Vehicle Routing Problem With Simultaneous Pickup And Delivery Research Articles

An efficient heuristic for very large-scale vehicle routing problems with simultaneous pickup and delivery

The paper discusses two variants of the Capacitated Vehicle Routing Problem (CVRP), in which each customer to be visited can require both pickup and delivery, or only either pickup and delivery but not both. These problems are referred to, respectively, as the Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD) and as the Vehicle Routing Problem with Mixed Pickup and Delivery (VRPMPD). Both VRPSPD and VRPMPD are particularly relevant in practical logistics distribution scenarios, especially when dealing with a substantial number of pickup and delivery locations. The aim of this work is to provide an algorithm which extends, through a variety of specializations, the FILO framework, originally proposed and specifically designed for the CVRP. This variant, called FSPD, has been developed to accomplish two objectives: first, being competitive with the state-of-the-art algorithms for the VRPSPD and VRPMPD; second, efficiently solving new benchmark instances for these problems with a very large number of customers, while maintaining linear scalability of the computing time with respect to the problem size. The extensive computational study and detailed analysis of the algorithm components conducted in this paper demonstrate the successful achievement of both objectives.

A unified exact approach for a broad class of vehicle routing problems with simultaneous pickup and delivery

The vehicle routing problem (VRP) with simultaneous pickup and delivery (VRPSPD) is a classical combinatorial optimization problem in which one aims at determining least-cost routes, starting and ending at the depot, while simultaneously meeting the pickup and delivery demands of geographically dispersed customers in a single visit, and without violating the capacity of the vehicle. In this work, we propose a unified branch-cut-and-price (BCP) algorithm to solve ten variants of the problem, considering not only the standard version but also those including additional attributes such as a heterogeneous fleet of vehicles, time windows, route duration, multiple depots, and location decisions. The resulting problem that generalizes all of these variants can be formulated as a heterogeneous location routing problem with simultaneous pickup and delivery and time windows (HLRPSPDTW), for which we propose a compact formulation to serve as the basis for an extended formulation associated with the BCP approach. The proposed exact method includes many of the features developed in recent years, such as a bucket graph-based labeling algorithm, rank-1 cuts with limited memory, and dynamic ng-sets. Extensive computational experiments were performed on more than 550 benchmark instances and our algorithm was capable of obtaining a number of new optimal solutions, as well as improved lower bounds, for all variants addressed in this paper.

An Improved Genetic Algorithm for the Granularity-Based Split Vehicle Routing Problem with Simultaneous Delivery and Pickup

The Split Vehicle Routing Problem with Simultaneous Delivery and Pickup (SVRPSDP) consists of two subproblems, i.e., the Vehicle Routing Problem with Simultaneous Delivery and Pickup (VRPSDP) and the Split Delivery Vehicle Routing Problem (SDVRP). Compared to the subproblems, SVRPSDP is much closer to reality. However, some realistic factors are still ignored in SVRPSDP. For example, the shipments are integrated and cannot be infinitely subdivided. Hence, this paper investigates the Granularity-based Split Vehicle Routing Problem with Simultaneous Delivery and Pickup (GSVRPSDP). The characteristics of GSVRPSDP are that the demands of customers are split into individual shipments and both the volume and weight of each shipment are considered. In order to solve GSVRPSDP efficiently, a Genetic-Simulated hybrid algorithm (GA-SA) is proposed, in which Simulated Annealing (SA) is inserted into the Genetic Algorithm (GA) framework to improve the global search abilities of individuals. The experimental results indicate that GA-SA can achieve lower total costs of routes compared to the traditional meta-algorithms, such as GA, SA and Particle Swarm Optimization (PSO), with a reduction of more than 10%. In the further analysis, the space utilization and capacity utilization of vehicles are calculated, which achieve 86.1% and 88.9%, respectively. These values are much higher than those achieved by GA (71.2% and 74.8%, respectively) and PSO (60.9% and 65.7%, respectively), further confirming the effectiveness of GA-SA. And the superiority of simultaneous delivery and pickup is proved by comparing with separate delivery and pickup. Specifically, the costs of separate delivery and pickup are more than 80% higher than that of simultaneous delivery and pickup.

Dynamic sustainable multiple-depot vehicle routing problem with simultaneous pickup and delivery in the context of the physical internet

PurposeThis paper proposes an approach to solve the vehicle routing problem with simultaneous pickup and delivery (VRPSPD) in the context of the Physical Internet (PI) supply chain. The main objective is to minimize the total distribution costs (transportation cost and holding cost) to supply retailers from PI hubs.Design/methodology/approachMixed integer programming (MIP) is proposed to solve the problem in smaller instances. A random local search (RLS) algorithm and a simulated annealing (SA) metaheuristic are proposed to solve larger instances of the problem.FindingsThe results show that SA provides the best solution in terms of total distribution cost and provides a good result regarding holding cost and transportation cost compared to other heuristic methods. Moreover, in terms of total carbon emissions, the PI concept proposed a better solution than the classical supply chain.Research limitations/implicationsThe sustainability of the route construction applied to the PI is validated through carbon emissions.Practical implicationsThis approach also relates to the main objectives of transportation in the PI context: reduce empty trips and share transportation resources between PI-hubs and retailers. The proposed approaches are then validated through a case study of agricultural products in Thailand. Social implicationsThis approach is also relevant with the reduction of driving hours on the road because of share transportation results and shorter distance than the classical route planning.Originality/valueThis paper addresses the VRPSPD problem in the PI context, which is based on sharing transportation and storage resources while considering sustainability.

ON DIFFERENT FORMULATIONS FOR THE MULTI-PERIOD VEHICLE ROUTING PROBLEM WITH SIMULTANEOUS PICKUP AND DELIVERY

In this paper, we extend the vehicle routing problem with simultaneous pickup and delivery (VRPSPD) with a consideration of multiple planning horizons. We propose three alternative mathematical formulations for Periodic-VRPSPD (P-VRPSPD) based on the available formulations for VRPSPD in the literatures, namely the three-index commodity flow formulation, four-index commodity flow formulation, and three-index vehicle flow formulation. We perform comparison analysis by conducting extensive numerical experiments on a set of instances with various complexities in order to evaluate the performance of these formulations. Overall, it is observed that the three-index commodity flow formulation returns the best results.

The Vehicle Routing Problem with Simultaneous Pickup and Delivery Considering the Total Number of Collected Goods

As a consequence of e-commerce development, large quantities of returned goods are shipped every day. The vehicle routing problem with simultaneous delivery and pickup (VRPSDP) has become one of the most important areas of logistics management. Most related studies are aimed at minimizing travel time. However, the total number of collected goods is also very important to logistics companies. Thus, only considering the traveling time cannot reflect actual practice. To effectively optimize these operations for logistics companies, this paper introduces the vehicle routing problem with simultaneous pickup and delivery considering the total number of collected goods. Based on the principles of considering the number of collected goods, a bi-objective vehicle routing model minimizing the total travel time and maximizing the total number of collected goods simultaneously is developed. A polynomial time approximation algorithm based on the ε-constraint method is designed to address this problem, and the approximation ratio of the algorithm is analyzed. Finally, the validity and feasibility of the proposed model and algorithm are verified by test examples, and several managerial insights are derived from the sensitivity analysis.

An Optimized Solution to Multi-Constraint Vehicle Routing Problem

A Vehicle Routing Problem (VRP) is a Non-Polynomial Hard Category (NP-hard) problem in which the best set of routes for a convoy of vehicles is traversed to deliver goods or services to a known set of customers. In VRP, some constraints are added to improve performance. Some variations of VRP are Capacitated Vehicle Routing Problem (CVRP), Vehicle Routing Problem with Stochastic Demands (VRPSD), Vehicle Routing Problem with Time Window (VRPTW), Dynamic Vehicle Routing Problem (DVRP), and Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD) where vehicle and routes have multiple constraints. Swarm intelligence is a well-used approach to solve VRPs. Moreover, different hybrid combinations of global and local optimization techniques are also used to optimize the said problem. In this research, an attempt is made to solve CVRP with VRPSD by using two different hybridized population-based approaches, that is, the Cuckoo Search Algorithm (CSA) and Particle Swarm Optimization (PSO). The experiments showed the accuracy of the improved CVRP that is superior to one obtained by using other classical versions and better than the results achieved by comparable algorithms. Besides, this improved algorithm can also improve search efficiency.

Grasshopper Optimizaton Algorithm (GOA) untuk Menyelesaikan Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD)

The purpose of this paper is to solve the Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD) using the Grasshopper Optimization Algorithm (GOA). Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD) is a problem of forming routes that serve each customer, by delivering and retrieving simultaneously. The purpose of VRPSPD is to minimize the total mileage to serve all customers with the limit that each customer is served exactly once, and the vehicle load does not exceed its maximum capacity. Grasshopper Optimization Algorithm (GOA) is an algorithm inspired by nature by mimicking the living behavior of grasshopper swarms in search of food sources. GOA has several main stages, namely initialization of parameters, determination of target grasshoppers, calculating the coefficient of decline, calculating the distance between grasshoppers, and calculating the new position of the grasshoppers. Implementation of the GOA program to complete VRPSPD using the C++ programming language using 3 types of data, data with 13 customers, 22 customers, and 100 customers. Based on the results of the running program, it can be concluded that the more iterations and the number of populations, the solution obtained tends to be better.

The Integrated Production-Inventory-Routing Problem with Reverse Logistics and Remanufacturing: A Two-Phase Decomposition Heuristic

Sustainable supply chains depend on three critical decisions: production, inventory management, and distribution with reverse flows. To achieve an effective level of operational performance, policymakers must consider all these decisions, especially in Closed-Loop Supply Chains (CLSCs) with remanufacturing option. In this research paper, we address the Integrated Production-Inventory-Routing Problem with Remanufacturing (IPIRP-R) of returned End-Of-Life (EOL) products. The aim behind solving this optimization problem is to minimize conjointly the total manufacturing, remanufacturing, setup, inventory, and routing costs over the planning horizon. A two-phase decomposition heuristic is developed to solve the model iteratively. Our study finds its originality in the fact of jointly optimizing the Capacitated Lot-Sizing Problem with Remanufacturing (CLSP-R) option and the Vehicle Routing Problem with Simultaneous Pick-up and Delivery (VRPSPD) in a single framework. Numerical results showed that our solution approach provides good solutions regarding small and medium-scale size instances under acceptable computational time, especially for problems occurring with significant manufacturing and remanufacturing costs under relatively low pickup requests.

Implementation of a new matheuristic to solve the vehicle routing problem with simultaneous deliveries and pick-ups – VRPSPD

The objective of this article is to present a new methodology for solving the Homogeneous Vehicle Routing Problem with Simultaneous Pickups and Deliveries (VRPSPD). The methodology integrates a matheuristic using the Chu-Beasley genetic algorithm and mixed integer linear programming, based on the Branch-and-Bound procedure. The best configuration obtained from the genetic algorithm is improved through the use of constructive heuristic methods in determining sub-problems, which contribute to the creation of the initial population necessary in the stage of local improvement. The goal is to determine minimum-cost routes that satisfy the pick-up and delivery demands of a group of geographically dispersed customers, while considering the restrictions of the system and the number of vehicles required. The methodology is implemented in C++ and a solver CPLEX software is used to find the solution. Results from test instances in specialized literature demonstrate the efficiency of this new hybrid model, showing good results in short computation times. This article is based on the author's doctoral thesis in engineering and presents similar topics discussed in international congresses, which may explain the similarities in the consulted bibliographical references.

A variable neighborhood search algorithm with constraint relaxation for the two-echelon vehicle routing problem with simultaneous delivery and pickup demands

This paper considers a special vehicle routing problem, the two-echelon vehicle routing problem with simultaneous delivery and pickup demands (2E-VRPSDP). The 2EVRPSDP differs from classic transportation and vehicle routing problems in two ways. First, freight delivery from the depot to the customers is managed by shipping the freight through intermediate satellites. Second, each customer in the 2E-VRPSDP may have simultaneous delivery and pickup demands. The 2E-VRPSDP is an extension of the two-echelon vehicle routing problem (2E-VRP) and the vehicle routing problem with simultaneous delivery and pickup (VRPSDP). A variable neighborhood search algorithm is designed to solve the 2E-VRPSDP in which both feasible and infeasible solutions can be explored. Numerical results show that the proposed algorithm is effective and that the algorithm can provide reasonable solutions within an acceptable computational time.

The efficient routing approach of a vehicle under the vehicle routing problem with simultaneous delivery and pickup for reducing the fuel consumption and pollutants emission

The vehicle routing problem with simultaneous delivery and pickup (VRPSDP) has received significant attention in logistics operations to minimise the travelling distance or travelling time. However, little is known regarding the VRPSDP in logistics operations which is used to reduce the emission of CO2 in the environment (i.e., critical requirements of green logistics). Our study proposes the fuel-optimisation model for the vehicle under VRPSDP referred to as an eco-friendly VRPSDP model. This model aims to determine the efficient vehicle route that will reduce the vehicle's fuel consumption as well as decrease the negative impact on the environment. The genetic algorithm (GA) is applied to solve the proposed fuel optimisation model. Here, two types of genetic mutation operator, i.e. swap and inverse are used to find out which one would give the optimum results regarding fuel consumption. The computational tests show that that the inverse mutation performs better than the swap mutation and saves the fuel consumption of 4.1% over the swap mutation.

The vehicle routing problem with simultaneous delivery and pickup: a taxonomic survey

In recent years, the vehicle routing problem with simultaneous delivery and pickup (VRPSDP) and its variants, which consist of a study field closely related to the reverse logistics, are becoming much more prevalent and more frequently studied. However, the problem features and assumptions diversify considerably and no literature review has classified the existing studies. The main contributions of this paper are: 1) to define the VRPSDP's field in its entirety; 2) to accomplish an all-encompassing taxonomic classification of the VRPSDP literature published since 1989; 3) to delimit all of VRPSDP's facets parsimoniously. Proposing an adapted taxonomy, we classify 191 papers and categorise the trends in the VRPSDP literature.

Logistics distribution de-carbonization pathways and effect in China: a systematic analysis using VRPSDP model

Abstract In order to reduce the carbon emission and improve the de-carbonization in distribution of vehicle routing problem with simultaneous delivery and pickup (VRPSDP), a two-stage disruption management method based on the changes of customer demand is proposed in this paper. Firstly, a mathematics model of VRPSDP is established to optimize the parameters and the distribution carbon emission cost and time window deviation. Next, several carbon emission equations in VRPSDP are inducted and designed. Considering the strong global searching ability of quantum particle swarm optimization (QPSO) and combing the advantages of superposition and parallelism of quantum computing, a multi-phase QPSO (MQPSO) is proposed to enhance global searching ability. At last, on the basis of Solomon examples, the validity of the proposed model and MQPSO is tested and compared with the classical algorithms at present. The simulation results show that the proposed method can not only achieve the goal of performance but also meet the practical requirements of reducing carbon emissions in VRPSDP.

Study of variable neighborhood descent and tabu search algorithm in VRPSDP

The application of graph theory can be used to solve distribution optimization problems, especially the Vehicle Routing Problems (VRP). One variant of VRP is the Vehicle Routing Problem with Simultaneous Delivery and Pickup (VRPSDP), which has particular constraints, namely requests, and returns, which are done simultaneously. The VRPSDP problems can be solved by a variety of algorithms, such as the tabu search algorithm and the Variable Neighborhood Descent (VND) algorithm. The basic principle of the VND algorithm is to determine the initial solution and repair the solution using the neighborhood operator. In this article, the initial solution is determined by the insertion heuristic algorithm and makes improvements using six neighborhood operators. The optimum condition of the VND algorithm is reached if there is the most optimal solution for all operators, and the solution has converged to a minimum value. The application of the VND algorithm on VRPSDP was made using Borland Delphi 7 software. The VND algorithm application program was tested using standardized data sets with 100 and 200 customers, then compared multiple routes, solutions, and gaps (%). For many customers, 100 and 200 on the two types of data sets, there are many more VND algorithm routes than the tabu search algorithm. The results of the distance solution for the four data sets show that the tabu search algorithm is smaller than the VND algorithm. The type c data set has a smaller gap compared to the type r data set for both 100 and 200 customers to test tabu search algorithms and VND algorithms.

ALGORITMA VARIABLE NEIGHBORHOOD DESCENT (VND) PADA VEHICLE ROUTING PROBLEM WITH SIMULTANEOUS DELIVERY AND PICKUP (VRPSDP) DAN IMPLEMENTASINYA

Vehicle Routing Problem with Simultaneous Delivery and Pickup (VRPSDP) is a variant of Vehicle Routing Problem (VRP). VRPSDP has special constraints, namely requests and returns are carried out simultaneously. In this article we will use the Variable Neighborhood Descent (VND) algorithm to solve VRPSDP problems. There are two steps taken to use the VND algorithm on VRPSDP, namely finding an initial solution with the Insertion Heuristic algorithm and improving the position of the customer by using the neighborhood operator on the VND algorithm. The implementation of the VND algorithm on VRPSDP has been successfully made using the Borland Delphi 7.0 programming language. Inputs contained in the program are point position, distance between points, customer requests and returns and vehicle capacity. The output contained in the program in the form of routes that have been completed using an algorithm and output in the form of images of the final solution that has been obtained. Based on the results obtained, an example with 6 customers produces 3 routes with a total distance of 266 km, while an example with 10 customers produces 4 routes with a total distance of 100 km.

A TACTICAL/STRATEGIC LEVEL COST ANALYSIS BASED ON VISIT TIME PREFERENCES FOR VEHICLE ROUTING PROBLEM WITH SIMULTANEOUS PICKUP AND DELIVERY

Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD) has been studied for long years and used to model for many real-life applications. The problem is basically to find optimal routes for vehicles that serve customers in a service area to collect as well as deliver goods at the same time. Many logistic companies such as cargo, post, online supermarket, etc. face this problem in their daily operations. Because of high competitions in the sector, companies start to provide visit time preferences that customers can select. Although this is highly preferred by customers, it causes additional costs for companies since it is highly probable that preferences of customers violate optimal routes of vehicles. This problem is called as VRPSPD in Time Windows (VRPSPDTW). In this study, we develop a tactical/strategic level pricing policy based on calculation of additional travel time cost caused by visit time preferences of customers. Idea behind the policy is that solving VRPSPD for same customer set with/without considering time windows in each trial. After conducting many trials for different customer sets, average differences between travel times of VRPSPD and VRPSPDTW are determined. A generic hourly additional prices based on the average differences are calculated. Both problems are modelled as mixed ineger linear programmings and solved with CPLEX 12.9. Differences among travel times vary from 32 to 180 minutes according to experimental settings. Furthermore, results do not only change with time windows, but also different service regions play important role on results.

Dynamic multi-stage failure-specific cooperative recourse strategy for logistics with simultaneous pickup and delivery

With the development of distribution logistics, the vehicle routing problem with simultaneous pickup and delivery (VRPSPD) has garnered attention in recent years due to its higher capacity-usage rate and transportation efficiency. This study proposes a novel dynamic multi-stage failure-specific cooperative (DMS-FSC) recourse strategy for solving the route failure which occurs in the VRPSPD. In the case of simultaneous transportation, the proposed DMS-FSC strategy allows a pair of cooperative vehicles to revisit each other’s customers with unmet demands by dividing the whole transportation route into multiple stages. An extended genetic algorithm with a new multi-stage paired vectors representation scheme is offered to more effectively deal with the proposed DMS-FSC strategy. It explores a more ideal a priori transportation plan for the VRPSPD by incorporating an acceptance criterion based on simulated annealing. Two sets of cases with middle and corner located depots are used to verify the proposed algorithm’s effectiveness in solving the presented problem. Furthermore, a comparison between the DMS-FSC strategy and the baseline strategy, which involves no cooperation, is also made to evaluate the cost savings generated from the proposed DMS-FSC strategy.

Vehicle Routing Problem with Simultaneous Pickup and Delivery

This paper focuses on the Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD) issue. VRPSPD is one of the extended problems related to the usual Vehicle Routing Problem (VRP). VRPSPD consists of both linehaul customers and backhaul customers with a known demand. In VPRSPD, only a single depot can receive and supply the loads. The vehicles can only visit each customer once and can serve all customers simultaneously by delivering or picking up the loads within a limited capacity. VPRSPD is a NP-hard problem. The huge data size has increased the difficulty for it to be solved by using mathematical programming or combinatorial optimisation. A heuristic approach based on the Variable Neighbourhood Search (VNS) is proposed. Heuristic based solutions offer feasible solutions that are approximately accurate to the exact solution. It is one of the most popular solutions to solve a complex problem. In this research, the algorithm solution consists of two main phases. A simple heuristic is used to generate the initial solution in the first phase. Then, the feasible solution obtained in the first phase will become the initial input for the improvement phase which applied the VNS algorithm. The proposed algorithm is tested using a list of benchmark dataset. The result obtained is compared with the best solution that can be found from the literature research. The comparison result shows that the heuristic algorithm is favourable to be used for this kind of vehicle routing problem.

Disruption management decision model for VRPSDP under changes of customer distribution demand

In order to solve the disruption of vehicle routing problem with simultaneous delivery and pickup (VRPSDP), a two-stage disruption management method based on the changes of customer demand is proposed. Firstly, a mathematical model of the problem is established to minimize the distribution cost and time window deviation, then to identify the disruption caused by the change of the original distribution scheme. Next, considering the strong global searching ability of bacteria foraging algorithm a hybrid quantum bacterial foraging scheduling algorithm (HQBFO) is designed combing with the advantages of superposition and parallelism of quantum computing. At last, the validity of the model and algorithm is tested by Solomon standard example and simulation test. The comparison with the global rescheduling scheme and additional vehicle scheme verifies the effectiveness of the proposed disruption management and improvement of the quantum algorithm performance.