Improved biogeography-based optimization using migration process adjustment: An approach for location-allocation of ambulances

Kaveh, Mehrdad; Mesgari, Mehran

doi:10.1016/j.cie.2019.06.058

Cited by 46 publications

(28 citation statements)

References 43 publications

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“…Chauhan et al [36] used MCLP to formulate their problem considering package weight, battery, and coverage constraints and developed a heuristic method to solve the problem with sensitivity analysis of the problem parameters. Furthermore, to locate ambulances in response to urban emergency requests, Kaveh and Mesgari [37] used MCLP in combination with an improved biogeographical-based optimization method. To refine the number of candidate locations for ambulance stations, they used geographic information system and the analytical hierarchy process method and compared the results of their developed solving procedure with similar methods.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Set Covering and Dynamic Modular Covering Location Problem: Application to an Emergency Humanitarian Logistics Problem

Alizadeh

Nishi

2020

Applied Sciences

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This paper presents an extension of the covering location problem as a hybrid covering model that utilizes the set covering and maximal covering location problems. The developed model is a multi-period model that considers strategic and tactical planning decisions. Hybrid covering location problem (HCLP) determines the location of the capacitated facilities by using dynamic set covering location problem as strategic decisions and assigns the constructive units of facilities and allocates the demand points by using dynamic modular capacitated maximal covering location problem as tactical decisions. One of the applications of the proposed model is locating first aid centers in humanitarian logistic services that have been addressed by studying a threat case study in Japan. In addition to validating the developed model, it has been compared to other possible combined problems, and several randomly generated examples have been solved. The results of the case study and model validation tests approve that the main hybrid developed model (HCLP) is capable of providing better coverage percentage compared to conventional covering models and other hybrid variants.

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Section: Literature Reviewmentioning

confidence: 99%

Hybrid Set Covering and Dynamic Modular Covering Location Problem: Application to an Emergency Humanitarian Logistics Problem

Alizadeh

Nishi

2020

Applied Sciences

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“…The habitats of biological populations have their corresponding Habitat Suitability Index (HSI), which is used to describe the quality of the habitat environment, and the factors that affect the fitness index are called Suitable Index Variables (SIVs). The BBO algorithm has the advantages of simple operation, fast convergence, and fewer parameters [28]. The standard BBO algorithm uses a simple linear migration model, but in the real biogeographic environment, species migration often occurs randomly and does not follow this rule.…”

Section: Routing Protocol Based On Bbo Algorithmmentioning

confidence: 99%

“…In Equation (28), m max is the maximum mutation rate, p s is the probability that habitat i has s species, and p max = max(p s ). Initialize population randomly 6:…”

Section: Routing Protocol Based On Bbo Algorithmmentioning

confidence: 99%

“…In Algorithm 2, p s is a parameter in Equation (28). The smaller the p s value is, the more it is prone to mutation.…”

Section: Parameter Settingsmentioning

confidence: 99%

“…The smaller the p s value is, the more it is prone to mutation. Hence the p s value is 0.1. p max is a parameter in Equation (28). It is the maximum value of p s and it takes a value of 1.…”

Section: Parameter Settingsmentioning

confidence: 99%

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Optimization of Submodularity and BBO-Based Routing Protocol for Wireless Sensor Deployment

Wang

Duan

et al. 2020

Sensors

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Wireless sensors are limited by node costs, communication efficiency, and energy consumption when wireless sensors are deployed on a large scale. The use of submodular optimization can reduce the deployment cost. This paper proposes a sensor deployment method based on the Improved Heuristic Ant Colony Algorithm-Chaos Optimization of Padded Sensor Placements at Informative and cost-Effective Locations (IHACA-COpSPIEL) algorithm and a routing protocol based on an improved Biogeography-Based Optimization (BBO) algorithm. First, a mathematical model with submodularity is established. Second, the IHACA is combined with pSPIEL-based on chaos optimization to determine the shortest path. Finally, the selected sensors are used in the biogeography of the improved BBO routing protocols to transmit data. The experimental results show that the IHACA-COpSPIEL algorithm can go beyond the local optimal solutions, and the communication cost of IHACA-COpSPIEL is 38.42%, 24.19% and 8.31%, respectively, lower than that of the greedy algorithm, the pSPIEL algorithm and the IHACA algorithm. It uses fewer sensors and has a longer life cycle. Compared with the LEACH protocol, the routing protocol based on the improved BBO extends the life cycle by 30.74% and has lower energy consumption.

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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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Improved biogeography-based optimization using migration process adjustment: An approach for location-allocation of ambulances

Cited by 46 publications

References 43 publications

Hybrid Set Covering and Dynamic Modular Covering Location Problem: Application to an Emergency Humanitarian Logistics Problem

Hybrid Set Covering and Dynamic Modular Covering Location Problem: Application to an Emergency Humanitarian Logistics Problem

Optimization of Submodularity and BBO-Based Routing Protocol for Wireless Sensor Deployment

A warehouse location‐allocation bilevel problem that considers inventory policies

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