Prefabricated building projects (PBPs) have contributed significantly to addressing Hong Kong’s serious housing shortage and accommodate almost half of the residents in this densely populated city. The supply chains of PBPs involve large numbers of stakeholders, including clients, designers, main contractors, transporters, and assembly subcontractors, all of which need to interact with each other by frequent information exchanges. However, cross-border supply chains are found to be quite complex, resulting in risks that significantly affect the performance of PBPs. Supply chain risks (SCRs) are closely associated with the stakeholders involved and have high interdependency with each other. This paper adopts social network analysis (SNA) to develop the risk network of the supply chain of a PBP in Hong Kong to prioritize the stakeholder-associated SCR. The research findings show that poor planning of resources and schedule, poor control of working flows, and poor information sharing between stakeholders are the major challenges to the supply chains of PBPs. This is the first study to consider the dynamic risk interdependency and associated stakeholders in SCRs. By providing a greater understanding of the risks embedded across the supply chains of PBPs in Hong Kong, this study could assist practitioners dealing with such risks more effectively and efficiently.

Purpose: The purpose of this paper is to propose an effective and economical management platform to realize real-time tracking and tracing for prepackaged food supply chain based on Internet of Things (IoT) technologies, and finally ensure a benign and safe food consumption environment. Design/methodology/approach: Following service-oriented architecture, a flexible layered architecture of tracking and tracing platform for prepackaged food is developed. Besides, to reduce the implementation cost while realizing fine-grained tracking and tracing, an integrated solution of using both the QR code and radio-frequency identification (RFID) tag is proposed. Furthermore, Extensible Markup Language (XML) is adopted to facilitate the information sharing among applications and stakeholders. Findings: The validity of the platform has been evaluated through a case study. First, the proposed platform is proved highly effective on realizing prepackaged food tracking and tracing throughout its supply chain, and can benefit all the stakeholders involved. Second, the integration of the QR code and RFID technologies is proved to be economical and could well ensure the real-time data collection. Third, the XML-based method is efficient to realize information sharing during the whole process. Originality/value: The contributions of this paper lie in three aspects. First, the technical architecture of IoT-based tracking and tracing platform is developed. It could realize fine-grained tracking and tracing and could be flexible to adapt in many other areas. Second, the solution of integrating the QR code and RFID technologies is proposed, which could greatly decrease the cost of adopting the platform. Third, this platform enables the information sharing among all the involved stakeholders, which will further facilitate their cooperation on guaranteeing the quality and safety of prepackaged food.

In this article, we study the reinforcement learning (RL) for vehicle routing problems (VRPs). Recent works have shown that attention-based RL models outperform recurrent neural network-based methods on these problems in terms of both effectiveness and efficiency. However, existing RL models simply aggregate node embeddings to generate the context embedding without taking into account the dynamic network structures, making them incapable of modeling the state transition and action selection dynamics. In this work, we develop a new attention-based RL model that provides enhanced node embeddings via batch normalization reordering and gate aggregation , as well as dynamic-aware context embedding through an attentive aggregation module on multiple relational structures. We conduct experiments on five types of VRPs: 1) travelling salesman problem (TSP); 2) capacitated VRP (CVRP); 3) split delivery VRP (SDVRP); 4) orienteering problem (OP); and 5) prize collecting TSP (PCTSP). The results show that our model not only outperforms the learning-based baselines but also solves the problems much faster than the traditional baselines. In addition, our model shows improved generalizability when being evaluated in large-scale problems, as well as problems with different data distributions.

The adoption of robotics is promising to improve the efficiency, quality, and safety of prefabricated construction. Besides technologies that improve the capability of a single robot, the automated assembly planning for robots at construction sites is vital for further improving the efficiency and promoting robots into practices. However, considering the highly dynamic and uncertain nature of a construction environment, and the varied scenarios in different construction sites, it is always challenging to make appropriate and up-to-date assembly plans. Therefore, this paper proposes a Deep Reinforcement Learning (DRL) based method for automated assembly planning in robot-based prefabricated construction. Specifically, a re-configurable simulator for assembly planning is developed based on a Building Information Model (BIM) and an open game engine, which could support the training and testing of various optimization methods. Furthermore, the assembly planning problem is modelled as a Markov Decision Process (MDP) and a set of DRL algorithms are developed and trained using the simulator. Finally, experimental case studies in four typical scenarios are conducted, and the performance of our proposed methods have been verified, which can also serve as benchmarks for future research works within the community of automated construction. Note to Practitioners —This paper is conducted based on the comprehensive analysis of real-life assembly planning processes in prefabricated construction, and the methods proposed could bring many benefits to practitioners. Firstly, the proposed simulator could be easily re-configured to simulate diverse scenarios, which can be used to evaluate and verify the operations’ optimization methods and new construction technologies. Secondly, the proposed DRL-based optimization methods can be directly adopted in various robot-based construction scenarios, and can also be tailored to support the assembly planning in traditional human-based or human-robot construction environments. Thirdly, the proposed DRL methods and their performance in the four typical scenarios can serve as benchmarks for proposing new advanced construction technologies and optimization methods in assembly planning.

Efficient urban emergency response is vital for saving lives and property after disasters. However, urban emergency response is challenging because it involves many demands, with a very tight time for making decisions, and is frequently threatened by the disruption of road networks and infrastructures. Taking these challenges into consideration, this article proposes a dynamic truck-UAV (DTU) collaboration strategy to realize efficient and resilient urban emergency response. Specifically, a DTU collaboration strategy is first proposed based on the characteristics of urban emergency management. Then, an integrated truck-UAV collaborative scheduling model is developed and indicated to be an NP-hard problem. Then, a tabu search-based integrated (TSI) scheduling algorithm is developed, with several tailored local search operators and a recursion-based evaluation (RE) algorithm. Finally, comprehensive experiments are conducted to demonstrate the superiority of our proposed strategy over existing truck-UAV collaboration strategies and verify the performance of our proposed solution algorithm. Managerial Relevance Statement—This article proposes a dynamic truck-UAV (DTU) collaboration strategy and a corresponding route planning method for urban emergency response. The findings of this work demonstrate that emergency management agencies can utilize the DTU collaboration strategy for efficient emergency response in urban areas with high-density road networks. Meanwhile, the emergency response under this strategy is resilient to cope with different degrees of road network disruptions. In addition, the DTU collaboration strategy shows a distinct improvement over the parallel and truck-UAV flying sidekick strategies in large-scale emergency response scenarios, and it will perform better when demand density increases. Furthermore, the proposed integrated scheduling algorithm is demonstrated to be suitable and efficient for solving the routing problems associated with the DTU collaboration strategy.