As an essential carrier of cross-border trade, the maritime supply chain has always been plagued by long service cycles, complex structures, and heterogeneous information from multiple sources. In this context, as a distributed database system, the emerging blockchain technology has the characteristics of decentralization, tamper-proofness, and traceability, which can be correctly applied in the maritime industry to promote its transformation and upgrading. However, blockchain application in the maritime supply chain remains notably absent, leaving this critical topic almost unexplored. Thus, we first conduct a comprehensive literature review and an industrial investigation to clarify the current status, problems, and challenges of the blockchain-based maritime supply chain system (BMSCS). We construct a new operation management mode of the maritime supply chain and propose an integrated BMSCS suitable for global economic development. Finally, based on comparison of the current research results, reasonable suggestions for the future operation and development of the BMSCS are put forward to enhance the coordination among members, accelerate the application of blockchain in the maritime industry, and gradually realise the intelligent operation of the maritime supply chain.

E-commerce, new retail, and other changes have highlighted the requirement of high efficiency and accuracy in the logistics service. As an important section in logistics and supply chain management, warehouses need to respond positively to the increasing requirement. The “smart warehouse” system, which is equipped with emerging warehousing technologies, is increasingly attracting the attention of industry and technology giants as an efficient solution for the future of warehouse development. This study provides a holistic view of operations management problems within the context of smart warehouses. We provide a framework to review smart warehouse operations management based on the characteristics of smart warehouses, including the perspectives of information interconnection, equipment automation, process integration, and environmental sustainability. A comprehensive review of relevant literature is then carried out based on the framework with four perspectives. This study could provide future research directions on smart warehouses for academia and industry practitioners.

This study investigates an integrated optimization problem on the three main types of resources used in container terminals: berths, quay cranes, and yard storage space. It presents a mixed integer linear programming model, which takes account of the decisions of berth allocation, quay crane assignment, and yard storage space unit assignment for incoming vessels. In addition, since the majority of the liner shipping services operate according to a weekly arrival pattern, the periodicity of the plan is also considered in the model and in the proposed algorithm. To solve the model on large-scale instances, a column generation (CG) procedure is developed to provide a lower bound for the integrated problem, in which an exact pseudopolynomial algorithm is designed for the pricing problems. Using this procedure, we propose a CG-based heuristic with different solution strategies and apply dual stabilization techniques to accelerate the algorithm. Based on some realistic instances, we conduct extensive numerical experiments to validate the effectiveness of the proposed model and the efficiency of the algorithm. The results show that the CG-based heuristic can yield a good solution with an approximate 1% optimality gap within a much shorter computation time than that of CPLEX. The online appendix is available at https://doi.org/10.1287/trsc.2018.0822 .

Air emissions from ships have become an important issue in sustainable shipping because of the low quality of the marine fuel consumed by ships. To reduce sulfur emissions from shipping, the International Maritime Organization has established emission control areas (ECAs) where ships must use low-sulfur fuel with at most 0.1% sulfur or take equivalent emission-reduction measures. The use of low-sulfur fuel increases the costs for liner shipping companies and affects their operations management. This study addresses a holistic liner shipping service planning problem that integrates fleet deployment, schedule design, and sailing path and speed optimization, considering the effect of ECAs. We propose a nesting algorithmic framework to address this new and challenging problem. Semianalytical solutions are derived for the sailing path and speed optimization problem, which are used in the schedule design. A tailored algorithm is applied to solve schedule design problems, and the solutions are used in fleet deployment. The fleet deployment problem is then addressed by a dynamic programming-based pseudo-polynomial time algorithm. Numerical experiments demonstrate that considering the effect of ECAs in liner shipping operations management can reduce over 2% of the costs, which is significant considering that the annual operating cost of a shipping company’s network can be as high as several billion dollars.