The modeling and solving of collaborative real-time feeder vehicle routing problem

نوع: Type: thesis

مقطع: Segment: PHD

عنوان: Title: The modeling and solving of collaborative real-time feeder vehicle routing problem

ارائه دهنده: Provider: Morteza Salehi Sarbijan

اساتید راهنما: Supervisors: DR. Javad Behnamian

اساتید مشاور: Advisory Professors:

اساتید ممتحن یا داور: Examining professors or referees: Amir Saman Kheirkhah, Mostafa Zandieh, َAli Husseinzadeh Kashan

زمان و تاریخ ارائه: Time and date of presentation: 2022

مکان ارائه: Place of presentation: Engineering Department

چکیده: Abstract: In real-time vehicle routing problems (VRPs), customer requests are identified over the time horizon of operations without previous knowledge. Transportation has become a vital service due to increased competition among businesses. In this regard, serving new requests is one of the major challenges of logistics units. Today, with the development of urbanism and technology, customers demand fast, flexible, and reliable delivery services at a low cost in a day or even hours. As a result, there is an increase in demand and, consequently, an increase in vehicles for the movement of goods and people, which, along with some other issues, causes congestion in urban transport networks. Also, in the VRP, vehicles' limited capacity forces them to return to the central depot on a regular basis to reload. Therefore, the travel costs and the number of return trips will increase if the customer demand considerably exceeds the vehicle capacity. To overcome these challenges, in this thesis, the feeder vehicle routing problem (FVRP) is presented as a new type of VRP to provide fast services in urban transportation. Unlike VRP, FVRP, consists of a fleet of heterogeneous vehicles, including trucks and motorcycles. In this issue, a mechanism called joint mechanism is used in which motorcycles, instead of returning to the depot, move and visit customers at joint points with trucks after loading and finally return to the depot. Therefore, in this research, the multi-fleet FVRP is presented, followed by developing and modeling its real-time and collaborative aspects. First, the multi-fleet FVRP with at least two trucks and motorcycles is developed. In this problem, there might be various joint points between motorcycles and trucks. After modeling this problem as a mixed-integer linear programming model, a particle swarm optimization algorithm, as well as a hybrid of particle swarm optimization-simulated annealing (PSO-SA) algorithm, is developed. Also, a Lagrangian relaxation method is presented for this problem.Then, the real-time feeder vehicle routing problem (RTFVRP) is modeled and solved in a situation where customer requests appear dynamically. After modeling this problem in the MILP form, a dynamic inertia weight PSO algorithm is proposed to solve the problem. Another problem developed in this research is the hybrid of FVRP with flexible time windows and collaborative strategy among the depots. The collaborative FVRP is formulated as a bi-objective MILP to minimize operating costs and maximize customer satisfaction. The proposed model was validated by applying the augmented epsilon constraint (AEC) method for the small-sized instances. Also, for large-sized instances, a multi-objective particle swarm optimization (MOPSO) algorithm is developed with adaptive learning strategies and dynamics in the inertia coefficient. Finally, the real-time collaborative feeder vehicle routing problem is modeled with a flexible time window. The proposed MILP model is solved with a CPLEX mathematical programming solver using the AEC. Also, Also, multi-objective particle swarm optimization (MOPSO) and MOPSO-variable neighborhood search (MOPSO-VNS) were developed regarding the complexity of the problem. Finally, in addition to statistical analysis, the AHP-TOPSIS method is employed to analyze and prioritize algorithms. The obtained results show the better performance of the MOPSO-VNS algorithm in both static and dynamic modes in small and large-size instances.

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