CEDoc-UM6P-AI MOV : Designing and Optimizing Last-Mile Delivery by Full-time

Mohammed VI Polytechnic University is an institution dedicated to research and innovation in Africa and aims to position itself among world-renowned universities in its fields

The University is engaged in economic and human development and puts research and innovation at the forefront of African development. A mechanism that enables it to consolidate Morocco’s frontline position in these fields, in a unique partnership-based approach and boosting skills training relevant for the future of Africa.

Located in the municipality of Benguerir, in the very heart of the Green City, Mohammed VI Polytechnic University aspires to leave its mark nationally, continentally, and globally.

1 Context

E-commerce has been expanding rapidly for several years, with events like the recent global pandemic further accelerating this growth. This sector involves the daily delivery of vast quantities of parcels, especially in urban areas, leading to a significant increase in traffic and its negative impacts on health and the environment, such as pollution, CO2, and greenhouse gas emissions, traffic congestion, stress, etc. The logistics of parcel delivery are particularly complex, involving numerous stakeholders and processes within the supply chain. To overcome these challenges, incorporating drones in delivering systems is promising solution. In fact, drones have been used for many years in various sectors, such as agriculture and surveillance. Recently, many logistics companies namely Amazon, DHL and Walmart uses drones to deliver parcels to their customers. Such solutions offers fast and cheap delivery, customer satisfaction, and reducing pollution [1]. In this thesis, we are interested in optimizing the last mile delivery by considering new delivery modes. The last mile can be defined as the last segment of the order delivery chain, from the logistic facility to the end customer. However, the use of drones is challenging due to the limitations on capacity and flight range that applies to commercially available drones, which may require multiple drone deliveries to the same address or alternatively delivery by truck. To address this issue, several collaborative delivery system was proposed in the-state-of-the-art, such as the Drone-Truck routing problem, an extended version of the Vehicle Routing Problem, which is falls within the field of combinatorial optimization. In such version, the objective is to determine an optimal tour for each truck/drone of the available fleet that minimizes several objectives including CO2 emissions, travel time, and cost. Some of them incorporate uncertainties about customer’s locations, delivery time, etc. Most prior research assumes that drone can be launched from the truck. In this case the role of the truck is to assist the drone. However, this approach can limit drone efficiency for longer distances [2]. Recently, few studies considered the capability of drones to perform tours starting and ending at depot but they don’t consider the capacity of trucks or multi-objective which would make the problem more realistic for real life situations [3].

2 Research Objectives The thesis goal is to research mathematical formulations that integrate the new characteristics of the Drone-Truck routing problem. The goal is to serve all customers in the shortest possible time using the combination of trucks and drones (initially parked at a depot). The first phase involves modeling the problem as mixed integer linear programming and then solve it using commercial solvers such as Gurobi on small instances. The second phase consists of designing matheuristics to deal with large instances, a new paradigm that involves integrating mathematical programming within heuristics by decomposing the problem into several sub-problems [4, 5]. The third phase is to consider the dynamic variant of this problem including demand of customers, battery discharge, charging stations, etc. For this, we will investigate machine learning techniques to predict certain inputs of the mathematical model. A state-of-the-art review will be conducted on both deterministic and stochastic of the Vehicle Routing Problem.

3 Admission Criteria The PhD position is available at Ai movement, the International Center for Artificial Intelligence of Morocco of UM6P. Applicants with excellent academic credentials must be holders of a Master’s, an engineering or an equivalent recognized degree with good skills in applied mathematics, in relation to optimization, operations research, and machine learning. The candidate should also be excellent in programming in Python, Java or C++, should have soft skills, and be fluent in English and French languages. Letters of recommendation are welcome.

References [1] Mohammad Moshref-Javadi and Matthias Winkenbach. Applications and research avenues for drone-based models in logistics: A classification and review. Expert Systems with Applications, 177:114854, 2021.

[2] Ho Young Jeong and Seokcheon Lee. Drone routing problem with truck: Optimization and quantitative analysis. Expert Systems with Applications, 227:120260, 2023.

[3] Raıssa G Mbiadou Saleu, Laurent Deroussi, Dominique Feillet, Nathalie Grangeon, and Alain Quilliot. The parallel drone scheduling problem with multiple drones and vehicles. European Journal of Operational Research, 300(2):571-589, 2022.

[4] Daniel Schermer, Mahdi Moeini, and Oliver Wendt. A matheuristic for the vehicle routing problem with drones and its variants. Transportation Research Part C: Emerging Technologies, 106:166-204, 2019. [5] Bernard Gendron, Saıd Hanafi, and Raca Todosijevic. Matheuristics based on iterative linear ‘ programming and slope scaling for multicommodity capacitated fixed charge network design.

European Journal of Operational Research, 268(1):70-81, 2018.

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