Simple Switch between Single Trip Vehicle Routing Problem and Multiple Trip Vehicle Routing Problem

The multiple trip vehicle routing problem involves several sequences of routes. Working shift of single vehicle can be scheduled in multiple trips. It is suitable for urban areas where the vehicle has very limited size and capacity over short travel distances. The size and capacity limit also requires the vehicle should be vacated frequently. As a result, the vehicle could be used in different trips as long as the total time or distance is not exceeded. Various approaches are developed to solve the vehicle routing problem (VRP). Except for the simplest cases, VRP is always a computationally complex issue in order to optimize the objective function in terms or both time and expense. Ant colony optimization (ACO) has been introduced to solve the vehicle routing problem. The multiple ant colony system is proposed to search for alternative trails between the source and destination so as to minimize (fuel consumption, distance, time) among numerous geographically scattered routes. The objective is to design adaptive routing so as to balance loads among congesting city networks and to be adaptable to connection failures. As the route number increases, each route becomes less densely packed. It can be viewed as the vehicle scheduling problem with capacity constraints. The proposed scheme is applied to typical cases of vehicle routing problems with a single depot and flexible trip numbers. Results show feasibility and effectiveness of the approach.

In everyday life almost all problems require the help of mathematics, one of them on the transportation or on the distribution prosess. Multiple Trip Vehicle Routing Problem (MTVRP) is one of the problems related to the transportation or distribution prosess. Multiple Trip Vehicle Routing Problem (MTVRP) is defined as the problem of the Vehicle Routing Problem (VRP) with the expansion and the addition of multiple trips on the each vehicle when it distributes goods and the time window of customer service. One algorithm to solve the Multiple Trip Vehicle Routing Problem (MTVRP) is memetic algorithms. Memetic algorithm is a combination of genetic algorithm and local search procedures that intensify the search. Memetic algorithms procedures are: initialization process, evaluation process, selection, crossover process, mutation process, repair process, local search, vehicle allocation, and the best route is formed. To facilitate the search for the solution of the Multiple Trip Vehicle Routing Problem (MTVRP) especially at the time had to submit to many customers, memetic algorithms implemented in the language programming Borland Delphi. By using an program application be made, produced together with the results obtained manually route 0 – 1 – 3 – 4 – 0 – 5 – 2 – 0 with a travel time "1.1675" hours and uses a vehicle. This program is designed applications up to 50 points in the Multiple Trip Vehicle Routing Problem (MTVRP) using memetic algorithms, which is already in trials with 11 points, 22 points, and 50 points in attachment. So that the application program can be used to solve the Multiple Trip Vehicle Routing Problem (MTVRP) using memetic algorithms on the transportation or on the distribution prosess.

We present a study of using genetic algorithms (GAs) to solve non-fixed destination multiple-depot capacitated vehicle routing problem. The genetic algorithm was developed on the basis of experiences in solving the travelling salesman problem and the single depot capacitated vehicle routing problem. Heuristic improvements in population initialization and crossover operators are made to prevent converging to local optima and to reduce the search space domain. To deal effectively with the constraints of the problem, and to prune the search space of GA in advance, the difficult capacity and supply reliability constraints are embedded in the decimal strings that are coded to represent the vehicle routes between depots. Computational results carried out on several instances indicate that the total distance travelled can be reduced significantly when such method is used.

This paper considers the deterministic vehicle routing problem with service time requirements for delivery. Service requests are also available at different times known at the initial time of route planning. This paper presents an approach based on the generation of service sequences (routes) using randomness. Both the single-vehicle and the multiple-vehicle cases are studied. Our approach is validated using random-generated data and compared against the optimal solution obtained by mathematical programming for small-sized instances, as well as against known lower bounds for medium to large-sized instances. Results show that our approach is competitive with reference to the value of the objective function, and requires less computational time in comparison with the exact resolution procedure.

This paper considers the deterministic vehicle routing problem with service time requirements for delivery. Service requests are also available at different times known at the initial time of route planning. This paper presents an approach based on the generation of service sequences (routes) using randomness. Both the single-vehicle and the multiple-vehicle cases are studied. Our approach is validated using random-generated data and compared against the optimal solution obtained by mathematical programming for small-sized instances, as well as against known lower bounds for medium to large-sized instances. Results show that our approach is competitive with reference to the value of the objective function, and requires less computational time in comparison with the exact resolution procedure.

We present a novel variation of the vehicle routing problem (VRP). Single commodity cargo with pickup and delivery service is considered. Customers are labeled as either cargo sink or cargo source, depending on their pickup or delivery demand. This problem is called a single commodity vehicle routing problem with pickup and delivery service (1‐VRPPD). 1‐VRPPD deals with multiple vehicles and is the same as the single‐commodity traveling salesman problem (1‐PDTSP) when the number of vehicles is equal to 1. Since 1‐VRPPD specializes VRP, it is 𝒩𝒫 hard in the strong sense. Iterative modified simulated annealing (IMSA) is presented along with greedy random‐based initial solution algorithm. IMSA provides a good approximation to the global optimum in a large search space. Experiment is done for the instances with different number of customers and their demands. With respect to average values of IMSA execution times, proposed method is appropriate for practical applications.

Purpose In this paper, the authors introduced a real world new problem, the multi-trip vehicle routing problem with time windows and the possible use of a less-than-truckload carrier to satisfy customer demands. The purpose of this paper is to develop a heuristic algorithm to route the private trucks with time windows and to make a selection between truckload and less-than-truckload carriers by minimizing a total cost function. Design/methodology/approach Both mathematical model and heuristic algorithm are developed for routing the private trucks with time windows and for selecting of less-than-truckload carriers by minimizing the total cost function. Findings In all, 40 test problems were examined with the heuristics. Computational results show that the algorithm obtains the optimal or near-optimal solutions efficiently in terms of time and accuracy. Originality/value The research described in this paper differs from the previous one on fleet planning or vehicle routing, in that it modifies the Clarke and Wright method by shifting the performance measure from a distance to cost and also incorporates the fixed cost of different types of trucks into the model. In addition, the authors simultaneously consider the multiple trip vehicle routing problems with time windows and the selection of less-than-truckload carriers that is an integrated scenario of real-world application. To the best of the authors’ knowledge, this scenario has not been considered in the literature.

We study the class of one-to-many-to-one single vehicle routing problems with pickups and deliveries, in which a single capacitated vehicle is used to serve a set of customers requiring a delivery, a pickup, or both. These problems have many real-world applications, including beverage distribution, courier service transportation, and reverse logistics. We first concentrate on a well-studied problem in this class, known as the single vehicle routing problem with deliveries and selective pickups (SVRPDSP), in which deliveries are mandatory but pickups are optional and generate a revenue if performed, and customers requiring both a delivery and a pickup (combined demand) can be visited either once or twice. Most exact algorithms in the literature solve SVRPDSP by looking for elementary tours on an extended network that is obtained by transforming each combined demand customer into two different customers, one requiring only the delivery and the other one only the pickup. Because this can result in a significant loss in performance, in this work we focus instead on the original problem network and present formulations that can yield non-elementary tours. Through the use of Benders decomposition, valid inequalities, and tailored optimization techniques based on branch-and-cut frameworks, we develop exact algorithms that outmatch previous results in the literature and obtain proven optimal solutions for all benchmark instances. We then generalize the algorithms to solve several other vehicle routing problems with pickups and deliveries, including the cases of split deliveries, intermediate drop-offs, mandatory pickups, and multiple vehicles. The online appendix is available at https://doi.org/10.1287/opre.2017.1639 .

Heuristic algorithms for single and multiple depot vehicle routing problems with pickups and deliveries

This study focuses on solving the vehicle routing problem (VRP) of E-logistics service providers. In our problem, each vehicle must visit some pick up nodes first, for instance, warehouses to pick up the orders then makes deliveries for customers in the list. Each pickup node has its own list of more than one customers requiring delivery. The objective is to minimize the total travelling cost while real-world application constraints, such as heterogeneous vehicles, capacity limits, time window, driver working duration, etc. are still considered. This research firstly proposes a mathematical model for this multiple pickup and multiple delivery vehicle routing problem with time window and heterogeneous fleets (MPMDVRPTWHF). In the next step, the ant colony optimization algorithm is studied to solve the problem in the large-scale.

This article puts forward a cloud theory-based particle swarm optimization (CTPSO) algorithm for solving a variant of the vehicle routing problem, namely a multiple decision maker vehicle routing problem with fuzzy random time windows (MDVRPFRTW). A new mathematical model is developed for the proposed problem in which fuzzy random theory is used to describe the time windows and bi-level programming is applied to describe the relationship between the multiple decision makers. To solve the problem, a cloud theory-based particle swarm optimization (CTPSO) is proposed. More specifically, this approach makes improvements in initialization, inertia weight and particle updates to overcome the shortcomings of the basic particle swarm optimization (PSO). Parameter tests and results analysis are presented to highlight the performance of the optimization method, and comparison of the algorithm with the basic PSO and the genetic algorithm demonstrates its efficiency.

PurposeThis research is designed to optimize the business process of a green tea dealer, who is a key supply chain partner of the Sri Lankan tea industry. The most appropriate trips for each vehicle in multiple trip routing systems are identified to minimize the total cost by considering the traveling distance.Design/methodology/approachThe study has followed the concepts in vehicle routing problems and mixed-integer programming mathematical techniques. The model was coded with the Python programming language and was solved with the CPLEX Optimization solver version 12.10. In total, 20 data instances were used from the subjected green tea dealer for the validation of the model.FindingsThe result of the numerical experiment showed the ability to access supply over the full capacity of the available fleet. The model achieved optimal traveling distance for all the instances, with the capability of saving 17% of daily transpiration cost as an average.Research limitations/implicationsThis study contributes to the three index mixed-integer programing model formulation through in-depth analysis and combination of several extensions of vehicle routing problem.Practical implicationsThis study contributes to the three index mixed-integer programming model formulation through in-depth analysis and combination of several extensions of the vehicle routing problem.Social implicationsThe proposed model provides a cost-effective optimal routing plan to the green tea dealer, which satisfies all the practical situations by following the multiple trip vehicle routing problems. Licensee green tea dealer is able to have an optimal fleet size, which is always less than the original fleet size. Elimination of a vehicle from the fleet has the capability of reducing the workforce. Hence, this provides managerial implication for the optimal fleet sizing and route designing.Originality/valueDeveloping an optimization model for a tea dealer in Sri Lankan context is important, as this a complex real world case which has a significant importance in export economy of the country and which has not been analyzed or optimized through any previous research effort.

The vehicle routing problem (VRP) has many applications in goods distribution and goods transportation. Today, many companies have requirements for VRP with multiple pickup and multiple delivery within due time. This problem is called multiple pickup and multiple delivery vehicle routing problem with time window (PDPTW). PDPTW has many constraints and ant colony optimization (ACO) has been used to solve it although ACO creates too many infeasible routes. Moreover, it often gets trapped in local optimum. To solve these problems, this paper proposed an improved ACO by using the route elimination technique and the pheromone reset technique. The ACO with route elimination technique, it has proven to solve the PDPTW problem with increased performance. The proposed technique was tested on datasets from the Li & Lim’s PDPTW benchmark problems and provided more satisfactory results compared to other ACO techniques.

We investigate an extension to the classical insertion-based heuristic for the vehicle routing problem with backhauling (VRPB). It is based on the idea of inserting more than one backhaul at a time. This method is tested on data sets with single and multiple depots with encouraging results at no additional computational burden. This approach can also be useful in generating good starting solutions for the more computer-intensive meta-heuristics.

Distribution is an action of supplying goods and services from one place to another. This company is an official agency which its main business is distributing subsidized Liquefied Petroleum Gas (LPG). Every day the company distributes to 29-agents that bounded by a company contract. The number of deliveries everyday varies depending on the agents’ demands. The company has 3 fleet vehicles, which are a six-wheel truck, 2 four-wheel trucks, and 2 three-wheel motorcycles. Those vehicles have different load capacity and some of them are able to distribute goods more than once in a day, therefore the company is facing Multiple Trips Heterogeneous Fleet Vehicle Routing Problem (MTHFVRP). At the moment the company does not know whether the vehicle routing they have now is the most effective and efficient. A solution is needed to determine a better distribution route in order to minimize the company's cost by using sweep algorithm to cluster all agents based on their vicinity. After clustering, establishment of distribution routes is created by using nearest neighbor algorithm with taking into account the minimum distance between the agents in order to produce an effective and efficient distribution route. Based on the result, distribution route created using sweep algorithm and nearest neighbor algorithm are able to generate effective and efficient distribution routes, so that the company can minimize the cost of current distribution route as much as 118,834.86 IDR/day or 2,852,036.64 IDR/month.