53 / 2023-03-30 20:52:02

A solution of using monarch butterfly optimization to vehicle routing problem with relief supplies in sudden disasters

Emergency vehicle routing problem; relief supplies; sudden disasters; intelligent algorithms; monarch butterfly optimization; self-adaptive; crossover operator

China is a country with frequent occurrence of sudden natural disasters in the world. In recent years, the establishment of a disaster relief system has received high attention from the government. The goal of this research was to resolve a key issue for emergency natural disaster relief: the emergency vehicle routing problem (EmVRP) with relief supplies in sudden disasters. In this paper, Firstly, we provided a description of the EmVRP and defined the boundary conditions, it’s a three-layer structure to involve movement from multiple parking areas (garages) to multiple storage sites, and then to multiple disaster points. Layer 1 includes the parking areas, Layer 2 is made up of the supplies reserve storage sites. We assume that the relief supplies at each reserve point are varied, and that the number of certain types of supplies is unchanged during the emergency period. The bottom layer, Layer 3, comprises the affected areas that are the destinations for relief distribution. After a disaster, demand for all kinds of emergency supplies will be generated at all the impact points according to the disaster assessment. This demand will determine the need to move reserves at all levels and locations, which in turn will call for a certain number of vehicles to begin distribution of aid supplies From Layer 1 to Layer 2, there is vehicle flow only, with no supplies flow, i.e., the vehicles run from Layer 1 to Layer 2, but they are not loaded. Moreover, this flow is one-way, with the vehicles moving only from the parking areas to the various supplies reserve sites. They do not return to the parking areas during the emergency period. However, both vehicle flow and supplies flow occur between Layer 2 and Layer 3. Supplies move in a one-way unidirectional flow from Layer 2 to Layer 3, while the vehicles form a two-way flow between these two layers. After a vehicle arrives at a disaster point, it does not immediately return to the parking areas, but instead remains in the arrival spot on standby. Once a new distribution task is ordered, the vehicle will go back to the appropriate supplies storage site to be loaded and start a new distribution. Therefore, after a vehicle arrives at an affected area from a reserve area, there are two possible states for the vehicle. Either it remains in the impacted area waiting for new orders, or it may be given a new task immediately, in which case it returns right way to the reserve to begin the next round of distribution.

Secondly, we constructed an optimization model of EmVRP with relief supplies in sudden disasters. To ger a better solution in the least amount of time, we proposed an enhanced monarch butterfly optimization (EMBO) algorithm, incorporating two modifications to the basic MBO: a self-adaptive strategy and a crossover operator. Finally, the EMBO algorithm was used to solve the EmVRP. Our experiments using two examples EmVRP with relief supplies in a sudden-onset disaster proved the suitability of EMBO. In addition, an array of comparative studies showed that the proposed EMBO algorithm can achieve satisfactory solutions in less time than the basic MBO algorithm and seven other intelligent algorithms.