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The last mile problem of E-grocery Distribution comprises one of the most costly and highest polluting components of the supply chain in which companies deliver goods to end customers. To reduce transport cost and fuel emissions, a new element of ground-based delivery services, autonomous delivery vehicles (ADVs), is included in the E-grocery distribution system for improving delivery efficiency. Thus, the objective of this study is to optimize a two-echelon distribution network for efficient E-grocery delivery, where conventional vans serve the delivery in the first echelon and ADVs serve delivery in the second echelon. The problem is formulated as a two-echelon vehicle routing problem with mixed vehicles (2E-VRP-MV) with a nonlinear objective function, in which the total transport and emission costs are optimized. This optimization is based on the flow assignment at each echelon and to realize routing choice for both the van and ADV. A two-step clustering-based hybrid Genetic Algorithm and Particle Swarm Optimization (C-GA-PSO) algorithm is proposed to solve the problem. First, the end customers are clustered to the intermediate depots, named satellites, based on the minimized distance and maximized demand. To enhance the efficiency of resolving the 2E-VRP-MV-model, a hybrid GA-PSO algorithm is adopted to solve the vehicle routing problem. Computational results of up to 21, 32, 50, and 100 customers show the effectiveness of the methods developed here. At last, the impacts of the layout of the depot-customer and customer density on the total cost are analyzed. This study sheds light on the tactical planning of the multi-echelon sustainable E-grocery delivery network.

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Despite advancements in the field of traffic planning and operations, many major cities still rely on pretimed signal settings. With only pretimed signal control strategies, the secondary effects of a terror attack are magnified by slow evacuation times resulting in further loss of life. However, the cost of implementing new infrastructure based solely on the chance of a no-notice evacuation is not something that city planners are willing to do. The purpose of the research is to define a cost-effective methodology to develop pretimed signal control strategies to assist evacuations in urban areas. To that end, a dynamic programming methodology was developed to assist critical intersections in urban corridors. To test this methodology, a microscopic traffic-simulation environment was created for a case study of a 10 intersection evacuation corridor in Washington, DC. Using the proposed methodology to optimizing signal splits of a critical intersection within an evacuation corridor, evacuation clearance time was reduced by approximately 1 h. Furthermore, this formulation can be used to develop pretimed signal control settings for evacuation scenarios. Results showed that peak-hour signal timings are not sufficient in the case of an emergency, and signal timing plans must be tailored for emergency evacuation.

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