Vietnamese e-commerce has recently experienced a robust growth, especially e-commerce platforms such as Shopee, Lazada, Tiki. Reverse logistics has been pointed out as having a significant impact on the performance of an e-commerce platform. To capture the actual impact of some reverse logistics factors, i.e, Return Processing Time (RPT), Return Policy (RP), Return Cost (RC), Customer Service (CSR), and Post-Return Product (PRP), on Customer Satisfaction (CS), an OLS model was conducted. The results indicated significant correlation between all independent variables and dependent variables, which CSR shows the greatest correlation and PRP shows the weakest correlation.  The study then made some suggestions for e-commerce platforms in Vietnam to enhance their reverse logistics process to get higher customer satisfaction.

Increasing the environmental friendliness of production systems is largely dependent on the effective organization of waste logistics within a single enterprise or a system of interconnected market participants. The purpose of this article is to develop and test a methodology for evaluating a data-based waste logistics model, followed by solutions to reduce the level of waste in production. The methodology is based on the principle of balance between the generation and beneficial use of waste. The information base is data from mandatory state reporting, which determines the applicability of the methodology at the level of enterprises and management departments. The methodology is presented step by step, indicating data processing algorithms, their convolution into waste turnover efficiency coefficients, classification of coefficient values and subsequent interpretation, typology of waste logistics models with access to targeted solutions to improve the environmental sustainability of production. The practical implementation results of the proposed approach are presented using the production example of chemical products. Plastics production in primary forms has been determined, characterized by the interorganizational use of waste and the return of waste to the production cycle. Production of finished plastic products, characterized by a priority for the sale of waste to other enterprises. The proposed methodology can be used by enterprises to diagnose existing models for organizing waste circulation and design their own economically feasible model of waste processing and disposal.

Finding the right technique to optimize a complex problem is not an easy task. There are hundreds of methods, especially in the field of metaheuristics suitable for solving NP-hard problems. Most metaheuristic research is characterized by developing a new algorithm for a task, modifying or improving an existing technique. The overall rate of reuse of metaheuristics is small. Many problems in the field of logistics are complex and NP-hard, so metaheuristics can adequately solve them. The purpose of this paper is to promote more frequent reuse of algorithms in the field of logistics. For this, a framework is presented, where tasks are analyzed and categorized in a new way in terms of variables or based on the type of task. A lot of emphasis is placed on whether the nature of a task is discrete or continuous. Metaheuristics are also analyzed from a new approach: the focus of the study is that, based on literature, an algorithm has already effectively solved mostly discrete or continuous problems. An algorithm is not modified and adapted to a problem, but methods that provide a possible good solution for a task type are collected. A kind of reverse optimization is presented, which can help the reuse and industrial application of metaheuristics. The paper also contributes to providing proof of the difficulties in the applicability of metaheuristics. The revealed research difficulties can help improve the quality of the field and, by initiating many additional research questions, it can improve the real application of metaheuristic algorithms to specific problems. The paper helps with decision support in logistics in the selection of applied optimization methods. We tested the effectiveness of the selection method on a specific task, and it was proven that the functional structure can help the decision when choosing the appropriate algorithm.

Rapid urban expansion gives rise to smart cities which pose immense logistical and supply chain challenges. The COVID-19 pandemic transformed the holistic system identified by Zhao et al. in 2021. The system encompasses logistics and supply chain integral to the concept of smart cities, with a focus on sustainability. This transformation requires an in-depth study on challenges of a common framework of policies for smart cities in countries comprising the Organisation for Economic Cooperation and Development (OECD). The study employs an extensive literature analysis for the period 2020–2022. an approach which contextualizes the model. The model identifies the causes, impact, and spillovers of new trends in logistics and supply, including the sustainability of adopted technologies. The study includes the variables involved, and barriers to creating a shared model. The results reveal that the two elements affecting the supply chain and transport in smart cities are Industry 4.0 and 5.0 technologies supporting specific sectors. The resilience of small and medium-sized enterprises positively impacts the sustainability of large urban centres. The study presents both factors that help and hinder the adoption of environmental, social, and economic sustainability technologies.

Amid the unfolding Fourth Industrial Revolution, the integration of Logistics 4.0 with agribusiness has emerged as a pivotal nexus, harboring potential for transformational change while concurrently presenting multifaceted challenges. Through a meticulous content analysis, this systematic review delves deeply into the existing body of literature, elucidating the profound capacities of Logistics 4.0 in alleviating supply chain disruptions and underscoring its pivotal role in fostering value co-creation within agro-industrial services. The study sheds light on the transformative potential vested within nascent technologies, such as Internet of Things (IoT), Blockchain, and Artificial Intelligence (AI), and their promise in shaping the future landscape of agribusiness. However, the path forward is not without impediments; the research identifies cardinal barriers, most notably the absence of robust governmental policies and a pervasive lack of awareness, which collectively stymie the seamless incorporation of Industry 4.0 technologies within the realm of agribusiness. Significantly, this inquiry also highlights advancements in sustainable supply chain management, drawing attention to pivotal domains including digitalization, evolving labor paradigms, supply chain financing innovations, and heightened commitments to social responsibility. As we stand on the cusp of technological evolution, the study offers a forward-looking perspective, anticipating a subsequent transition towards Industry 5.0, characterized by the advent of hyper-cognitive systems, synergistic robotics, and AI-centric supply chains. In its culmination, the review presents prospective avenues for future research, emphasizing the indispensable need for relentless exploration and pragmatic solutions. This comprehensive synthesis not only sets the stage for future research endeavors but also extends invaluable insights for practitioners, policymakers, and academicians navigating the intricate labyrinthstry of Logistics 4.0 in agribusiness.

Recent times have seen significant advancements in AI and NLP technologies, poised to revolutionize logistical decision-making across industries. This study investigates integrating ChatGPT, an advanced AI language model, into strategic, tactical, and operational logistics. Examining its applicability, benefits, and limitations, the study delves into ChatGPT's capacity for strategic logistics planning, facilitating nuanced decision-making through natural language interactions. At the tactical level, it explores ChatGPT's role in optimizing route planning and enhancing real-time decision support. The operational aspect scrutinizes ChatGPT's capabilities in micro-level logistics and emergency response. Ethical implications, encompassing data security and human-AI trust dynamics, are also analyzed. This report furnishes valuable insights for the logistics sector, emphasizing AI's potential in reshaping decision-making while underscoring the necessity for foresight, evaluation, and ethical considerations in AI integration. In this publication, it is assumed that ChatGPT is not entirely reliable for decision-making in the logistics field: at the strategic level, it can be effectively used for "brainstormin" in preparing decisions, but at the tactical and operational level, the depth of the knowledge is not sufficient to make appropriate decisions. Therefore, the answers provided by ChatGPT to the defined logistic tasks are compared with real logistic solutions. The article highlights ChatGPT's effectiveness at different levels of logistics and clarifies its potential and limitations in the logistics field.