Vehicle Routing Problem With Pickup And Delivery Research Articles

Green last-mile route planning for efficient e-commerce distribution

Abstract This study aims to design vehicle routes based on cost minimisation and the minimisation of greenhouse gasses (GHG) emissions to help companies solve the vehicle routing problem with pickup and delivery (VRPPD) via particle swarm optimisation (PSO). An effective metaheuristics search technique called particle swarm optimisation (PSO) was applied to design the optimal route for these problems. Simulated data from Li and Lim (2001) were used to evaluate the PSO performance for solving green vehicle routing problems with pickup and delivery (Green VRPPD). The findings suggest that green vehicle routing problems with pickup and delivery should be used when distributing products to customers living in a specific area called a cluster. However, the design of vehicle routes by Green VRPPD costs more when used to distribute products to customers living randomly in a coverage service area. When logistics providers decide to use Green VRPPD instead of VRPPD, they need to be concerned about possible higher costs if an increase in the number of vehicles is needed. PSO has been confirmed for solving VRPPD effectively. The study compared the results based on the use of two different objective functions with fuel consumption from diesel and liquefied petroleum gas (LPG). It indicates that solving VRPPD by considering the emissions of direct greenhouse gases as an objective function provides cleaner routes, rather than considering total cost as the objective function for all test cases. However, as Green VRPPD requires more vehicles and longer travel distances, this requires a greater total cost than considering the total cost as the objective function. Considering the types of fuels used, it is obvious that LPG is more environmentally friendly than diesel by up to 53.61 %. This paper should be of interest to a broad readership, including those concerned with vehicle routing problems, transportation, logistics, and environmental management. The findings suggest that green vehicle routing problems with pickup and delivery should be used when distributing products to a cluster. However, the design of vehicle routes by Green VRPPD costs more when used to distribute products to customers living randomly in a coverage service area. When logistics providers decide to use Green VRPPD instead of VRPPD, they need to be concerned about possible higher costs if an increase in the number of vehicles is needed.

Mixed Integer Linear Programming Models to Solve a Real-Life Vehicle Routing Problem with Pickup and Delivery

This paper presents multiple readings to solve a vehicle routing problem with pickup and delivery (VRPPD) based on a real-life case study. Compared to theoretical problems, real-life ones are more difficult to address due to their richness and complexity. To handle multiple points of view in modeling our problem, we developed three different Mixed Integer Linear Programming (MILP) models, where each model covers particular constraints. The suggested models are designed for a mega poultry company in Tunisia, called CHAHIA. Our mission was to develop a prototype for CHAHIA that helps decision-makers find the best path for simultaneously delivering the company’s products and collecting the empty boxes. Based on data provided by CHAHIA, we conducted computational experiments, which have shown interesting and promising results.

Multi-objective Optimization of Vehicle Routing with Resource Repositioning

This paper deals with a vehicle routing problem with resource repositioning (VRPRR) which is a variation of well-known vehicle routing problem with pickup and delivery (VRPPD). VRPRR in which static repositioning of public bikes is a representative case, can be defined as a multi-objective optimization problem aiming at minimizing both transportation cost and the amount of unmet demand. To obtain Pareto sets for the problem, famous multi-objective optimization algorithms such as Strength Pareto Evolutionary Algorithm 2 (SPEA2) can be applied. In addition, a linear combination of two objective functions with weights can be exploited to generate Pareto sets. By varying weight values in the combined single objective function, a set of solutions is created. Experiments accomplished with a standard benchmark problem sets show that Variable Neighborhood Search (VNS) applied to solve a number of single objective function outperforms SPEA2. All generated solutions from SPEA2 are completely dominated by a set of VNS solutions. It seems that local optimization technique inherent in VNS makes it possible to generate near optimal solutions for the single objective function. Also, it shows that trade-off between the number of solutions in Pareto set and the computation time should be considered to obtain good solutions effectively in case of linearly combined single objective function.

Optimization of capacitated vehicle routing problem with alternative delivery, pick-up and time windows: A modified hybrid approach

In this paper, the optimization of Capacitated Vehicle Routing Problem with Alternative Delivery, Pick-up and Time windows is considered. The development of this problem was motivated by analysis of postal and courier delivery issues. In some generalization, the problem examined can be classified as a combination of many variants of the classical VRP (Vehicle Routing Problem), such as CVRP (Capacitated Vehicle Routing Problem), VRPPD (Vehicle Routing Problem with Pickup and Delivery), VRPTW (Vehicle Routing Problem with Time Windows), etc. What distinguishes the presented problem from known variants of VRPs is the introduction of alternative delivery points and parcel lockers incorporated into the distribution network and the ability to take into account logical constraints. The problem model was formulated in the form of BIP (Binary Integer Programming). Moreover, the original hybrid approach integrating CP (Constraint Programming), GA (Genetic Algorithm) and MP (Mathematical Programming) was proposed for the model implementation and optimization. Numerous computational experiments verifying the correctness of the model and the effectiveness of the hybrid approach were also presented.

Optimal Express Bus Routes Design with Limited-Stop Services for Long-Distance Commuters

This research aimed to propose a route optimization method for long-distance commuter bus service to improve the attraction of public transport as a sustainable travel mode. Taking the express bus services (EBS) in Changping Corridor in Beijing as an example, we put forward an EBS route-planning method for long-distance commuter based on a solving algorithm for vehicle routing problem with pickups and deliveries (VRPPD) to determine the length of routes, number of lines, and stop location. Mobile phone location (MPL) data served as a valid instrument for the origin–destination (OD) estimation, which provided a new perspective to identify the locations of homes and jobs. The OD distribution matrices were specified via geocoded MPL data. The optimization objective of the EBS is to minimize the total distance traveled by the lines, subject to maximum segment capacity constraints. The sensitivity analysis was done to several key factors (e.g., the segment capacity, vehicle capacity, and headway) influencing the number of lines, the length of routes. The results suggest that the scenario with the segment capacity of 4000 passengers/h has a minimum of number and length of lines, but we recommend that the transit agency adopt 3000 passengers/h as the route segment capacity because this scenario results in minimum fleet size and minimum total operation length.

Dynamic Memory Memetic Algorithm for VRPPD With Multiple Arrival Time and Traffic Congestion Constraints

With the new distribution demands emerging continuously in the last decade, the distribution mode is changing gradually in various applications, such as e-commerce, emergency relief supplies distribution, the last-mile delivery, and so on. We formulate vehicle routing problem with pickup and delivery (VRPPD), while simultaneously considering multiple arrival time and traffic congestion constraints. Our model focuses on the clear changes of the distribution mode to meet the fast-delivery requirements in the last decade, which is characterized by the multi-batch arrival of goods in 24 hours and time-varying congestion in various time windows. Besides, we propose dynamic memory memetic algorithm, which updates its dynamic memory by whether to promote populations to find new better solutions or not. This is an effective acceleration mechanism to promote the population progress. Meanwhile, dynamic memory memetic algorithm determines the serious congestion tasks in the delivery route and transforms them into normal congestion or even non-congestion tasks. Test sets with 30 test problems are constructed by using real distribution data from Alibaba Cloud and traffic congestion data from Baidu Map in Shanghai. By comparing with four compared algorithms, the effectiveness, efficiency, and robustness of our proposed algorithm in non-congestion and congestion tests are simultaneously demonstrated.

Distributed forecasting and ant colony optimization for the bike-sharing rebalancing problem with unserved demands.

Bike-sharing systems (BSS) have widely spread over many cities in the world as an environmentally friendly means to reduce air pollution and traffic congestion. This paper focuses on the bike-sharing rebalancing problem (BRP), which consists of two aspects: determining desired demands at each station and designing routes to redistribute bikes among stations. For the first task, we firstly apply the random forest, a very efficient machine learning algorithm, to forecast desired demands for each station, which can be easily implemented with distributed computing. For the second task, it belongs to the broad class of the vehicle routing problem with pickup and delivery (VRPPD). In most existing settings, all of the demands being strictly satisfied can lead to longer routes and add operational costs. In this paper, we propose a new model with unserved demands by relaxing demands satisfying constraints. Then, we design a distributed ant colony optimization (ACO) based algorithm with some specific modifications to increase its efficiency for the proposed model. We propose to use the percentage of average cost saving per bike as a metric to evaluate the performance of our method on cost-reducing and compare with existing methods and best-known values. Computational results on benchmarks show the advantage of our approach. Finally, we provide a real case study of BSS in Hangzhou, China, with insightful elaborations.

Optimization of Vehicle Routing in Simultaneous Pickup and Delivery Service: A Case study

In this case study, considered the vehicle routing problem with pickup and delivery which is a generalization of the capacitated vehicle routing problem (CVRP). The vehicle routing problem with pickup and delivery (VRPPD) arises whenever pickup demand and delivery demand is to be satisfied by the same vehicle. The problem is encountered in many real life situations. In this paper problem arises from the distribution of beverages and collection of recyclable containers. It can be modeled as a variant of the vehicle routing problem with a heterogeneous vehicle fleet, capacity and volume constraints, and an objective function combining routing distance or minimizing the total travel distance to serve customers located to different locations. Three construction heuristics and an improvement procedure are developed for the problem and designing a set of routes with minimum cost to serve a delivery of beverages and a collection of recyclable material with a fleet of vehicles. The aim of this paper is to develop a vehicle routing Problem (VRP) model that addresses simultaneous pickup and delivery in the beverage distribution. To this effect, a mathematical model is adopted and fitted with real data collected from MOHA Soft drinks Summit Plant located in Ethiopia, and solved using Clark-Wright saving algorithm. The form-to-distance is computed from the data collected from Google Earth and the customer’s data from the MOHA. The findings of the study show that the model is feasible and showed an improvement as compared to the current performances of the plant with respect to product distribution and collection and the total distance covered is minimized about 27.79%. The average performances of the model show that on average 5 routes are required to serve customers’ demands.

An ILP-based local search procedure for the VRP with pickups and deliveries

In this paper we address the Vehicle Routing Problem with Pickups and Deliveries (VRPPD), an extension of the classical Vehicle Routing Problem (VRP) where we consider precedences among customers, and develop an Integer Linear Programming (ILP) based local search procedure. We consider the capacitated one-to-one variant, where a particular precedence must be satisfied between pairs of origin-destination customers. We extend the scheme proposed in De Franceschi et al. (Math Program 105(2–3):471–499, 2006) for the Distance-Constrained Capacitated VRP, which has been successfully applied to other variants of the VRP. Starting from an initial feasible solution, this scheme follows the destroy/repair paradigm where a set of vertices is removed from the routes and reinserted by solving heuristically an associated ILP formulation with an exponential number of variables, named Reallocation Model. In this research, we propose two formulations for the Reallocation Model when considering pickup and delivery constraints and compare their behavior within the framework in terms of the trade off between the quality of the solutions obtained and the computational effort required. Based on the computational experience, the proposed scheme shows good potential to be applied in practice to this kind of problems and is a good starting point to consider more complex versions of the VRPPD.

Swarm-based approach for solving the ambulance routing problem

The efficient management of ambulance routing for emergency requests is vital to save patients when a disaster response scenario occurs. In today’s road traffic, the transportation of patients from emergency points becomes more difficult in such tragic situation. We consider an urgent situation where a lot of hurts/patients require simultaneously an urgent medical care. Patients are either (1) slightly injured which can be assisted on the spot or (2) seriously injured which should be transferred to hospitals. In this paper, we aim to enhance the response-time performance of emergency medical service providers by handling the ambulance routing problem (ARP). The problem can be modeled as either the Open Vehicle Routing Problem (OVRP) or a Vehicle Routing Problem with Pickup and Delivery (VRPPD). We propose a cluster-first route-second algorithm based on the Petal algorithm and the particle swarm optimization (PSO) approach in order to handle efficiently the ARP.

Revisión del estado del arte del problema de ruteo de vehículos con recogida y entrega (VRPPD)

Revisión del estado del arte del problema de ruteo de vehículos con recogida y entrega (VRPPD)

Distribution Route Optimization of Gallon Water Using Genetic Algorithm and Tabu Search

Abstract Distributions of drinking water in gallons often do not pay attention to the problem of finding the most optimal route, thus causing inefficiency in the cost of shipping. To minimize incurred costs, it is necessary to minimize vehicle fleet and amount of travel distance, with the restriction that the vehicle must have sufficient capacity to transport the goods to be shipped and return it back to the depots. This problem could be framed as a Vehicle Routing Problem with pick-up and delivery (VRPPD). In this paper, we propose a method to optimize delivery route in a drinking water depot by combining genetic algorithm (GA) and Tabu search. GA has advantages by providing possible solutions while Tabu covers up its shortfall in identifying local solutions so that searching will able to avoid loop in the area of the same solution. Experimental results show that the proposed method is more efficient than a manually predetermined route.

An algorithm based on granular tabu search for the problem of balancing public bikes by using multiple vehicles

The use of public bicycle systems has gained great importance in European countries and around the globe; this has led to the need to seek advanced techniques to help decision making. A public bicycle system consists of a set of points where you can pick up and deliver bicycles; a headquarters where a group of vehicles taking leftover bikes and transported to the points where a deficit (the demand exceeds supply) exists. One of the major problems that arise in systems of public bike is balanced, which involves sending bikes from the point where an offer (bicycles left over) to the point where there is a demand (bikes missing) occurs. The way to model this problem is with an adaptation of the vehicle routing problem with pickup and delivery (VRPPD), allowing each route make partial deliveries to customers and limiting the number of customers to visit by each route. In this paper an integer linear programming model is introduced and a metaheuristic based on granular tabu search to find a local optimum. Instances from 15 to 500 customers adapted from the literature are used. The computational results show that the proposed algorithm finds solutions in short computational time.

A new saving-based ant algorithm for the Vehicle Routing Problem with Simultaneous Pickup and Delivery

The Vehicle Routing Problem with Pickup and Delivery (VRPPD) is a variant of the Vehicle Routing Problem where the vehicles are not only required to deliver goods but also to pick up some goods from the customers. Given a fleet of vehicles and a set of customers with known delivery and/or pickup demands for one commodity VRPPD determines a set of vehicle routes originating and ending at a single depot and visiting all customers exactly once. The objective is to minimize the total distance traversed. In this paper, we consider a special case of the VRPPD where each customer has both a delivery and a pickup demand to be satisfied simultaneously. For this problem, we propose an ant colony algorithm employing a new saving-based visibility function and pheromone updating procedure. The numerical tests with the well-known benchmark problems from the literature show that the proposed approach provides competitive results and improves several best-known solutions.

A Geometrical Center based Two-way Search Heuristic Algorithm for Vehicle Routing Problem with Pickups and Deliveries

Abstract: The classical vehicle routing problem (VRP) can be extended by including customers who want to send goods to the depot. This type of VRP is called the vehicle routing problem with pickups and deliveries (VRPPD). This study proposes a novel way to solve VRPPD by introducing a two-phase heuristic routing algorithm which consists of a clustering phase and uses the geometrical center of a cluster and route establishment phase by applying a two-way search of each route after applying the TSP algorithm on each route. Experimental results show that the suggested algorithm can generate bet-ter initial solutions for more computer-intensive meta-heuristics than other existing methods such as the giant-tour-based partitioning method or the insertion-based method. Keywords: Vehicle Routing Problem, Heuristic Algorithm, Initial Solution 1. Introduction The vehicle routing problem (VRP) is a combinatorial optimization and nonlinear programming problem seeking to service a number of customers with a fleet of vehicles. VRP has been an important problem in the fields of trans-portation, distribution and logistics since Dantzig and Ramser [1] first proposed the problem. The vehicle routing problem with pickups and deliveries (VRPPD) is an exten-sion of the classical VRP. Recently, research on VRPPD has peaked essentially because pickup demands for pack-aging and used product returns from customer locations have increased substantially due to environmental and gov-ernment regulations, and the fact that integrating pickups with deliveries maximally utilizes vehicle capacity and saves money. So, VRPPD must take into account the goods that customers return to the delivery vehicle. This restric-tion makes the planning problem more difficult and in-creases travel distances or the number of vehicles. Re-stricted situations, in which there are no interchanges of goods between customers and all delivery demands start from the depot and all pickup demands are brought back to the depot, are usually considered in VRPPD. The VRPPD model can be classified into three: Deliv-ery-first and pickup-second; mixed pickups and deliveries; and simultaneous pickup and deliveries. The assumption of the delivery-first and pickup-second model is that all deliv-eries must be made before any pickups. This assumption was due to the fact that vehicles were rear-loaded and be-cause rearranging delivery loads onboard to accommodate new pickup loads was difficult. However, in recent days, most vehicles have side-loading as well as rear-loading functions. To accommodate new pickup loads, rearranging delivery loads onboard is no longer a requirement. Hence, the assumption that all deliveries must be made before any pickup can occur can be relaxed, allowing for the mixed pickups and deliveries model in which deliveries and pick-ups may occur in any sequence on a vehicle route. When customers can simultaneously receive and send goods, it is referred to as simultaneous pickup and delivery. A VRPPD solution is feasible only if the following three conditions are satisfied: delivery-feasibility, pickup-feasibility and load-feasibility. Delivery-feasibility and pickup-feasibility mean that both the total delivery and the total pickup de-mands on any vehicle route do not exceed the maximum capacity of the vehicle; and load-feasibility means that the maximum capacity of the vehicle is not exceeded at any point on the route. This study focuses on solving mixed pickups and deliv-eries by applying the two-phase heuristic algorithm. The first phase selects cluster seed as the farthest node (cus-tomers) among unclustered nodes and cluster nodes by using the notion of the geometrical center of a cluster. The result of the first phase satisfies the delivery-feasibility and pickup-feasibility since the total deliveries and pickups on any route is less than or equal to the maximum capacity of the vehicle. And the second phase of the algorithm applies the TSP algorithm on each cluster to find the shortest route regardless of load-feasibility and then finds routes that sat-DOI : 10.3745/JIPS.2009.5.4.237

Lasso solution strategies for the vehicle routing problem with pickups and deliveries

This paper considers the vehicle routing problem with pickups and deliveries (VRPPD) where the same customer may require both a delivery and a pickup. This is the case, for instance, of breweries that deliver beer or mineral water bottles to a set of customers and collect empty bottles from the same customers. It is possible to relax the customary practice of performing a pickup when delivering at a customer, and postpone the pickup until the vehicle has sufficient free capacity. In the case of breweries, these solutions will often consist of routes in which bottles are first delivered until the vehicle is partly unloaded, then both pickup and delivery are performed at the remaining customers, and finally empty bottles are picked up from the first visited customers. These customers are revisited in reverse order, thus giving rise to lasso shaped solutions. Another possibility is to relax the traditional problem even more and allow customers to be visited twice either in two different routes or at different times on the same route, giving rise to a general solution. This article develops a tabu search algorithm capable of producing lasso solutions. A general solution can be reached by first duplicating each customer and generating a Hamiltonian solution on the extended set of customers. Test results show that while general solutions outperform other solution shapes in term of cost, their computation can be time consuming. The best lasso solution generated within a given time limit is generally better than the best general solution produced with the same computing effort.

Decision Support for Optimal Repositioning of Containers in a Feeder System

The transport of empty containers represents a seriousproblem in the fast growing sphere of maritime container transport.The most widespread type of container transport organizationin maritime transport is the hub and spoke mode, whichenables the transport of a great number of containers via largevessels between hub ports, from where feeder ships transport tosmaller ports that thus gravitate to the central hub port. The articlecontains a detailed analysis of the northern Adriatic portsand the feeder connections with the hub ports of the Mediterranean.A two-level VRPPD (Vehicle Routing Problem withPickup and Delivery) problem is modelled on a graph, wherethe transport of full containers is privileged over the transport ofempty containers. This enables the simulation of the feeder systemin the n01them Adriatic, meaning that it shows the ship'soperator the movement programme with minimal transportcosts for the superfluous empty containers in the complex of theregular transports of full containers in the feeder system.

A reactive tabu search algorithm for the vehicle routing problem with simultaneous pickups and deliveries

The vehicle routing problem with pickups and deliveries (VRPPD) extends the vehicle routing problem (VRP) by allowing customers to both send and receive goods. The main difficulty of the problem is that the load of vehicles is fluctuating rather than decreasing as in the VRP. We design a reactive tabu search metaheuristic that can check feasibility of proposed moves quickly and reacts to repetitions to guide the search. Several new best solutions are found for benchmark problems.

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

The Vehicle Routing Problem with Pickups and Deliveries (VRPPD) is an extension to the classical Vehicle Routing Problem (VRP), where customers may both receive and send goods. We do not make the assumption common in the VRPPD literature, that goods may only be picked up after all deliveries have been completed. We also eschew the concept of insertion and propose a method that treats pickups and deliveries in an integrated manner. This method finds a solution to the corresponding VRP problem and modifies this solution to make it feasible for the VRPPD. Such modification is achieved mainly by heuristic routines taken from VRP methodology but modified such that their aim becomes the reduction of infeasibilities, although a number of problem-specific routines are also constructed. To render our procedures efficient when checking feasibility, we built appropriate mathematical relationships to describe changes in the maximum load of routes. Furthermore, several enhancements are introduced. Our methodology is also capable of solving multi-depot problems, which has not been done before for this challenging general version of the VRPPD. The methods are tested for single and multiple depot problems with encouraging results.