Robust Optimization for the Connected Facility Location Problem

Bardossy, M. Gisela; Raghavan, S.

doi:10.1016/j.endm.2013.10.023

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Other Variants Of the Connected Facility Location Problem1

Scientific Programming1

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Cited by 4 publications

(3 citation statements)

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“…Bardossy and Raghavan (2013) proposed a robust version of the problem based on the framework introduced by Bertsimas and Sim (2003). In particular, they proposed a heuristic based on the dual-ascent based local search for the basic ConFL problem proposed in the latter paper.…”

Section: Other Variants Of the Connected Facility Location Problemmentioning

confidence: 99%

Location Science

2015

132

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Section: Other Variants Of the Connected Facility Location Problemmentioning

confidence: 99%

Location Science

2015

132

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“…Eiselt and Marianov [24] studied WTE facility location planning problems under uncertainty from a government perspective in a cost-effective and environmental-friendly way. Bardossy and Raghavan [25] presented a robust optimization model for the connected facility location problem within the framework and extend the BS robust optimization approach using heuristics in conjunction with a lower bounding procedure for the subproblems. de Rosa et al [26] presented the robust capacitated facility location problem to cope with uncertainty by dynamically assigning multilevel production allocations, locations, and capacity adjustments for uncertain parameter development over time.…”

Section: Scientific Programmingmentioning

confidence: 99%

Location Model for Distribution Centers for Fulfilling Electronic Orders of Fresh Foods under Uncertain Demand

Zhang

Xiong

et al. 2017

Scientific Programming

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The problem of locating distribution centers for delivering fresh food as a part of electronic commerce is a strategic decision problem for enterprises. This paper establishes a model for locating distribution centers that considers the uncertainty of customer demands for fresh goods in terms of time-sensitiveness and freshness. Based on the methodology of robust optimization in dealing with uncertain problems, this paper optimizes the location model in discrete demand probabilistic scenarios. In this paper, an improved fruit fly optimization algorithm is proposed to solve the distribution center location problem. An example is given to show that the proposed model and algorithm are robust and can effectively handle the complications caused by uncertain demand. The model proposed in this paper proves valuable both theoretically and practically in the selection of locations of distribution centers.

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“…According to the duality relationships between (4),(5), (6) and (10), (11), (12), we formulate the robust counterpart model as follows: …”

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confidence: 99%