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Urban population increase results in more supply chain operations in these areas, which leads to increased energy consumption and environmental pollution. City logistics represents a strategy of efficient freight transportation and material handling to fulfill customer and business demands. Within the frame of this paper, the authors describe an optimization model of a multi-echelon collection and distribution system, focusing on downtown areas and energy efficiency, sustainability, and emission reduction. After a systematic literature review, this paper introduces a mathematical model of collection and distribution problems, including package delivery, municipal waste collection, home delivery services, and supply of supermarkets and offices. The object of the optimization model is twofold: firstly, to design the optimal structure of the multi-echelon collection and distribution system, including layout planning and the determination of required transportation resources, like e-cars, e-bikes, and the use of public transportation; and secondly, to optimize the operation strategy of the multi-echelon supply chain, including resource allocation and scheduling problems. Next, a heuristic approach is described, whose performance is validated with common benchmark functions, such as metaheuristic evaluation. The scenario analysis demonstrates the application of the described model and shows the optimal layout, resource allocation, and operation strategy focusing on energy efficiency.

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Water distribution system (WDS) aims to distribute water from reservoirs or aqueducts to the end-users. This system is part of the water supply network that carries potable water from a central treatment plant or wells to water consumers in order to deliver water sufficiently to meet residential, commercial, industrial, and firefighting requirements. Modern systems aim to solve water distribution systems management problems, such as the lowest cost, and most efficient design by using linear/nonlinear optimization schemes, which are limited by the system size, the number of constraints, and the number of loading conditions. After a literature review for the articles that dealt with this topic, designing two parts of the water distribution system is discussed as a case study in Erbil. Pumps and storage tanks, while optimizing the water distribution system by minimizing the project cost through minimizing the volume of the elevated tank according to the pump working hours.

Industry 4.0 tools, such as the Internet of Things, artificial intelligence, digital twinning, and cloud computing, create a technological revolution that accelerates efforts to optimize the efficiency of cyber-physical operations and services. The waste management system requires a critical share of city logistics optimization, especially when using cyber-physical systems. Modern tools reduce the required municipal waste handling, such as loading and unloading, transportation, and warehousing, which leads to an increase in efficiency and flexibility, saving energy and time, and protecting the environment. In this paper, we present a cyber-physical waste management system solution by providing a cyber-physical model design and description, mathematical modeling, and two cases to investigate the impact on energy consumption and emissions. After an introduction and literature review, we describe the design of the cyber-physical model and tackle the first echelon. The designed system incorporates the IoT, smart bins with multi-percentage sensors, data and information analysis, vehicles’ actual routes, energy and emissions optimization, multi-echelon systems, time windows, and flexibility. Mathematical modeling equations for the optimized total energy consumption are presented. Thirty and twenty smart bins located in VIII District in Budapest are detailed as two case studies, where solutions for the optimized real routes and energy consumption are found using three metaheuristic algorithms: genetic, particle swarm, and simulated annealing optimization algorithms. The accrued emissions of CO, NMHC, CH4, NOx, and PM for the optimized solutions are calculated. Finally, the results are compared with a random traditional solution to measure the effectiveness.