A Hybrid Code Genetic Algorithm for VRP in Public-Private Emergency Collaborations

Abstract

To minimize the total time for the distribution of relief commodities participated by both private companies and the government, a vehicle routing problem (VRP) model in emergencies was proposed. Considering the differences in the starting points of vehicles, the VRP of general logistics, and departments of vehicles, constraints, such as vehicle capacity limitation and time windows, were introduced into the model, which was close to meeting the practical demands of emergency relief. A hybrid code genetic algorithm (HCGA) was proposed, and it used dynamic mutations to avoid early traps in local optimization and to accelerate convergence. This algorithm was programmed by MATLAB. Furthermore, the vehicle routing optimization plans in an emergency was calculated by a simple genetic algorithm (SGA) and the HCGA, respectively. Results demonstrate that the total time for relief distribution in the HCGA is 11.62 % lower and the calculation time is 14.24 % shorter than that of the SGA. The HCGA is not only convenient in processing the constraints of the model and the natural description of problem solutions, but it is also effective in improving the complexity.

Dynamic Optimization of Emergency Logistics for Major Epidemic Considering Demand Urgency

“…Xue et al developed a multi-objective optimization model under capacity-constrained conditions, minimizing the shortest average waiting time for rescue at the affected point with access constraints [2]. Wang et al proposed a scenario-specific emergency material distribution model with time windows to minimize emergency material loading and unloading time and distribution time [3]. Wang et al developed a dual-objective mixed-integer programming model based on state-space-time networks with the minimum cost and the maximum emergency response speed to meet demands [9].…”

“…The issue of how to allocate scarce emergency materials reasonably has become a key concern for ensuring patient safety and reducing losses. Researchers [2][3][4][5] have addressed this issue from a variety of perspectives, primarily studying how to optimize the allocation of scarce emergency materials to reduce delivery times, reduce costs, and maximize demand satisfaction. The current research on emergency management material allocations still has limitations, as most models only focus on singleperiod allocations.…”

An Improved Genetic Algorithm for Location Allocation Problem with Grey Theory in Public Health Emergencies

“…Some of the most renowned emergencies include the SARS outbreak in China in 2003 and the H1N1 flu outbreak that started in the United States in 2009 and spread across 214 countries and regions. In 2013, the H7N9 epidemic caused panic among the public [ 1 ]. The occurrence of these public health emergencies is usually followed by a shortage of specialized hospitals and hospital beds.…”

Urban domestic garbage collection and transportation scheduling under the background of garbage classification

“…Puspitasari F. H. et al [4] , aiming at minimizing travel time, solved and optimized the classical VRP, effectively shortening the delivery time and distance of couriers. Wang S. R. et al [5] proposed a hybrid code genetic algorithm (HCGA) to solve VRP to minimize the total time for private enterprises and the government to participate in disaster relief material distribution. Wang Heng et al [6] applied the method of solving VRP to the intelligent distribution of fresh agricultural products in smart cities, effectively improving the efficiency of distribution.…”