A Novel Optimization Method and Its Application for Hazardous Materials Vehicle Routing Problem Under Different Road Conditions

The vehicle routing problem with backhauls is a variant of the classical capacitated vehicle routing problem. The difference is that it contains two distinct sets of customers: those who receive goods from the depot, who are called linehauls, and those who send goods to the depot, who are referred to as backhauls. In this paper, we describe a new deterministic iterated local search algorithm, which is tested using a large number of benchmark problems chosen from the literature. These computational tests have proven that this algorithm competes with the best known algorithms in terms of the quality of the solutions and at the same time, it is simpler and faster.

Fuel consumption accounts for a large and increasing part of transportation costs. In this paper, the Fuel Consumption Rate (FCR), a factor considered as a load dependant function, is added to the classical capacitated vehicle routing problem (CVRP) to extend traditional studies on CVRP with the objective of minimizing fuel consumption. We present a mathematical optimization model to formally characterize the FCR considered CVRP (FCVRP) as well as a string based version for calculation. A simulated annealing algorithm with a hybrid exchange rule is developed to solve FCVRP and shows good performance on both the traditional CVRP and the FCVRP in substantial computation experiments. The results of the experiments show that the FCVRP model can reduce fuel consumption by 5% on average compared to the CVRP model. Factors causing the variation in fuel consumption are also identified and discussed in this study.

This paper addresses the non-preemptive unrelated parallel machine scheduling problem with machine-dependent and job sequence-dependent setup times. All jobs are available at time zero, all times are deterministic, and the objective is to minimize the makespan. This is a NP-hard problem and in this paper, a two-stage ant colony optimization (ACO) algorithm is introduced and its performance is evaluated by comparing its solutions to the solutions of Tabu Search and an existing heuristic for the same problem. The results show that ACO outperformed the other algorithms.

Metaheuristics like ant colony optimization (ACO) can be used to solve combinatorial optimization problems. In this paper we refer to its successful application to the vehicle routing problem (VRP). At the beginning, we introduce the VRP and some of its variants. The variants of VRP were designed to reproduce the kind of situations faced in the real-world. Further, we introduce the fundamentals of ant colony optimization, and we present in few words its application to the solution of the VRP. At the end, we discuss the applications of ACO to a number of real-world problems: a VRP with time windows for a major supermarket chain in Switzerland; a VRP with pickup and delivery for a leading distribution company in Italy and an on-line VRP in the city of Lugano, Switzerland, where clients' orders arrive during the delivery process.

Driving behavior is an important factor affecting the risk of hazardous materials transportation. In this paper, we propose a transport risk evaluation method that considers driving risk. We consider driving risk and establish a model of vehicle routing problems with a soft time window for the transportation of hazardous materials and design a non-dominated genetic algorithm to solve the bi-objective optimization model. Taking a network of 23 nodes and 38 road segments as an example, 59 pareto-optimal solutions were obtained for six drivers on nine different paths. Comparing different solutions, it was found that driving risk, road population density, and transportation distance have different impacts on transport cost and risk. Choosing drivers and routes can adjust the propensity of cost and risk, allowing the decision-maker to select a solution for allocating drivers and routing vehicles according to their risk preference.

Mixed Integer Linear Programming Model for Vehicle Routing Problem for Hazardous Materials Transportation

The Vehicle Routing Problem (VRP) is a well-known NP-hard combinatorial optimization problem, which has wide spread applications in real world, such as logistics, bus route planning, and urban path planning. To solve VRP, traditional optimization methods usually start the search from scratch and ignore the VRPs solved in the past, which could lead to repeated explorations of the search space of related problems, and thus results in slow optimization process involving unnecessary computational cost. Keeping this in mind, to speed up the optimization for vehicle routing, this article presents a new study towards faster vehicle routing by transferring knowledge from customer representations which are learned from past solved VRPs. In particular, we propose to capture the useful traits buried in previous optimized routing solutions by learning a new customer representation, which can be transferred across VRPs, serving as the prior knowledge, to bias the optimization in the target VRP. In contrast to existing approaches, the proposed knowledge transfer is consist of a learning of new customer representation based on the optimized routing solution, which is general to VRPs possessing different structural properties, and a weighted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula> norm-regularized formulation for building sparse mapping across VRPs, that is easy to solve. Further, the proposed knowledge transfer across VRPs occurs along the whole optimization search process, and is thus able to guide the routing optimization process consistently. To verify the efficacy of the proposed method, by using population-based optimization method as the VRP solver, comprehensive empirical studies on both commonly used VRP benchmarks and real world vehicle routing application are presented.

This paper presents a variant of vehicle routing problem, incorporating factors of transportation costs in conjunction with adverse situations. The effects of hazardous materials (HazMat) transportation are examined on air-pollution exposure levels, as well as the risk and damage in the case of accidents. In an urban network, the various weights that can be are assigned to each road of a route are associated to the following factors: time, cost, pollution exposure levels, and the risk of damage in HazMat transportation. These weights express the travel cost and the estimated provoked adverse consequences at each road of a vehicle transit route between two disparate points of the network concerning the pollution and risk of unforeseen accident during hazardous materials transportation. The sum of the weights assigned to every road of a route represents the route value according to the corresponding factors. We introduce an optimization criterion in order to select the optimal route among all vehicle transition routes joining any given pair of the network points that does not surpass a predefined threshold route value for each factor. The predefined weights for each factor are a percentage increment of its corresponding shortest route value. The optimal location of a central depot is determined where each vehicle serves a customer using one specific route with respect to the posed restrictions. Subsequently, a suitable partition into sub-networks is carried out in the case of large urban areas and a depot is assigned to each sub-network by applying a specific optimization criterion. A branch and bound tree search algorithm is developed for the determination of the optimal routes while the locations of the depots are investigated within the framework of graph theory. Finally, small numerical examples are given.

Vehicle routing problem is a typical NP hard problem in combinatorial optimization. This paper proposes an improved algorithm, namely Immune Ant Colony Algorithm to solve the problem of vehicle routing problem. The algorithm is based on the intelligent optimization idea- Ant Colony Algorithm and Artificial Immune Algorithm. In this paper, in order to solve the problem of vehicle routing problem effectively four aspects are modified to improve the basic ant colony algorithm. And the improved Artificial Immune Algorithm is used to solve the problem of Ant Colony Algorithm to be apt to plunge into as the local optimum solution. On the basis of these improvements, the framework of the new algorithm—Immune Ant Colony Algorithm is given. Finally, it was proved that Immune Colony Algorithm can quicken the convergence rate to obtain optimal solution and decrease computing time. In addition, the computing simulation examples show its validity.

Increased public concern about the transportation of hazardous materials has caused the assurance of safe transport of this material to be a pressing national need. United States Department of Energy (DOE) programs involve the transportation of many hazardous and radioactive materials, and the national need to assure that these materials are transported in a safe manner is being addressed through the application of technology developed as part of the DOE/Albuquerque Operations Office's Transportation Base Technology Program. Currently the Albuquerque Operations Office is the integrator of DOE's Transportation Base Technology Program. Activities in this area include: packaging development, engineering analysis, testing, advanced technology development, packaging certification support, regulatory standards development, information systems management, and risk and systems analysis support. This paper addresses the specifics of DOE activities in these areas.

The transportation of hazardous materials is always accompanied by considerable risk that will impact public and environment security. As an efficient and reliable transportation organization, a multimodal service should participate in the transportation of hazardous materials. In this study, we focus on transporting hazardous materials through the multimodal service network and explore the hazardous materials multimodal routing problem from the operational level of network planning. To formulate this problem more practicably, minimizing the total generalized costs of transporting the hazardous materials and the social risk along the planned routes are set as the optimization objectives. Meanwhile, the following formulation characteristics will be comprehensively modelled: (1) specific customer demands; (2) multiple hazardous material flows; (3) capacitated schedule-based rail service and uncapacitated time-flexible road service; and (4) environmental risk constraint. A bi-objective mixed integer nonlinear programming model is first built to formulate the routing problem that combines the formulation characteristics above. Then linear reformations are developed to linearize and improve the initial model so that it can be effectively solved by exact solution algorithms on standard mathematical programming software. By utilizing the normalized weighted sum method, we can generate the Pareto solutions to the bi-objective optimization problem for a specific case. Finally, a large-scale empirical case study from the Beijing–Tianjin–Hebei Region in China is presented to demonstrate the feasibility of the proposed methods in dealing with the practical problem. Various scenarios are also discussed in the case study.

General Background: Efficient distribution routing is fundamental in logistics, directly influencing operational costs, fuel consumption, and customer satisfaction. Specific Background: PT Threelog Kencana Mandiri in Batam currently experiences inefficiencies due to suboptimal routing strategies for spare part deliveries. Knowledge Gap: Despite various routing solutions, few are tailored for dynamic, real-world constraints like vehicle capacity and varying delivery points in mid-scale operations. Aim: This study aims to optimize PT Threelog’s delivery routes using the Ant Colony Optimization (ACO) algorithm by modeling the problem as a Vehicle Routing Problem (VRP) with specific constraints. Results: The implementation of ACO significantly reduced total travel distance compared to the company’s existing manual routing, resulting in lower fuel usage, faster delivery times, and enhanced customer service. Novelty: Unlike generic routing systems, the proposed ACO-based model dynamically adapts to real operational variables through pheromone-based local and global updates, improving solution quality iteratively. Implications: This research offers a practical, intelligent decision-support framework for logistics firms, proving that metaheuristic algorithms like ACO can robustly handle complex, real-world delivery challenges and scale to broader applications. Highlights: Improves route efficiency using ACO in real delivery operations. Reduces distance, fuel usage, and delivery time significantly. Provides a scalable model for intelligent logistics planning. Keywords: Ant Colony Optimization, Vehicle Routing Problem, Logistics Efficiency, Route Optimization, Metaheuristic Algorithm

This work proposes a multi-objective extension of a real-world inventory routing problem (IRP), a generalisation of the classical vehicle routing problem (VRP) with vendor managed inventory (VMI) replenishment. While many mathematical formulations and solution models already exist, this study incorporates business-related and risk considerations that makes it unique. It is known that a significant volume of hazardous materials travels every day. Consideration of risks arising from the transportation of hazardous materials as a criterion for selecting distribution routes could potentially reduce the likelihood of accidents and/or the expected consequences of accidents.

The Vehicle Routing Problem (VRP) is a complex and high-level set of routing problems. Two of its important variants are the Dynamic Vehicle Routing Problem (DVRP) and the Green Vehicle Routing Problem (GVRP). The first one has become a challenging research topic in the last two decades, in which not all informations are known in advance, but are revealed as the system progresses. The second one is seen as a new application and solution in new logistics patterns, more specifically, for finding routes of vehicles to serve a set of customers while minimizing the total amount of CO 2 emissions, by increasing the loading rate and reducing the number of empty trips could reduce from 10% to 40% km traveled and therefore CO 2 emissions [1]. In this paper, we combine these two variants (DVRP and GVRP), where we try to minimize, in the dynamic environment, the greenhouse gas especially the carbon dioxide CO 2 , which is the immediate consequence of the depletion of the ozone layer and time duration. We will call this combination the Dynamic Green Vehicle Routing Problem (DGVRP). To study this new problem, we present the technique employed to estimate the amount of CO 2 emissions, the emission matrix and integrate them into the DGVRP model, then we propose the hybridization based on Ant Colony Optimization (ACO) algorithm with a Large Neighborhood Search (LNS) algorithm to solve our problem. The effectiveness of this approach is tested on a set of the dynamic green problems instances, which are adopted in this work from the static GVRP benchmark datasets.

CV Atham Toys (CVAT) produces wooden toys and furniture, comprises 13 small and medium industries. CVAT always attempt to deliver customer orders on time but delivery costs are high. This is because of inadequate infrastructure such that delivery routes are long, car maintenance costs are high, while fuel subsidy by the government is still temporary. This study seeks to minimize the cost of product distribution based on the shortest route using one of five Ant Colony Optimization (ACO) algorithms to solve the Vehicle Routing Problem (VRP). This study concludes that the best of the five is the Ant Colony System (ACS) algorithm. The best route in 1st week gave a total distance of 124.11 km at a cost of Rp 66,703.75. The 2nd week route gave a total distance of 132.27 km at a cost of Rp 71,095.13. The 3rd week best route gave a total distance of 122.70 km with a cost of Rp 65,951.25. While the 4th week gave a total distance of 132.27 km at a cost of Rp 74,083.63. Prior to this study there was no effort to calculate these figures.