Logistics Network Research Articles

Life Cycle Assessment of reusable plastic containers throughout the fruit and vegetables supply chain

In the food packaging industry, waste, resource utilisation, and transport are critical issues that significantly influence the food supply chain footprint. Several studies evaluated the Life Cycle Assessment (LCA) of food packaging systems by comparing alternative materials and sizes, such as plastic, cardboard, and wood. This study illustrates the results of a LCA conducted on a novel reusable plastic container (RPC) for fresh products travelling within a logistic network made of growers, retailers, package manufacturers, and RPC poolers. It is based on an Italian country-wide distribution system of fruit and vegetables. The novel contributions of this research deal with the utilisation of primary data and the development of a multi-scenario analysis considering different packaging solutions, production recipes, and energy mix at the production plant.

Aptenodytes forsteri optimization algorithm for low-carbon logistics network under demand uncertainty.

As China's "double carbon" goal continues to advance, logistics as a key area of carbon emissions and low-carbon logistics center site selection are key links in the process. However, existing studies on logistics center location often ignore the impact of demand uncertainty, which leads to a waste of resources in the planning and construction processes. We take logistics cost and carbon emission as the objectives, and the multi-objective site selection model established based on stochastic programming theory takes demand uncertainty as a stochastic constraint. We transform the stochastic constraint model into a 0-1 mixed integer multi-objective planning model by utilizing the idea of equivalence transformation. The Aptenodytes Forsteri Optimization (AFO) algorithm is combined with the Ideal Point Method to solve the model, and the algorithm is compared with the Particle Swarm Optimization (PSO), Differential Evolutionary (DE), Tabu Search (TS), Sparrow Search (SS) algorithms, and the exact solver Linear Interactive and General Optimizer (LINGO). The examples verify the validity of the models and algorithms, with an average reduction of 6.2% and 3.6% in logistics costs and carbon emissions in the case of demand determination, and at the confidence level of 0.9 under demand uncertainty, both logistics costs and carbon emissions are decreased to varying degrees. This study provides a new research idea for the low-carbon logistics location problem under demand uncertainty, which helps to promote the transformation of the logistics industry to low-carbon and high-efficiency.

An exploratory study for product-as-a-service (PaaS) offers development for electrical and electronic equipment

Manufacturing enterprises continue to grapple with transforming from their existing business model, which revolves around designing and selling tangible products, to business models centered on providing a blend of products and services. Product-as-a-Service (PaaS) emerges as a business strategy capable of catalyzing the shift toward higher levels of circularity. However, effecting changes in operational methodologies and value delivery mechanisms is imperative, consequently prompting modifications in the core business strategies of these companies. The objective of this study is to discern and categorize prevailing PaaS business models for electrical and electronic equipment (EEE). The Business Model Canvas (BMC) is introduced as a foundational tool to facilitate this transition, emphasizing the central role of the value proposition within the business model. Additionally, the study underscores the importance of robust take-back systems and repairability in PaaS implementation, particularly within the EEE sector. It highlights the need for novel partnerships, redesigned reverse logistics networks, and efficient product assessment methods to facilitate environmentally responsible decisions. Furthermore, the research evaluates the financial implications of PaaS adoption through Life Cycle Costing (LCC), emphasizing its value in predicting and addressing temporary financial complications, thereby ensuring a smoother transition to service-oriented business models.

Research on Logistics Network Based on ARIMA Prediction Model and Multi-Objective Optimization

Research on Logistics Network Based on ARIMA Prediction Model and Multi-Objective Optimization

A Parallel Logistic Network Simulation Method and System to Improve Logistics Efficiency

A Parallel Logistic Network Simulation Method and System to Improve Logistics Efficiency

Development of the Northeast China Logistics Network: A Bibliometric Analysis from the Perspectives of South Korea and China

Purpose - This study aims to disclose latest progress in the field of logistics networks in Northeast China through bibliometric analysis to uncover research topics from 2002 to 2022, and explore future research trends through bibliometric research.
 Design/Methodology/Approach - In this study, 230 CSSCI and 95 KCI articles were selected through a screening process using the PRISMA guidelines flowchart, eliminating research-irrelevant articles and duplicate articles. Citespace and Vosviewer visualization software were used to conduct bibliometric analysis on articles with search terms of “Northeast China” and “Logistics” from 2002 to 2022. It mainly discussed the following issues: (1) the salient conceptual dimensions using keywords analysis; (2) research hot spots using cluster analysis; and (3) likely future research trends.
 Findings - Based on the KCI and CSSCI databases (articles published from 2002 to 2022), this paper found that logistics development in Northeast China is expected to be accompanied by a number of experiments. Therefore, we firstly need to improve the level and efficiency of logistics activities. Border cities in Northeast China must ensure a smooth process of distribution. Further, it is also necessary to form a multinational logistics cooperation system by activating exchanges with neighboring countries.
 Research Implications - This study will help establish the status of individual research study in the future, promote the development of the logistics industry in Northeast China, and boost the construction of international cities. It will explore the direction of future research in relevant research fields of South Korea and China.

이산화탄소 배출 저감을 위한 국제개발협력과 물류네트워크의 영향력 분석

Purpose: This study analyzes the causal relationships between CO2 emissions and ODA, logistics, renewable energy, economic growth, and industrialization.
 Research design, data, and methodology: From 2006 to 2019, panel data related to CO2 emissions, ODA, logistics networks, renewable energy, economic growth, and industrialization were collected from a total of 84 countries. To analyze this model, panel unit root tests and panel co-integration tests were performed to confirm the stability and long-term causal relationships in the data. Subsequently, panel FMOLS and panel DOLS were used to identify variables affecting carbon dioxide, and pairwise panel Granger causality analysis was used to analyze causality between these variables.
 Results: According to panel FMOLS and panel DOLS analysis, ODA and renewable energy reduce CO2 emissions, but economic growth increases CO2 emissions.
 Conclusions: As a result of panel FMOLS and panel DOLS analysis, economic growth was identified as a factor in increasing CO2 emissions, and international development cooperation and renewable energy were identified as factors in reducing CO2 emissions.

코로나 팬데믹에 따른 항만물류 네트워크 변화 분석 연구

코로나 팬데믹에 따른 항만물류 네트워크 변화 분석 연구

Sistemas de Prescripción y Medicación de Control Sanitario para Ciudadanos Vulnerables en el Perú

This article discusses the challenges and solutions to providing healthcare to vulnerable citizens in remote areas, focusing on Peru as a case study. It highlights the need to improve health monitoring, prescribing, and medication systems to improve healthcare delivery. The paper identifies existing barriers and explores innovative and accessible strategies to overcome them. The proposed solution through a Medication and Prescription Health Monitoring System leverages digital, web, IoT, and mobile technology for remote medication administration and health monitoring. This system allows physicians to conduct virtual consultations and diagnose and prescribe medications delivered to remote communities through a logistics network. The proposed approach concludes that it effectively improves access to health care and quality of life for remote populations in Peru and alleviates the burden on overburdened health professionals.

Optimising hierarchical emergency logistics network under ambiguous demand and transportation cost

This article studies the response problem of an emergency logistics network with a decision hierarchy relationship under uncertainty. To account for the partial distribution information about uncertain demand and transportation costs, we construct a moment-based ambiguity set based on limited historical data, where the pivot variable method is employed to determine the confidence interval of the mean value. Based on the constructed ambiguity set, we develop a novel distributionally robust bi-level post-disaster emergency logistics location-routeing model. By exploiting the structural characteristic, chance-constrained models under box-ellipsoid and budget perturbation sets are reformulated as bi-level mixed-integer conic programming models. To accelerate the solution procedure, the bi-level models are further converted into single-level ones via Karush-Kuhn-Tucker condition, which can be directly solved to optimality using CPLEX software. Supply risk value for each supplier is obtained by applying analytic hierarchy process. We conduct extensive experiments using the Iranian flood as the case study to address the computational performance of our proposed optimisation method.

Vehicle Exhausts Emission Pattern Decisions for Logistic Services and Packing Industries with Orthogonal Array-Based Rough Set Theory

Precise monitoring of vehicle emissions in green logistics, focusing on the contributions of vehicles from packing industries, is crucial for many issues. It helps to understand the total emissions and gain insights into the mechanism of vehicle-associated environmental concerns. Notwithstanding, a key issue when monitoring vehicle emissions is the effective discrimination problem for different patterns generated from the parameters. Data from the packing industry are available from distribution networks but its pattern cannot be discriminated. Given this background, this article presents a new method of the orthogonal array-based rough set to discern patterns of the parametric behaviors to monitor emissions from vehicle exhausts in the packing industry. The proposed method is based on an Indian logistics network and delivery system data, which was obtained from previous work in the literature. By setting controls on the parameters of the packing industry which includes revenue obtained, packing units sold, growth rate, carbon-dioxide equivalent, materials utilized, and quantity consumed, the method was able to discern the patterns of the parametric behavior. The orthogonal arrays, which are developed, form factors (parameters) and levels to ascertain a balanced and uniform analysis of the various groups of options. Indiscernibility and approximation concepts of fuzzy sets are then applied to arrive at the outcome. Unlike previous studies, this study eliminates the need for tracking data, assumptions, and external information to establish the set membership. However, it utilizes the available information within the data. The rough set analysis indicates that there are no discernable patterns or rules that distinguish between "Yes" and "No" decisions. The method of rough set illustrated in this work shows the feasibility of the approach in the Indian packing industry. The method is useful for the logistics manager and government agencies responsible for the control of vehicle-generated greenhouse emissions.

State of the art in determining the optimal reverse logistics network for your product

The primary focus of this study is to explore and answer the following research question: “In what ways can organizations enhance the efficiency and responsiveness of their reverse logistics network design?” The study presents a conceptual framework that the authors have built, incorporating several essential variables to guide the process of network design. The conceptual framework for reverse logistics networks has been developed by conducting an analysis of the fundamental factors that influence network design decisions. This paradigm facilitates the decision-making process for firms when considering the centralization or decentralization of their reverse logistics operations and the outsourcing or insourcing of specific operational aspects. Moreover, the authors have discovered and highlighted relevant literature that exhibits strong alignment with our proposed framework, hence offering useful insights for decision-making in the domain of reverse logistics network design. Within our framework, two crucial dimensions are considered at each stage: the choice between internal processing vs . outsourcing and the decision to centralize or decentralize operations. With consideration of the factors noted above, this paper proposes a mixed integer linear programming model for the design of a reverse logistics network. This model aims to minimize the total cost at each stage of reverse logistics operations.

A new measure of node centrality on schedule-based space-time networks for the designation of spread potential

Node centrality is one of the most frequently revisited network theoretical concepts, which got many calculation method alternatives, each of them being conceived on different empirical or theoretical network abstractions. The vast majority of centrality measures produced up to date were conceived on static network abstractions (the so-called “snapshot” networks), which arguably are less realistic than dynamic (temporal) network abstractions. The new, temporal node centrality measure that we offer with this article, is based on an uncommon abstraction, of a space-time network derived from service schedules (timetables). The proposed measure was designed to rank nodes of a space-time network based on their spread or transmission potential, and was subsequently implemented on the network of sea ferry transportation derived from the aggregated schedules for sea ferry liner shipping services in Europe, as they occurred in the month of August, 2015. The main feature of our measure, named “the Spread Potential”, is the evaluation of the potential of a node in the network for transmitting disease, information (e.g. rumours or false news), as well as other phenomena, whichever support a space-time network abstraction from regular and scheduled services with some known carrying capacities. Such abstractions are, for instance, of the transportation networks (e.g. of airline or maritime shipping or the wider logistics (delivery) networks), networks of medical (hospital) services, educational (teaching) services, and virtually, of any other scheduled networked phenomenon. The article also offers the perspectives of the measure’s applicability on the non-scheduled space-time network abstractions.

Exploring the cross-border e-commerce of agricultural exports and its logistics supply chain innovation and development strategy under digital technology

Abstract This paper constructs a cross-border e-commerce supply chain information synergy platform for agricultural products, which screens and processes data of relevant business information and finally outputs information that helps cross-border e-commerce business of agricultural products. Through in-depth analysis of the structural characteristics of cross-border e-commerce maritime and air transport international logistics network under the mode of overseas warehouse, we first describe this type of problem and the basic assumptions of the model, set the model parameters, take a set of optimal solutions of Pareto, and model the cross-border e-commerce logistics network under the mode of overseas warehouse under the mode of maritime and air transport, respectively. According to different objectives, the multi-objective model of the international logistics network of cross-border e-commerce sea transportation mode and the multi-objective model of the international logistics network of cross-border e-commerce air transportation mode are established, respectively. After the optimization of the logistics supply chain, the turnover days of accounts receivable are 20 days faster than in the past, which is still 5 days less than the specified target value. The inventory stock days have been decreased from 150 days to 110 days. In other aspects, although there is still a small gap between the actual situation and the target value, most of the score values are close to the established value, and there is a significant improvement compared with the original operational value after improvement through the method of this paper cross-border e-commerce enterprises’ procurement cycle time and order fulfillment cycle time are reduced by 10 days compared with the past and reach the target values.

Neural Network Estimators for Optimal Tour Lengths of Traveling Salesperson Problem Instances with Arbitrary Node Distributions

It is essential to solve complex routing problems to achieve operational efficiency in logistics. However, because of their complexity, these problems are often tackled sequentially using cluster-first, route-second frameworks. Unfortunately, such two-phase frameworks can suffer from suboptimality due to the initial phase. To address this issue, we propose leveraging information about the optimal tour lengths of potential clusters as a preliminary step, transforming the two-phase approach into a less myopic solution framework. We introduce quick and highly accurate Traveling Salesperson Problem (TSP) tour length estimators based on neural networks (NNs) to facilitate this. Our approach combines the power of NNs and theoretical knowledge in the routing domain, utilizing a novel feature set that includes node-level, instance-level, and solution-level features. This hybridization of data and domain knowledge allows us to achieve predictions with an average deviation of less than 0.7% from optimality. Unlike previous studies, we design and employ new instances replicating real-life logistics networks and morphologies. These instances possess characteristics that introduce significant computational costs, making them more challenging. To address these challenges, we develop a novel and efficient method for obtaining lower bounds and partial solutions to the TSP, which are subsequently utilized as solution-level predictors. Our computational study demonstrates a prediction error up to six times lower than the best machine learning (ML) methods on their training instances and up to 100 times lower prediction error on out-of-distribution test instances. Furthermore, we integrate our proposed ML models with metaheuristics to create an enumeration-like solution framework, enabling the improved solution of massive-scale routing problems. In terms of solution time and quality, our approach significantly outperforms the state-of-the-art solver, demonstrating the potential of our features, models, and the proposed method. History: This paper has been accepted for the Transportation Science Special Issue on Machine Learning Methods and Applications in Large-Scale Route Planning Problems. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0015 .

An Integrated Methodology for Enhancing Reverse Logistics Flows and Networks in Industry 5.0

Background: This paper explores the potential of Industry 5.0 in driving societal transition to a circular economy. We focus on the strategic role of reverse logistics in this context, underlining its significance in optimizing resource use, reducing waste, and enhancing sustainable production and consumption patterns. Adopting sustainable industrial practices is critical to addressing global environmental challenges. Industry 5.0 offers opportunities for achieving these goals, particularly through the enhancement of reverse logistics processes. Methods: We propose an integrated methodology that combines binary logistic regression and decision trees to predict and optimize reverse logistics flows and networks within the Industry 5.0 framework. Results: The methodology demonstrates effective quantitative modeling of influential predictors in reverse logistics and provides a structured framework for understanding their interrelations. It yields actionable insights that enhance decision-making processes in supply chain management. Conclusions: The methodology supports the integration of advanced technologies and human-centered approaches into industrial reverse logistics, thereby improving resource sustainability, systemic innovation, and contributing to the broader goals of a circular economy. Future research should explore the scalability of this methodology across different industrial sectors and its integration with other Industry 5.0 technologies. Continuous refinement and adaptation of the methodology will be necessary to keep pace with the evolving landscape of industrial sustainability.

Measuring productivity in networks: A game-theoretic approach

Measuring individual productivity (or equivalently distributing the overall productivity) in a network structure of workers displaying peer effects has been a subject of ongoing interest in many areas ranging from academia to industry. In this paper, we propose a novel approach based on cooperative game theory that takes into account the peer effects of worker productivity represented by a complete bipartite network of interactions. More specifically, we construct a series of cooperative games where the characteristic function of each coalition of workers is equal to the sum of each worker intrinsic productivity as well as the productivity of other workers within a distance discounted by an attenuation factor. We show that these (truncated) games are balanced and converge to a balanced game when the distance of influence grows large. We then provide an explicit formula for the Shapley value and propose an alternative coalitionally stable distribution of productivity which is computationally much more tractable than the Shapley value. Lastly, we characterize this alternative distribution based on three sensible properties of a logistic network. This analysis enhances our understanding of game-theoretic analysis within logistics networks, offering valuable insights into the peer effects’ impact when assessing the overall productivity and its distribution among workers.

Мathematical model of the problem of optimizing logistics networks under conditions of interval determination of input data

Problem. In the conditions of rapid changes in demand and supply in transportation markets, there is a need for corresponding changes in logistics technologies and networks, which are carried out through their reengineering. Existing mathematical models for the optimization of logistics networks are designed for conditions with point-wise, clearly defined input data, which does not allow obtaining solutions resistant to changes in the external environment. This determined the relevance of the scientific and applied task of developing mathematical models of multi-criteria optimization problems of logistics networks for the conditions of interval submission of input data. Goal. The goal is to increase the efficiency of the logistics network optimization technology at the stage of re-engineering by developing a mathematical model of the multi-criteria optimization problem for the conditions of interval submission of input data. Methodology. According to the results of the decomposition of the problem of system optimization of logistics networks as the most complex defined task of their structural and topological optimization. When developing the model, the methodology of system design of territorially distributed objects, methods of multi-criteria optimization, theories of utility and decision-making were used. The apparatus of interval mathematics was used to compare solution options with vaguely specified input data. The results. The result are the formulated and completed formalization of the task of optimizing logistics networks as territorially distributed objects. The relationship for the objective function and constraints of the optimization task of topological structures of centralized three-level logistics networks is proposed. Originality. A mathematical model of the task of reengineering topological structures of centralized three-level logistics networks according to cost and efficiency indicators for interval defined input data and values of local criteria using comparison indices based on the Hukuhara difference has been developed. Practical value. The obtained results make it possible to increase the reliability of optimization results in the processes of design, development planning and reengineering of logistics networks, to obtain variants of the construction of their topological structures that are resistant to changes in external conditions.

A simulation-based genetic algorithm approach for the simultaneous consideration of reverse logistics network design and disassembly line balancing with sequencing

Reverse logistics (RL) network design and disassembly line balancing (DLB) decisions are generally considered separately. In addition, the effect of disassembly sequencing on DLB is ignored. However, companies can take better-informed decisions by simultaneously considering RL, DLB and disassembly sequencing issues. This integrated approach results in more efficient processes which reduce cost and idle time, while maximizing throughput and customer satisfaction. In this study, we propose a simulation-based genetic algorithm approach for the joint optimization of RL network design and DLB with sequencing decisions. The use of simulation modeling allows for the consideration of stochastic aspects associated with RL and DLB such as transportation and disassembly times. A numerical example was provided to present the applicability of the proposed approach. Moreover, a sensitivity analysis was carried out to study the impact of various parameters. The results indicate the superior performance of the proposed approach with respect to total cost.

Feed-forward networks using logistic regression and support vector machine for whole-slide breast cancer histopathology image classification

The performance of an image classification depends on the efficiency of the feature learning process. This process is a challenging task that traditionally requires prior knowledge from domain experts. Recently, representation learning was introduced to extract features directly from the raw images without any prior knowledge. Deep learning using a Convolutional Neural Network (CNN) has gained massive attention for performing image classification, as it achieves remarkable accuracy that sometimes exceeds human performance. But this type of network learns features by using a back-propagation approach. This approach requires a huge amount of training data and suffers from the vanishing gradient problem that deteriorates the feature learning. The forward-propagation approach uses predefined filters or filters learned outside the model and applied in a feed-forward manner. This approach is proven to achieve good results with small size labeled datasets. In this work, we investigate the suitability of using two feed-forward methods such as Convolutional Logistic Regression Network (CLR), and Convolutional Support Vector Machine Network for Histopathology Images (CSVM-H). The experiments we have conducted on two small breast cancer datasets (Sultan Qaboos University Hospital (SQUH) and BreaKHis dataset) demonstrate the advantage of using feed-forward approaches over the traditional back-propagation ones. On those datasets, the proposed models CLR and CSVM-H were faster to train and achieved better classification performance than the traditional back-propagation methods (VggNet-16 and ResNet-50) on the SQUH dataset. Importantly, our proposed approach CLR and CSVM-H efficiently learn representations from small amounts of breast cancer whole-slide images and achieve an AUC of 0.83 and 0.84, respectively, on the SQUH dataset. Moreover, the proposed models reduce memory footprint in the classification of Whole-Slide histopathology images since their training time is significantly reduced compared to the traditional CNN on the SQUH and BreaKHis datasets.