Rich Vehicle Routing Problem Research Articles

Planning of Vehicle Routes for the Exam Booklet Distribution: A GIS-based Solution Approach

A vehicle routing problem (VRP) motivated by the case of examination booklet distribution is considered in this paper. In this practical and repetitive problem, the boxes include exam booklets that are taken from a depot in the university campus and distributed to the 49 exam buildings (schools) where the exams are held in. For the distribution, there are 18 vehicles with different box capacities. The exam is organized in four different sessions. Visiting the maximum three schools by each vehicle is one of the main constraints of the problem. This rich VRP variant concerns the capacitated heterogonous vehicle fleet, multi-distribution and a limited number of schools visited. Due to the complexity and NP-hardness of the problem, a geographic information system (GIS)-based solution approach, which uses a tabu search heuristic method, is applied to obtain an acceptable solution in a reasonable time. Our numerical results show that total traveled distance, average capacity utilization and the total number of routes are improved by 19.39%, 9.14%, and 9.38%, respectively, in comparison with the current distribution plan.

Integrated planning of inbound and outbound logistics with a Rich Vehicle Routing Problem with backhauls

This paper addresses the integration of the planning decisions concerning inbound logistics in an industrial setting (from the suppliers to the mill) and outbound logistics (from the mill to customers). The goal is to find the minimum cost routing plan, which includes the cost-effective outbound and inbound daily routes (OIRs), consisting of a sequence of deliveries of customer orders, pickup of a full truck-load at a supplier, and its delivery to the mill. This study distinguishes between three planning strategies: opportunistic backhauling planning (OBP), integrated inbound and outbound planning (IIOP) and decoupled planning (DIOP), the latter being the commonly used, particularly in the case of the wood-based panel industry under study. From the point of view of process integration, OBP can be considered as an intermediate stage from DIOP to IIOP. The problem is modelled as a Vehicle Routing Problem with Backhauls, enriched with case-specific rules for visiting the backhaul, split deliveries to customers and the use of a heterogeneous fleet. A new fix-and-optimise matheuristic is proposed for this problem, seeking to obtain good quality solutions within a reasonable computational time. The results from its application to the wood-based panel industry in Portugal show that IIOP can help to reduce total costs in about 2.7%, when compared with DIOP, due to better use of the delivery truck and a reduction of the number of dedicated inbound routes. Regarding OBP, fostering the use of OIRs does not necessarily lead to better routing plans than DIOP, as it depends upon a favourable geographical configuration of the set of customers to be visited in a day, specifically, the relative distance between a linehaul that can be visited last in a route, a neighboring backhaul, and a mill. The paper further provides valuable managerial insights on how the routing plan is impacted by the values of business-related model parameters which are set by the planner with some degree of uncertainty. Results suggest that increasing the maximum length of the route will likely have the largest impact in reducing transportation costs. Moreover, increasing the value of a reward paid for visiting a backhaul can foster the percentage of OIR in the optimal routing plan.

Efficient implementation of the genetic algorithm to solve rich vehicle routing problems

The aim of this paper is to further study the rich vehicle routing problem (RVRP), which is a well-known combinatorial optimization problem arising in many transportation and logistics settings. This problem is known to be subject to a number of real life constraints, such as the number and capacity limitation of vehicles, time constraints including ready and due dates for each customer, heterogeneous vehicle fleets and different warehouses for vehicles. A Genetic Algorithm (GA)-based approach is proposed to tackle this highly constrained problem. The proposed approach efficiently resolves the problem despite its high complexity. To the best of our knowledge, no GA have been used for solving multi-depot heterogeneous limited fleet VRP with time windows so far. The new algorithm has been tested on benchmark and real-world instances. In fact, promising computational results have shown its good cost-effectiveness.

Rich vehicle routing problem with last-mile outsourcing decisions

This paper addresses a Rich Vehicle Routing Problem (RVRP) characterized by the following attributes: long-haul transport, driver hours regulation, incompatibility among goods, multiple depots and pickup locations, heterogeneous vehicles, time windows, and outsourcing decisions for last-mile delivery. Addressing the latter aspect is especially challenging, since it requires substantial changes in the algorithms for generating initial feasible solutions (construction heuristic) and for modifying existing solutions (improvement heuristics). Our work develops new heuristics adapted to the above attributes, and evaluates their performance in combination with common solving metaheuristics. We also assess the impact and the cost-effectiveness of the outsourcing attribute.

Rich vehicle routing with auxiliary depots and anticipated deliveries: An application to pharmaceutical distribution

We present and solve a rich vehicle routing problem based on a practical distribution problem faced by a third-party logistics provider, whose aim is to deliver pharmaceutical products to healthcare facilities in Tuscany. The problem is characterized by having multiple depots, a heterogeneous fleet of vehicles, flexible time windows, periodic demands, incompatibilities between vehicles and customers, a maximum duration for the routes, and a maximum number of customers per route. A multi-start iterated local search algorithm making use of several neighborhoods is proposed to solve the problem. The algorithm has been tested on a large number of instances and obtained good results, both on the real case study and on a number of artificially generated instances.

An Exact Algorithm for a Rich Vehicle Routing Problem with Private Fleet and Common Carrier

The vehicle routing problem with private fleet and common carrier (VRPPC) is a generalization of the classical vehicle routing problem in which the owner of a private fleet can either visit a customer with one of the owner’s vehicles or assign the customer to a common carrier. The latter case occurs if the demand exceeds the total capacity of the private fleet or if it is more economically convenient to do so. The owner’s objective is to minimize the variable and fixed costs for operating the owner’s fleet plus the total cost charged by the common carrier. This family of problems has many practical applications, particularly in the design of last-mile distribution services and has received some attention in the literature, in which some heuristics were proposed. We extend here the VRPPC by considering more realistic cost structures that account for quantity discounts on outsourcing costs and by considering time windows resulting in a rich VRPPC (RVRPPC). We present an exact approach based on a branch-and-cut-and-price algorithm for the RVRPPC and test the algorithm on instances from the literature.

New Shades of the Vehicle Routing Problem: Emerging Problem Formulations and Computational Intelligence Solution Methods

This paper presents an overview of recent advances in the field of the vehicle routing problem (VRP), based on papers published in high-quality journals during the period from January 2015 to July 2017. A distinctive feature of the presented survey is its focus on new versions of the VRP, which have recently emerged or gained momentum, and the corresponding new solution methods, with particular emphasis on computational intelligence (CI) approaches. The list of newly proposed or currently popular VRP formulations include last mile and same day delivery , crowdshipping , bike sharing systems , post-disaster response plans , local routing in large production or cargo plants , customer-centric VRP , autonomous delivery , unnamed aerial vehicle delivery , green VRP , waste collection VRP , rich VRP, or VRP with backhauls . Simultaneously, an adequate increase of interest in the application of traditional CI methods (e.g., genetic , memetic , ant colony or particle swarm optimization , simulated annealing , or their various hybrid versions) can be observed in the VRP domain. At the same time, approaches proven efficient in other optimization areas (e.g., hyperheuristics , methods based on Monte Carlo simulations , algorithms rooted in game theory and bi-level optimization—Stackelberg games, or cognitively motivated methods ) have lately entered the VRP field and become a viable alternative to more traditional techniques. Since VRP is one of the fastest growing fields in the operations research area, we believe that an analysis of the recently published VRP papers from the perspective of their novelty in problem formulation and/or applied solution method can provide a true value for the CI community, especially young researchers entering the field and seeking challenges in this interesting and fast developing research area.

Solving Rich Vehicle Routing Problem Using Three Steps Heuristic

Vehicle Routing Problem (VRP) relates to the problem of providing optimum service with a fleet of vehicles to customers. It is a combinatorial optimization problem. The objective is usually to maximize the profit of the operation. However, for public transportation owned and operated by the government, accessibility takes priority over profitability. Accessibility usually reduces profit, while increasing profit tends to reduce accessibility. In this research, we look at how accessibility can be increased without penalizing the profitability. This requires the determination of routes with minimum fuel consumption, the maximum number of ports of call and maximum load factor satisfying a number of pre-determined constraints: hard and soft constraints. To solve this problem, we propose a heuristic algorithm. The results from this experiment show that the algorithm proposed has better performance compared to the partitioning set.

Solving Rich Vehicle Routing Problem Using Three Steps Heuristic

Vehicle Routing Problem (VRP) relates to the problem of providing optimum service with a fleet of vehicles to customers. It is a combinatorial optimization problem. The objective is usually to maximize the profit of the operation. However, for public transportation owned and operated by the government, accessibility takes priority over profitability. Accessibility usually reduces profit, while increasing profit tends to reduce accessibility. In this research, we look at how accessibility can be increased without penalizing the profitability. This requires the determination of routes with minimum fuel consumption, the maximum number of ports of call and maximum load factor satisfying a number of pre-determined constraints: hard and soft constraints. To solve this problem, we propose a heuristic algorithm. The results from this experiment show that the algorithm proposed has better performance compared to the partitioning set.

Heuristics for Rich Vehicle Routing Problem : A Case of a Korean Mixed Feed Company

The vehicle routing problem is one of the vibrant research problems for half a century. Many studies have extensively studied the vehicle routing...

Comparison of GA-Shift Neighbourhood Mutation and GA-Pairs Exchange Mutation with Multi Cut Point Crossover in Solving RICH-VRP

This research was focused on a heterogeneous fleet of passenger ships to solve multi depot by using genetic algorithm (GA) to solve combinatorial problem i.e. vehicle routing problem (VRP). The objective of this study is to compare the roulette wheel selection, multi cut point crossover, and shift neighbourhood mutation with roulette wheel selection, multi cut point crossover, and pairs exchange mutation to minimize the sum of the fuel consumption travelled, the cost for violations of the ship draft and sea depth, and penalty cost for violations of the load factor; maximize number port of call; and maximize load factor. Problem solving in this study is how to generate feasible route combinations for rich VRP that meets all the requirements with optimum solution. Route generated by roulette wheel selection, multi cut point crossover, and shift neighbourhood mutation could decrease fuel consumption about 19.4350% compared to roulette wheel selection, multi cut point crossover, and pairs exchange mutation about 18.6738%.

A rich vehicle routing problem arising in the replenishment of automated teller machines

This paper introduces, models, and solves a rich vehicle routing problem (VRP) motivated by the case study of replenishment of automated teller machines (ATMs) in Turkey. In this practical problem, commodities can be taken from the depot, as well as from the branches to efficiently manage the inventory shortages at ATMs. This rich VRP variant concerns with the joint multiple depots, pickup and delivery, multi-trip, and homogeneous fixed vehicle fleet. We first mathematically formulate the problem as a mixed-integer linear programming model. We then apply a Geographic Information System (GIS)-based solution method, which uses a tabu search heuristic optimization method, to a real dataset of one of the major bank. Our numerical results show that we are able to obtain solutions within reasonable solution time for this new and challenging practical problem. The paper presents computational and managerial results by analyzing the trade-offs between various constraints.

A unified framework for rich routing problems with stochastic demands

We introduce a unified framework for rich vehicle and inventory routing problems with complex physical and temporal constraints. Demands are stochastic, can be non-stationary, and are forecast using any model that provides the expected demands and their error term distribution, which can be any theoretical or empirical distribution. We offer a detailed discussion on the modeling of demand stochasticity, focusing on the probabilities and cost effects of undesirable events, such as stock-outs, breakdowns and route failures, and their associated recourse actions. Tractability is achieved through the ability to pre-compute or at least partially pre-process the stochastic information, which is possible under mild assumptions for a general inventory policy. We integrate the stochastic aspect into a mixed integer non-linear program, illustrate applications to various problem classes, and show how to model specific problems through the lens of inventory routing. The case study is based on two sets of realistic instances, representing a waste collection inventory routing problem and a facility maintenance problem, respectively. We analyze the effects of our assumptions on modeling realism and tractability, and demonstrate that our framework significantly outperforms deterministic policies in its ability to limit the number of undesirable events for the same routing cost.

A Discrete and Improved Bat Algorithm for solving a medical goods distribution problem with pharmacological waste collection

Abstract The work presented in this paper is focused on the resolution of a real-world drugs distribution problem with pharmacological waste collection. With the aim of properly meeting all the real-world restrictions that comprise this complex problem, we have modeled it as a multi-attribute or rich vehicle routing problem (RVRP). The problem has been modeled as a Clustered Vehicle Routing Problem with Pickups and Deliveries, Asymmetric Variable Costs, Forbidden Roads and Cost Constraints. To the best of authors knowledge, this is the first time that such a RVRP problem is tackled in the literature. For this reason, a benchmark composed of 24 datasets, from 60 to 1000 customers, has also been designed. For the developing of this benchmark, we have used real geographical positions located in Bizkaia, Spain. Furthermore, for the proper dealing of the proposed RVRP, we have developed a Discrete and Improved Bat Algorithm (DaIBA). The main feature of this adaptation is the use of the well-known Hamming Distance to calculate the differences between the bats. An effective improvement has been also contemplated for the proposed DaIBA, which consists on the existence of two different neighborhood structures, which are explored depending on the bat's distance regarding the best individual of the swarm. For the experimentation, we have compared the performance of our presented DaIBA with three additional approaches: an evolutionary algorithm, an evolutionary simulated annealing and a firefly algorithm. Additionally, with the intention of obtaining rigorous conclusions, two different statistical tests have been conducted: the Friedman's non-parametric test and the Holm's post-hoc test. Furthermore, an additional experimentation has been performed in terms of convergence. Finally, the obtained outcomes conclude that the proposed DaIBA is a promising technique for addressing the designed problem.

Collaborative transportation planning distinguishing old and new shippers for small-medium enterprise

This paper conducted a research on the vehicle routing problem (VRP) for small–medium enterprise (SMEs) considering new and old shippers. The problem is motivated by a real-world scenario found between Changzhou’s small or medium enterprises (Shippers) and transportation companies (Carriers). Since the service time is different for new and old shippers in the real world, we obtained a set of rich VRP considering time windows and the working time of the vehicle, as well as distinguishing between old and new shippers. We also propose a hybrid algorithm that consists of ant colony optimization (ACO) and tabu search (TS). Carrier’s trade-off point of orders’ number is calculated by the hybrid algorithm. Meanwhile, scenarios of deterministic versus stochastic shippers’ demand and collaborative versus non-collaborative carriers are optimized by the algorithm. The results show that the collaborative transportation planning could improve the profits of carriers.

A rich heterogeneous fleet vehicle routing problem with flexible time windows: a case study of dairy supply chain

Routing is a particularly important concept of cargo transport and has always enticed the interest of researchers and decision makers from different industries. The rich vehicle routing problem considers more realistic features of the real-world problems than the conventional problem. This article aims at solving a vehicle routing problem with heterogeneous fleet and flexible hard and soft time windows where flexible time windows allow for early and tardy deliveries in a given tolerance interval considering earliness and tardiness penalties through defining the earliness and tardiness as decision variables. In this paper, the proposed mathematical programming model is verified through several numerical examples using GAMS software. In order to demonstrate the applicability of the model, a real-world case study is provided. The results show the high potential of the proposed model for real-life application. Specifically, by applying the results of the case study, a 156,000 USD annual cost reduction is possible.

A rich heterogeneous fleet vehicle routing problem with flexible time windows: a case study of dairy supply chain

Routing is a particularly important concept of cargo transport and has always enticed the interest of researchers and decision makers from different industries. The rich vehicle routing problem considers more realistic features of the real-world problems than the conventional problem. This article aims at solving a vehicle routing problem with heterogeneous fleet and flexible hard and soft time windows where flexible time windows allow for early and tardy deliveries in a given tolerance interval considering earliness and tardiness penalties through defining the earliness and tardiness as decision variables. In this paper, the proposed mathematical programming model is verified through several numerical examples using GAMS software. In order to demonstrate the applicability of the model, a real-world case study is provided. The results show the high potential of the proposed model for real-life application. Specifically, by applying the results of the case study, a 156,000 USD annual cost reduction is possible.

Adaptive cooperation in parallel memetic algorithms for rich vehicle routing problems

Adaptive cooperation in parallel memetic algorithms for rich vehicle routing problems

Adaptive cooperation in parallel memetic algorithms for rich vehicle routing problems

Designing and implementing cooperation schemes for parallel algorithms has become a very important task recently. The scheme, which defines the cooperation topology, frequency and strategies for handling transferred solutions, has a tremendous influence on the algorithm search capabilities, and can help balance the exploration and exploitation of the vast solution space. In this paper, we present both static and dynamic schemes – the former are selected before the algorithm execution, whereas the latter are dynamically updated on the fly to better respond to the optimisation progress. To understand the impact of such cooperation approaches, we applied them in the parallel memetic algorithms for solving rich routing problems, and performed an extensive experimental study using well-known benchmark sets. This experimental analysis is backed with the appropriate statistical tests to verify the importance of the retrieved results.

A hybrid heuristic for a broad class of vehicle routing problems with heterogeneous fleet

We consider a family of rich vehicle routing problems (RVRP) which have the particularity to combine a heterogeneous fleet with other attributes, such as backhauls, multiple depots, split deliveries, site dependency, open routes, duration limits, and time windows. To efficiently solve these problems, we propose a hybrid metaheuristic which combines an iterated local search with variable neighborhood descent, for solution improvement, and a set partitioning formulation, to exploit the memory of the past search. Moreover, we investigate a class of combined neighborhoods which jointly modify the sequences of visits and perform either heuristic or optimal reassignments of vehicles to routes. To the best of our knowledge, this is the first unified approach for a large class of heterogeneous fleet RVRPs, capable of solving more than 12 problem variants. The efficiency of the algorithm is evaluated on 643 well-known benchmark instances, and 71.70% of the best known solutions are either retrieved or improved. Moreover, the proposed metaheuristic, which can be considered as a matheuristic, produces high quality solutions with low standard deviation in comparison with previous methods. Finally, we observe that the use of combined neighborhoods does not lead to significant quality gains. Contrary to intuition, the computational effort seems better spent on more intensive route optimization rather than on more intelligent and frequent fleet re-assignments.