The maximal covering location problem with accessibility indicators and mobile units

Vicencio-Medina, Salvador J.; Ríos-Solís, Yasmín Á.; Ibarra-Rojas, Omar J.; Cid-García, Néstor M.; Rios‐Solis, Leonardo

doi:10.1016/j.seps.2023.101597

Cited by 2 publications

(1 citation statement)

References 33 publications

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“…Rosni et al [27] proposed a model for the allocation of recycling facilities in Seremban urban area, Malaysia, using a classical method of facility locations while considering their capacity. Medina et al [28] developed MCLM with accessibility indicators and mobile units to expand service coverage and optimize accessibility indicators at the service area of facilities. Another study by Zhuo and Yan [10] has developed a Multi-Maximal Covering Location Model (MMCLM) to address the challenge of determining waste collection site allocations by considering the level of community satisfaction while taking into account site capacities and distances from municipal services.…”

Section: Introductionmentioning

confidence: 99%

Sustainable waste solutions: Optimizing location-allocation of 3R waste management sites in Gondokusuman, Yogyakarta, Indonesia through multi-maximal covering location approach

Leuveano,

Kasih,

Ridho

et al. 2024

Int. J. Ind. Optim.

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Developing a Multi-Maximal Covering Location Model (MMCLM) for waste management in Gondokusuman Sub-district, Yogyakarta, Indonesia, is urgently needed. The closure of the Piyungan landfill has resulted in the need for additional Reduce, Reuse, and Recycle Waste Management Sites (3R-WMSs) to reduce waste that the landfill cannot accommodate. The primary objective of this model is to optimize the location and allocation of demand volume nodes, representing the resident population, to a specific set of 3R-WMS. These demand nodes are located at different distances from 3R-WMSs, including high and low coverage areas. The research in the Gondokusuman Sub-district employed MMCLM with facility capacity constraints and was developed using mixed integer linear programming methodology. The study identified five optimal locations for a 3R-WMS establishment that comprehensively cover all demand nodes (15301) and population clusters (45903) in the sub-district, including both high (5085) and low coverage areas (10216). This research represents a significant step forward in developing a sustainable environment by ensuring easy access to reducing, reusing, and recycling-based waste management facilities for residents.

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Section: Introductionmentioning

confidence: 99%