The stochastic -median problem with unknown cost probability distribution

Tadei, Roberto; Ricciardi, Nicoletta; Perboli, Guido

doi:10.1016/j.orl.2009.01.005

Cited by 24 publications

(16 citation statements)

References 25 publications

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“…Taking into consideration of the integral constraints on the paths, Lin, et al [22] developed a general capacitated p-hub median problem for the optimizing the cost and operations of a Chinese air cargo network. Considering the uncertainty related to the cost and its probability distribution, Tadei, et al [23] improved a p-median model with stochastic parameters for minimizing the total cost expectation. Goldengorin and Krushinsky [24] investigated the computational performance of a Pseudo-Boolean method for resolving a p-median problem.…”

Section: B the Application Of P-median Problemmentioning

confidence: 99%

A Comparison of Two Location Models in Optimizing the Decision-making on the Relocation Problem of Post Offices at Narvik, Norway

Solvang

2018

2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

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Since 2013, the postal service provider in Norway: Posten has implemented a new strategic plan to close their large post offices across the country for improving the cost and operational efficiency. Instead, with the cooperation with large retailers, the local postal service is provided at the Posten sub-branches at some supermarkets. In this paper, an optimization problem for relocating the post offices at local supermarkets at Narvik, Norway, is investigated. For resolving the optimization problem, two classical location models: p-median problem and maximal covering problem, are employed. The problems are coded and resolved with Lingo optimization solver. The result has provided the optimal relocation plans for the sub-branches of Posten at Narvik with respect to different scenarios. In addition, a comparison between the optimal strategy and the current relocation plan is given in order to show the improvement on the system performance.

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Section: B the Application Of P-median Problemmentioning

confidence: 99%

A Comparison of Two Location Models in Optimizing the Decision-making on the Relocation Problem of Post Offices at Narvik, Norway

Solvang

2018

2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

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“…The authors formulate the problem as a two-stage stochastic programming model and propose a solution method based on Lagrangean relaxation. Tadei et al (2009) consider a stochastic p-MP where the cost for siting a facility is a stochastic variable with an unknown probability distribution and the goal is to minimize the expected total cost. Modifying from the original stochastic p-MP formulation, the authors develop a deterministic integer nonlinear problem to determine the optimal value of location variables.…”

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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“…random utilities has an asymptotically exponential behavior in its tail [9,10,22]. The effectiveness of such an asymptotic approximation have been proved in several applications in the context of location, routing, loading, packing, and other logistics operations [17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

The multi-stage dynamic stochastic decision process with unknown distribution of the random utilities

2019

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We consider a decision maker who performs a stochastic decision process over a multiple number of stages, where the choice alternatives are characterized by random utilities with unknown probability distribution. The decisions are nested each other, i.e. the decision taken at each stage is affected by the subsequent stage decisions. The problem consists in maximizing the total expected utility of the overall multistage stochastic dynamic decision process. By means of some results of the extreme values theory, the probability distribution of the total maximum utility is derived and its expected value is found. This value is proportional to the logarithm of the accessibility of the decision maker to the overall set of alternatives in the different stages at the start of the decision process. It is also shown that the choice probability to select alternatives becomes a Nested Multinomial Logit model.

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The stochastic -median problem with unknown cost probability distribution

Cited by 24 publications

References 25 publications

A Comparison of Two Location Models in Optimizing the Decision-making on the Relocation Problem of Post Offices at Narvik, Norway

A Comparison of Two Location Models in Optimizing the Decision-making on the Relocation Problem of Post Offices at Narvik, Norway

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

The multi-stage dynamic stochastic decision process with unknown distribution of the random utilities

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