An Improved Shuffled Frog Leaping Algorithm and Its Application in Dynamic Emergency Vehicle Dispatching

Duan, Xiaohong; Niu, Tianyong; Huang, Qi

doi:10.1155/2018/7896926

Cited by 14 publications

(8 citation statements)

References 24 publications

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“…1) Convergence rate: The suitability of an algorithm for an optimization problem can be evaluated using convergence rate [36]. The convergence graph can also estimate an algorithm's best, average, worst results, and standard deviation.…”

Section: B Results and Discussionmentioning

confidence: 99%

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Optimally Allocating Ambulances in Delhi using Mutation based Shuffled Frog Leaping Algorithm

Zaheeruddin¹,

Gupta²

2022

IJACSA

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This paper presents a reliable and competent evolutionary-based approach for improving the response time of Emergency Medical Service (EMS) by efficiently allocating ambulances at the base stations. As the prime objective of EMS is to save people's lives by providing them with timely assistance, thus increasing the chances of a person's survivability, this paper has undertaken the problem of ambulance allocation. The work has been implemented using the proposed mutation-based Shuffled Frog Leaping Algorithm (mSFLA) to provide an optimal allocation plan. The authors have altered the basic SFLA using the concept of mutation to improve the quality of the solution obtained and avoid being trapped in local optima. Considering a set of assumptions, the new algorithm has been applied for allocating 50 ambulances among 11 base stations in Southern Delhi. The working environment of EMS, which includes stochastic requests, travel time, and dynamic traffic conditions, has been considered to attain accurate results. The work has been implemented in the MATLAB simulation environment to find an optimized allocation plan with a minimum average response time. The authors have reduced the average response time by 12.23% with the proposed algorithm. The paper also compares mSFLA, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) for the stated problem. The algorithms are compared in terms of objective value (average response time), convergence rate, and constancy repeatability to conclude that mSFLA performs better than the other two algorithms.

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Section: B Results and Discussionmentioning

confidence: 99%

“…Another algorithm called Shuffled Frog Leaping Algorithm (SFLA) has been used in some works to explore the field of EMS. The authors [35], [36] used SFLA to optimize the working of EMS. SFLA has also been used in different domains for optimally allocating resources [37], [38].…”

Section: Litertaure Reviewmentioning

confidence: 99%

Optimally Allocating Ambulances in Delhi using Mutation based Shuffled Frog Leaping Algorithm

Zaheeruddin¹,

Gupta²

2022

IJACSA

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“…Step 5: The best individual X b and worst individual X w in the subpopulation are marked, and the frog individuals with the best fitness value are updated using equation (23).…”

Section: Application Of Isfla In the Stacker-scheduling Optimization ...mentioning

confidence: 99%

“…SFLA is a metaheuristic algorithm based on memetic evolution; its main advantages are fast convergence speed and effective algorithm structures involving local search and global information exchanges. 20 The algorithm has been widely applied to several optimization problems, including economic load dispatch, 21 vehicle routing, 22,23 component sequencing, [24][25][26] and shop scheduling. [27][28][29] However, the initial solution will be distributed unevenly in the feasible region, resulting in a weaker global optimization ability, because the initial population of the SFLA is generated by a random method.…”

Section: Introductionmentioning

confidence: 99%

A scheduling optimization method for stacker path in double-ended compact storage system

Yan

Shao

et al. 2023

Advances in Mechanical Engineering

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Given the low space usage rate of the traditional automated storage/retrieval system and the long aisle, it is easy for a stacker to take a long time to enter/leave the warehouse. Thus, a new type of double-ended compact storage system is proposed. This paper addresses the scheduling problem for the stacker to execute the single and dual commands mixed tasks in the system where the I/O ports are located at both ends of the aisle, and the power conveyor devices on the rack can meet the requirement of multi-depth storage and generate displacement. An improved shuffled frog leaping algorithm (ISFLA) is developed for the scheduling problem. In order to eliminate the disadvantages of local optimum and slow convergence in the standard shuffled frog leaping algorithm, a set of hybrid perturbation update methods are designed based on a role model learning strategy, and the feasibility of the improved algorithm is verified by a numerical simulation. The experimental results show that the solution quality and the convergence ability of the ISFLA are significantly improved, and it can effectively solve the stacker-scheduling problem in the double-ended compact storage system.

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“…They developed a modified simulated annealing (SA) and variable neighborhood search (VNS) algorithm to solve the problem with the aim of minimizing the total traveling time. An improved shuffled frog leaping algorithm (SFLA) was proposed by Duan et al (2018) to study the dynamic emergency vehicle dispatching problem. They considered response time, accident severity and accident time windows as the main factors to develop an emergency vehicle dispatching model.…”

Section: Introductionmentioning

confidence: 99%

A robust bi-objective location-routing model for providing emergency medical services

Adarang

Bozorgi-Amiri

Khalili‐Damghani

et al. 2020

JHLSCM

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PurposeThis paper addresses a location-routing problem (LRP) under uncertainty for providing emergency medical services (EMS) during disasters, which is formulated using a robust optimization (RO) approach. The objectives consist of minimizing relief time and the total cost including location costs and the cost of route coverage by the vehicles (ambulances and helicopters).Design/methodology/approachA shuffled frog leaping algorithm (SFLA) is developed to solve the problem and the performance is assessed using both the ε-constraint method and NSGA-II algorithm. For a more accurate validation of the proposed algorithm, the four indicators of dispersion measure (DM), mean ideal distance (MID), space measure (SM), and the number of Pareto solutions (NPS) are used.FindingsThe results obtained indicate the efficiency of the proposed algorithm within a proper computation time compared to the CPLEX solver as an exact method.Research limitations/implicationsIn this study, the planning horizon is not considered in the model which can affect the value of parameters such as demand. Moreover, the uncertain nature of the other parameters such as traveling time is not incorporated into the model.Practical implicationsThe outcomes of this research are helpful for decision-makers for the planning and management of casualty transportation under uncertain environment. The proposed algorithm can obtain acceptable solutions for real-world cases.Originality/valueA novel robust mixed-integer linear programming (MILP) model is proposed to formulate the problem as a LRP. To solve the problem, two efficient metaheuristic algorithms were developed to determine the optimal values of objectives and decision variables.

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An Improved Shuffled Frog Leaping Algorithm and Its Application in Dynamic Emergency Vehicle Dispatching

Cited by 14 publications

References 24 publications

Optimally Allocating Ambulances in Delhi using Mutation based Shuffled Frog Leaping Algorithm

Optimally Allocating Ambulances in Delhi using Mutation based Shuffled Frog Leaping Algorithm

A scheduling optimization method for stacker path in double-ended compact storage system

A robust bi-objective location-routing model for providing emergency medical services

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