To cope with the globalisation of online shopping, ecological environment deterioration and increasing traffic pressure, a novel logistics service paradigm – joint distribution (JD) – for tackling the bottlenecks in operational efficiency, quality of service and environment-friendly development is put forward under online shopping combining with the internet of things, service-oriented technologies and other advanced management methods and tools. Then, the concept of JD is defined under online shopping, the differences among JD, third-party express service system (TESS) and self-built logistics system (SLS) are discussed. JD architecture and typical characteristics and the key technologies for developing a JD service platform (JDSP) are introduced. Four core components, i.e. Management system, optimisation on resource service, operational strategies during the delivery, etc. are studied for developing the JDSP. Finally, we develop some key systems and integrate them into the JDSP, also the platform is applied to a joint distribution alliance, and future research directions are discussed.

With the rapid depletion of natural resources and undesired environmental changes globally, more interest has been shown in the research of green supply chain practices, including end-of-life vehicle (ELV) recycling. The ELV recycling is mandatory for auto-manufacturers by legislation for the purpose of minimizing potential environmental damages. The purpose of the present research is to determine the best choice of ELV recycling service provider by employing an integrating hybrid multi-criteria decision making (MCDM) method. In this research, economic, environmental and social factors are taken into consideration. The linguistic variables and trapezoidal fuzzy numbers (TFNs) are applied into this evaluation to deal with the vague and qualitative information. With the combined weight calculation of criteria based on fuzzy aggregation and Shannon Entropy techniques, the normative multi-criteria optimization technique (FVIKOR method) is applied to explore the best solution. An application was performed based on the proposed hybrid MCDM method, and sensitivity analysis was conducted on different decision making scenarios. The present study provides a decision-making approach on ELV recycling business selection under sustainability and green philosophy with high robustness and easy implementation.

With the globalization of online shopping, deterioration of the ecological environment and the increasing pressure of urban transportation, a novel logistics service mode—joint distribution (JD)—was developed. Selecting the optimal partner combination is important to ensure the joint distribution alliance (JDA) is sustainable and stable, taking into consideration conflicting criteria. In this paper, we present an integrated fuzzy entropy weight, fuzzy analytic hierarchy process (fuzzy EW-AHP) and fuzzy technique for order preference by similarity to an ideal solution (TOPSIS) approach to select the optimal partner combination of JDA. A three-phase approach is proposed. In the first phase, we identify partner combination evaluation criteria using an economy-society-environment-flexibility (ESEF) framework from a perspective that considers sustainability. In the second phase, the criteria weights and criteria combination performance of different partner combinations were calculated by using an integrated fuzzy EW-AHP approach considering the objective and subjective factors of experts. In the third phase, the JDA partner combinations are ranked by employing fuzzy TOPSIS approach. The sensitivity analysis is considered for the optimal partner combination. Taking JDA in Chongqing for example, the results indicate the alternative partner combination 3 (PC3) is always ranked first no matter how the criteria weights change. It is effective and robust to apply the integrated fuzzy EW-AHP and TOPSIS approach to the partner selection of JDA.

The disassembly activity, regarding as the crucial stage in recycling operations, has attracted increasing focus owing to the significance of eco-economics and environmental issues. This paper examines the capacitated disassembly scheduling with demand and disassembly operation time uncertainty consideration, which is the problem of determining the quantity of the end-of-life (EOL) products (root item) to be disassembled while satisfying recycling market. The addressed problem is formulated as a novel stochastic programming model and a hybrid genetic-based algorithm (HGA) is proposed to derive the best solution. To deal with the uncertain demand of disassembled parts/modules (leaf item) and the disassembly operation time, the fixed sample size (FSS) sampling strategy is employed and embedded into the designed heuristic algorithm, lunched by the Monte Carlo Simulation. The numerical instances under different scales are performed, and results show that the developed HGA manifests good performance in terms of accuracy and efficiency.