Delivery Route Optimization Research Articles

OPTIMIZING CARTON PRODUCT DELIVERY BY SOLVING TRAVELLING SALESMAN PROBLEM AT PACKAGING COMPANIES

Optimization of delivery routes is one form of increasing business productivity to achieve the company's main goal of distributing each product to customers. The Traveling Salesman Problem (TSP) is a combinatorial optimization problem where a salesman goes on a distribution journey that starts from the depot, then visits all customers exactly once, and ends with returning to the depot. This study aims to optimize the distribution route of carton products for packaging companies using the TSP model. The research methodology includes observation, interviews, and document studies to understand the distribution process of carton products at packaging companies. To complete the TSP model, Branch and Bound (BB) and Nearest Neighbor (NN) methods are applied to find the best solution for determining the distribution route of carton products. The way the BB method works is by utilizing branch cuts and boundaries to reduce search space and speed up the resolution process. In the NN method, the nearest point is chosen to get the shortest route distance. Research findings show that the use of the BB method results in a mileage difference from the initial route of 125 km (53% more efficient) and a fuel cost difference of 121,231 IDR (45% cheaper). Meanwhile, the NN method results in a mileage difference from the initial route of 115 km (48.94%) and a fuel cost difference of 109,901 IDR (41.27%). So, the method that produces the best solution is the BB method. The limitations of this study lie in the scale of the model used and the assumptions underlying the analysis. Future research can broaden the scope of the model and consider other factors that may affect the distribution of carton products. The results of this study contribute to improving the efficiency of carton product distribution.

Optimization of Drone Delivery Routes for E-commerce in Urban Areas

Optimization of Drone Delivery Routes for E-commerce in Urban Areas

THE SAMPLE SCHEDULING APPLICATION OF THE ANT COLONY OPTIMIZATION ALGORITHM IN VEHICLE ROUTING PROBLEM TO FIND THE SHORTEST ROUTE

In this collaborative research initiative with the Research Center and Industry Standardization in Surabaya, the primary objective is to obtain Indonesian National Standard certificates through the collection of samples from companies in East Java. The focal challenge revolves around the optimization of delivery routes for vehicles with specific capacities, constituting the Vehicle Routing Problem (VRP), and is addressed through the application of the Ant Colony Optimization (ACO) algorithm. The study confronts constraints, including the limitation of time and resources during the sample collection process, and grapples with challenges associated with travel distances that impact overall efficiency. The utilization of Sample Scheduling software (Si Dull) developed in Visual Basic introduces configurational constraints for route planning with the aim of minimizing distances. The overarching aim is to implement the ACO algorithm, culminating in the development of the Si Dull application, to elevate the efficacy of the sample collection process for industrial certification in Surabaya, thereby contributing to enhanced efficiency in travel distances for sample collection endeavors

Medical Supplies Delivery Route Optimization under Public Health Emergencies Incorporating Metro-based Logistics System

To effectively mitigate the spread of infections during public health crises, precise and timely distribution of medical supplies is crucial. This paper proposes the integration of the metro-based underground logistics system (M-ULS) into the delivery process to address the vehicle routing problem (VRP). Considering various factors related to the metro-based VRP in public health emergencies, we formulate a mixed-integer nonlinear function model that aims to minimize total delivery costs while maximizing the load factor of medical vans and the average demand index concerning demand urgency and satisfaction rate of medical centers. To achieve this, we develop an improved adaptive genetic algorithm (IAGA) that incorporates adaptive crossover, adaptive mutation, and an elitist strategy. A case study is conducted to verify and analyze the performance of the optimized model and solving algorithm. Extensive numerical experiments are carried out to assess the economical and efficient advantages of M-ULS and the computational efficiency and solution quality of IAGA. Sensitivity analysis is conducted to evaluate the impact of medical van capacity on the routes. Lastly, we consider inter-medical-center transfer (IMCT) to further enhance emergency health response.

Can language models be used for real-world urban-delivery route optimization?

Language models have contributed to breakthroughs in interdisciplinary research, such as protein design and molecular dynamics understanding. In this study, we reveal that beyond language, representations of other entities, such as human behaviors, that are mappable to learnable sequences can be learned by language models. One compelling example is the real-world delivery route optimization problem. We here propose a novel approach based on the language model to optimize delivery routes on the basis of drivers' historical experiences. Although a broad range of optimization-based approaches have been designed to optimize delivery routes, they do not capture the implicit knowledge of complex delivery operating environments. The model we propose integrates this knowledge in the route optimization process by learning from driving behaviors in experienced drivers. A real-world delivery route that preserves drivers' implicit behavioral patterns is first analogized to a sentence in natural language. Through unsupervised learning, we then learn the vector representations of words and infer the drivers' delivery chains on the basis of the tailored chain-reaction-based algorithm. We also provide insights into the fusion of language models and operations research methods. In our approach, language models are applied to learn drivers' delivery behaviors and infer new deliveries at the delivery zone level, while the classic traveling salesman problem (TSP) model is embedded into the hybrid framework for intra-zone optimization. Numerical experiments performed on real-world data from Amazon's delivery service demonstrate that the proposed approach outperforms pure optimization, supporting the effectiveness, efficiency, and extensibility of our model. As a versatile approach, the proposed framework can easily be extended to various disciplines in which the data follow certain grammar rules. We anticipate that our work will serve as a stepping stone toward the understanding and application of language models in tackling interdisciplinary research problems.

A double-adaptive general variable neighborhood search for an unmanned electric vehicle routing and scheduling problem in green manufacturing systems

In recent years, more and more manufacturing factories have employed unmanned electric vehicles (UEVs) to perform the materials delivery, and efficient routing and scheduling approaches are required to improve the delivery efficiency. However, most of existing papers studied the routing problem of fuel vehicles or the drones. This paper investigates the optimization of UEVs delivery routes and charging strategies. Due to the limited payload capacity and battery capacity, the UEV must return to the depot multiple times to load the materials and go to the recharging station during the route to charge, and thus the problem is modeled as a multi-trip UEV routing and scheduling problem, which assigns the recharging station to the UEV’s route and optimizes a set of UEV’s trips. The investigated problem is firstly formulated as a mixed integer programming model. Then, considering the routing and scheduling challenges, we develop an efficient double-adaptive variable neighborhood search (DA-GVNS), which incorporates the general variable neighborhood search with a new adaptive mechanism, for this problem. The experimental results on the test and benchmark instances highlight that the DA-GVNS outperforms other existing algorithms. Furthermore, sensitivity analyses are conducted to study the impact of model’s characteristics on the optimality and complexity of solutions. Finally, a case study is proposed to present how the developed approach works for a manufacturing factory. This paper can offer decision-makers a valuable approach to construct a high-quality routing and scheduling scheme for the UEV delivery while providing potential managerial insights for implementing efficient green manufacturing systems.

Logistics management in e-commerce: challenges and opportunities

Logistics management is a crucial component of success for companies operating in the retail industry. As the popularity of online shopping continues to grow, businesses must implement efficient logistics practices to satisfy customers, cut costs, and maintain a competitive edge. This article offers a comprehensive overview of logistics management in e-commerce, with a focus on identifying challenges, opportunities, strategies, and solutions that can help companies improve their logistics processes and succeed in a highly competitive market. It was employed the methodology of a literature review, which involves a structured and organized analysis of published scientific articles related to the topic. The study follows a five-step process for conducting a comprehensive and critical review of knowledge related to logistics management. The literature review conducted analyzed a selection of 70 studies and highlighted numerous noteworthy challenges in logistics management faced by e-commerce companies, such as managing order volume and storage, accurately tracking inventory, and ensuring timely shipping and delivery. To overcome these challenges, companies can invest in infrastructure, inventory management strategies, transportation management systems, optimization of delivery routes, and emerging technologies such as drones and robots for deliveries. The adoption of these technologies can significantly enhance logistics process efficiency, minimize errors, and boost customer satisfaction. By implementing efficient logistics practices, companies can offer their customers a smooth and hassle-free shopping experience, which can translate into long-term customer loyalty and higher sales.

Development of Software and Tools to Optimize a Manufacturing Enterprise Logistics in Food Industry

The article discusses the logistics automation for a manufacturing enterprise engaged in food production and delivery. In particular, optimization of product delivery routes to customers. Light vehicles are used to deliver products around the city. Vehicle route optimization that delivers loads to the shipment terminal within the city. Routes are specified with maldistribution of shipment terminals on the map and limited delivery time. The enterprise delivers finished products to stores daily, reducing the cost and time of delivery will increase not only the profit of the enterprise, but also customer satisfaction, because they will be able to receive goods earlier, the goods will be fresher, which gives the enterprise a competitive advantage. The tasks of optimizing delivery routes are relevant for all enterprises in the sphere of production or trade that have in their structure fleet vehicles for the delivery of products, or enterprises for which delivery is the main activity. The complexity of finding the optimal route when drawing up a transportation plan increases significantly with the increase in the number of delivery terminals and their distance from each other within the complex road network of the city. An increase in the number of buyers increases the number of possible routes, finding the optimal route without using optimization methods becomes a difficult task. The article proposes an algorithm for minimizing the product delivery plan by reducing the length of the route. A comparison of changes in the controlled variable U with a fixed production plan Q, as well as with a change in the production plan Q and delivery plan D(R) to analyze the profitability of product delivery is made.

Application of Swarm Intelligence Optimization Algorithm in Logistics Delivery Path Optimization under the Background of Big Data

Traditional logistics delivery route optimization algorithm has some problems such as long time to find the optimal route. Based on this, this paper discusses swarm intelligence optimization algorithm and logistics delivery route optimization. To solve the logistics vehicle routing problem, considering that the basic ACO (ant colony optimization) has the disadvantages of slow convergence speed and easy to fall into local optimum, this paper proposes a new hybrid population optimization algorithm and applies it to VRPTW (vehicle routing problem with time windows). In addition, the concept of crowding degree in AFSA (artificial fish swarm algorithm) is introduced into ACO. In the early stage of the optimization process, a strong crowding degree limit is set to ensure that most ants are not affected by pheromone concentration to conduct random optimization. The simulation results show that the AC (accuracy rate) of this algorithm is 95.08%, which is higher than the traditional PSO (particle swarm optimization) algorithm and general heuristic algorithm. The hybrid algorithm can effectively improve the optimization efficiency of VRPTW, lay a foundation for solving large-scale VRPTW, and provide new research ideas and methods. At the same time, the results fully show that the algorithm in this paper has certain advantages in performance, and it can be applied to logistics delivery route optimization.

DELIVERY ROUTES OPTIMIZATION USING MACHINE LEARNING ALGORITHMS

Delivery route optimization is a crucial concern in the logistics industry, affecting delivery times, costs, and customer satisfaction. The conventional methods for optimizing delivery routes are time-consuming and require substantial manual efforts. To address these limitations, they have increasingly used machine learning algorithms for more efficient and effective optimization. This paper reviews modern techniques for delivery route optimization using machine learning algorithms, including the key challenges faced by delivery companies. Metaheuristic methods, reinforcement learning, and machine learning are discussed, along with their advantages and limitations. In developing a delivery route optimization system, factors such as the number of vehicles, their capacity, delivery time windows, road networks, and customer demand are considered. Different optimization objectives, such as minimizing delivery time, reducing transportation costs, and maximizing customer satisfaction, are presented. Finally, the paper highlights future research directions, including multi-agent systems, swarm intelligence, and hybrid algorithms. This paper provides a comprehensive review of delivery route optimization using machine learning algorithms and can be useful for practitioners and researchers in the logistics industry.

ПОСТАНОВКА ЗАДАЧИ ОПТИМИЗАЦИИ МАРШРУТОВ ГРУЗОВЫХ МУЛЬТИМОДАЛЬНЫХ ПЕРЕВОЗОК

The article deals with the urgent task of increasing the efficiency of multimodal transportation of goods based on the optimization of delivery routes. The task is formulated in terms of the subject area of interest to the transportation customer. The mathematical statement of the problem is presented in the form of a graph model that takes into account dynamic changes in the requirements of both the consignor and the consignee. According to the authors, this approach to solving the logistics problem allows the multimodal operator to respond to changes in a timely manner and gives some advantages when choosing an effective route in the face of sanctions pres-sure and border closures.

Economic and Managerial Aspects of Energy Efficiency of Economic Entities in Territorial Communities

The theoretical and methodological principles of the formation of energy efficiency management have been clarified. The concepts of “energy efficiency of technical systems”, “energy efficiency of biological systems”, “energy efficiency of socio-economic systems”, “energy efficiency of a business entity”, and “energy efficiency of a territorial community” have been distinguished. It has been proven that increasing the energy efficiency of economic entities in the context of territorial community development should not allow: deterioration of working conditions of economic entity’s personnel and life safety of residents of communities (imperative of the human factor); complication of the ecological situation on the community territory (ecological imperative); reduction of economic efficiency of a business entity and well-being of the community population (priority of economic assessment). The types of energy effect of the territorial community have been established. The essence of the concepts of “energy saving efficiency of territorial community” and “energy saving effect of territorial community” have been defined. The types of energy saving effect of the territorial community have been reasoned. The fuel flow matrix has been formed. The functional components of the energy efficiency management process have been defined. The directions for optimization of energy flows in territorial communities have been substantiated as follows: the choice of energy supply enterprises; optimization of energy resource delivery routes; substantiation of energy-optimal sizes and structure of the transport fleet of territorial communities and oil industry (if available); optimization of storage stocks of energy resources and their management.

МОБИЛЬНОЕ ПРИЛОЖЕНИЕ ДЛЯ ТРАНСПОРТИРОВКИ СЕЛЬСКОХОЗЯЙСТВЕННОЙ ПРОДУКЦИИ

This article explores the possibilities and advantages of using mobile applications for the transportation of agricultural products. The authors analyze the benefits of mobile applications intended for the optimization of delivery routes, cargo tracking, automation of warehouse management and integration with other systems. In addition, the article discusses the benefits of using mobile applications to improve communication and cooperation between farmers, suppliers and consumers of agricultural products. This allows one to reduce costs and increase the efficiency of the entire agricultural value chain. Based on this study and practical examples presented in the article, it was concluded that mobile applications may significantly improve the transportation of agricultural products, increase operational efficiency and improve the quality of service. For each industry, it is vital to increase profits and reduce costs. In this regard, this article is devoted to the study of the possibilities of improving the efficiency of road transportation of agricultural goods. In order to create a high-performance advanced transport logistics system, Kazakhstan needs to ensure transparency of transport tariffs and the mechanism of their control and regulation concerning set of certain issues. It's important to focus on refining the performance of current networks while also improving the management of their usage intensity. Moving towards eco-friendly fuel options, cutting maintenance expenses through the upsizing of transportation companies, and embracing technological advancements and innovations are key elements in this matter.

Optimization of Food Delivery Routes and Distribution Costs Using the Nearest Neighbor in PT. XYZ

Optimization of Food Delivery Routes and Distribution Costs Using the Nearest Neighbor in PT. XYZ

Study on distribution routing optimization for part-time riders under crowdsourcing Ordering takeout

In recent years, "Ordering takeout" has become an important part of today's fast-paced life, so the crowdsourcing takeout delivery platform has been born. In this paper, we considers the characteristics and distribution process of part-time riders, takes path minimization as the objective function and distribution cost as the constraint condition, establishes a delivery crowdsourcing part-time rider distribution model, To win over Excel "planning solution" function. Combined with VLOOKUP and INDEX functions, the method of adding All Different constraint conditions to the mathematical model was optimized for the distribution path of external sales. This method reduces the high cost of purchasing professional programming software, making the optimization of delivery route become a routine office affair, and improving the overall optimization efficiency of the delivery enterprise.

Artificial Bee Colony Algorithm for Fresh Food Distribution without Quality Loss by Delivery Route Optimization

This paper enlightens the use of artificial intelligence (AI) for distribution of fresh foods by searching more viable route to keep intact the food attributes. In recent years, very hard-hitting competition is for food industries because of the individuals living standards and their responsiveness for fresh food products demand within stipulated time period. Food industry deals with the extensive kind of activities such as food processing, food packaging and distribution, and instrumentation and control. To meet market demand, customer satisfaction, and maintaining its own brand and ranking on global scale, artificial intelligence can play a vibrant role in decision-making by providing analytical solutions with adjusting available resources. Therefore, by integrating innovative technologies for fresh food distribution, potential benefits have been increased, and simultaneously risk associated with the food quality is reduced. Time is a major factor upon which food quality depends; hence, time required to complete the task must be minimized, and it is achieved by reducing the distance travelled; so, path optimization is the key for the overall task. Swarm intelligence (SI) is a subfield of artificial intelligence and consists of many algorithms. SI is a branch of nature-inspired algorithm, having a capability of global search, and gives optimized solution for real-time problems adaptive in nature. An artificial bee colony (ABC) optimization and cuckoo search (CS) algorithm also come into the category of SI algorithm. Researchers have implemented ABC algorithm and CS algorithm to optimize the distribution route for fresh food delivery in time window along with considering other factors: fixed number of delivery vehicles and fixed cost and fuel by covering all service locations. Results show that this research provides an efficient approach, i.e., artificial bee colony algorithm for fresh food distribution in time window without penalty and food quality loss.

운영자원 규모 및 고객서비스 수준을 동시 고려한 운송 라우트 설계

Purpose Among the costs incurred by operating a logistics center, shipping costs vary depending on changes in international oil prices, development of vehicle technology, and optimization of delivery routes. As a result, they suggest ways to reduce shipping costs. Methods In this work, we propose a study that provides a delivery network design that can be considered in the design of a real delivery network by deriving an optimal solution that satisfies the objective function through genetic algorithms. Results The experimental results showed that the increase in operating resource t is highly correlated with operating cost and CSL. As the increase in operating resource t decreases the trend of CSL growth, the establishment of operating resource to maintain CSL 100% is very inefficient. Conclusion For companies considering changes in logistics network design and operation resources, it was important to establish mid-term or long-term business plan targets for companies and to find and make decisions where logistics operating costs and CSLs conflict to an appropriate level.

Optimizing Living Material Delivery During the COVID-19 Outbreak.

The coronavirus disease 2019 (COVID-19) epidemic has spread worldwide, posing a great threat to human beings. The stay-home quarantine is an effective way to reduce physical contacts and the associated COVID-19 transmission risk, which requires the support of efficient living materials (such as meats, vegetables, grain, and oil) delivery. Notably, the presence of potential infected individuals increases the COVID-19 transmission risk during the delivery. The deliveryman may be the medium through which the virus spreads among urban residents. However, traditional delivery route optimization methods don't take the virus transmission risk into account. Here, we propose a novel living material delivery route approach considering the possible COVID-19 transmission during the delivery. A complex network-based virus transmission model is developed to simulate the possible COVID-19 infection between urban residents and the deliverymen. A bi-objective model considering the COVID-19 transmission risk and the total route length is proposed and solved by the hybrid meta-heuristics integrating the adaptive large neighborhood search and simulated annealing. The experiment was conducted in Wuhan, China to assess the performance of the proposed approach. The results demonstrate that 935 vehicles will totally travel 56,424.55 km to deliver necessary living materials to 3,154 neighborhoods, with total risk [Formula: see text]. The presented approach reduces the risk of COVID-19 transmission by 67.55% compared to traditional distance-based optimization methods. The presented approach can facilitate a well response to the COVID-19 in the transportation sector.

Rapid and efficient cataract gene evaluation in F0 zebrafish using CRISPR-Cas9 ribonucleoprotein complexes.

Cataract is the leading cause of blindness worldwide. Congenital or paediatric cataract can result in permanent visual impairment or blindness even with best attempts at treatment. A significant proportion of paediatric cataract has a genetic cause. Therefore, identifying the genes that lead to cataract formation is essential for understanding the pathological process of inherited paediatric cataract as well as to the development of new therapies. Despite clear progress in genomics technologies, verification of the biological effects of newly identified candidate genes and variants is still challenging. Here, we provide a step-by-step pipeline to evaluate cataract candidate genes in F0 zebrafish using CRISPR-Cas9 ribonucleoprotein complexes (RNP). Detailed descriptions of CRISPR-Cas9 RNP design and formulation, microinjection, optimization of CRISPR-Cas9 RNP reagent dose and delivery route, editing efficacy analysis as well as cataract formation evaluation are included. Following this protocol, any cataract candidates can be readily and efficiently evaluated within 2weeks using basic laboratory supplies.

IMMU-31. QUANTITATIVE EVALUATION OF ROUTES OF ADMINISTRATION OF IMMUNOTHERAPEUTIC T CELLS TO ORTHOTOPIC GLIOMA

Abstract SIGNIFICANCE Adoptive T cell therapy (ACT) has emerged as the most effective treatment against advanced malignant melanoma, eliciting remarkable objective clinical responses in up to 75% of patients with refractory metastatic disease, including within the central nervous system. Immunologic surrogate endpoints correlating with treatment outcome have been identified in these patients, with clinical responses being dependent on the migration of transferred T cells to sites of tumor growth. OBJECTIVE We investigated the biodistribution of intravenously or intraventricularly administered T cells in a murine model of glioblastoma at whole body, organ, and cellular levels. METHODS gp100-specific T cells were isolated from the spleens of pmel DsRed transgenic C57BL/6 mice and injected intravenously or intraventricularly, after in vitro expansion and activation, in murine KR158B-Luc-gp100 glioma-bearing mice. To determine transferred T cell spatial distribution, the brain, lymph nodes, heart, lungs, spleen, liver, and kidneys of mice were processed for 3D imaging using light-sheet and multiphoton imaging. ACT T cell quantification in various organs was performed ex vivo using flow cytometry, 2D optical imaging (IVIS), and magnetic particle imaging (MPI) after ferucarbotran nanoparticle transfection of T cells. T cell biodistribution was also assessed in vivo using MPI. RESULTS Following T cell intravenous injection, the spleen, liver, and lungs accounted for more than 90% of transferred T cells; the proportion of DsRed T cells in the brains was found to be very low, hovering below 1%. In contrast, most ACT T cells persisted in the tumor-bearing brains following intraventricular injections. ACT T cells mostly concentrated at the periphery of tumor masses and in proximity to blood vessels. CONCLUSIONS The success of ACT immunotherapy for brain tumors requires optimization of delivery route, dosing regimen, and enhancement of tumor-specific lymphocyte trafficking and effector functions to achieve maximal penetration and persistence at sites of invasive tumor growth.