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Transport and distribution management is a crucial aspect of supply chain logistics, as it allocates resources to optimize delivery times for finished products. This study proposes a vehicle routing system to minimize delivery times for milk to customers and retailers in the dairy supply chain in Magangué and Mompóx, Department of Bolívar, Colombia. The research involves using mathematical models to optimize routes and the utilization of the vehicle fleet. Two mathematical vehicle routing models were developed: the first addresses the Traveling Salesman Problem (TSP) for a single vehicle. In contrast, the second model deals with the capacitated vehicle routing problem (CVRP) for multiple vehicles. Both models were solved using the IBM ILOG CPLEX Optimization software. The study successfully identified viable CVRP and TSP mathematical models for milk distribution, resulting in minimized delivery times to meet the demands of the TSP model. [ABSTRACT FROM AUTHOR]

The Capacitated Vehicle Routing Problem (CVRP) is an NP-optimization problem (NPO) that arises in various fields including transportation and logistics. The CVRP extends from the Vehicle Routing Problem (VRP), aiming to determine the most efficient plan for a fleet of vehicles to deliver goods to a set of customers, subject to the limited carrying capacity of each vehicle. As the number of possible solutions increases exponentially with the number of customers, finding high-quality solutions remains a significant challenge. Recently, the Quantum Approximate Optimization Algorithm (QAOA), a quantum–classical hybrid algorithm, has exhibited enhanced performance in certain combinatorial optimization problems, such as the Max-Cut problem, compared to classical heuristics. However, its ability diminishes notably in solving constrained optimization problems including the CVRP. This limitation primarily arises from the typical approach of encoding the given problems as unconstrained binary optimization problems with penalty terms. In this case, the QAOA faces challenges in sampling solutions satisfying all constraints. Addressing this, our work presents a new binary encoding for the CVRP, with an alternative objective function of minimizing the shortest path that bypasses the vehicle capacity constraint of the CVRP. The search space is further restricted by the constraint-preserving mixing operation. We examine and discuss the effectiveness of the proposed encoding under the framework of the variant of the QAOA, Quantum Alternating Operator Ansatz (AOA), through its application to several illustrative examples. Compared to the typical QAOA approach, our proposed method not only preserves the feasibility but also achieves a significant enhancement in the probability of measuring optimal solutions. [ABSTRACT FROM AUTHOR]

This paper presents an optimal vehicle routing model for an efficient waste collection process using the Ogun State Waste Management Agency (OGWAMA) as a case study. Just like in many cases, the current manual predetermined routing method used by OGWAMA is inefficient and contributes to excessive fuel usage. These challenges, in addition to the small instances reported in most literature, inspire this research to propose an improved routing scheme that takes into account real-time costs and eventually develops a novel instance based on OGWAMA's operation mode. The developed model was optimized using a new discrete smell agent optimization (SAO) algorithm and compared to firefly algorithm (FA) and particle swarm optimization (PSO). The SAO outperformed FA and PSO, achieving 3.92 % and 19.38 % improvements in service cost (SC) and 2.65 % and 14.96 % improvements in total travel distance (TTD), respectively. The convergence rates of the algorithms were also compared; using the Optimized Depot (OD) techniques and results shows the acceptability of the proposed approaches.

The Capacitated Vehicle Routing Problem (CVRP) is a combinatorial optimization problem that seeks to determine the optimal set of routes for a fleet of vehicles, with limited capacity, to deliver goods to customers while minimizing the total cost. Due to its NP-hard nature, finding exact solutions for the large-scale CVRP instances is computationally intractable. Therefore, heuristics and metaheuristics are widely employed to find approximate optimal solutions. Among these, the Clarke-Wright (CW) algorithm is a popular greedy approach that constructs routes by iteratively merging nodes to minimize transportation costs. This study presents an implementation of the CW algorithm in graphics processing units (GPUs) using the CUDA (Compute Unified Device Architecture) framework. The GPU implementation is compared to its CPU counterpart in terms of execution time and performance. The results demonstrate significant speed-ups achieved by the GPU implementation, particularly for large-scale instances. Performance gains can be attributed to the parallel processing capabilities of GPUs, enabling efficient execution of the algorithm computational steps. [ABSTRACT FROM AUTHOR]

In recent years, the development of graph neural network (GNN) and deep reinforcement learning provides new methods to solve combinatorial optimization problems. Currently, most of these methods do not consider the problem of algorithm parameter learning. This paper developed an intelligent optimization model based on graph attention networks to solve this problem. The model automatically learned neighborhood search operators and destructive sequence terminators according to a significant amount of problem data, and trained model parameters based on reinforcement learning. This article used standard examples to test the model and conducted three different groups of experiments. The experimental results show that the learned neighborhood search operator has a remarkable ability to optimize and converge, while significantly reducing the training memory occupation. It can solve CVRP problems containing hundreds of nodes in a short time and has the potential for expansion. [ABSTRACT FROM AUTHOR]

This work proposes an effective solution to the Vehicle Routing Problem, taking into account all phases of the delivery process. When compared to real-world data, the findings are encouraging and demonstrate the value of Machine Learning algorithms incorporated into the process. Several algorithms were combined along with a modified Hopfield network to deliver the optimal solution to a multiobjective issue on a platform capable of monitoring the various phases of the process. Additionally, a system providing viable insights and analytics in regard to the orders was developed. The results reveal a maximum distance saving of 25% and a maximum overall delivery time saving of 14%.

E-cargo bikes, i.e., electric bicycles equipped with baskets for transporting goods, are perceived as an advantageous solution for urban last-mile logistics. Particularly for short distances and low volumes of goods, the use of e-cargo bikes is ideal in urban areas where access of conventional cars may be prohibited. In this context, this study presents an e-cargo bike routing model for urban deliveries. The problem is formulated as a Capacitated Vehicle Routing Problem (CVRP), which takes several aspects into account, such as restrictions on bicycle capacity, road grade and workload balancing. A mathematical model is constructed to solve the problem, and a Genetic Algorithm (GA) is applied. Results on a case study in Athens, Greece are presented and analyzed, highlighting the applicability of the model in real-world operations, yielding reasonable results in short computational times.

This work proposes an effective solution to the Vehicle Routing Problem, taking into account all phases of the delivery process. When compared to real-world data, the findings are encouraging and demonstrate the value of Machine Learning algorithms incorporated into the process. Several algorithms were combined along with a modified Hopfield network to deliver the optimal solution to a multiobjective issue on a platform capable of monitoring the various phases of the process. Additionally, a system providing viable insights and analytics in regard to the orders was developed. The results reveal a maximum distance saving of 25% and a maximum overall delivery time saving of 14%. [ABSTRACT FROM AUTHOR]

E-cargo bikes, i.e., electric bicycles equipped with baskets for transporting goods, are perceived as an advantageous solution for urban last-mile logistics. Particularly for short distances and low volumes of goods, the use of e-cargo bikes is ideal in urban areas where access of conventional cars may be prohibited. In this context, this study presents an e-cargo bike routing model for urban deliveries. The problem is formulated as a Capacitated Vehicle Routing Problem (CVRP), which takes several aspects into account, such as restrictions on bicycle capacity, road grade and workload balancing. A mathematical model is constructed to solve the problem, and a Genetic Algorithm (GA) is applied. Results on a case study in Athens, Greece are presented and analyzed, highlighting the applicability of the model in real-world operations, yielding reasonable results in short computational times. [ABSTRACT FROM AUTHOR]

In companies, the transportation process involves all stages of production and distribution of products to customers with geographically dispersed locations that directly impact the cost of the operation and products, addressing vehicle routing problems (VRP) that minimize costs of transport. In accordance with the above, the playful activity "Route your route" is created as a learning tool, to publicize and understand the resolution of routing problems. For its development three phases were followed, the first is Learning, where the mathematical model of the problem is reviewed; the second consists of the Execution, which includes the aspects that intervene in the development of the playful activity and ends with the analysis and discussion where feedback is obtained from the participants. The results allow us to verify the relevance and acceptance of play as a learning tool that promotes the appropriation of the factors that intervene in the logistics management of distribution such as routing, resource allocation, decision-making, among others.

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The vehicle routing problem is one of the most well-known optimization problems, which aims to design an optimum set of routes with the lowest cost for servicing the customers in a way that is consistent with the existing constraints. The wide practical application and scope of this problem has attracted much attention from researchers. But in return, the severity of solving has created difficulties and increased the need for heuristic and meta-heuristic solutions. This research represents a greedy heuristic method based on first categorizing then routing methods, to solve the capacitated vehicle routing problem (CVRP) using the capabilities of problem reduction to the knapsack problem. The advantages of this method include the consideration of effective criteria such as distance between customers, distance between customers to depot and demand of points in decision making, decent speed and quality of solution and the ability to utilize the benefits of reduction. Standard samples from CVRPLIB were used to evaluate the results and comparing them.

At the present time, business and industrial organizations are given the task of good planning when determining any type of acquisition for their services. That is, identify and evaluate the different options of provision of inventory that allow the company to maintain and improve its offer. Within this evaluation, it can be noticed that the decision of the management and administration of resources, involves the problem of distribution (goods and services), being also of great importance within the supply chain. In this way, in this paper, we analyze the Capacitated Vehicle Routing Problem with ellipsoidal distances, which includes an inventory model with uniformly distributed demands, so that inventory costs and distances are minimized. With this contribution, the way to minimize total costs in a distribution period can be refined. For validation, computational evaluations obtained from the mathematical model and backed by the software are presented (LINGO 18.0 - MATLAB). [ABSTRACT FROM AUTHOR]

En la ciudad de Tunja no se cuenta con fuentes de información e indicadores exactos que establezcan la cantidad de llantas usadas y desechadas en tiraderos clandestinos, patios de casas y centros de acopio, las cuales generan impacto negativo en el medio ambiente y la salud de los habitantes, evidenciando la necesidad de generar una solución logística. Se presenta una investigación de enfoque cuantitativo de tipo estudio de caso; en esta se utilizó el método CVRP, que consiste en el diseño de un conjunto de rutas de menor coste, donde cada cliente se visite una única vez por un único vehículo y todas las rutas comienzan y finalizan en un centro de gravedad. Como resultados se logró la caracterización y diseño de un modelo logístico, que integró la ubicación de punto de acopio, demanda, rutas de recolección, capacidades, como herramienta sólida y actualizada que generara alternativas ambientales para la ciudad.

Efficient vehicle routing is a major concern for any supply chain, especially when dealing with last-mile deliveries in highly urbanized areas. In this paper problems considering last-mile delivery in areas with the restrictions of motorized traffic are described and different types of cargo bikes are reviewed. The paper describes methods developed in order to solve a combination of problems for cargo bicycle logistics, including efficient packing, routing and load-dependent speed constraints. Proposed models apply mathematical descriptions of problems, including the Knapsack Problem, Traveling Salesman Problem and Traveling Thief Problem. Based on synthetically generated data, we study the efficiency of the proposed algorithms. Models described in this paper are implemented in Python programming language and will be further developed and used for solving the problems of electric cargo bikes’ routing under real-world conditions.

Many optimization problems require heuristic methods to solve the problem. Variable Neighborhood Search (VNS) is a metaheuristic form that systematically changes its "neighborhood" in search of solutions. One method in VNS is Variable Neighborhood Descent (VND), which performs a deterministic neighborhood change. The change of the neighborhood in VND can be done in a random and sequential order. This paper compares sequential and random neighborhood selection methods in solving Capacitated Vehicle Routing Problem (CVRP) problems. There are 6 intra-route neighborhood structures and 4 inter-route structures used. CVRP problems are taken from several dataset providers. The initial solution is formed by Sequential Insertion method. The experimental results show that the random selection of neighborhood operators can provide a more optimal route length (in 10 of 13 datasets used) than that of sequential selection (only better in 3 dataset). However, the random selection takes more iterations to reach convergent state than the sequential one. For sequential selection, determination of the neighborhood structure's order affects the speed to the convergent state. Hence, a random selection in VND method is more preferable than sequential selection. [ABSTRACT FROM AUTHOR]

Along this paper, we present a new multi agent-based system to gather waste on cities and villages. We have developed a low cost wireless sensor prototype to measure the volume level of the containers. Furthermore a route system is developed to optimize the routes of the trucks and a mobile application has been developed to help drivers in their working days. In order to evaluate and validate the proposed system a practical case study in a real city environment is modeled using open data available and with the purpose of identifying limitations of the system.

The proposal of a model of intelligent logistics that allows help influence the optimization of a Smart City is presented in this investigation, and thereby achieve reduce traffic accidents specifically those related to transport any type of product to generate energy. In addition, the problem of routing of vehicles (VRP) is the approach from which faces the problem of logistics, management, management and distribution of goods from one point to a destination. When is required to distribute loads to the classic problem, then it is considered the VRP with the capacity extension or (CVRP). The present work focuses on using an instance with the purpose of implementing an improvement to transport of hydrocarbonsation in the Metropolitan Area of Cuernavaca (MAoC). It uses the language for technical calculation called (MATLAB). The algorithm used in this work is described below: a) Start from the depot, b) Examine the outputs that have not been served, outputs may be feasible and infeasible) For choose the best feasible outputs, for example having the shortest distance, insert it into the route and position before the last exit on that route, d) If there are more feasible outputs, repeat from point b), but to create a new route and start the flow from the point a), e) If all exits were covered, but finish initiate flow from the point a). The results show that the route can be optimized by distributing and reordering of units so that all points are covered.

Background: The Capacitated Vehicle Routing Problem (CVRP) is one of the most important transportation problems in logistics and supply chain management. The standard CVRP considers a fleet of vehicles with homogeneous capacity that depart from a warehouse, collect products from (or deliver products to) a set of customer locations (points) and return to the same warehouse. However, the operation of carrier companies and third-party transportation providers may follow a different network flow for collection and delivery. This may lead to non-optimal route planning through the use of the standard CVRP. Objective: To propose a model for carrier companies to obtain optimal route planning. Method: A Capacitated Vehicle Routing Problem for Carriers (CVRPfC) model is used to consider the distribution scenario where a fleet of vehicles depart from a vehicle storage depot, collect products from a set of customer points and deliver them to a specific warehouse before returning to the vehicle storage depot. Validation of the model’s functionality was performed with adapted CVRP test problems from the Vehicle Routing Problem LIBrary. Following this, an assessment of the model’s economic impact was performed and validated with data from a real carrier (real instance) with the previously described distribution scenario. Results: The route planning obtained through the CVRPfC model accurately described the network flow of the real instance and significantly reduced its distribution costs. Conclusion: The CVRPfC model can thus improve the competitiveness of the carriers by providing better fares to their customers, reducing their distribution costs in the process.