Applied p-median and p-center algorithms for facility location problems

Dantrakul, Sittipong; Likasiri, Chulin; Pongvuthithum, Radom

doi:10.1016/j.eswa.2013.11.046

Cited by 81 publications

(39 citation statements)

References 28 publications

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“…The multi-criteria/objective facility location problem (MCFLP/MOFLP) for infectious waste disposal in this case is one the MCDM problem. A group of researchers (Dantrakul et al, 2014;Guo, Cheng, & Wang, 2017;Kalcsics, Nickel, Pozo, Puerto, & Rodríguez-Chía, 2014;Steiner, Datta, Steiner Neto, Scarpin, & Rui Figueira, 2015) proposed mathematical techniques to solve MCDM problems which need to deal with environmental restrictions, whereas another group (Choudhary & Shankar, 2012;Hanine et al, 2016;Kahraman et al, 2003) often proposed MCDM tools to solve complex problems which are difficult to interpret. The disadvantages of MCDM tools alone cause them to be unable to handle existing environmental restrictions in some special problems, and the disadvantages of mathematical techniques make them unable to address complex problems which are difficult to interpret.…”

Section: Related Literaturementioning

confidence: 99%

“…The location network (depots, customers and arcs) that incurs the minimum total distance/lowest total cost is regarded as an optimal solution. Many researchers (Dantrakul et al, 2014;Etemadnia et al, 2015;Guastaroba & Speranza, 2014;Rahmani & MirHassani, 2014) have proposed solving techniques such as mathematical techniques (heuristic and optimization techniques) only. However, with some complex problems such as selecting sites for hazardous material waste disposal, choosing sites for nuclear power plants, location selection for infectious waste disposal, these problems are very important decisions because they are costly and hard to reverse.…”

Section: Related Literaturementioning

confidence: 99%

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Solving a multi-objective location routing problem for infectious waste disposal using hybrid goal programming and hybrid genetic algorithm

Wichapa¹,

Khokhajaikiat²

2018

10.5267/j.ijiec

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Infectious waste disposal remains one of the most serious problems in the medical, social and environmental domains of almost every country. Selection of new suitable locations and finding the optimal set of transport routes for a fleet of vehicles to transport infectious waste material, location routing problem for infectious waste disposal, is one of the major problems in hazardous waste management. Determining locations for infectious waste disposal is a difficult and complex process, because it requires combining both intangible and tangible factors. Additionally, it depends on several criteria and various regulations. This facility location problem for infectious waste disposal is complicated, and it cannot be addressed using any standalone technique. Based on a case study, 107 hospitals and 6 candidate municipalities in UpperNortheastern Thailand, we considered criteria such as infrastructure, geology and social & environmental criteria, evaluating global priority weights using the fuzzy analytical hierarchy process (Fuzzy AHP). After that, a new multi-objective facility location problem model which hybridizes fuzzy AHP and goal programming (GP), namely the HGP model, was tested. Finally, the vehicle routing problem (VRP) for a case study was formulated, and it was tested using a hybrid genetic algorithm (HGA) which hybridizes the push forward insertion heuristic (PFIH), genetic algorithm (GA) and three local searches including 2-opt, insertion-move and interexchange-move. The results show that both the HGP and HGA can lead to select new suitable locations and to find the optimal set of transport routes for vehicles delivering infectious waste material. The novelty of the proposed methodologies, HGP, is the simultaneous combination of relevant factors that are difficult to interpret and cost factors in order to determine new suitable locations, and HGA can be applied to determine the transport routes which provide a minimum number of vehicles and minimum transportation cost under the actual situation efficiently in this case.

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Section: Related Literaturementioning

confidence: 99%

Section: Related Literaturementioning

confidence: 99%

Solving a multi-objective location routing problem for infectious waste disposal using hybrid goal programming and hybrid genetic algorithm

Wichapa¹,

Khokhajaikiat²

2018

10.5267/j.ijiec

View full text Add to dashboard Cite

show abstract

“…Differently, in P-median problems (p-MP) the decision maker aims to allocate p facilities in such a way that total distance traveled between demand points and facilities is minimized. Dantrakul et al (2014) apply median and center model to allocate waste system facilities in Chiang Mai city and 5 provinces of Northern Thailand. As emergency systems facility location problem applications, Jia et al (2007) utilize both covering, center and median models for allocating medical supply storage to distribute antibiotics, vaccines and drugs rapidly under a possible chemical, biological, radiological and nuclear (CBRN) terrorist attack in LA.…”

Section: Related Workmentioning

confidence: 99%

An ILP and simulation model to optimize search and rescue helicopter operations

Karataş

Razi

Günal

2017

Journal of the Operational Research Society

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Maritime search and rescue (SAR) operations, conducted for rendering aid to the victims in need of help at sea, play a crucial role in dropping the number of causalities. Therefore, it is of high importance to organize SAR operations properly. In this paper, we compose a hybrid methodology which combines optimization and simulation to allocate SAR helicopters. First, we build an integer linear programming (ILP) model to provide an effective deployment plan and use it as an input to a simulation model which includes constraints that the ILP model cannot tackle. Next, using a rule-based algorithm, we generate alternative solutions and seek better plans that exist in the vicinity of the ILP model solution. We perform our methodology on the historical incident data in the Aegean Sea region. Results show that the hybrid methodology we adopted leads to a more effective utilization of resources than the optimization model alone.

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“…The CPMP problem is NP-complete according to the proof of [11], so its generalization BMCLP is too. Several other variants of CPMP are treated by [18], [3], [1] and [7]. Dynamic location problems are solved by [2], [8] and [6].…”

Section: Introductionmentioning

confidence: 99%

Multi-Stage Algorithms for Solving a Generalized Capacitated P-median Location Problem

Amrani¹,

Benadada²

2018

ijacsa

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Abstract-The capacitated p-median location problem is one of the famous problems widely discussed in the literature, but its generalization to a multi-capacity case has not. This generalization, called multi-capacitated location problem, is characterized by allowing facilities to use one of several capacity levels. For this purpose, a predefined list of capacity levels supported by all potential facilities is established. In this paper, we will detail the mathematical formulation and propose a new solving method. We try to construct, indeed, a multi-stage heuristic algorithm that will be called BDF (Biggest Demand First). This new method appears in two approaches: Integrated BDF (IBDF) and Hybridized BDF (HBDF) will be improved by using a local search optimization. A valid lower bound to the optimal solution value is obtained by solving a lagrangian relaxation dual of the exact formulation. Computational results are presented at the end using new instances with higher ratio between the number of customers, facilities and capacity levels or adapted from those of p-median drawn from the literature. The obtained results show that the IBDF is much faster with medium quality solution while HBDF is slower but provides very good solutions close to the optimality.

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Applied p-median and p-center algorithms for facility location problems

Cited by 81 publications

References 28 publications

Solving a multi-objective location routing problem for infectious waste disposal using hybrid goal programming and hybrid genetic algorithm

Solving a multi-objective location routing problem for infectious waste disposal using hybrid goal programming and hybrid genetic algorithm

An ILP and simulation model to optimize search and rescue helicopter operations

Multi-Stage Algorithms for Solving a Generalized Capacitated P-median Location Problem

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