5] attractiveness of a resource for demand points is determined which is based on size of resource, distance of resource and accessibility to resource. To provide a quality of service with some fixed cost, it may also be possible to have a mix of small and large resources in a way so that small facilities are available to all demand points in minimum access time, at the same time these small resources should be capable of providing basic services to the demand points.…”

Section: Continuous Resourse Locationmentioning

confidence: 99%

Computational Geometry for Continuous Resource Location Model in a Competitive Environment

Mangla¹,

Garg²

2011

IJCA

View full text Add to dashboard Cite

Resource location has become a fundamental question to be answered in the day today life. It has found its place in the life for over hundred years from the smallest application like where to locate any electric appliance in a kitchen to opening a new departmental store by a company. Depending upon the context in which the location model is being used, various objective functions may be designed while main features will always remain same [1]. Here in this paper, we have studied the continuous resource location model where competing resources are already known to exist in the system under study. The newly located resources compete for its share of customer demands with the pre-existing resources in the system and with each other also [2]. The paper also discusses how the applications of computational geometry relate to this spatial problem.

show abstract

Multi-source facility location–allocation and inventory problem

Cited by 70 publications

References 26 publications

Otimização da rede de uma cadeia de suprimentos com a utilização de uma heurística baseada em Busca Tabu

Otimização da rede de uma cadeia de suprimentos com a utilização de uma heurística baseada em Busca Tabu

A capacitated location-allocation problem with stochastic demands using sub-sources: An empirical study

Computational Geometry for Continuous Resource Location Model in a Competitive Environment

Contact Info

Product

Resources

About