Route Optimization Research Articles

Optimizing warehouse management system with blockchain and machine learning predictive data analytics

Blockchain technology is proving to be a disruptive technology in many areas of supply chain, manufacturing, medical, agriculture, and so on. Warehouses are an inevitable part of the supply chain. Issues like space optimization, route optimization, quick item pick-up, demand forecasting, and transaction management are of importance to address in warehouse management systems (WMS). Traditional database systems have limitations of interoperability among different entities involved in warehouses. This paper presents an innovative application of blockchain technology and machine learning (ML) to build a smart warehouse management system in Web3 (SWMW3). We developed a decentralized application (DApp) using Web3.0 principles, integrating ReactJS for the frontend, express for the backend, and blockchain through smart contracts. This integration enhances security and transparency by storing WMS operational data in the blockchain and automating payments and verifications through smart contracts. Additionally, we implemented a ML model for predicting the total time from order receipt to delivery, leveraging historical data to optimize workflow, reduce delays, and improve overall efficiency. This combination of blockchain for secure transactions and ML for predictive analytics generates a robust, efficient, and optimized management system for the warehouse.

Optimizing food transportation processes for grocery stores across the US

In the rapidly evolving landscape of food logistics, optimizing transportation processes for grocery stores across the United States has become paramount to enhancing operational efficiency and ensuring the delivery of high-quality, perishable goods. This article examines the multifaceted challenges inherent in food transportation, including logistical complexities, environmental concerns, and rising operational costs. It highlights the importance of data-driven strategies, such as route optimization and machine learning algorithms, to improve delivery times and reduce waste. Additionally, the integration of advanced technologies, such as Internet of Things (IoT) sensors for temperature monitoring and automation in warehouse management, is explored to enhance the cold chain and ensure food safety. Moreover, the article emphasizes the critical role of strategic partnerships with third-party logistics providers and suppliers in streamlining operations and enhancing overall performance. Through in-depth case studies of successful grocery chains, it illustrates effective practices that have led to significant improvements in food transportation processes. The regulatory landscape surrounding food transportation is also analysed, underscoring compliance with safety standards and sustainability initiatives. Finally, the article looks ahead to future trends, including the potential impact of autonomous vehicles, blockchain technology, and sustainable practices on the food supply chain. By providing a comprehensive overview of best practices and innovative solutions, this article aims to equip grocery store operators and stakeholders with actionable insights to optimize their food transportation processes, ultimately leading to improved customer satisfaction and reduced environmental impact.

Development of Evacuation Route Optimization Model Considering Congestion Degree Using Dijkstra’s Algorithm during Earthquake for Underground Station

Development of Evacuation Route Optimization Model Considering Congestion Degree Using Dijkstra’s Algorithm during Earthquake for Underground Station

Redesigning quick commerce fresh and short food supply chains: circular economy strategies for sustainable last-mile operations

PurposeFresh and short food supply chains in grocery quick commerce (q-grocery FSFSCs), while valued for swift delivery, face challenges in sustainability, resource efficiency and scalability. This study aims to redesign the last mile of q-grocery FSFSCs by identifying and validating a framework of applicable circular economy (CE) practices. Furthermore, the study assesses the impact of these practices across various sustainability dimensions to understand their level of contribution to sustainable livelihoods and supply chain resilience.Design/methodology/approachThe study employs a multi-phase approach. First, an integrative literature review identifies CE practices and strategies. Second, a survey gathers data on the relationship between these practices and strategies and their impact on sustainability. Third, the framework is validated using the partial least squares structural equation modeling (PLS-SEM) method in WarpPLS 8.0. Finally, k-means cluster analysis in Tableau 2019.4 groups CE practices based on their impact across five sustainability dimensions: environmental, economic, social, resource efficiency and scalability.FindingsThe study validates a framework and identifies high-impact CE practices, such as waste reduction, sustainable packaging, sustainable sourcing, route optimization, sustainable processes and customer convenience. Medium-impact practices include energy efficiency, sustainable fleet, resale and redistribution and information flow. Low-impact practices cover composting, reverse logistics and appropriate storage.Research limitations/implicationsThe findings assist q-grocery companies to adopt CE practices and strategies that support sustainable livelihoods and strengthen supply chain resilience while offering policymakers guidance to promote circularity.Originality/valueThis study is unique in the exploration of the intersection of quick commerce, FSFSCs and CE principles, providing actionable insights for sustainability in the q-grocery industry.

Building an AI Portfolio: A Technical Guide for Modern Logistics Specialists

This technical article provides a comprehensive framework for logistics specialists to build an effective AI portfolio in modern supply chain management. As the industry processes massive volumes of data annually, the integration of artificial intelligence has revolutionized logistics operations. The article addresses critical aspects, including demand forecasting, route optimization, inventory management, and security implementations. With AI implementations demonstrating significant performance improvements across cycle time reduction, transportation expense optimization, and inventory accuracy enhancement, the need for structured portfolio development has become increasingly important. The article outlines detailed technical requirements, implementation frameworks, and best practices for developing AI solutions in logistics, supported by real-world performance metrics and industry standards. Special attention is given to security considerations, scalability architecture, and professional development strategies, providing logistics professionals with a holistic approach to AI integration. The comprehensive coverage encompasses technical depth and practical implementation guidelines, making it a valuable resource for professionals seeking to advance their careers in AI-driven logistics operations.

Evaluating Strategies for Cost Reduction in Supply Chain Management Relating to Exports and Imports

This study explores different approaches to supply chain management cost reduction, with an emphasis on imports and exports. As the economy becomes more globalized, controlling supply chain expenses is essential to preserving profitability and competitive advantage. According to the study, procurement, transportation, inventory control, and technology integration are important areas where cost reductions can be made. Highlighting the main cost drivers in international trade, the paper starts with a review of the body of research on supply chain cost management. After that, it looks at particular tactics like utilizing economies of scale, transport route optimization, and shipment consolidation. The potential of technology to simplify processes and cut expenses is examined, including the use of automation, supply chain management software, and block chain technology for transparency. For an organization to keep a competitive edge in a globalized economy, effective supply chain management is essential. Exports and imports are the main focus of the current research paper, which provides a thorough assessment of various cost-cutting strategies in supply chain management. Transportation optimization, inventory management, supplier collaboration, technology integration, process improvement, regulatory compliance, sustainability, risk management, and continuous improvement are all topics covered in this study in an effort to give a comprehensive overview of effective cost-reduction strategies.

Numerical Data Processing by The Implementation of Trees and Graphs

Trees and Graphs play a vital role in transport and logistics. In tree, decision tree is one of the important, not only implemented for data processing, but also considered for Numerical data analysis. The decision tree is a flow chart-like structure, in which each internal node represents a ‘test’ on an attribute, which has a node known as root being at the top, which further divides the given data into branches depending upon the conditions. Every branch consists of a rule, and each leaf node is its outcome. A support tool with a tree-like structure that models probable outcomes, cost of resources, utilities, and possible consequences. Decision trees are also used in operations research along with planning logistics. They can help in determining appropriate plans that will help a company achieve its target. In Graph, Dijkstra's algorithm is an admired algorithm used to find the shortest paths between nodes in a graph, which may represent road networks for example. Dijkstra's algorithm finds the shortest path from a given source node to every other node. It can also be used to find the shortest path to a specific destination node, by concluding the algorithm once the shortest path to the destination node is found. It is also commonly used on graphs where the edge weights are in real numbers. A common application of shortest path algorithms is network routing protocols and also support in route optimization for delivery services, ensuring timely and cost-effective deliveries by finding the best paths for transportation.

Refining Dijkstra's Algorithm: Employing Smart Solutions for Efficient Waste Management and Pollution Mitigation

This study explores the application of Dijkstra's algorithm to enhance waste disposal management systems, focusing on optimizing routing for efficient waste disposal. By modeling waste disposal pathways, the research aims to identify the shortest and most cost-effective routes, thereby reducing both pollution and operational costs. The analysis demonstrates that Dijkstra's algorithm can effectively streamline waste collection processes in Abakaliki, Ebonyi State, Nigeria, enabling the waste management board to minimize distance, time, and expenses associated with waste transportation. The developed model is both scalable and adaptable, promoting the integration of Geographic Information Systems (GIS) and real-time data for dynamic route optimization. The findings suggest significant potential for this approach not only in waste management but also in other domains such as traffic management and urban planning. This paper advocates for further exploration of user-friendly interfaces and alert systems to enhance operational efficiency. This research highlights the overall transformative potential of Dijkstra's algorithm in improving the sustainability and effectiveness of municipal services.

Multiobjective Route Optimization for Multimodal Cold Chain Networks Considering Carbon Emissions and Food Waste

The cold chain logistics industry faces significant challenges in terms of transportation costs and carbon emissions. It is imperative to plan multimodal transportation routes efficiently to address these issues, minimize food waste, and reduce carbon emissions. This paper focuses on four key optimization objectives for multimodal cold chain transport: minimizing total transportation time, costs, carbon emissions, and food waste. To tackle these objectives, we propose a high-dimensional multiobjective route optimization model for multimodal cold chain networks. Our approach involves the development of a multiobjective evolutionary algorithm, utilizing Monte Carlo simulation and a one-by-one selection strategy. We evaluate the proposed algorithm’s performance by analyzing various convergence and distribution indicators. The average values for the minimum total transportation time, transportation cost, carbon emission cost, and cargo loss rate derived from the proposed algorithm ultimately converge to 6721.7, 5184.4, 301.5, and 0.21, respectively, demonstrating the effectiveness of the algorithmic solution. Additionally, we benchmark our algorithm against the existing literature to showcase its efficiency in solving high-dimensional multi-objective route optimization problems. Furthermore, we investigate the impact of different parameters, such as carbon tax rates, temperature, and cargo activation energy, on carbon emissions, and food waste. Moreover, we conduct a real-world case study to apply our approach to solving a practical business problem related to multimodal cold chain transportation. The insights gained from this research offer valuable decision-making support for multimodal carriers in developing low-carbon and environmentally friendly transportation strategies to efficiently transport perishable goods.

Rethinking and redesigning storage, packaging and distribution practices for food supply chain

PurposeThe study aims to add to the existing literature on food supply chains by specifically taking into the redesigning of the alignment of storage, packaging and distribution practices in the modern complex supply chain. The redesign of the food supply chain’s storage, distribution and packaging is a transformative endeavor ultimately aimed at enhancing efficiency, sustainability and reliability.Design/methodology/approachIn order to identify, classify and prioritize the main challenges, this study conducted an extensive analysis of the literature and experts’ opinions in the areas of academia, information technology and the food supply chain (FSC) using combined compromise solution method (CoCoSo) and complex proportional assessment (COPRAS).FindingsThe top three classes of key indicators revealed in this study are dynamic route optimization and on-demand delivery pods (RD4), implementation of active packaging with nanotechnology (RP3) and collaborative last-mile (RD2). The findings reveal that dynamic route optimization and on-demand delivery pods (RD4) and collaborative last-mile (RD2) are maintaining a balance between collaborative delivery networks through route optimization which is a very discussable theme in recent literature.Originality/valueThe research provides fresh insights into how perishable food shelf life parameters and the use of distribution networks within the short supply chain can be taken into consideration when redesigning the storage, packaging and distribution system for food supply chains.

Performance Evaluation of Various Path Planning Methods for Robotics and Computational Geometry

INTRODUCTION: In integrating Spiral Coverage into Cellular Decomposition, which combines structured grid-based techniques with flexible, quick spiral traversal, time efficiency is increased.OBJECTIVES: In the field of robotics and computational geometry, the study proposes a comparative exploration of two prominent path planning methodologies—Boustrophedon Cellular Decomposition and the innovative Spiral Coverage. Boustrophedon coverage has limitations in time efficiency due to its back-and-forth motion pattern, which can lead to lengthier coverage periods, especially in congested areas. Nevertheless, it is useful in some situations. It is critical to address these time-related issues to make Boustrophedon algorithms more useful in practical settings. METHODS: The research centres on achieving comprehensive cell coverage, addressing the complexities arising from confined spaces and intricate geometries. While conventional methods emphasise route optimization between points, the coverage path planning approach seeks optimal paths that maximize coverage and minimize associated costs. This study delves into the theory, practical implementation, and application of Spiral Coverage integrated with established cellular decomposition techniques.RESULTS: Through comparative analysis, it illustrates the advantages of spiral coverage over boustrophedon coverage in diverse robotics and computational applications. The research highlights Spiral Coverage's superiority in terms of path optimization, computational efficiency, and adaptability, proposing a novel perspective into cell decomposition. The methodology integrates the Spiral Coverage concept, transcending traditional techniques reliant on grids or Voronoi diagrams. Rigorous evaluation validates its potential to enhance path planning, exemplifying a substantial advancement in robotics and computational geometry.CONCLUSION: Our findings show that spiral coverage is on an average 45% more efficient than conventional Boustrophedon coverage. This paper set the basis for the future work on how different algorithms can traverse different shapes more efficiently.

GREEN LOGISTICS PRACTICES AND FIRM PERFORMANCE: THE MEDIATING EFFECT OF ENVIRONMENTAL PERFORMANCE AMONG LOGISTICS FIRMS IN KENYA

Objective: The objective of this study was to determine the effect of green logistics practices on performance and mediating effect of environmental performance on this relationship in logistics companies in Kenya. Theoretical Framework: This study is anchored on the resource-based view and the natural resource-based view. Method: The study employs cross-sectional survey research design. A structured questionnaire is used to collect data from a sample of 300 firms which is analyzed using CB-SEM. Results and Discussion: Study establishes a significant positive relationship between green logistics practices and performance which is in line with the argument of the natural resource-based view. Secondly, the mediating effect of environmental performance on the relationship between green logistics practices and firm performance is found to be non-significant. Research Implications: The study recommends that logistics firms should implement environment-friendly practices of green packaging, route optimization, fuel efficiency, carbon emission measurement and reverse logistics. The expenses associated with implementing these practices impede the firm’s ability to improve performance in the context of a developing country. The findings provide a basis for the development regulatory framework in pursuit of environmental sustainability. Originality/Value: Research on ecological sustainability has mainly concentrated on the entire supply chain. This study concentrates on the logistics aspect which is responsible for a quarter of emissions of Green House Gases (CHGs). The research employs a measurement model of green logistics practices construct in the service sector hence moving away from the manufacturing organizations.

MXenes and artificial intelligence: fostering advancements in synthesis techniques and breakthroughs in applications.

This review explores the synergistic relationship between MXenes and artificial intelligence (AI), highlighting recent advancements in predicting and optimizing the properties, synthesis routes, and diverse applications of MXenes and their composites. MXenes possess fascinating characteristics that position them as promising candidates for a variety of technological applications, including energy storage, sensors/detectors, actuators, catalysis, and neuromorphic systems. The integration of AI methodologies provides a robust toolkit to tackle the complexities inherent in MXene research, facilitating property predictions and innovative applications. We discuss the challenges associated with the predictive capabilities for novel properties of MXenes and emphasize the necessity for sophisticated AI models to unravel the intricate relationships between structural features and material behaviors. Moreover, we examine the optimization of synthesis routes for MXenes through AI-driven approaches, underscoring the potential for streamlining and enhancing synthesis processes via data-driven insights. Furthermore, the role of AI is elucidated in enabling targeted applications of MXenes across multiple domains, illustrating the correlations between MXene properties and application performance. The synergistic integration of MXenes and AI marks the dawn of a new era in material design and innovation, with profound implications for advancing diverse technological frontiers.

Vessel Trajectory Prediction Based on AIS Data: Dual-Path Spatial–Temporal Attention Network with Multi-Attribute Information

With the rapid growth of the global shipping industry, the increasing number of vessels has brought significant challenges to navigation safety and management. Vessel trajectory prediction technology plays a crucial role in route optimization and collision avoidance. However, current prediction methods face limitations when dealing with complex vessel interactions and multi-dimensional attribute information. Most models rely solely on global modeling in the temporal dimension, considering spatial interactions only later, failing to capture dynamic changes in trajectory interactions at different time points. Additionally, these methods do not fully utilize the multi-attribute information in AIS data, and the simple concatenation of attributes limits the model’s potential. To address these issues, this paper proposes a dual spacial–temporal attention network with multi-attribute information (DualSTMA). This network models vessel behavior and interactions through two distinct paths, comprehensively considering individual vessel intentions and dynamic interactions. Moreover, we divide vessel attributes into dynamic and static categories, with dynamic attributes fused during feature preprocessing, and with static attributes being controlled through a gating mechanism during spatial interactions to regulate the importance of neighboring vessel features. Benchmark tests on real AIS data show that DualSTMA significantly outperforms existing methods in prediction accuracy. Ablation studies and visual analyses further validate the model’s reliability and advantages.

POTENCIAL TRANSFORMADOR DA INTELIGÊNCIA ARTIFICIAL ​​NA LOGÍSTICA

Artificial intelligence (AI) has become a fundamental tool for many sectors of society, and the transport and logistics sector is no exception. This study aims to review the use of Artificial Intelligence (AI) in the transport and logistics sector, focusing on logistics control towers, routing and telemetry. This is a literature review of articles taken from databases such as Scielo and Google Scholar, published in the last 5 years (2018-2023) that were in the public domain and could be read in full. The results showed that AI has the potential to revolutionize industrial sectors, with applications ranging from route optimization to demand forecasting and efficient warehouse management. However, implementing AI also presents challenges, including the need for large volumes of high-quality data, investment in infrastructure and training.

Effect of Extreme Weather Conditions on Delivery Services of India Post and Private Couriers in Uttar Pradesh

The impact of extreme weather conditions on delivery services, including India Post and private couriers, is increasingly becoming a concern in Uttar Pradesh, India. This paper explores how severe weather events such as heavy rains, floods, heat waves, and storms disrupt the logistical operations of these services. The study highlights that extreme weather can lead to delays, damaged goods, and operational inefficiencies due to road blockages, infrastructure damage, and health risks for delivery personnel as per Pekka L. et-al (2014). India Post, with its extensive rural reach, and private couriers, which focus on urban areas, both face challenges in maintaining timely deliveries. While technological interventions like GPS tracking and route optimization are being used, infrastructure improvements and weather-resilient planning are needed to mitigate these disruptions. The study is an attempt to understand the probable impacts on delivery service and the company of India Post and private courier companies due to various weather conditions such as delay in delivery, concern about safety of shipment, and delivery person, financial loses, risk of accidents, blockage of roads, time sensitivity of parcel etc. These challenges may be faced by both India Post and private courier service companies. Apart from ascertaining the effects and impacts, this study also emphasizes the necessity for adaptive strategies to ensure the continuity of delivery services and to safeguard the safety of parcel and staff involved in delivery amidst climate uncertainties in Uttar Pradesh.

Waste collection route optimisation for the second waste-to-energy plant in Budapest

The city of Budapest produces approximately 680–700 000 tonnes of municipal solid waste every year, of which <2/3 is being recycled or used for energetic purposes, the rest ends up in a landfill. To combat this environmental and logistical problem the installation of a second waste incineration plant has been proposed in the south of the city. The only cost associated with fuel consumption in the case of waste to energy powerplants (WtE plants) is the transport cost, as the city council provides economic support for waste disposal. Since the transportation has a huge influence on the cost of opertation, logistical optimisation of the transport routes promises a direct impact on cost savings. In this study the logistical optimisation of the Southern Budapest area was carried out using image processing and logic based algorithm programming. As a result the optimal transport of 230 000 tonnes of municipal solid waste (MSW) was solved resulting in a 4 835.2 km monthly travel distance reduction.This value can be translated to 7 823 €/month cost, 9 459.6 kg/month CO2 and 45.3 kg/month NOx emissions reduction in the urban areas.

Optimization of emergency material distribution routes in flood disaster with truck‐speedboat‐drone coordination

AbstractTo improve the effectiveness of flood disaster relief operations, by ensuring timely and accurate delivery of urgently needed supplies to affected areas, this study focuses on the problem of emergency material distribution during floods. With the objective of minimizing the overall delivery time of emergency materials, we propose a coordinated optimization model that integrates trucks, speedboats, and drones for effective distribution of emergency supplies in flood‐affected areas. To solve this optimization problem, we introduce an improved adaptive large neighborhood search (IALNS) algorithm, which builds on the traditional ALNS framework through refined tuning of deletion and insertion operators. Comparative analyses are conducted with a genetic algorithm, simulated annealing algorithm, and tabu search algorithm. The results reveal that the average performance gap of IALNS compared to these methods is 91.13%, 152.72%, and 16.92%, respectively. The experimental results demonstrate that the efficiency of the proposed model and algorithm in addressing the emergency supply distribution problem during flood disasters, highlighting the superior performance of IALNS. This research contributes to enhancing disaster response strategies, ultimately leading to improved outcomes for flood‐affected communities.

Combining OpenStreetMap mapping and route optimization algorithms to inform the delivery of community health interventions at the last mile.

Community health programs are gaining relevance within national health systems and becoming inherently more complex. To ensure that community health programs lead to equitable geographic access to care, the WHO recommends adapting the target population and workload of community health workers (CHWs) according to the local geographic context and population size of the communities they serve. Geographic optimization could be particularly beneficial for those activities that require CHWs to visit households door-to-door for last mile delivery of care. The goal of this study was to demonstrate how geographic optimization can be applied to inform community health programs in rural areas of the developing world. We developed a decision-making tool based on OpenStreetMap mapping and route optimization algorithms in order to inform the micro-planning and implementation of two kinds of community health interventions requiring door-to-door delivery: mass distribution campaigns and proactive community case management (proCCM) programs. We applied the Vehicle Routing Problem with Time Windows (VRPTW) algorithm to optimize the on-foot routes that CHWs take to visit households in their catchment, using a geographic dataset obtained from mapping on OpenStreetMap comprising over 100,000 buildings and 20,000 km of footpaths in the rural district of Ifanadiana, Madagascar. We found that personnel-day requirements ranged from less than 15 to over 60 per CHW catchment for mass distribution campaigns, and from less than 5 to over 20 for proCCM programs, assuming 1 visit per month. To illustrate how these VRPTW algorithms can be used by operational teams, we developed an "e-health" platform to visualize resource requirements, CHW optimal schedules and itineraries according to customizable intervention designs and hypotheses. Further development and scale-up of these tools could help optimize community health programs and other last mile delivery activities, in line with WHO recommendations, linking a new era of big data analytics with the most basic forms of frontline care in resource poor areas.

Decision-making optimization for post-disaster restoration of multimodal transport networks in terms of resilience

IntroductionPublic health emergencies can have a ripple effect on the resilience of multimodal transport network, which will lead to problems such as route disruptions or blockages, route selection change and information transmission delay spreading to the whole network, further hindering the transportation planning and operational efficiency of the network. MethodsThis study constructs a multimodal transport route optimization model under uncertainty with the objective of the sum of transportation cost, transshipment cost, penalty cost and carbon emission cost. To enhance the computational efficiency of the model, a novel invasive weed optimization with memory and encoding value clustering capabilities is proposed. In addition, by fusing the Q-learning algorithm in reinforcement learning with the novel invasive weed algorithm, the action-value function table obtained from the training facilitates the selection of optimal routes. Based on empirical data, explore the sensitivity analysis of node disruptions, time windows, and fuzzy demand on route decision-making under public health emergencies. ResultsThe transport network is affected by public health emergencies, which makes the optimal route deviate from the expected goal, resulting in an increase in the total cost. The proportion of total cost is determined by the position of nodes in the network, with critical nodes suffering more losses than ordinary nodes. Reasonable setting of time windows and fuzzy demand intervals is an effective way to improve the resilience and transportation efficiency of multimodal transport network. ConclusionsThis study provides more applicable decision-making references for enterprises to prevent the risk of supply chain disruptions caused by public health emergencies.