Sustainable urban logistics require support of sustainable logistics center location planning. Nevertheless, urban public authorities generally experience difficulties in choosing a successful planning scheme, due to the lack of a rigorous analytical tool designed to help understand how logistics center location affects sustainability throughout the supply chain. A poor location can keep a firm or a supply chain from reaching its economic, environmental, and social sustainability goals. A good location can help meet these goals. To address the pressing cause of policy failure, this paper developed an analytical framework by integrating the multi-criteria decision-making method and the shortest path model with multiple objectives. The developed framework can be used to evaluate the effects of logistics center location schemes on travel time, transport costs, carbon emission, and road traffic, and further explore the conflict level among them. It is applied to Beijing in this paper. The analysis results reveal that: (1) travel time, transport costs, and carbon emissions all vary along with the distance from logistics center location to the city center; (2) encouraging cargo companies to plan their truck paths with the navigation objective of minimizing travel time is an excellent way to achieve sustainable urban logistics, because in this scenario, freight transport is the quickest, and transport costs and carbon emissions are all more moderate. Additionally, this paper confirmed the conflict level among sustainability goals of urban logistics based on actual contextual data. The proposed framework is a helpful guide tool for urban planners to develop sustainable logistics land-use planning and responsive policy interventions.

The robotic mobile fulfillment (RMF) system is a parts-to-picker warehousing system and a sustainable technology used in human–robot collaborative order picking. Storage location assignment (SLA) tactically benefits order-picking efficiency. Most studies focus on the retrieval efficiency of robots to solve SLA problems. To further consider the crucial role played by human pickers in RMF systems, especially in the context that the sustainable performance of human workers should be paid attention to in human–robot collaboration, we solve the SLA problem by aiming to improve human–robot collaborative order-picking efficiency. This study specifically makes decisions on assigning multiple items of various products to the slots of pods in the RMF system, in which human behavioral factors are taken into account. To obtain the solution in one mathematical model, we propose the heuristic algorithm under a two-stage optimization method. The results show that assigning correlated products to pods improves the retrieval efficiency of robots compared to class-based assignment. We also find that assigning items of each product to slots of pods, considering behavioral factors, benefits the operation efficiency of human pickers compared to random assignment. Improving human–robot collaborative order-picking efficiency and increasing the capacity usage of pods benefits sustainable warehousing management.

With the development of industrial production technologies and market economy, Postponement is increasingly being adopted by continuous production enterprise as a method that enables multi-product, mass customization production. In order to make use of the Postponement in manufacturing production enterprises need to achieve customized production and reduce enterprise costs. A modeling is conducted in the production process of Postponement in iron and steel enterprises in this paper. With the goal of minimizing total costs on a specific customer service level, mixed integer nonlinear programming and particle swarm algorithms are adopted for modeling and solving, to determine the optimal location and optimal semi-finished product inventory of PDP and CODP. Finally, taking an iron and steel enterprise as an example, the feasibility and effectiveness of the model and algorithm are verified. The study shows that the change of the optimal locations of PDP and CODP is affected by the change of customer service level and delay penalty coefficient, but the speed of change of the optimal location of PDP shows longer delays. In addition, the size of the capacity of semi-finished product inventory corresponding to CODP has a direct influence on whether the semi-finished product inventory corresponding to PDP participates in production, which in turn affects the optimal semi-finished product inventory on all levels. Through the analysis, it is found that the model constructed in this paper can better describe the overall situation and the influence relationship of the Postponement, and the study supplements the deficiency of the research on Postponement in the continuous manufacturing enterprises and enriches the content of the quantitative research on the Postponement.

Clarifying the internal supply relationships between subsidiaries and identifying the core members is important for manufacturing groups to be able to highlights the focus of management and solve operational problems. This paper uses social network analysis (SNA), which refers to the use of network level and node level indices to investigate the JD Cement Group supply network for fuel, raw materials, and cement clinker. According to the analysis, various subsidiaries, that seem to play the same role in the supply network, actually have different statuses and functions. To enhance the core competitive advantage, the group needs to manage the different subsidiaries pointedly and make a reasonable plan for the future development.