With the increasing interest in online shopping, the Last Mile delivery is regarded as one of the most expensive and pollutive—and yet the least efficient—stages of the e-commerce supply chain. To address this challenge, a novel location-routing problem with simultaneous home delivery and customer’s pickup is proposed. This problem aims to build a more effective Last Mile distribution system by providing two kinds of service options when delivering packages to customers. To solve this specific problem, a hybrid evolution search algorithm by combining genetic algorithm (GA) and local search (LS) is presented. In this approach, a diverse population generation algorithm along with a two-phase solution initialization heuristic is first proposed to give high quality initial population. Then, advantaged solution representation, individual evaluation, crossover and mutation operations are designed to enhance the evolution and search efficiency. Computational experiments based on a large family of instances are conducted, and the results obtained indicate the validity of the proposed model and method.

To stimulate the development of the Internet of Health Things and the construction of a robotic automation system in the healthcare industry, the mobile robot selection problem for a hospital pharmacy is studied. We target the mobile robot selection as a multi-criteria decision-making problem. The VIKOR-based implementation steps integrating fuzzy extended analytic hierarchy process technique are developed. To avoid any loss of information and ensure the veracity of the VIKOR-based calculations, we employ the fuzzy ranking technique based on the degree of possibility to select the best mobile robot alternative by deriving the minimum fuzzy comprehensive utility value. The results of a case study and the accompanying sensitivity analysis show the effectiveness and robustness of the fuzzy extended VIKOR approach for the mobile robot selection problem.

A sharp increasing number of online shopping parcels and scattered customers lead each express company to invest a lot logistics resources, but its utilization rate is low. Each express company faces the challenge of improving customer satisfaction and market competitiveness while ensuring the total costs are under control. This paper develops a novel joint delivery (JD) model to reduce operation costs and carbon emission via strengthening horizontal cooperation and resource sharing among express companies. Furthermore, it designs a multi-depot two-echelon joint delivery location routing problem (MD-2E-JDLRP) mathematic model for JD considering multi-objectives and proposes a hybrid heuristic algorithm to solve the MD-2E-JDLRP. The performance of this algorithm was tested by benchmarks and a case study. Finally, a case study is conducted to verify the effectiveness of the MD-2E-JDLRP model. The obtained results show the JD model can effectively reduce costs and carbon emissions while ensuring higher customer satisfaction.

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.

This paper examines the dual-channel supply chain in which the differentiation of the environmental sustainability of channels is considered. We analyze the influences of the level of environmental sustainability of channels on the pricing policies for the supply chain members in both centralized and decentralized models using the Stackelberg game model under inconsistent price policy. We obtain the optimal level of environmental sustainability of channels and pricing decisions for the players in the centralized and decentralized dual-channel supply chains. Results show that the influence mechanisms of the level of environmental sustainability of channels on the pricing decisions are different in the centralized and decentralized models. Furthermore, numerical analysis has been conducted to investigate the effects of the cross-environmental-sustainability sensitivity factor and the initial proportion of consumers who prefer the retail channel on the level of environmental sustainability of channels, pricing policies and players’ profits.