Zheyi Tan’s research while affiliated with Shanghai University and other places

... Vehicles like yard trucks [17,18] reach stackers [19] and forklift [20] already demonstrated their hydrogen potential, showing easier management (mostly in terms of vehicle refueling if compared to operating daily schedule) and investment costs comparable to BEVs. The management of hydrogen (H 2 ) in ports in terms of safety (mostly looking at regulatory imposed distances) and refueling/supply is a challenging aspect [21,22]. ...

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Multi-aspect assessment for the retrofitting of operating vessels in ports by using advanced power systems

... Deep learning is a subfield of Machine learning wherein the algorithm is built with multiple layers of neurons that can analyze abstract features of given data, [2]. Deep learning is an interesting upand-coming branch of machine learning that has found its applications in many fields, such as computer vision, natural language processing, and logistics, [ [18]. ...

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Deep Learning Empowered Intermodal Path Optimization in Logistics: Deep Shortest Approach

... Although RMFS has its advantages over manual order picking, it also poses further planning problems regarding system structure, layout planning, storage allocation, and order processing, as Boysen and De Koster (2024) point out. Previous studies have aimed to enhance the performance of RMFS by solving these planning problems and providing design and operational guidance for logistics managers (Merschformann et al., 2019;Wang et al., 2022;Zhen et al., 2023). However, they have concluded that optimizing RMFS is problematic because it emerges as a complex system. ...

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The power of Robotic Mobile Fulfilment Systems — A sensitivity analysis

... Instant delivery is limited by the short-order engagement time ranges, e.g., one hour, so advanced technologies and decision-making methods are essential for saving time and speeding the delivery processes. Table 1 lists studies on the operations research of instant delivery, which differs from general delivery in the order engagement time limits [16]. Most studies reviewed apply UAVs (Unmanned Aerial Vehicles) or drones to cooperate with logistics vehicles. ...

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Drone-Based Instant Delivery Hub-and-Spoke Network Optimization

... This service, tailored for passengers, ensures direct transportation to one's destination without intermediate stops upon request for demandresponsive transport. Implementing this service can significantly enhance passenger satisfaction by minimizing both waiting and riding times, thereby reducing vehicle idle times [31]. For instance, DRT X in Figure 3 Upon receiving a new service request from Passenger D, the DRT control system must determine whether to assign the call to DRT X or DRT Y and identify an optimal insertion point within the existing routes. ...

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Analyzing New Operation Strategy of Demand-Responsive Transports Using Discrete-Event Simulation Framework

... Compared with TSSP models that uncertainty information becomes known when the first-stage decisions are determined, MSSP models indicate uncertainty in sequential stages, where only information related to the current stage is revealed after decisions in the previous stages are made. Wu et al. (2023) propose a multi-stage stochastic programming (MSSP) model with uncertain demand for the fleet deployment problem in shipping networks. Then, they develop a Benders-based branchand-cut algorithm to solve it. ...

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A distribution-free-based approach for stochastic food closed- loop supply chain

... Guo et al. [28] established a rescue traffic route optimization model of considering safety risk and time cost of rescue vehicles in the case where multiple rescuer groups can be dispatched from the departure point and return to replenishment point. To minimize the total risk and total cost of emergency responses, Chen et al. [29] studied the emergency rescue route optimization problem based on a bi-objective robust optimization model. Tan et al. [30] provided a decision tool for urban traffic accident rescue vehicles' integrated deployment problem based on a simulation optimization model. ...

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Multiclass dynamic emergency traffic collaborative assignment with parallel two-stage optimization

... In research on urban logistics focusing on the last mile distribution, Fontaine et al. (2023) studied the design of city logistics networks with multiple logistics service providers. Zhen et al. (2023) studied territorial design and urban delivery services, and Calabrò et al. (2023) assistance in planning future-generation transit systems. Service design in urban events has gained attention in recent research on design research, particularly from a data management perspective pointing to inconsistent consideration of data, see, e.g. ...

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From urban design to service design in urban informatics: spectators' attitudes, expectations and privacy concerns in mass urban events

... Papers Label for problems Label for methods Strategy [31] SA&YA-L/U&IT&YO-OL-QCASP LO-MetaH-/ Double-cycling [187] SA&YA-L/U&IT-OL-QCASP LO-H&MetaH-/ Single-cycling [188] SA&YA-L/U&IT-TL&OL-QCASP LO-MetaH-/ Double-cycling [189] SA&YA-L/U&IT&YO-TL&OL-QCASP LO-MetaH-/ Double-cycling [190] SA&YA-L/U&IT-OL-QCASP LO-MetaH-/ Single-cycling [191] SA&YA-L/U&YO-TL&OL-QCASP LO-EA-/ Single-cycling [192] SA&YA-L/U&IT&YO-OL-QCASP LO-EA&MetaH-/ Single-cycling [193] SA&YA-L/U&IT&YO-OL-QCASP LO-MetaH-/ Double-cycling/Bidirectional [194] SA&YA-L/U&IT&YO-OL-QCASP LO-MetaH-/ Single-cycling/Double-cycling [195] SA&YA-L/U&IT&YO-OL-QCASP LO&HM-EA-/ Single-cycling [196] SA&YA-L/U&IT&YO-OL-QCASP HM-H-/ Single-cycling [197] SA&YA-L/U&IT-OL-QCASP LO-MetaH-/ Single-cycling/Unidirectional [198] SA&YA-L/U&IT-TL-QCASP OuU-EA-/ Single-cycling [199] SA&YA-L/U&YO-OL-QCASP LO-MetaH-/ Single-cycling different approaches, and emphasize k-shifts and multiple ports as areas for future research. Among recent studies, only one has applied machine learning techniques to solve the slot planning problem (SPP). ...

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Operations Research, Machine Learning, and Integrated Techniques for Decision Problems in the Seaside Area of Container Terminals

... Another important dimension of relief distribution is managing uncertainties, which include variability in demand for relief, disruptions to transportation networks, fluctuating resource availability, and potential facility failures (Desi-Nezhad et al., 2022;Wang et al., 2024a). For example, Siddig and Song (2024) addressed evacuation-related uncertainties in disaster relief planning with a robust optimization model. ...

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Recent Advances in Disaster Emergency Response Planning: Integrating Optimization, Machine Learning, and Simulation