A Novel Approach for Multiplicative Linguistic Group Decision Making Based on Symmetrical Linguistic Chi-Square Deviation and VIKOR Method

Most linguistic-based approaches to multi-attribute group decision making (MAGDM) use symmetric, uniformly distributed sets of additive linguistic terms to express the opinions of decision makers. However, in reality, there are also some problems that require the use of asymmetric, uneven, i.e., non-equilibrium, multiplicative linguistic term sets to express the evaluation. The purpose of this paper is to propose a new approach to MAGDM under multiplicative linguistic information. The aggregation of linguistic data is an important component in MAGDM. To solve this problem, we define a chi-square for measuring the difference between multiplicative linguistic term sets. Furthermore, the linguistic generalized weighted logarithm multiple averaging (LGWLMA) operator and linguistic generalized ordered weighted logarithm multiple averaging (LGOWLMA) operator are proposed based on chi-square deviation. On the basis of the proposed two operators, we develop a novel approach to GDM with multiplicative linguistic term sets. Finally, the evaluation of transport logistics enterprises is developed to illustrate the validity and practicality of the proposed approach.

Methodical Approach of Detecting and Calculating Key Indicators of the Competitive Potential of a Trade-Logistics Company in the Conditions of Uncertainty

The significantly changed conditions of the activity of trade-logistics enterprises place heavy demands on the accuracy of calculation and planning key indicators of competitive potential. The article formulates and solves a scientific problem, which consists in the absence of a unified approach, within which not only the strategic potential of an enterprise is assessed and key indicators of competitive potential are determined, but also a toolkit for calculating values of these indicators in the conditions of fuzzy input data is proposed. It is suggested to determine the key indicators of competitive potential based on the characteristics of the corporate profile that have the potential for temporary competitive advantages with the possibility of increasing their organization, as well as the key weaknesses of the organization. To calculate the values of the key indicators of competitive potential and their planning in the conditions of uncertainty, it is proposed to use fuzzy inference systems. The article presents the results of approbation of the developed methodological approach in a particular trade and logistics company.

Research on Enterprise Supply Chain Optimization Model and Algorithm Based on Fuzzy Clustering

Reasonable logistics distribution network structure can not only effectively reduce the cost of logistics enterprises themselves but also reduce the social cost. Through effective supply chain management, enterprises can significantly reduce costs, improve competitiveness, and enhance their ability to resist risks. Because the single-level distribution network structure of production enterprises is not suitable for large-scale logistics distribution, this paper proposes a distribution network structure design that accords with economies of scale and establishes an enterprise supply chain optimization model based on the fuzzy clustering algorithm. Using this optimization method to optimize the inventory of enterprise logistics supply chain, the operation is fast, the result is correct and reasonable, and it can provide good decision support for the distribution network of logistics enterprises. Through information technology and modern management technology, we should effectively control and coordinate the logistics, information flow, and capital flow in the production and operation process and organically integrate the internal supply chain with the external supply chain for management, so as to achieve the goal of global optimization.

The Influence of Trust on Crowd Logistics Enterprise’s Operational Performance: A SEM-PLS Model

With the development of information communication and satellite positioning, crowd logistics has gradually emerged as a popular urban distribution mode for logistics enterprises. Trust and synergy between the crowd workers and the crowd logistics enterprises have become key issues in urban logistics development. Based on commitment-trust theory and logistics synergy, this paper developed a theoretical model exploring ability trust, goodwill trust, logistics synergy, and crowd logistics operational performance. We used SmartPLS software to analyze the questionnaire survey from 50 senior managers in crowd logistics enterprises. The results show that ability trust has a significant positive impact on logistics synergy and operational performance. Logistics synergy was also found to increase the operational performance of crowd logistics enterprises and plays a mediating role in the impact of ability trust and goodwill trust on operational performance. We recommend that logistics enterprises evaluate their crowd workers’ service abilities, promote trust relationships with them, strengthen the construction of crowd logistics mechanisms, and invest in crowd worker training and development.

Optimization of VRR for Cold Chain with Minimum Loss Based on Actual Traffic Conditions

Recently, fresh agricultural cold-chain logistics have been greatly developed with the increasing needs of people’s life. Reducing costs of cold-chain distribution has become the main object of loss control in logistics enterprises. The objective of this research is to find a set of optimal routes that minimize the total loss, including fuel cost, refrigeration cost, soft time window penalty cost, and cargo damage cost over transit time. In this paper, the definition and model construction of vehicle routing problem (VRP) with multiobjective minimum lost are introduced first. Then, an ant colony optimization (ACO) algorithm combined with Pareto local search (PLS) is put forward to solve the minimum loss model. In order to avoid the influence of complex road conditions during distribution, the distance matrix and the transit time matrix are both derived from the recommended navigation road based on E-map API. At last, a compare experiment between the traditional method and our proposed method is performed. The results indicate that our method has strong applicability and potential advantages in cold-chain logistic and has important practical significance and application value.

Multiweight Cross-Multimedia Logistics Optimal Path Exploration by Integrating High-Dimensional Deep Learning

High-dimensional deep learning has been applied in all walks of life at present, among which the most representative one is the logistics path optimization combining multimedia with high-dimensional deep learning. Using multimedia logistics to explore and operate the best path can make the whole logistics industry get innovation and leap forward. How to use high-dimensional deep learning to conduct visual logistics operation management is an opportunity and a problem facing the whole logistics industry at present. The application of high-dimensional deep learning technology can help logistics enterprises improve their management level, realize intelligent decision-making, and enable accurate prediction. Starting from the total amount of logistics, regional layout, enterprise scale, and high-dimensional deep learning algorithm, this paper analyzes the current situation of China’s logistic development through multiweight analysis and explores the best path for multimedia logistics.

Analysis of Factors Affecting to The Competitiveness of The Logistics Enterprises in Mekong Delta, Vietnam under The Integration of Descriptive Statistics and Fuzzy-ISM Approach

This paper analyzes the influence of the factors belonging to the Logistics Performance Index (LPI) on the competitiveness of Logistics Enterprises (LEs) in the Mekong Delta (MD), Vietnam. The input factors will be selected from a rigorous document review process combined with an in-depth survey of leading experts in the logistics field. Since then, an integrated approach including Improved Descriptive Statistics (IDS) combined with Fuzzy Interpretive Structure Modeling (FISM) has been structured to achieve this purpose. Specifically, the factors that have the closest relationship with competitiveness will be determined from the results of an improved descriptive statistical method, then the results will be developed using the FISM method under trapezoidal fuzzy number format to realize the importance of factors contributing to improving the competitiveness of logistics companies. The results have shown the impact of 16 sub-factors, which belong in the 4 main factors of Reputation (REP); Timeliness (TIM); Customs (CUS); and Ease of arranging shipments (EAS), on the competitiveness of logistics enterprises in the region analyzed in 4 clusters (Dependency, Association, Autonomy and independence). In which, 6 out of 16 factors have a significant impact on competitiveness that logistics enterprises need to pay more attention to in their operations. These factors will be ranked as REP4, REP 5, REP3, REP1, REP2, EAS1, respectively.

Application of machine learning technologies for Drone Network in logistics and port activities of Russia

Cтатья посвящена проведению исследовательского анализа особенностей практического использования беспилотных дронов и квадрокоптеров. Актуальность исследования обусловлена тем, что технологии Drone Network позволяют решить основную проблему логистики – провести оптимизацию финансовых расходов, путем сокращения затрат на реализацию цепочек поставок. Это помощь интеграции логистики, дронов и технологии машинного обучения. В рамках статьи рассмотрены теоретические аспекты понятия технологии «Drone Network». Проанализирован зарубежный опыт и международные тенденции в использовании беспилотных дронов при формировании цепочки поставок логистики морских предприятий. Рассмотрены ключевые преимущества, которые предоставляют данные технологии в совершенствовании транспортной логистики компаний. Проанализированы перспективы развития технологий Drone Network на территории Российской Федерации. Рассмотрены основные проблемы, препятствующие их практическому применению российскими компаниями. Проанализированы недостатки, с которыми сталкиваются организации в рамках использования технологий беспилотных дронов в логистике. Описаны перспективы развития технологий Drone Network в международном и российском рынке. Проанализированы перспективы применения беспилотных дронов, управляемых технологиями машинного обучения, в рамках развития портовой деятельности, внутрипортовой логистики и для поиска бедствующих кораблей. The scientific article is devoted to the research analysis of the features of the practical use of unmanned drones and frame copters in the framework of the transport logistics of goods and orders by foreign companies. The relevance of the study is due to the fact that Drone Network technologies allow solving the main problem of logistics - to optimize financial costs by reducing the cost of implementing supply chains. Perhaps this is helping the integration of logistics, drones and machine learning technology. The article discusses the theoretical aspects of the concept of the "Drone Network" technology. Analyzed foreign experience and international trends in the use of unmanned drones in the formation of the supply chain of logistics enterprises. The key advantages that these technologies provide in improving the transport logistics of companies are considered. The prospects for the development of Drone Network technologies on the territory of the Russian Federation are analyzed. The main problems that hinder their practical application by Russian companies are considered. The paper analyzes the shortcomings faced by organizations in the use of unmanned drone technologies in logistics. The prospects for the development of Drone Network technologies in the international and Russian markets are described. The prospects for the use of unmanned drones controlled by machine learning technologies in the development of port activities, intra-port logistics and for the search for distressed ships are analyzed.

Analysis on Evolutionary Stability Strategies of Carbon Emission of Logistics Enterprises Based on Carbon Tax

In view of the problem of high carbon emissions of logistics enterprises, the government’s carbon tax policy, consumers’ willingness to purchase low-carbon services, and the carbon emission reduction behavior of logistics enterprises, the evolutionary game model between the government, consumers, and logistics enterprises is established by using the theory and method of evolutionary game, and the evolutionary stabilization strategies of the three parties under different parameters are analyzed. The research results show the following. (1) When setting the carbon tax rate, the government can ignore the impact on consumers and give more consideration to the influencing factors of logistics enterprises, which is conducive to the formulation of carbon emission reduction policies for logistics enterprises. (2) When the government sets a lower carbon tax rate, it can not only promote the carbon emission reduction of logistics enterprises but also be conducive to government supervision. (3) The evolution direction of the government’s final decision will not change due to the size of Y and Z. The government’s final decision is to adopt a regulatory strategy. The study provides theoretical guidance for the government to formulate carbon tax policies, guides consumers to purchase low-carbon services, and promotes carbon emission reduction in logistics enterprises.

Research on LRP Integration of E-Commerce Logistics under the Background of Integration of Collection and Distribution

The distribution network composed of location and route is an important part of e-commerce logistics. With the continuous improvement of e-commerce requirements for logistics level, the practice of planning logistics network only from the perspective of the network location or the vehicle route can no longer meet the actual demand. In addition to the comprehensive consideration of the location-routing problem, the reverse logistics caused by customers’ returning goods should be taken into account. In this paper, the destruction and reorganization strategy of adaptive large-scale neighborhood search algorithm was introduced into the traditional genetic algorithm, so as to conduct research on the logistics location-routing problem under the background of integration of collection and distribution. Finally, the effectiveness of the optimized genetic algorithm was verified by Matlab tools and the existing bench-marking data set of the location-routing problem, which provided reference for the planning and decision-making of logistics enterprises.