Reliable p-median facility location problem: two-stage robust models and algorithms

An, Yu; Zeng, Bo; Zhang, Yunlong; Zhao, Long

doi:10.1016/j.trb.2014.02.005

Cited by 159 publications

(82 citation statements)

References 31 publications

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“…For this problem, there are two mainstream approaches, including spatial approaches and flow-based approaches. Spatial approaches, such as the p-median approach, minimize the distances between the location of a facility and the possible customers that are served by this facility [31,32]. Furthermore, in this paper, the facility corresponds to a recharging station, and the customer to a BEV driver.…”

Section: Literature Reviewmentioning

confidence: 99%

Multiple Types of Plug-In Charging Facilities’ Location-Routing Problem with Time Windows for Mobile Charging Vehicles

2018

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Abstract:Increasing attention is being paid to the technology of battery electric vehicles (BEVs) because of their environmental friendliness. However, their short range, extended recharging times, and insufficient charging facilities hinder the improvement in the market share of BEVs. As a remedy, this paper presents a novel approach to providing a service for the battery charge replenishment of BEVs. Instead of using traditional alternative methods by only providing a charging service in a fixed location, such as battery-swapping and charging lanes, the novel charge replenishment is provided by mobile charging vehicles (MCVs), which could offer a charging service at any time and at location requested. To consider the limited running range and the opportunity to recharge from MCVs, as well as to determine the location strategy of multiple types of plug-in charging facility locations and the routing plan of the MCVs simultaneously, the location routing problem (LRP) that can integrate two decision levels, with a strategic level (location) and tactical level (routing), is applied. Then, we present the multiple types of plug-in charging facilities' location-routing problem with time windows for mobile charging vehicles (MTPCF-LRPwTW-MCVs), and formulate the MTPCF-LRPwTW-MCVs as a mixed integer linear program for the convenience of solving. To demonstrate the model, test instances are designed and computational results are presented. Furthermore, sensitivity analyses on battery capacity, recharging rate, and so on, are also examined. The results show that with the increase of the battery capacity or the improvement of the charging rate of the charging facilities, the service efficiency of the MCVs can reasonably be improved. Therefore, the proposed method could be used in real world problems.

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Section: Literature Reviewmentioning

confidence: 99%

Multiple Types of Plug-In Charging Facilities’ Location-Routing Problem with Time Windows for Mobile Charging Vehicles

2018

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“…An, Zeng, Zhang and Zhao [33] presented a robust p-median model for a facility location problem dealing with an optimisation model that was subject to disruption. The two-stage robust optimisation scheme caters for the demand changes due to disruption and facility capacities.…”

Section: Facility Location and P-medianmentioning

confidence: 99%

Facility Location of Organ Procurement Organisations in Indian Health Care Supply Chain Management

Rajmohan

Theophilus

Sumalatha

et al. 2017

SAJIE

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In health care supply chain management, particularly in the area of organ transplantation, organ procurement and the transplantation network play an important role. The organ procurement organisation (OPO) should coordinate so that organs are prepared and transported to the recipients when donors become available. The scarcity of organ supply leads to life-challenging issues for the organ recipient. In this research, the importance of the location of OPOs to coordinate with the transplant centres in India is considered, and a solution is provided by facilitating the identification of locations where organs can be procured and distributed to the nearest transplant location. The location of the distribution centres of organs are identified, based on the p-median model. This model minimises the weighted distance of the organ recipients. Initially, the demand or the population density of organ recipients with respect to particular location is recognised. Then, based on the p-median model, the location of OPOs is effectively identified. Experimental analysis proves that the proposed model performs well in facilitating the location of OPOs. The robustness of the proposed work is validated using a sensitivity analysis of the differences in the selection of OPOs when the estimated demand for organs varies. OPSOMMINGOrgaanverkryging en die oorplantingsnetwerk speel ŉ belangrike rol in die voorsieningkettingbestuur van gesondheidsorg. Die orgaan verkrygingsorganisasie moet koördineer dat organe voorberei en vervoer word na ontvangers soos wat skenkers beskikbaar word. Die skaarsheid gekoppel aan orgaanverskaffing lei tot lewensgevaarlike situasies vir die orgaanontvanger. Die belangrikheid van die ligging van orgaan verkrygingsorganisasies word oorweeg en ŉ oplossing word verskaf deur die liggings waar organe verkry en versprei kan word na die naaste oorplantingsentrum te identifiseer. Die ligging van die verspreidingsentrums word deur middel van die p-mediaan model identifiseer. Die model minimeer die geweegde afstand tot by die orgaanontvangers. Eksperimentele analise bewys dat die voorgestelde model daarin slaag om die orgaan verkrygingsorganisasies se liggings te fasiliteer. Die robuustheid van die voorgestelde model is gevalideer deur middel van ŉ sensitiwiteitsanalise van die verskille in die seleksie van orgaan verkrygingsorganisasies wanneer die geskatte vraag na organe wissel.

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“…The disruption systems are great examples of this type of uncertainty sets. An et al [1] used a combinatorial uncertainty set to formulate a p-median facility location problem prone to disruptions. Moreover, the uncertainty set U can be modified in order to encompass the data of uncertain parameters.…”

Section: Namakshenas Pishvaee and Mahdavi Mazdehmentioning

confidence: 99%

Event-driven and Attribute-driven Robustness

Namakshenas

Pishvaee

Mazdeh

2017

IJOR

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Reliable p-median facility location problem: two-stage robust models and algorithms

Cited by 159 publications

References 31 publications

Multiple Types of Plug-In Charging Facilities’ Location-Routing Problem with Time Windows for Mobile Charging Vehicles

Multiple Types of Plug-In Charging Facilities’ Location-Routing Problem with Time Windows for Mobile Charging Vehicles

Facility Location of Organ Procurement Organisations in Indian Health Care Supply Chain Management

Event-driven and Attribute-driven Robustness

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