A synthesis of assignment and heuristic solutions for vehicle routing with time windows

This paper proposes a three-stage method for the vehicle-routing problem with time window constraints (VRPTW). Using the Hungarian method the optimal customer matching for an assignment approximation of the VRPTW, which is a travel time-based relaxation that partially respects the time windows, is obtained. The assignment matching is transformed into feasible routes of the VRPTW via a simple decoupling heuristic. The best of these routes, in terms of travelling and vehicle waiting times, form part of the final solution, which is completed by the routes provided by heuristic methods applied to the remainder of the customers. The proposed approach is tested on a set of standard literature problems, and improves the results of the heuristic methods with respect to total travel time. Furthermore, it provides useful insights into the effect of employing optimal travel time solutions resulting from the assignment relaxation to derive partial route sets of the VRPTW.     

Open vehicle routing problem with driver nodes and time deadlines

In this paper, we consider a variant of the open vehicle routing problem in which vehicles depart from the depot, visit a set of customers, and end their routes at special nodes called driver nodes. A driver node can be the home of the driver or a parking lot where the vehicle will stay overnight. The resulting problem is referred to as the open vehicle routing problem with driver nodes (OVRP-d). We consider three classes of OVRP-d: with no time constraints, with a maximum route duration, and with both a maximum route duration as well as time deadlines for visiting customers. For the solution of these problems, which are not addressed previously in the literature, we develop a new tabu search heuristic. Computational results on randomly generated instances indicate that the new heuristic exhibits a good performance both in terms of the solution quality and computation time.     

Clustering for routing in densely populated areas

This paper introduces a new approach for generating school bus routes in a dense urban area. First, a districting algorithm is used to determine clusters including appropriate numbers of students. Then, for each cluster, a route and the stops along this route are determined. Numerical results are reported and compared with those obtained previously. Although the algorithm has been developped and tested in a specific context, it could easily be extended to more general vehicle routing problems.     

Managing pharmaceuticals delivery service using a hybrid particle swarm intelligence approach

Medicines or drugs have unique characteristics of short life cycle, small size, light weight, restrictive distribution time and the need of temperature and humidity control (selected items only). Thus, logistics companies often use different types of vehicles with different carrying capacities, and considering fixed and variable costs in service delivery, which make the vehicle assignment and route optimization more complicated. In this study, we formulate the problem to a multi-type vehicle assignment and mixed integer programming route optimization model with fixed fleet size under the constraints of distribution time and carrying capacity. Given non-deterministic polynomial hard and optimal algorithm can only be used to solve small-size problem, a hybrid particle swarm intelligence (PSI) heuristic approach, which adopts the crossover and mutation operators from genetic algorithm and 2-opt local search strategy, is proposed to solve the problem. We also adapt a principle based on cost network and Dijkstra’s algorithm for vehicle scheduling to balance the distribution time limit and the high loading rate. We verify the relative performance of the proposed method against several known optimal or heuristic solutions using a standard data set for heterogeneous fleet vehicle routing problem. Additionally, we compare the relative performance of our proposed Hybrid PSI algorithm with two intelligent-based algorithms, Hybrid Population Heuristic algorithm and Improved Genetic Algorithm, using a real-world data set to illustrate the practical and validity of the model and algorithm.

     

A Survey of Local Delivery Vehicle Routing Methodology

Vehicle routing techniques which have offered fascinating combinatorial problems to the academic operational research worker, become of real concern to practitioners of Operational Research as managements become increasingly aware of the need to control the rising costs of the physical distribution activity. The systematic construction of efficient vehicle route structures for local delivery operations provides an important tool for the control of costs in the short-term, for adapting the vehicle fleet size and composition in the medium-term, and even for the location of depots in the longer term.The bases of the many heuristic algorithms which have been proposed in the literature for the design of efficient vehicle route structures are critically appraised. The issues which arise when integrating such techniques into an operational setting are described in the light of the real needs of management. It is concluded that the design of "flexible fixed routes" is the real target which only a few of the many algorithms can approach.     

An Integer Programming Approach to the Vehicle Scheduling Problem

This paper describes an integer programming formulation of the vehicle scheduling problem and illustrates how such a formulation can be extended to incorporate restrictions on work load, coverage and service that occur in real world vehicle scheduling problems. The integer programme is solved using the Revised Simplex method, additional constraints being introduced to retain integrality during convergence. The feasible region of this integer programme is initially restricted so that only routes constructed through sets of radially contiguous locations are considered. The effect of relaxing these over-constraints is explored. The method is demonstrated on fifteen problems ranging in size from 21 to 100 locations and the results generally show an improvement on previously published results. This is particularly true of the larger problems. This method compares favourably with other methods in computational efficiency.     

An exact algorithm for solving a capacitated location-routing problem

In location-routing problems, the objective is to locate one or many depots within a set of sites (representing customer locations or cities) and to construct delivery routes from the selected depot or depots to the remaining sites at least system cost. The objective function is the sum of depot operating costs, vehicle acquisition costs and routing costs. This paper considers one such problem in which a weight is assigned to each site and where sites are to be visited by vehicles having a given capacity. The solution must be such that the sum of the weights of sites visited on any given route does not exceed the capacity of the visiting vehicle. The formulation of an integer linear program for this problem involves degree constraints, generalized subtour elimination constraints, and chain barring constraints. An exact algorithm, using initial relaxation of most of the problem constraints, is presented which is capable of solving problems with up to twenty sites within a reasonable number of iterations.     

Real-time vehicle rerouting problems with time windows

This paper introduces and studies real-time vehicle rerouting problems with time windows, applicable to delivery and/or pickup services that undergo service disruptions due to vehicle breakdowns. In such problems, one or more vehicles need to be rerouted, in real-time, to perform uninitiated services, with the objective to minimize a weighted sum of operating, service cancellation and route disruption costs. A Lagrangian relaxation based-heuristic is developed, which includes an insertion based-algorithm to obtain a feasible solution for the primal problem. A dynamic programming based algorithm solves heuristically the shortest path problems with resource constraints that result from the Lagrangian relaxation. Computational experiments show that the developed Lagrangian heuristic performs very well.     

A heuristic algorithm for the asymmetric capacitated vehicle routing problem

We consider the Asymmetric Capacitated Vehicle Routing Problem (ACVRP[, a particular case of the standard asymmetric Vehicle Routing Problem arising when only the vehicle capacity constraints are imposed. ACVRP is known to be NP-hard and finds practical applications, e.g. in distribution and scheduling. In this paper we describe the extension to ACVRP of the two well-known Clarke-Wright and Fisher-Jaikumar heuristic algorithms. We also propose a new heuristic algorithm for ACVRP that, starting with an initial infeasible solution, determines the final set of vehicle routes through an insertion procedure as well as intea-route and inter-route arc exchanges. The initial infeasible solution is obtained by using the additive bounding procedures for ACVRP described by Fischetti, Toth and Vigo in 1992. Extensive computational results on several classes of randomly generated test problems involving up to 300 customers and on some real instances of distribution problems in urban areas, are presented. The results obtained show that the proposed approach favourably compares with previous algorithms from the literature.     

A heuristic for the multi-period petrol station replenishment problem

In the multi-period petrol station replenishment problem (MPSRP) the aim is to optimize the delivery of several petroleum products to a set of petrol stations over a given planning horizon. One must determine, for each day of the planning horizon, how much of each product should be delivered to each station, how to load these products into vehicle compartments, and how to plan vehicle routes. The objective is to maximize the total profit equal to the revenue, minus the sum of routing costs and of regular and overtime costs. This article describes a heuristic for the MPSRP. It contains a route construction and truck loading procedures, a route packing procedure, and two procedures enabling the anticipation or the postponement of deliveries. The heuristic was extensively tested on randomly generated data and compared to a previously published algorithm. Computational results confirm the efficiency of the proposed methodology.     

Two-echelon vehicle routing problem with satellite bi-synchronization

In considering route optimization at a series of express stages from pickup to delivery via the intercity linehaul, we introduce the two-echelon vehicle routing problem with satellite bi-synchronization (2E-VRP-SBS) from the perspective of modeling the routing problems of two-echelon networks. The 2E-VRP-SBS involves the inter-satellite linehaul on the first echelon, and the pickups from senders to origin satellites (i.e., satellites for cargo collection) and deliveries from destination satellites (i.e., satellites for cargo deliveries) to receivers on the second echelon. The 2E-VRP-SBS integrates satellite bi-synchronization constraints, multiple vehicles, and time window constraints on the two-echelon network and aims to find cost-minimizing routes for various types of trucks. Satellite bi-synchronization constraints, which synchronously guarantee the synchronization at origin satellites and the synchronization at destination satellites, provide an innovative method to formulate the two-echelon routing problem. In this study, we develop a mixed-integer programming model for the 2E-VRP-SBS. An exact method using CPLEX solver is presented and a modified adaptive large neighborhood search is conducted. Furthermore, the effectiveness of the 2E-VRP-SBS formulation and the applicability of the heuristic for various instances are experimentally evaluated.     

A Greedy Look-ahead Heuristic for Combinatorial Optimization: An Application to Vehicle Scheduling with Time Windows

This paper describes a greedy heuristic for a class of combinatorial optimization problems; a central feature of the method being a look-ahead capability. The power of the heuristic is demonstrated through experimentation using a large, real-life vehicle scheduling problem with tight time-window constraints. Incorporation of the look-ahead feature gave an improvement in performance that was at least as great as, and in addition to, that which had been obtained through use of the well-known ‘savings’ method. Based upon the experimental results, some guidelines are proposed for the application of the heuristic to other problems. One of the conclusions is that designers of heuristics should give greater consideration to the inclusion of a look-ahead element in their algorithms.     

A tabu search algorithm for the multi-trip vehicle routing and scheduling problem

This paper describes a novel tabu search heuristic for the multi-trip vehicle routing and scheduling problem (MTVRSP). The method was developed to tackle real distribution problems, taking into account most of the constraints that appear in practice. In the MTVRSP, besides the constraints that are common to the basic vehicle routing problem, the following ones are present: during each day a vehicle can make more than one trip; the customers impose delivery time windows; the vehicles have different capacities considered in terms of both volume and weight; the access to some customers is restricted to some vehicles; the drivers' schedules must respect the maximum legal driving time per day and the legal time breaks; the unloading times are considered.     

Reduction of job-shop problems to flow-shop problems with precedence constraints

We study the problem of scheduling N independent jobs in a job-shop environment. Each job must be processed on M machines according to individual routes. The objective is to minimize the maximum completion time of the jobs. First, the job-shop problem is reduced to a flow-shop problem with job precedence constraints. Then, a set of flow-shop algorithms are modified to solve it. To evaluate the quality of these heuristics, several lower bounds on the optimal solution have been computed and compared with the heuristic solutions for 3040 problems. The heuristics appear especially promising for job-shop problems with ‘flow-like’ properties.     

Vehicle routing with a sparse feasibility graph

In this paper we introduce the concept of a feasibility graph for vehicle routing problems, a graph where two customers are linked if and only if it is possible for them to be successive (adjacent) customers on some feasible vehicle route. We consider the problem of designing vehicle routes when the underlying feasibility graph is sparse, i.e. when any customer has only a few other customers to which they can be adjacent on a vehicle route. This problem arose during a consultancy study that involved the design of fixed vehicle routes delivering to contiguous (adjacent) postal districts. A heuristic algorithm for the problem is presented and computational results given for a number of test problems involving up to 856 customers.     

The Impact of a Decision-Support System for Vehicle Routeing in a Foodservice Supply Situation

Efficient vehicle routeing is critical to the competitive position of firms in many industries. The foodservice industry encounters complex and constantly changing routeing problems in which time is a critical factor. With the cooperation and support of management and staff of Kraft Inc., we implemented an heuristic-based decision-support system, which utilizes computer-graphic pictures of routes in a large foodservice distribution region of Kraft Inc.The system provides the route scheduler with a tool to enable the rapid evaluation of computer-proposed solutions and to easily modify them. In tests conducted by Kraft personnel, system users found potential variable cost savings of 10.7% on 10 actual delivery problems of up to 223 stops. A potential for reductions in fixed costs was also indicated. The system allows the scheduler great flexibility in dealing with unpredictable factors and also aids management in strategic planning for distribution. It is currently in day-to-day use at Kraft.     

Combined vehicle routing and scheduling with temporal precedence and synchronization constraints

We present a mathematical programming model for the combined vehicle routing and scheduling problem with time windows and additional temporal constraints. The temporal constraints allow for imposing pairwise synchronization and pairwise temporal precedence between customer visits, independently of the vehicles. We describe some real world problems where in the literature the temporal constraints are usually remarkably simplified in the solution process, even though these constraints may significantly improve the solution quality and/or usefulness. We also propose an optimization based heuristic to solve real size instances. The results of numerical experiments substantiate the importance of the temporal constraints in the solution approach. We also make a computational study by comparing a direct use of a commercial solver against the proposed heuristic, where the latter approach can find high quality solutions within specific time limits.     

Job routing and operations scheduling: a network-based virtual cell formation approach

Virtual cellular manufacturing inherits the benefits of traditional cellular manufacturing and maintains the responsiveness to the changing market and routing flexibility of a job shop by integrating machine-grouping, shop layout design and intercellular flow handling. The primary goal of virtual cell formation is to minimize the throughput time of a given job. This paper proposes a method for virtual cell formation by adopting the double-sweep algorithm for the k-shortest path problem, and a heuristic is devised to schedule the virtual cells for the multiple job orders. Results generated from this method include not only the optimal candidates of the virtual cell with the shortest throughput time with sub-optimal alternative route(s) and throughput time(s) as the alternative candidates in case some resources are restricted or are not available. The procedure of virtual cell creation and scheduling is illustrated explicitly with examples. Since most of the scheduling problems are NP-hard and virtual cell scheduling is even more complex due to the bottleneck machines that are demanded by jobs at other cells. For multiplicity of possible virtual cell candidates, in addition to the precedence and resource constraints, heuristic solutions are found to be reasonable.     

Solving routing problems with pairwise synchronization constraints

Pairwise route synchronization constraints are commonly encountered in the field of service technician routing and scheduling and in the area of mobile care. Pairwise route synchronization refers to constraints that require that two technicians or home care workers visit the same location at exactly the same time. We consider constraints of this type in the context of the well-known vehicle routing problem with time windows and a generic service technician routing and scheduling problem. Different approaches for dealing with the problem of pairwise route synchronization are compared and several ways of integrating a synchronization component into a metaheuristic algorithm tailored to the original problems are analyzed. When applied to benchmark instances from the literature, our algorithm matches almost all available optimal values and it produces several new best results for the remaining instances.

     

An exact algorithm for a vehicle routing problem with time windows and multiple use of vehicles

The vehicle routing problem with multiple use of vehicles is a variant of the classical vehicle routing problem. It arises when each vehicle performs several routes during the workday due to strict time limits on route duration (e.g., when perishable goods are transported). The routes are defined over customers with a revenue, a demand and a time window. Given a fixed-size fleet of vehicles, it might not be possible to serve all customers. Thus, the customers must be chosen based on their associated revenue minus the traveling cost to reach them. We introduce a branch-and-price approach to address this problem where lower bounds are computed by solving the linear programming relaxation of a set packing formulation, using column generation. The pricing subproblems are elementary shortest path problems with resource constraints. Computational results are reported on euclidean problems derived from well-known benchmark instances for the vehicle routing problem with time windows.