Thesis title
Mixed freight transport: Proposal of a high capacity-feeder urban distribution system based on a two-tier consolidation network
Thesis subject
The fact that we cannot do without private means of transportation is driving up urban traffic congestion rates. This phenomenon is being compounded by poorly-filled heavy goods vehicles on the roads, making long-haul journeys which are not maximising efficiency for delivery workers. In towns not widely equipped with logistics infrastructure (Urban Distribution Centre/UDC, delivery areas, etc.) but seeing a boom in the number of local businesses requiring deliveries, this trend is even more acute, and the cause of myriad problems including congestion, pollution (air and noise) and unsafe roads. In light of this situation, we propose to reconsider urban mobility in a more inclusive manner, from the point of view of flows (passengers and goods) by pooling transport resources so as to free cities from the physical challenges.
The Urban Goods Transportation system outlined is grounded in the concept of a passenger/goods mix, with our focus on the capacity rather than on the vehicle itself: the idea is to use the residual capacity of vehicles that use the existing network. In order to eliminate unnecessary journeys made by public transport vehicles, the system is based on a two-tier consolidation plan. The first is concerned with high-volume logistics areas situated upstream of the high-capacity network aimed at accommodating traffic heading for the city and at organising the goods to transport them via public transport. The second tier involves urban platforms laid out all along the main circuit to pick up the goods and organise rounds towards their final recipient. Feeder fleets would then take over to deliver the goods to their final destination by making rounds.
Urban transport is a sector with little in the way of flexibility, and any poor decision can undermine the performance of the logistics system. We are thus helping to develop a new method for structured analysis divided into two decision tiers. The strategic level encompasses the stages for working out the best configuration of nodal points (number and location) within the logistics chain by following the geographic distribution of the delivery points. The operational level corresponds to the management of goods flows by adopting the plan configured in the first level. Both tiers are complementary in the modelling and evaluation of the concepts put into practice.
To mitigate the problem and factor in each local area’s specific characteristics, we adopt spatial approaches aimed at dividing the urban area into uniform zones. The idea is to build clusters representing the geographic distribution of the transport demand. Each zone will be assigned a weight that reflects the significance of the logistics movements generated by the delivery points it contains. To that end we have developed zoning architecture grounded in more than 10 supervised and unsupervised machine learning algorithms. Significant results have been obtained on the basis of (almost all) the machine learning performance indicators existing in the literature. The search perimeter for the aforementioned points in our problem is limited by the existing outline of the passenger transport system and therefore by the locations of nearby hubs. We then consider the problem of assigning to the hubs the predetermined zones according to the logistical need. In this part, we have set up a Hybrid Robust Machine Learning-Heuristic Algorithm (HR-MLHA) with a ROBUST control to assess the long-term location accuracy. By comparing the findings with three heuristics, this algorithm has led to location cost savings and maximised coverage of the customer demand.
Biography
Jihane El Ouadi is a State Engineer in Industrial Engineering, who graduated from Errachidia Faculty of Sciences and Technologies at Moulay Ismaïl University (2016) and is a member of the Integrated Mobility research team at EIGSI. During her purely industrial studies, she also earned a vocational bachelor’s degree and Diploma of Higher Education (DEUP) in Industrial Engineering from Khouribga Multidisciplinary Faculty, Sultan Moulay Slimane University.
Jihane El Ouadi continued her academic studies by embarking on a PhD in urban logistics at the Engineering Research Laboratory of the National Graduate School for Electricity and Mechanical Engineering, Hassan II University (2017). At the same time, she joined the Foundation for Research, Development and Innovation in Science and Engineering (FRDISI). In partnership with the FRDISI, her research work explores the planning aspects of shared mobility and the way in which urban transport sharing systems could be improved and optimised.
Her research has previously included a range of subjects across different disciplines, among them artificial intelligence, optimisation, urban zoning systems, multi-agent dynamic systems and the impact of inclusive management of passenger and goods flows on traffic and road safety in cities.
