Real-life Vehicle Routing Problem Research Articles

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.

A VNS solution and objectives tradeoffs for real-life large-scale vehicle routing problems

A VNS solution and objectives tradeoffs for real-life large-scale vehicle routing problems

Solving a Periodic Capacitated Vehicle Routing Problem using Simulated Annealing Algorithm for a Manufacturing Company

Goal: This paper aims to implement a periodic capacitated vehicle routing problem with simulated annealing algorithm using a real-life industrial distribution problem and to recommend it to industry practitioners. The authors aimed to achieve high-performance solutions by coding a manually solved industrial problem and thus solving a real-life vehicle routing problem using Julia language and simulated annealing algorithm.
 Design / Methodology / Approach: The vehicle routing problem (VRP) that is a widely studied combinatorial optimization and integer programming problem, aims to design optimal tours for a fleet of vehicles serving a given set of customers at different locations. The simulated annealing algorithm is used for periodic capacitated vehicle routing problem. Julia is a state-of-art scientific computation language. Therefore, a Julia programming language toolbox developed for logistic optimization is used.
 Results: The results are compared to savings algorithms from Matlab in terms of solution quality and time. It is seen that the simulated annealing algorithm with Julia gives better solution quality in reasonable simulation time compared to the constructive savings algorithm.
 Limitations of the investigation: The data of the company is obtained from 12 periods with a history of four years. About the capacitated vehicle routing problem, the homogenous fleet with 3000 meters/vehicle is used. Then, the simulated annealing design parameters are chosen rule-of-thumb. Therefore, better performance can be obtained by optimizing the simulated annealing parameters.
 Practical implications: In this study, a furniture roving parts manufacturing company that have 30 customers in Denizli, an industrial city in the west part of Turkey, is investigated. Before the scheduling implementation with Julia, the company has no effective and efficient planning as they have been using spreadsheet programs for vehicle scheduling solutions. In this study, the solutions with Julia are used in practice for the distribution with higher utilization rate and minimum number of vehicles. The simulated annealing and savings algorithms are compared in terms of solution time and performance. The savings algorithm has produced better solution time, the simulated annealing approach has minimum total distance objective value, minimum number of required vehicles, and maximum vehicle utilization rate for the whole model. Thus, this paper can contribute to small scale business management in the sense of presenting a digitalization solution for the vehicle scheduling solution. Also, Julia application of simulated annealing for vehicle scheduling is demonstrated that can help both academicians and practitioners in organizations, mainly in logistics and distribution problems.
 Originality / Value: The main contribution of this study is a new solution method to capacitated vehicle routing problems for a real-life industrial problem using the advantages of the high-level computing language Julia and a meta-heuristic algorithm, the simulated annealing method.
 Keywords: Capacitated vehicle routing problem, Simulated annealing algorithm, Julia programming language.

Solving a real-life roll-on–roll-off waste collection problem with column generation

In this paper, we develop an intelligent solution to a complex, real-life vehicle routing problem in the waste management sector. Waste management companies deliver various types of empty waste containers to industrial customers, to be filled with different types of waste, after which the containers are picked up again. The full containers are transported to a so-called waste management depot or waste handling depot, where they are emptied, after which they are reused. Empty containers are stocked in various stock depots. Time windows in which containers may be picked up and delivered at the customers and opening hours of the different depots have to be taken into account additionally. Very importantly, there are two types of waste collection trucks that can, respectively, carry one or two containers. The resulting vehicle routing problem belongs to a class of so-called roll-on–roll-off problems, characterized by unit demand. Additionally, the problem discussed in this paper has several characteristics (multiple waste types, multiple container types, multiple depots, a heterogeneous fleet, pickup and delivery, time window constraints, and other) that make solving it a difficult task. To solve this problem, we develop a novel column generation scheme that incorporates a heuristic approach to generate new columns. The master problem is solved by linear relaxation of a set partitioning problem. New routes (columns) are generated by a constructive heuristic loosely based on Solomon’s insertion heuristic for the vehicle routing problem with time windows. The construction heuristic operates on a reformulated version of the roll-on–roll-off problem, i.e., a generalized vehicle routing problem with time windows. The algorithm—called wmpopt—is submitted to an extensive sensitivity analysis to determine its response to different parameter settings. After this, we test it on four real-life problem instances and compare its results to those obtained by a commercial solver. We show that wmpopt achieves much better solutions than the commercial solver in similar computing times.

The planning of selective collection in a real-life vehicle routing problem: A case in Rio de Janeiro

Considering the importance of the waste collection sector, mainly in developing countries, this paper evaluates the implementation of the selective collection of recyclable waste so that it can be reused to have an appropriate final destination other than the landfill. The analysis of this study was carried out using the vehicle routing software ORTEC Routing and Dispatch in order to optimize the operation of a company that performs the collection of municipal solid waste from 1764 commercial establishments in Rio de Janeiro. We have considered political and economic aspects and all the actors involved in a real case. The results of 19 scenarios show that, despite the increase in total mileage traveled, it was possible to obtain a reduction of 45.40% in the total cost per client when the whole collection region gradually carries out recyclable waste separation. Therefore, despite the difficulties of planning and implementation of the selective collection of recyclable waste, we show that this can be beneficial to private companies and presents social and environmental benefits for the city.

The heterogeneous fleet vehicle routing problem with light loads and overtime: Formulation and population variable neighbourhood search with adaptive memory

In this paper we consider a real life Vehicle Routing Problem inspired by the gas delivery industry in the United Kingdom. The problem is characterized by heterogeneous vehicle fleet, demand-dependent service times, maximum allowable overtime and a special light load requirement. A mathematical formulation of the problem is developed and optimal solutions for small sized instances are found. A new learning-based Population Variable Neighbourhood Search algorithm is designed to address this real life logistic problem. To the best of our knowledge Adaptive Memory has not been hybridized with a classical iterative memoryless method. In this paper we devise and analyse empirically a new and effective hybridization search that considers both memory extraction and exploitation. In terms of practical implications, we show that on a daily basis up to 8% cost savings on average can be achieved when overtime and light load requirements are considered in the decision making process. Moreover, accommodating for allowable overtime has shown to yield 12% better average utilization of the driver's working hours and 12.5% better average utilization of the vehicle load, without a significant increase in running costs. We also further discuss some managerial insights and trade-offs.

Optimizing Real-Life Freight-Distribution Problems

Real-life freight-distribution problems present high degrees of complexity mostly derived from needing to respect a variety of constraints and addressing complex objective functions in which different factors are taken into account. The aim of this article is to point out relevant issues arising in real-life freight-distribution problems and to describe still-open issues and gaps between models and real-life applications. Furthermore, a real-life vehicle-routing problem is presented and a real instance related to regional freight distribution in Piedmont (northwest Italy) is solved by a fast heuristic method. The extreme portability of this method is then discussed.

Vehicle routing in a Spanish distribution company: Saving using a savings-based heuristic

In this article we present a Vehicle Routing Problem (VRP) faced by a large distribution company in the Northeast of Spain. The company distributes products from its central facilities to a chain of around 400 stores all over the country. One of the peculiarities of the VRP of this company – which is common among real-life VRPs – is the presence of a heterogeneous fleet where vehicles with different capacities can make multiple trips during a single day. This variant of the problem, which we refer as Heterogeneous Fleet and Multi-trip VRP, has been barely studied in the literature. To solve the problem, we use an algorithm based on the well-known savings heuristic with a biased-randomization effect and three local search operations. Our approach is simple to implement as it needs few parameters and no fine-tuning processes, which are usually cumbersome and require experts’ involvement. We obtain savings of around 12 per cent in transportation costs, which represent around €30 000 saved per week.

REAL-LIFE VEHICLE ROUTING WITH TIME WINDOWS FOR VISUAL ATTRACTIVENESS AND OPERATIONAL ROBUSTNESS

This paper describes an algorithm for producing visually attractive and operationally robust solutions to a real-life vehicle routing problem with time windows. Visually attractive and operationally robust solutions are usually synonymous with compact, nonoverlapping routes with little or no intra-route cross over. The visual attractiveness of routes, for practical routing applications, is often of paramount importance. We present a two phase solution approach. The first phase is inspired by the sequential insertion algorithm of Solomon (1987) and includes a range of novel enhancements to ensure visually attractive solutions are produced in the face of a range of nonstandard real-life constraints: A constrained and heterogeneous vehicle fleet; tight time windows and banned delivery windows; multiple route capacity measures; driver breaks; minimum route volumes; vehicle-location compatibility rules; nonreturn to base routes; peak hour traveling times; vehicle type dependent service times; and replenishment back at the depot. The second phase is based on the guided local search algorithm of Kilby et al. (1999). It uses an augmented objective function designed to seek solutions which strike a balance between minimizing traditional cost measures, whilst maximizing the visual attractiveness of the solution. Our two phase solution approach is particularly adept at producing solutions that both aggressively minimize the total number of routes, a feature that we believe has been missing in algorithms presented in equivalent literature, as well as minimizing traditional cost measures whilst delivering a very high degree of visual attractiveness. The algorithm presented has been successfully implemented and deployed for the real-life, daily beverage distribution problem of Schweppes Australia Pty. Ltd. for a range of capital cities throughout Australia.

The minimum duration truck driver scheduling problem

Truck driver scheduling problems are important subproblems of real-life vehicle routing and scheduling problems, because rest periods required by government regulations have a significant impact on travel and arrival times. Vehicle routes generated without considering these regulations are often practically infeasible. This paper identifies common constraints imposed by hours of service regulations world wide and presents a mixed integer programming formulation for a variant of the truck driver scheduling problem in which truck drivers may only rest at customer locations and at suitable rest areas. The objective of the problem is to find a feasible truck driver schedule with minimal duration. The model presented in this paper is very flexible and can be configured to consider different sets of rules imposed by government regulations and union contracts. A dynamic programming approach is presented and its effectiveness is demonstrated for working hour regulations in the United States and in the European Union.

The Minimum Duration Truck Driver Scheduling Problem

Truck driver scheduling problems are important subproblems of real-life vehicle routing and scheduling problems because rest periods required by government regulations have a significant impact on travel and arrival times. Vehicle routes generated without considering these regulations are often practically infeasible. This paper identifies common constraints imposed by hours of service regulations world wide and presents a mixed integer programming formulation for a variant of the truck driver scheduling problem in which truck drivers may only rest at customer locations and at suitable rest areas. The objective of the problem is to find a feasible truck driver schedule with minimal duration. The model presented in this paper is very flexible and can be configured to consider different sets of rules imposed by government regulations and union contracts. A dynamic programming approach is presented and its effectiveness is demonstrated for working hour regulations in the United States and in the European Union.

The balanced cargo vehicle routing problem with time windows

This paper addresses a variant of the vehicle routing problem with time windows (VRPTW) that targets the balancing of the load carried by each active vehicle (balanced cargo VRPTW or BCVRPTW). A new approach, based on the free disposal hull (FDH) method of data envelopment analysis, is proposed; it consists of an initial, feasible route generation procedure that employs an effective heuristic for the VRPTW, followed by an iterative method for selecting routes on the efficiency frontier, according to the criteria of the BCVRPTW and the notion of non-dominated solutions embedded within FDH. This new approach produces, according to our computational evaluation, very good results for the BCVRPTW, and can serve as the basis for solving real-life vehicle routing problems.

An efficient variable neighborhood search heuristic for very large scale vehicle routing problems

In this paper, we present an efficient variable neighborhood search heuristic for the capacitated vehicle routing problem. The objective is to design least cost routes for a fleet of identically capacitated vehicles to service geographically scattered customers with known demands. The variable neighborhood search procedure is used to guide a set of standard improvement heuristics. In addition, a strategy reminiscent of the guided local search metaheuristic is used to help escape local minima. The developed solution method is specifically aimed at solving very large scale real-life vehicle routing problems. To speed up the method and cut down memory usage, new implementation concepts are used. Computational experiments on 32 existing large scale benchmarks, as well as on 20 new very large scale problem instances, demonstrate that the proposed method is fast, competitive and able to find high-quality solutions for problem instances with up to 20,000 customers within reasonable CPU times.

Genetic local search with distance preserving recombination operator for a vehicle routing problem

The paper describes a systematic adaptation of the genetic local search algorithm to a real life vehicle routing problem. The proposition is motivated by successful implementations of genetic local search-based heuristics for a number of combinatorial optimization problems. The key element of the proposed approach is the use of global convexity tests. The tests allow finding the types of solution features that are essential for solution quality. The results of the tests are used to construct an appropriate distance preserving recombination operator. Results of computational experiments demonstrating the efficiency of the proposed approach are reported.

APPLICATION OF GENETIC ALGORITHMS TO A LARGE-SCALE MULTIPLE-CONSTRAINT VEHICLE ROUTING PROBLEM

This paper presents a study on the application of a hybrid genetic algorithm (HGA) to an extended instance of the Vehicle Routing Problem. The actual problem is a complex real-life vehicle routing problem regarding the distribution of products to customers. A non homogenous fleet of vehicles with limited capacity and allowed travel time is available to satisfy the stochastic demand of a set of different types of customers with earliest and latest time for servicing. The objective is to minimize distribution costs respecting the imposed constraints (vehicle capacity, customer time windows, driver working hours and so on). The approach for solving the problem was based on a "cluster and route" HGA. Several genetic operators, selection and replacement methods were tested until the HGA became efficient for optimization of a multi-extrema search space system (multi-modal optimization). Finally, High Performance Computing (HPC) has been applied in order to provide near-optimal solutions in a sensible amount of time.

A List Based Threshold Accepting Algorithm for the Capacitated Vehicle Routing Problem

The aim of this study is to describe a new stochastic search meta-heuristic algorithm for solving the Capacitated Vehicle Routing Problem (CVRP), termed as the List Based Threshold Accepting (LBTA) algorithm. The main advantage of this algorithm over the majority of other meta-heuristics is that it produces quite satisfactory solutions in reasonable amount of time by tuning only one parameter of the algorithm. This property makes this algorithm a reliable and a practical tool for every decision support system designed for solving real life vehicle routing problems.

A savings based method for real-life vehicle routing problems

This paper describes a Savings Based algorithm for the Extended Vehicle Routing Problem. This algorithm is compared with a Sequential Insertion algorithm on real-life data. Besides the traditional quality measures such as total distance traveled and total workload, we compare the routing plans of both algorithms according to non-standard quality measures that help to evaluate the visual attractiveness of the plan. Computational results show that, in general, the Savings Based algorithm not only performs better with respect to these non-standard quality measures, but also with respect to the traditional measures.

A savings based method for real-life vehicle routing problems

This paper describes a Savings Based algorithm for the Extended Vehicle Routing Problem. This algorithm is compared with a sequential insertion algorithm on real-life data. Besides the traditional quality measures such as total distance travelled and total workload, we compare the routing plans of both algorithms according to non-standard quality measures that help to evaluate the ‘visual attractiveness’ of the plan. Computational results show that, in general, the savings based algorithm not only performs better with respect to these non-standard quality measures, but also with respect to the traditional measures.

Solving real-life vehicle routing problems efficiently using tabu search

This paper presents a tabu search based method for finding good solutions to a real-life vehicle routing problem. The problem considered deals with some new features beyond those normally associated with the classical problems of the literature: in addition to capacity constraints for vehicles and time windows for deliveries, it takes the heterogeneous character of the fleet into account, in the sense that utilization costs are vehicle-dependent and that some accessibility restrictions have to be fulfilled. It also deals with the use of trailers. In spite of the intricacy of the problem, the proposed tabu search approach is easy to implement and can be easily adapted to many other applications. An emphasis is placed on means that have to be used to speed up the search. In a few minutes of computation on a personal workstation, our approach obtains solutions that are significantly better than those previously developed and implemented in practice.

Solving real-life vehicle routing problems efficiently using tabu search

Solving real-life vehicle routing problems efficiently using tabu search