A location-allocation-improvement heuristic for districting with multiple-activity balancing constraints and p-median-based dispersion minimization

Ríos‐Mercado, Roger Z.; Álvarez-Socarrás, Ada M.; Castrillón, Andrés; López-Locés, Mario C.

doi:10.1016/j.cor.2020.105106

Cited by 16 publications

(5 citation statements)

References 35 publications

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“…It mainly consists of police patrol districting, emergency vehicle patrol districting and maritime patrol districting problems [13,14,21,22]. It uses the urban road network or census streets as a whole to achieve some of the goals of the patrol objectives, such as maximum coverage and workload standards [23][24][25]. It makes sense to use the urban road network as an area to patrol, but there is currently little research into the problem based on the road network.…”

Section: Literature Reviewmentioning

confidence: 99%

A hybrid spectral clustering simulated annealing algorithm for the street patrol districting problem

Jiang

Zhao

et al. 2022

Complex Intell. Syst.

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Reasonable districting plays an important role in the patrolling process. In this paper, workload attributes are considered, and a mixed integer programming model is developed to solve the street patrol districting problem (SPDP). The improved spectral clustering algorithm named spectral clustering algorithm based on the road network (SCRn) and simulated annealing algorithm (SA) are combined. This results in a hybrid algorithm called SCRn-SA. The SCRn-SA algorithm is tested on small examples and real instances in Zhengzhou, China. The experimental results show that the proposed algorithm is effective for solving SPDP. It has better performance when compared to other advanced algorithms.

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Section: Literature Reviewmentioning

confidence: 99%

A hybrid spectral clustering simulated annealing algorithm for the street patrol districting problem

Jiang

Zhao

et al. 2022

Complex Intell. Syst.

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“…This assumption leads to the well‐known

p

‐median problem proposed in [24], where the criterion for opening facilities is based on the minimization of the weighted total distance from the customers to the opened facilities. Some recent papers addressing this problem can be found in [44] and [60].…”

Section: Introductionmentioning

confidence: 99%

A warehouse location‐allocation bilevel problem that considers inventory policies

Camacho‐Vallejo,

Dávila,

Cárdenas‐Barrón

2024

Networks

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A location‐allocation problem faced by a company that aims to locate warehouses to supply products to a set of customers is addressed in this paper. The company's objective is to minimize the total cost of locating the warehouses and the cost due to inventory policies. However, these inventory decisions are made by a different decision‐maker. In other words, once the company makes the location decisions, the decision‐maker associated with each warehouse must determine its own order quantity. Warehouses are allowed to have a certain maximum number of backorders, which represents an extra cost for them. This situation can be modeled as a bilevel programming problem, where the upper level is associated with the company that needs to minimize the costs related to location‐allocation and the total orders of each warehouse. Each warehouse is associated with an independent lower level, in which a warehouse manager aims to minimize the total inventory cost. The bilevel problem results in a single‐objective upper‐level problem with non‐linear, multiple independent lower‐level problems, making it generally challenging to find an optimal solution. A population‐based metaheuristic under the Brain Storm Optimization algorithm scheme is proposed. To solve each non‐linear problem associated with the lower level, the Lagrangian method is applied. Both decision levels are solved in a nested manner, leading to obtaining bilevel feasible solutions. To validate the effectiveness of the proposed algorithm, an enumerative algorithm is implemented. A set of benchmark instances has been considered to conduct computational experiments. Results show that optimality is achieved by the proposed algorithm for small‐sized instances. In the case of larger‐sized instances, the proposed algorithm demonstrates the same efficiency and consistent results. Finally, interesting managerial insights deduced from the computational experimentation and some proposals for future research directions are included.

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“…Due to the complexity of solving these combinatorial problems, heuristic solutions are mainly considered, such as search algorithms. These algorithms have guaranteed efficient performance for obtaining global solutions [25,26,27]. To the best of our knowledge, there are no studies in the literature that focus on developing testing strategies in LTCFs that take formal optimization methods into account.…”

Section: Introductionmentioning

confidence: 99%

Modeling COVID-19 optimal testing strategies in long-term care facilities: An optimization-based approach

Davoodi¹,

Batista²,

Senapati³

et al. 2022

Preprint

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Long-term care facilities have been widely affected by the COVID-19 pandemic. Retirement homes are particularly vulnerable due to the higher mortality risk of infected elderly individuals. Once an outbreak occurs, suppressing the spread of the virus in retirement homes is challenging because the residents are in contact with each other and isolation measures cannot be widely enforced. Regular testing strategies, on the other hand, have been shown to effectively prevent outbreaks in retirement homes. However, high frequency testing may consume substantial staff working time, which results in a trade-off between the time invested in testing, and the time spent providing essential care to residents. Thus, developing an optimal testing strategy is crucial to proactively detect infections while guaranteeing efficient use of limited staff time in these facilities. Although numerous efforts have been made to prevent the virus from spreading in longterm care facilities, this is the first study to develop testing strategies based on formal optimization methods. This paper proposes two novel optimization models for testing schedules. The models aim to minimize the risk of infection in retirement homes, considering the trade-off between the probability of infection and staff workload. We employ a probabilistic approach in conjunction with the optimization models, to compute the risk of infection, including contact rates, incidence status, and the probability of infection of the residents. To solve the models, we propose an enhanced local search algorithm by leveraging the symmetry property of the optimal solution. We perform several experiments with realistically sized instances and show that the proposed approach can derive * Corresponding author.

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A location-allocation-improvement heuristic for districting with multiple-activity balancing constraints and p-median-based dispersion minimization

Cited by 16 publications

References 35 publications

A hybrid spectral clustering simulated annealing algorithm for the street patrol districting problem

A hybrid spectral clustering simulated annealing algorithm for the street patrol districting problem

A warehouse location‐allocation bilevel problem that considers inventory policies

Modeling COVID-19 optimal testing strategies in long-term care facilities: An optimization-based approach

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