Optimal allocation of material dispatch in emergency events using multi-objective constraint for vehicular networks

Liu, Chen; Yongfu, Qian

doi:10.1007/s11276-022-03069-8

Cited by 11 publications

(6 citation statements)

References 24 publications

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“…With the advancement of research, various studies have delved into the dynamic multi-region scheduling problem of agricultural machinery. Some works have focused on non-dominated neighborhood selection and taboo search immunity algorithms 9 , while others have explored the challenges of multiregion emergency scheduling, taking into account factors such as scheduling cost and time due to the time-sensitive nature of agricultural operations [10][11][12] . Additionally, there have been investigations into emergency scheduling models and algorithms for multi-region farm machinery operations, aiming to minimize scheduling costs and losses 13 .…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Scheduling of Agricultural Machines in Hilly Mountainous Areas Based on NSGA-II-SA Hybrid Algorithm with Applications

Liu,

Luo,

Zhao

et al. 2024

Preprint

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Optimizing the scheduling of farm machinery is essential to meet farmers' requirements, minimize scheduling costs, and save time. This study focuses on scheduling farm machinery in multiple cooperatives across various regions, aiming to minimize scheduling costs and reduce scheduling time. Initially, a multi-constraint hybrid clustering algorithm is employed to assign farmland to each farm machinery cooperative by clustering before scheduling. Subsequently, an enhanced version of the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is proposed, integrating a local search strategy based on congestion-based neighborhood search and the Simulated Annealing (SA) algorithm to develop the NSGA-II-SA algorithm. This hybrid multi-objective evolutionary algorithm effectively optimizes scheduling costs and time. The model's validity and the algorithm's superiority are demonstrated through a Web-based multi-region agricultural machine scheduling system and an example study. Experimental results show that the NSGA-II-SA algorithm significantly reduces scheduling costs and time, as well as the number of dispatched farm machines, outperforming other algorithms with reductions of 9.8%, 3.1%, and 8.7% in total scheduling costs, and 12.5%, 13.4%, and 11.6% in total scheduling time. This research establishes a theoretical framework for multi-region agricultural machine scheduling in hilly and mountainous areas, enhancing agricultural production efficiency.

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Section: Literature Reviewmentioning

confidence: 99%

Optimal Scheduling of Agricultural Machines in Hilly Mountainous Areas Based on NSGA-II-SA Hybrid Algorithm with Applications

Liu,

Luo,

Zhao

et al. 2024

Preprint

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show abstract

“…is enhances the fairness of rescue dispatching, especially for disaster areas with small demand. Sheu and Pan [15] proposed a multiobjective model to establish the shortest path for vehicle travel in order to meet the shortest delivery time and material demand. Wang et al [16] proposed a cooperative approach to correct the supply-demand imbalance in post-disaster relief by first identifying potential relief suppliers using a clustering mechanism.…”

Section: Materials Movement Modelmentioning

confidence: 99%

“…If (15) e particle positions and velocities are updated according to equations ( 15) and ( 16) ( 16) End End while As shown in Table 5, the emergency material dispatching scheme for which the GP-APSO solution yields the optimal solution of 399.63, and Figure 6 shows the dispatching of different supplies between the material distribution center and the rescue point under this scheme.…”

Section: Model Solvingmentioning

confidence: 99%

An Improved PSO Algorithm for Optimized Material Scheduling in Emergency Relief

2022

Mathematical Problems in Engineering

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Efficient emergency material dispatch, amid the aftermath of an emergency event, can help control the spread of the disaster and reduce disaster losses. Herewith, we propose a model with the urgency of material demand as the target coefficient, and the minimum load time and the minimum transportation cost as the total cost. For this model, an improved particle swarm optimization (PSO) algorithm is proposed as the means to optimize the initial positions of particles with good point sets and improve the convergence speed with adaptive dynamic weights to improve the optimization of the emergency material dispatch model. In order to verify the effectiveness of the proposed model and algorithm improvement strategy, the experimental results are verified by means of simulation experiments and algorithm comparison experiments, which show that the proposed emergency material dispatch model and the improved PSO algorithm cannot only solve the post-disaster relief material distribution and dispatching problem but also effectively reduce the total cost of emergency material dispatching.

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“…In soft computing, the general strategy is to identify a complete Pareto optimal solution set or a descriptive subset [23,[38][39][40]. A Pareto optimal set gives a set of solutions that are non-dominated to each other.…”

Section: Related Workmentioning

confidence: 99%

A multi-objective evolutionary optimisation model for heterogeneous vehicles routing and relief items scheduling in humanitarian crises

Mishra

Dahal

Pervez

et al. 2022

Decision Analytics Journal

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Optimal allocation of material dispatch in emergency events using multi-objective constraint for vehicular networks

Cited by 11 publications

References 24 publications

Optimal Scheduling of Agricultural Machines in Hilly Mountainous Areas Based on NSGA-II-SA Hybrid Algorithm with Applications

Optimal Scheduling of Agricultural Machines in Hilly Mountainous Areas Based on NSGA-II-SA Hybrid Algorithm with Applications

An Improved PSO Algorithm for Optimized Material Scheduling in Emergency Relief

A multi-objective evolutionary optimisation model for heterogeneous vehicles routing and relief items scheduling in humanitarian crises

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