Vehicle Routing Problem With Simultaneous Pickup And Delivery Research Articles

A robust optimization approach for the production-inventory-routing problem with simultaneous pickup and delivery

The production routing problem (PRP) merges the lot-sizing problem and the vehicle routing problem, two classical problems that have been the focus of comprehensive studies for over half a century. Solving the PRP is an effort to optimize decisions about the production, inventory, distribution, and routing in an integrated manner. In the literature of the recent decade, due to economic changes and regulatory issues, reverse logistics has become a focal point. Subsequently, the vehicle routing problem with simultaneous pickup and delivery (VRPSPD) has drawn more and more attention for its considerable effect on the reverse logistics problem. In addition, one of the major arguments in supply chain management is uncertainty of demand, which it is highly crucial to make accurate decisions. Most of the previous studies have disregarded the concept of uncertainty and reverse flow in the PRP. In this study, we present a mixed integer linear programming model (MILP) for the production-inventory-routing problem with simultaneous pickup and delivery, while considering a reverse flow of products. To reduce the total cost of the system, the proposed model plans the decisions with respect to production–reproduction setups, quantities of the production–reproduction, visiting/not-visiting the retailers, supplier inventory management, retail inventory management under the vendor-managed inventory (VMI) policy, the quantity of the defect-free products delivered to retailers and that of the defective products collected from them at the same time, and vehicle routing in each period. We also propose a new robust MILP formulation for the problem, while considering multiple uncertainty conditions, including the quantity of each retailer’s demand, the quantity of the defective products collected from each retailer in each period, and the reproduction cost. Presenting the mentioned problem with a robust optimization approach which applies to equality constraints is the main contribution of this research. The performance of the model under uncertainty was depicted based on comparison between deterministic and robust model considering various random examples. Given the NP-hardness of the problem and to evaluate the performance of various algorithms in solving the model, we develop two meta-heuristic algorithms, viz. the simulated annealing algorithm and genetic algorithm. The performances of the proposed algorithms are analyzed and compared based on the numerical examples in three small, medium, and large sizes which for each size three states, including standard, high transportation cost, and high inventory cost are considered.

The vehicle routing problem with simultaneous pickup and delivery and handling costs

In this paper we introduce the vehicle routing problem with simultaneous pickup and delivery and handling costs (VRPSPD-H). In the VRPSPD-H, a fleet of vehicles operates from a single depot to service all customers, which have both a delivery and a pickup demand such that all delivery items originate from and all pickup items go to the depot. The items on the vehicles are organized as a single linear stack where only the last loaded item is accessible. Handling operations are required if the delivery items are not the last loaded ones. We implement a heuristic handling policy approximating the optimal decisions for the handling sub-problem, and we propose two bounds on the optimal policy, resulting in two new myopic policies. We show that one of the myopic policies outperforms the other one in all configurations, and that it is competitive with the heuristic handling policy if many routes are required. We propose an adaptive large neighborhood search (ALNS) metaheuristic to solve our problem, in which we embed the handling policies. Computational results indicate that our metaheuristic finds optimal solutions on instances of up to 15 customers. We also compare our ALNS metaheuristic against best solutions on benchmark instances of two special cases, the vehicle routing problem with simultaneous pickup and delivery (VRPSPD) and the traveling salesman problem with pickups, deliveries and handling costs (TSPPD-H), and on two related problems, the vehicle routing problem with divisible pickup and delivery (VRPDPD) and the vehicle routing problem with mixed pickup and delivery (VRPMPD). We find or improve 39 out of 54 best known solutions (BKS) for the VRPSPD, 36 out of 54 BKS for the VRPDPD, 15 out of 21 BKS for the VRPMPD, and 69 out of 80 BKS for the TSPPD-H. Finally, we introduce and analyze solutions for the variations of the VRPDPD and VRPMPD with handling costs – the VRPDPD-H and the VRPMPD-H, respectively.

A Fast Randomized Algorithm for the Heterogeneous Vehicle Routing Problem with Simultaneous Pickup and Delivery

In the vehicle routing problem with simultaneous pickup and delivery (VRPSPD), customers demanding both delivery and pickup operations have to be visited once by a single vehicle. In this work, we propose a fast randomized algorithm using a nearest neighbor strategy to tackle an extension of the VRPSPD in which the fleet of vehicles is heterogeneous. This variant is an NP-hard problem, which in practice makes it impossible to be solved to proven optimality for large instances. To evaluate the proposal, we use benchmark instances from the literature and compare our results to those obtained by a state-of-the-art algorithm. Our approach presents very competitive results, not only improving several of the known solutions, but also running in a shorter time.

A metaheuristic solution approach to capacitied vehicle routing and network optimization

The vehicle routing problem (VRP) is one of the problem types that are sought after for a long time by trying out different techniques and attracting attention in terms of optimization. In most VRP types, route cost is associated with distance, and a shorter distance solution is considered a more successful solution. While the shortest distance goal provides significant advantages in terms of cost and time to businesses, this makes it attractive for further research. When examining the types of problems having different directions and areas devised from different points of view on vehicle routing, it can be said that the closest approach to practical application is the vehicle routing problem with simultaneous delivery and pickup (VRPSDP). In this study, a solution proposal is presented for the VRPSDP using the Artificial Bee Colony (ABC) algorithm and the application is tested with the benchmark problem data sets commonly used for VRPSDP in the literature. When the results are compared with the least cost route solutions in the literature, it is observed that despite the few parameters, the proposed method can produce low-cost solutions very close to the most successful solutions in the literature.

Penyelesaian Vehicle Routing Problem with Simultaneous Pick Up and Delivery dengan Algoritma Tabu Search

Vehicle routing problem with simultaneous pickup and delivery (VRPSPD) is a problem of vehicle routes where the delivery and pickup of products to each consumer is conducted simultaneously. Pangkalan Agisindo Alam Mandiri is engaged in the distribution of 3 kg LPG. At present,pangkalan already has a schedule of visits to retail stores, but the visit route is still subjectively set which causes long mileage and high transportation costs. The problem of determining the route at this pangkalanis calledVRPSPD because pangkalandelivered filled LPG tubes to retail stores and at the same time picked up empty LPG tubes to be brought back. To determine the route, one must consider the vehicle capacity and the ratio of the number of LPG tubes delivered and picked up. This research used a tabu search algorithm to get the proposed route. The steps in the tabu search algorithm includes determination of the initial solution, the neighborhood search, intensification phase, diversification phase, standard phase, and interactive phase. By using the tabu search algorithm, the proposed route provided a savings of transportation costs of 13.72% rather than the initial route of the company.

A Modified Meta-Heuristic Approach for Vehicle Routing Problem with Simultaneous Pickup and Delivery

The aim of this work is to develop an intelligent optimization software based on enhanced VNS meta-heuristic to tackle Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD). An optimization system developed based on enhanced Variable Neighborhood Search with Perturbation Mechanism and Adaptive Selection Mechanism as the simple but effective optimization approach presented in this work. The solution method composed by combining Perturbation based Variable Neighborhood Search (PVNS) with Adaptive Selection Mechanism (ASM) to control perturbation scheme. Instead of stochastic approach, selection of perturbation scheme used in the algorithm employed an empirical selection based on each perturbation scheme success along the search. The ASM help algorithm to get more diversification degree and jumping from local optimum condition using most successful perturbation scheme empirically in the search process. A comparative analysis with a well-known exact approach is presented to test the solution method in a generated VRPSPD benchmark instance in limited computation time. Then a test to VRPSPD scenario provided by a liquefied petroleum gas distribution company is performed. The test result confirms that solution method present superior performance against exact approach solution in giving best solution for larger sized instance and successfully obtain substantial improvements when compared to the basic VNS and original route planning technique used by a distributor company.

A selective adaptive large neighborhood search heuristic for the profitable tour problem with simultaneous pickup and delivery services

The Vehicle Routing Problem with Simultaneous Pickups and Deliveries (VRPSPD) is a variant of the Vehicle Routing Problem. In this variant, an unlimited fleet of capacitated vehicles is used to satisfy both pickup and delivery demands of each customer simultaneously. In many practical situations, such a fleet is costly. The present study extends the VRPSPD by assuming a fixed number of vehicles when the constraint of visiting all customers is relaxed. More specifically, profits are assigned to the customers with the goal of maximizing the difference between collected profits and routing costs. This variant is named Profitable Tour Problem with Simultaneous Pickup and Delivery services (PTPSPD). We present a mathematical model run with the CPLEX solver. We also present an extension of the Adaptive Large Neighborhood Search heuristic (ALNS) called selective ALNS (sALNS). sALNS uses a new operator selection that executes two phases alternately: the random and the score-dependent phases. An appropriate update of scores is employed. Furthermore, sALNS explores missed regions of the search space by evaluating solutions after the destruction step. Finally, we give tuned insertion and removal operators that handle the constraints of the PTPSPD, as well as a new update of temperature, that helps avoiding local optima, in the Simulated Annealing embedded in sALNS. sALNS is evaluated on 117 new instances with 50–199 customers. A comparison is made between the components of sALNS, the classical ALNS and a recent ALNS heuristic from the literature. sALNS is also evaluated on some VRPSPD instances from the literature. The computational results show that our heuristic provides good quality solutions in reasonable computing time.

A Bee Evolutionary Algorithm for Multiobjective Vehicle Routing Problem with Simultaneous Pickup and Delivery

A new closed-loop supply chain logistics network of vehicle routing problem with simultaneous pickups and deliveries (VRPSPD) dominated by remanufacturer is constructed, in which the customers are originally divided into three types: distributors, recyclers, and suppliers. Furthermore, the fuel consumption is originally added to the optimization objectives of the proposed VRPSPD. In addition, a bee evolutionary algorithm guiding nondominated sorting genetic algorithm II (BEG-NSGA-II) with a two-stage optimization mechanism is originally designed to solve the proposed VRPSPD model with three optimization objectives: minimum fuel consumption, minimum waiting time, and the shortest delivery distance. The proposed BEG-NSGA-II could conquer the disadvantages of traditional nondominated sorting genetic algorithm II (NSGA-II) and algorithms with a two-stage optimization mechanism. Finally, the validity and feasibility of the proposed model and algorithm are verified by simulating an engineering machinery remanufacturing company’s reverse logistics and another three test examples.

A hybrid algorithm for the vehicle routing problem with backhauls, time windows and three-dimensional loading constraints

This paper deals with a special vehicle routing problem with backhauls where customers may want to receive items from a depot and, at the same time, return items back to the depot. Moreover, time windows are assumed and three-dimensional loading constraints are to be observed, i.e. the items are three-dimensional boxes and packing constraints, e.g. regarding load stability, are to be met. The resulting problem is the vehicle routing problem with simultaneous delivery and pickup (VRPSDP), time windows and three-dimensional loading constraints (3L-VRPSDPTW). This problem occurs, for example, if retail stores are supplied by a central warehouse and wish to return packaging material. A particular challenge of the problem consists of transporting delivery and pickup items simultaneously on the same vehicle. In order to avoid any reloading effort during a tour, we consider two different approaches for loading the vehicles: (i) loading from the back with separation of the loading space into a delivery section and a pickup section and (ii) loading from the (long) side. A hybrid algorithm is proposed for the 3L-VRPSDPTW consisting of an adaptive large neighbourhood search for the routing and different packing heuristics for the loading part of the problem. Extensive numerical experiments are conducted with VRPSDP instances from the literature and newly generated instances for the 3L-VRPSDPTW.

An ant colony system empowered variable neighborhood search algorithm for the vehicle routing problem with simultaneous pickup and delivery

Along with the progress in computer hardware architecture and computational power, in order to overcome technological bottlenecks, software applications that make use of expert and intelligent systems must race against time where nanoseconds matter in the long-awaited future. This is possible with the integration of excellent solvers to software engineering methodologies that provide optimization-based decision support for planning. Since the logistics market is growing rapidly, the optimization of routing systems is of primary concern that motivates the use of vehicle routing problem (VRP) solvers as software components integrated as an optimization engine. A critical success factor of routing optimization is quality vs. response time performance. Less time-consuming and more efficient automated processes can be achieved by employing stronger solution algorithms. This study aims to solve the Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD) which is a popular extension of the basic Vehicle Routing Problem arising in real world applications where pickup and delivery operations are simultaneously taken into account to satisfy the vehicle capacity constraint with the objective of total travelled distance minimization. Since the problem is known to be NP-hard, a hybrid metaheuristic algorithm based on an ant colony system (ACS) and a variable neighborhood search (VNS) is developed for its solution. VNS is a powerful optimization algorithm that provides intensive local search. However, it lacks a memory structure. This weakness can be minimized by utilizing long term memory structure of ACS and hence the overall performance of the algorithm can be boosted. In the proposed algorithm, instead of ants, VNS releases pheromones on the edges while ants provide a perturbation mechanism for the integrated algorithm using the pheromone information in order to explore search space further and jump from local optima. The performance of the proposed ACS empowered VNS algorithm is studied on well-known benchmarks test problems taken from the open literature of VRPSPD for comparison purposes. Numerical results confirm that the developed approach is robust and very efficient in terms of both solution quality and CPU time since better results provided in a shorter time on benchmark data sets is a good performance indicator.

A hybrid metaheuristic algorithm for heterogeneous vehicle routing problem with simultaneous pickup and delivery

The Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD) is a variant of the classical Vehicle Routing Problem (VRP) where the vehicles serve a set of customers demanding pickup and delivery services at the same time. The VRPSPD can arise in many transportation systems involving both distribution and collection operations. Originally, the VRPSPD assumes a homogeneous fleet of vehicles to serve the customers. However, in many practical situations, there are different types of vehicles available to perform the pickup and delivery operations. In this study, the original version of the VRPSPD is extended by assuming the fleet of vehicles to be heterogeneous. The Heterogeneous Vehicle Routing Problem with Simultaneous Pickup and Delivery (HVRPSPD) is considered to be an NP-hard problem because it generalizes the classical VRP. For its solution, we develop a hybrid local search algorithm in which a non-monotone threshold adjusting strategy is integrated with tabu search. The threshold function used in the algorithm has an adaptive nature which makes it self-tuning. Additionally, its implementation is very simple as it requires no parameter tuning except for the tabu list length. The proposed algorithm is applied to a set of randomly generated problem instances. The results indicate that the developed approach can produce efficient and effective solutions.

Solving vehicle routing problem with simultaneous pickup and delivery using parallel simulated annealing algorithm

The vehicle routing problem with simultaneous pickup and delivery (VRPSPD) is an NP-hard combinatorial optimisation problem that has received considerable attention in the past few decades. A parallel simulated annealing (parallel-SA) algorithm that uses four different neighbourhood structures has been developed to solve this problem. The parallel-SA algorithm follows the traditional sequential SA algorithm with an integrated asynchronous and synchronous multiple Markov chains (MMC) approach and incorporates a master-slave structure in the algorithm. Computational results are reported for 72 test problems with 50 to 400 customers from Dethloff's medium-scale benchmark, Salhi and Nagy's medium-to-large-scale benchmark and Montane and Galvao's large-scale benchmark. Computational results indicate that the proposed parallel-SA algorithm is competitive with other well-known algorithms for VRPSPD from both quality of solution and computational time.

The Vehicle Routing Problem with Simultaneous Pick-ups and Deliveries and Two-Dimensional Loading Constraints

We introduce and solve the Vehicle Routing Problem with Simultaneous Pick-ups and Deliveries and Two-Dimensional Loading Constraints (2L-SPD). The 2L-SPD model covers cases where customers raise delivery and pick-up requests for transporting non-stackable rectangular items. 2L-SPD belongs to the class of composite routing-packing optimization problems. However, it is the first such problem to consider bi-directional material flows dictated in practice by reverse logistics policies. The aspect of simultaneously satisfying deliveries and pick-ups has a major impact on the underlying loading constraints: feasible loading patterns must be identified for every arc traveled in the routing plan. This implies that 2L-SPD generalizes previous routing problem variants with two-dimensional loading constraints which call for one feasible loading per route. From a managerial perspective, the simultaneous service of deliveries and pick-ups may bring substantial cost-savings, but the generalized loading constraints are very hard to tackle in reasonable computational times. To this end, we propose an optimization framework which employs memorization techniques designed for the 2L-SPD model, to accelerate the solution methodology. To assess the performance of our routing and packing algorithmic components, we have solved the Vehicle Routing Problem with Simultaneous Pick-ups and Deliveries (VRPSPD) and the Vehicle Routing Problem with Two-Dimensional Constraints (2L-CVRP). Computational results are also reported on newly constructed 2L-SPD benchmark problems. Apart from the basic 2L-SPD version, we introduce the 2L-SPD with LIFO constraints which prohibit item rearrangements along the routes. Computational experiments are conducted to understand the impact of the LIFO constraints on the routing plans obtained.

A dynamic closed-loop vehicle routing problem with uncertainty and incompatible goods

This study investigates a dynamic closed-loop vehicle routing problem (VRP) with uncertain pickup and deterministic delivery of incompatible goods, which is an extension of the VRP with simultaneous pickup and delivery (VRPSPD) in closed-loop logistics, where the incompatibility between goods of pickup and delivery is considered. The problem involves minimizing transportation cost, incompatibility and number of customers visited twice. A solution method based on variable neighborhood search (VNS) is developed for solving the VRPSPD. The pickup uncertainty is handled in two stages: first, a priori routes are generated by solving a VRPSPD whose pickup demands are estimated; second, the a priori routes are simulated by dynamically satisfying the pickup demand under incompatibility, and the second-round routes are generated to meet the unmet demands. The effects of considering the incompatibility are examined by experiments. A case of centralized tableware disinfection and logistics services in China’s catering industry is used for demonstration. Disinfected tableware for delivery and used tableware for pickup are incompatible because of potential cross-contamination. The experimental results quantitatively provide insights for managers who must solve the dynamic closed-loop VRP with uncertain pickup and incompatible goods. The proposed method also proves competitive for the VRPSPD.

An adaptive local search algorithm for vehicle routing problem with simultaneous and mixed pickups and deliveries

The Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD) is an extension to the classical Vehicle Routing Problem (VRP), where customers may both receive and send goods simultaneously. The Vehicle Routing Problem with Mixed Pickup and Delivery (VRPMPD) differs from the VRPSPD in that the customers may have either pickup or delivery demand. However, the solution approaches proposed for the VRPSPD can be directly applied to the VRPMPD. In this study, an adaptive local search solution approach is developed for both the VRPSPD and the VRPMPD, which hybridizes a Simulated Annealing inspired algorithm with Variable Neighborhood Descent. The algorithm uses an adaptive threshold function that makes the algorithm self-tuning. The proposed approach is tested on well-known VRPSPD and VRPMPD benchmark instances derived from the literature. The computational results indicate that the proposed algorithm is effective in solving the problems in reasonable computation time.

A combination of modified tabu search and elite ant system to solve the vehicle routing problem with simultaneous pickup and delivery

One of the most important extensions of the vehicle routing problem is the vehicle routing problem with simultaneous pickup and delivery (VRPSPD) in which the objective is to minimize the distance traveled by using a fleet of vehicles for servicing a set of customers. Because the VRPSPD is an NP-hard problem, practical large-scale instances of this problem cannot be solved by exact solution methodologies within acceptable computational time, and most of the researchers have focused on metaheuristic solution approaches. For this reason, a combined modified tabu search and elite ant system (MTSEAS) is proposed for solving the VRPSPD in this paper. The proposed algorithm is tested on 54 standard instances available from the literature. The computational result shows that the MTSEAS obtains 39 best known solutions of the benchmark problem, and it is competitive with other metaheuristic algorithms for solving VRPSPD.

Exact Solutions to the Symmetric and Asymmetric Vehicle Routing Problem with Simultaneous Delivery and Pick-Up

AbstractIn reverse logistics networks, products (e.g., bottles or containers) have to be transported from a depot to customer locations and, after use, from customer locations back to the depot. In order to operate economically beneficial, companies prefer a simultaneous delivery and pick-up service. The resulting Vehicle Routing Problem with Simultaneous Delivery and Pick-up (VRPSDP) is an operational problem, which has to be solved daily by many companies. We present two mixed-integer linear model formulations for the VRPSDP, namely a vehicle-flow and a commodity-flow model. In order to strengthen the models, domain-reducing preprocessing techniques, and effective cutting planes are outlined. Symmetric benchmark instances known from the literature as well as new asymmetric instances derived from real-world problems are solved to optimality using CPLEX 12.1.

A Hybrid PSO Algorithm for Vehicle Routing Problem with Simultaneous Delivery and Pickup

In this paper a hybrid algorithm named IPSO-VND is proposed and applied to solving the vehicle routing problem with simultaneous pickup and delivery (VRPSPD). The IPSO-VND algorithm combines two meta-heuristics: Improved Particle Swarm Optimization (IPSO) is used to find a group of excellent solutions, and then the Variable Neighborhood Descent (VND) is implemented to deeply search to achieve the optimal solution around these solutions. During the IPSO procedure, in order to make up for the change of a particle’s position, a velocity component is added to the movement of any particle which has been optimized or made feasible. During the VND procedure, three different neighborhood structures: insertion, swap and cross are successively used. Computational results on the benchmark problems show that our IPSO-VND algorithm is effective.

AIS-based Algorithm for Solving Vehicle Routing Problem with Simultaneous Pick-up and Delivery

Vehicle Routing Problem with Simultaneous Pick-up and Delivery (VRP-SPD) is regarded as an NP-hard problem, which takes unacceptable time to use traditional algorithms to solve. This article presents an artificial immune systems or AIS-based algorithm to solve the problem and the results shows competitive performance. This algorithm is embedded with a sweep approach to generate random initial population. For the mutation operator, a variety of local search techniques are applied to realize the diversity. The algorithm is tested with examples which used by many of other works and compared with the results obtained by an exact algorithm. Experimental results suggest that the algorithm is a valuable alternative to other metaheuristics for solving VRP-SPD. 

GENVNS-TS-CL-PR: A heuristic approach for solving the vehicle routing problem with simultaneous pickup and delivery

This work addresses the Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD). Due to its complexity, we propose a heuristic algorithm for solving it, so-called GENVNS-TS-CL-PR. This algorithm combines the heuristic procedures Cheapest Insertion, Cheapest Insertion with multiple routes, GENIUS, Variable Neighborhood Search (VNS), Variable Neighborhood Descent (VND), Tabu Search (TS) and Path Relinking (PR). The first three procedures aim to obtain an good initial solution, and the VND and TS are used as local search methods for VNS. TS is called after some iterations without any improvement through of the VND. The PR procedure is called after each VNS iteration and it connects a local optimum with an elite solution generated during the search. The algorithm uses an strategy based on Candidate List to reduce the number of solutions evaluated in the solution space. The algorithm was tested on benchmark instances taken from the literature and it was able to generate high quality solutions.