Optimization of Fuzzy Electric Vehicle Routing Problem

Objective/Contributions:

The issue of sustainable development is a global one. As a result, green innovation has emerged as a critical avenue to resolving environmental issues, gaining a competitive edge, achieving carbon neutrality, and further promoting sustainable development. One of the biggest sustainable development initiatives is the United Nations’ Sustainable Development Goals (SDG). The two main SDGs this proposal targets are Goal 11: Make cities inclusive, safe, resilient and sustainable, and Goal 7 Ensure access to affordable, reliable, sustainable and modern energy

Products are distributed across Egypt through an interactive network of freight, distribution centers, and logistical facilities. This logistical network is in a continuous improvement state in efforts to make it more sustainable to maximize the value delivered. The main means of transportation of goods inside Egypt is road freight, building a complex network across the country. Road freight transportation is an essential enabler of economic growth but also a substantial user of fossil fuels, posing a challenge to the achievement of a low-carbon future. Cairo is now considered to be one of the most polluted cities in the world. A key reason behind the city’s pollution is Internal Combustion Engine Vehicles (ICEVs). Therefore, the need for the replacement of high-emissions trucks for environmentally friendly Electric Vehicles (EVs) is on the rise.  

Optimization of the route of the freight is essential. When the route is optimized, less distance is driven by trucks, and in return, the process is more sustainable. The Vehicle Routing Problem (VRP) is a logistical problem that addresses this issue. A fleet of trucks is released from a central depot, delivering quantities of products demanded by several consumers, in the typical vehicle routing problem. Each vehicle is to provide its corresponding demand to a set of allocated consumers before returning to the depot. It is necessary to determine the customers to visit for each vehicle, as well as the sequence in which these customers should be visited, to keep the overall cost of all vehicles' journeys to a minimum. Customers usually have preferred times for vehicle visits, which results in an additional requirement that vehicles visit customers during a specific time window. Visiting customers outside this time windows might be permitted with an additional cost, known as a penalty, and resulting in a VRP with soft time windows. Moreover, real-time traffic conditions may affect the preplanned routing of vehicles, resulting in a potential re-route. The electric vehicle routing problem (EVRP) has been subject to extensive research by a large number of researchers recently. In addition, it is currently an emerging area within Egyptian industries. Due to the nature of VRP and its variants, fuzzy based approaches have been extensively proposed to solve the regular VRP efficiently (Shalaby et. al 2020, and Shalaby et. al. 2021). In the field of EVRP, new parameters have been introduced, such as charging time, expected travel distance with respect to the state of the charge,…etc. Hence, these new parameters are better modeled and/or represented using fuzzy-based approaches to address the uncertain nature of such parameters (Zhang et al., 2020). 

This proposal is to develop a proof of concept of an optimization process for fuzzy EVRP.