Multifacility Location Problem

Daneshzand, Farzaneh; Shoeleh, Razieh

doi:10.1007/978-3-7908-2151-2_4

Cited by 8 publications

(6 citation statements)

References 56 publications

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“…Evidently, the quantity

p

has a direct impact on the execution time of any solution algorithm, whereby, according to complexity theory, the general problem of finding the optimal locations of

p

facilities in a graph with

n

vertices is NP‐hard . Surveys concerning multiple facility location problems can be found, for example, in Daneshzand and Shoeleh (2009) or Reese (2006). Capacitated versus uncapacitated location problems : While in elementary location‐allocation problems, the capacities of all facilities are assumed to be unrestricted, advanced models overcome this apparent shortcoming by incorporating additional constraints concerning individual capacity limitations of the facilities.…”

Section: Review Of Location‐allocation Literaturementioning

confidence: 99%

“…Apart from a few special cases, all network location problems are based on the assumptions that (a) the demand centers as well as the facility locations coincide with the vertices of a graph and (b) transport occurs only along the edges of this graph, whereby the associated edge-weights may not only specify metric distances, but also quantities like travel times or transportation cost. For a thorough discussion of these two types of location-allocation models, confer, for instance, Current et al (2002) or Daneshzand and Shoeleh (2009).…”

Section: Planar Versus Network Location Problemsmentioning

confidence: 99%

“…Evidently, the quantity A p has a direct impact on the execution time of any solution algorithm, whereby, according to complexity theory, the general problem of finding the optimal locations of A p facilities in a graph with A n vertices is NP-hard. Surveys concerning multiple facility location problems can be found, for example, in Daneshzand and Shoeleh (2009) or Reese (2006).…”

Section: Planar Versus Network Location Problemsmentioning

confidence: 99%

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Solving location‐allocation problems with professional optimization software

Wolf

2022

Transactions in GIS

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Location‐allocation modeling has always been regarded as a core part of spatial analysis. A review of the literature, however, reveals that, nevertheless, publications related to facility location disappeared almost completely from geographic journals in the meantime. It can be assumed that one major reason for this development is the limited capability of the location‐allocation modules implemented in GIS packages that prevents this software from being used to solve the increasingly complex real‐world problems of today. The main purpose of the present article is to demonstrate how location‐allocation problems can be solved efficiently with professional optimization software, more precisely, with the two program packages CPLEX® and AIMMS®. Starting from two classic problems, viz., the p$$ p $$‐median and the location set covering problem, the article presents how structurally much more complicated problems, which are far beyond the capabilities of the respective GIS modules, can be solved quite easily with these program packages.

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“…Evidently, the quantity

p

has a direct impact on the execution time of any solution algorithm, whereby, according to complexity theory, the general problem of finding the optimal locations of

p

facilities in a graph with

n

Section: Review Of Location‐allocation Literaturementioning

confidence: 99%

Section: Planar Versus Network Location Problemsmentioning

confidence: 99%

Section: Planar Versus Network Location Problemsmentioning

confidence: 99%

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Solving location‐allocation problems with professional optimization software

Wolf

2022

Transactions in GIS

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“…More complicated approaches on FLP has been done, including: multi FLP (Daneshzand and Shoeleh, 2009); facility location-allocation problemthese two objectives are searched as optimized solution, location of facilities and allocating them to clients in order to respond to demands; discrete FLP (Boloori and Farahani, 2012).…”

Section: Imds 1162mentioning

confidence: 99%

Developing service supply chains by using agent based simulation

Rouzafzoon

Helo

2016

Industr Mngmnt & Data Systems

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The Master thesis present a novel approach to model a service supply chain with agent based simulation. Also, the case study of thesis is related to healthcare services and research problem includes facility location of healthcare centers in Vaasa region by considering the demand, resource units and service quality. Geographical information system is utilized for locating population, agent based simulation for patients and their illness status probability, and discrete event simulation for healthcare services modelling. Health centers are located on predefined sites based on managers' preference, then each patient based on the distance to health centers, move to the nearest point for receiving the healthcare services. For evaluating cost and services condition, various key performance indicators have defined in the modelling such as Number of patient in queue, patients waiting time, resource utilization, and number of patients ratio yielded by different of inflow and outflow. Healthcare managers would be able to experiment different scenarios based on changing number of resource units or location of healthcare centers, and subsequently evaluate the results without necessity of implementation in real life.

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“…So HAP can be improved by successively reducing the value of ε, which is called SHAP. For more details about HAP please refer to [6,29].…”

mentioning

confidence: 99%

ADMM-type methods for generalized multi-facility Weber problem

Zhang¹,

Jiang²,

Su³

et al. 2022

JIMO

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The well-known multi-facility Weber problem (MFWP) is one of fundamental models in facility location. With the aim of enhancing the practical applicability of MFWP, this paper considers a generalized multi-facility Weber problem (GMFWP), where the gauge is used to measure distances and the locational constraints are imposed to new facilities. This paper focuses on developing efficient numerical methods based on alternating direction method of multipliers (ADMM) to solve GMFWP. Specifically, GMFWP is equivalently reformulated into a minmax problem with special structure and then some ADMM-type methods are proposed for its primal problem. Global convergence of proposed methods for GMFWP is established under mild assumptions. Preliminary numerical results are reported to verify the effectiveness of proposed methods.

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Multifacility Location Problem

Cited by 8 publications

References 56 publications

Solving location‐allocation problems with professional optimization software

Solving location‐allocation problems with professional optimization software

Developing service supply chains by using agent based simulation

ADMM-type methods for generalized multi-facility Weber problem

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