Truckload Pickup Research Articles

Resources synchronization in a full truckload pickup and delivery problem: An exact approach

In this work, the Unpaired Full Truckload Pickup and Delivery Problem with Resource Synchronization is modeled and solved, where routes must be determined to transport commodities from pickup to delivery locations by a set of vehicles, subject to timing and resource synchronization constraints, to satisfy demands at minimum cost. Unlike previous works, the use of multiple resources for loading and unloading tasks at each location are considered and appropriately managed through a representation using discrete shifts. An integer linear programming model is proposed to simultaneously solve allocation, routing and resources synchronization optimization problems. In order to improve the model performance for large size instances, diverse reformulations, including additional inequalities and symmetric-breaking constraints, are implemented and tested. Moreover, a heuristic procedure is proposed to provide good initial feasible solutions. The capabilities of the proposed approach are assessed through several examples.

Mixed-integer linear optimization for full truckload pickup and delivery

This paper is focused on solving an industrially-motivated, rich routing variant of the so-called full truckload pickup and delivery problem. It addresses a setting where the distributor has to transport full truckload shipments between distribution centers and customer locations, yet the distributor’s owned fleet is inadequate to perform the totality of the required deliveries and thus a subset of the deliveries has to be outsourced to third-party carriers. In this work, we propose a novel mixed-integer linear programming formulation to model this problem. Using datasets inspired from industrial practice, we evaluate the computational tractability of this model and demonstrate its potential to serve as a decision-support system for real-life operations. Furthermore,we hypothesize that the distributor may realize cost savings when the later portion of the distribution period is utilized to pre-load cargo for delivery during the following period. To that end, we augment the original model to allow for such cargo pre-loading, and we conduct a rolling horizon-based simulation study to quantify its overall economic effect.

Flexible truckload pickup and delivery problem considering reserved orders and fuel consumption

The orders of a transportation enterprise can be fulfilled by the private fleet or outsourced to a common carrier for a flexible transportation planning. In reality, some of the orders have to be reserved for the private fleet due to some constraints (such as trade secret), which can reduce the flexibility of transportation, thereby increasing the operational cost. To handle this problem, a flexible truckload pickup and delivery problem is proposed, where time widows, outsourcing, reserved orders, and fuel consumption of the private fleet are considered, and truck speed is taken as a decision variable instead of a parameter. Then, the proposed problem is described as a directed graph, and its attributes are analyzed to reduce the solution space; consequently, a mixed integer nonlinear programming model based on truck visiting time is established. After that, an updated time window partition based (UTWPB) method is developed, which is described by a sparse matrix to further reduce the solution space, and it can obtain a lower bound of the proposed problem by relaxing the constraints of the partitioned time windows. Numerical experiments demonstrate that the UTWPB method performs well compared with existing approximate methods, and it can offer reasonably accurate solutions for real-life scale instances. Additionally, sensitivity analysis is also conducted to confirm the effectiveness of the proposed model.

Multiple vehicle synchronisation in a full truck-load pickup and delivery problem: A case-study in the biomass supply chain

The search for higher efficiency in transportation planning processes in real life applications is challenging. The synchronisation of different vehicles performing interrelated operations can enforce a better use of vehicle fleets and decrease travelled distances and non-productive times, leading to a reduction of logistics costs. In this work, the full truck-load pickup and delivery problem with multiple vehicle synchronisation (FT-PDP-mVS) is presented. This problem is motivated by a real-life application in the biomass supply chain “hot-system”, where it is necessary to simultaneously perform chipping and transportation operations at the forest roadside. The FT-PDP-mVS consists in determining the integrated routes for three distinct types of vehicles, which need to perform interrelated operations with minimum logistics costs. We extend existing studies in synchronisation of multiple routes by acknowledging several synchronisation aspects, such as operations and movement synchronisation. A novel mixed integer programming model (MIP) is presented, along with valid inequalities to tighten the formulation. A solution method approach is developed based on the fix-and-optimise principles under a variable neighbourhood decomposition search. Results of its application to 19 instances based on a real-world case-study demonstrate its performance. For a baseline instance, the synchronisation aspects tackled in this problem allowed for significant gains when compared to the company’s current planning approach. Furthermore, the proposed approach can enhance planning and decision making processes by providing valuable insights about the impact of key parameters of biomass logistics over the routing results.

A GENERALIZED DECOMPOSITION ALGORITHM FOR REAL-TIME TRUCK ROUTING PROBLEMS

ABSTRACT This paper is based on a practical project jointly conducted by a major trucking company and a renowned operations research consulting firm. It studies a large-scale, real-time truckload pickup and delivery problem. A number of cost factors are carefully measured such as loaded/empty travel distance, travel time, crew labor, equipment rental or operational cost, and revenue for completing the movements. This paper proposes a generalized decomposition algorithm that is capable of considering sophisticated business rules. The goal is to recommend executable and efficient truck routing decisions to minimize operating costs. Numerical tests are conducted with operational data from J.B.HUNT. A fleet of 5,000 trucks is considered in this experiment. The test result not only shows significant cost savings but also demonstrates computational efficiency for real-time application.

An iterative auction for carrier collaboration in truckload pickup and delivery

While carriers can collaborate to reduce empty traveling miles in truckload shipping, this is difficult to realize because each party is self-interested who may not share his private information that is necessary for the cooperation. In this paper, we propose an iterative auction scheme, which enables carriers to collaborate by exchanging their shipping requests iteratively. The auction is shown to be incentive-compatible, individually rational, budget balanced, monotonic, and convergent. Computational experiments indicate that it has small efficiency loss and can significantly improve carriers’ profits. We also develop two acceleration methods and extend the auction to more general problems.

An adaptive large neighborhood search for the full truckload pickup and delivery problem with resource synchronization

This paper presents the Full Truckload Pickup and Delivery Problem with Resource Synchronization (FT-PDP-RS). It consists of optimizing the transport of materials between sites, using a heterogeneous fleet of trucks, in the context of public works. Full truckload pickup and delivery requests have to be served within time windows. Trucks are synchronized on pickup or delivery locations based on unitary loading and unloading resources. We propose an Adaptive Large Neighborhood Search (ALNS) to solve this problem. Custom destroy and repair operators and an efficient feasibility insertion procedure have been designed to solve it. The method has been evaluated on real instances from the literature and on real case instances from a public works company. Computational experiments confirm the efficiency of the method.

An exact algorithm for the full truckload pick-up and delivery problem with time windows: concept and implementation details

Intermodal goods transport is characterised by a main transport by rail, barge or seaborne vessel, preceded and followed by short in time but expensive road transport. Many times trucks pick–up or deliver a single container which leads to a full truckload vehicle routing problem in terms of economically efficient transport services. Time windows induced by the customer or due to external situations increase the complexity of an efficient planning by a logistics provider. An exact algorithm for this type of pick–up and delivery problem is developed and the details of its implementation are explained. A set partitioning problem is automatically generated, which may be solved to optimality by an optimising software.

Dynamic Truckload Pick-Up and Delivery Problem with Time Windows

A rolling horizon framework for the dynamic assignment and sequencing of trucks to jobs consisting of picking up and delivering full truckloads when requests for service arise on a continuous basis. A mathematical formulation of the problem faced at each stage is presented; its solution allows for the dynamic reassignment of trucks to loads. Loads have associated time windows for pickup and delivery, and the objective function includes explicit penalty cost for not serving a particular load. A solution algorithm is presented and implemented, and computational results are presented.

Strategic Design and Analysis of a Relay Network in Truckload Transportation

The truckload industry faces a serious and chronic problem of high driver turnover rate—typically more than 100%—with staggering associated costs. Among the major causes of this problem are extended on-the-road times where drivers handle several truckload pickup and deliveries successively; nonregular schedules and get-home rates; and low utilization, i.e., less mileage/unit-time per driver, which leads to low pay. We consider the strategic design of a relay network that may potentially help to alleviate this problem by providing an efficient underlying network that facilitates an assignment of drivers to home bases (domiciles to which they stay close) and generation of more predictable schedules with continuity and higher get-home rates. In relay network design, we are interested in determining a number of relay point locations, assigning network nodes to these relay points (i.e., defining domiciles), and determining the actual route (from the origin to the destination) for each truckload on the network. In doing so, we explicitly consider driver tour lengths, load imbalance at relay points, and the percentage circuity constraints. We develop an efficient Benders' decomposition-based algorithm that is significantly enhanced via strengthened Benders' cuts, cut disaggregation schemes, heuristics for improved upper bounds, and surrogate constraints. Our approach provides the ability to solve large size instances within reasonable solution times and very small optimality gaps. We also examine the effects of changes in the problem parameters on the performance of solution algorithms. Furthermore, in our computational experiments, we provide an analysis of the conditions for which relay network presents most benefits as well as incorporation of direct shipments within relay network operations.

Are Stacker Crane Problems easy? A statistical study

Many full truckload pick-up and delivery problems in the intermodal freight container transport industry can be modeled as Asymmetric Traveling Salesman Problems (ATSPs). Several authors have noted that while ATSPs are NP-hard, some instances are readily solved to optimality in only a short amount of time. Furthermore, the literature contains several references to the Stacker Crane Problem (SCP) as an “easy” problem amidst the ATSPs. We put this hypothesis to test by using statistical methods to build a model relating measurable distance matrix structures to the amount of time required by two existing exact solvers in finding solutions to over 500 ATSP instances. From this analysis we conclude that SCPs are not necessarily easier than other ATSPs, but a special subset of SCPs, termed drayage problems, are more readily solved. We speculate that drayage problems are “easy” because of a comparatively high number of zeros in symmetric locations within the distance matrix. In real-world drayage problems (i.e. the movement of containers a short distance to/from a port or rail terminal), these zeros correspond to the prevalence of jobs originating at or destined to a fixed number of freight terminals.

Simulation and optimisation of intermodal barge transport networks

This is a summary of the author’s PhD thesis supervised by Gerrit Janssens and Cathy Macharis and defended on 7 May 2010 at the Hasselt University, Belgium. The thesis is written in English and is available from the author upon request. This work deals with two key aspects in the competitiveness of intermodal transport making use of inland navigation. The first part of the thesis studies bundling of freight in intermodal barge transport networks. Alternative bundling strategies for container barge transport are compared by means of discrete event simulation. Cooperation between inland barge terminals to bundle freight flows is modelled as a service network design problem. The second part relates to the initial and final journey by road in the intermodal barge transport chain. Pre- and end-haulage by road is modelled as a Full Truckload Pickup and Delivery Problem with Time Windows. A local search algorithm and deterministic annealing algorithm are proposed to find near-optimal solutions.

A deterministic annealing algorithm for the pre- and end-haulage of intermodal container terminals

The drayage of containers in the service area of an intermodal barge terminal is modelled as a full truckload pickup and delivery problem with time windows (FTPDPTW). Initial solutions are generated with an insertion heuristic and improved with three local search operators. In a post-optimisation phase, the three search operators are integrated in a deterministic annealing (DA) framework. The mechanism of the heuristic procedures is demonstrated with a numerical example. A sensitivity analysis indicates that the DA algorithm is robust with respect to variations in threshold value and quality of the initial solution.

A local search heuristic for the pre- and end-haulage of intermodal container terminals

Pre- and end-haulage of intermodal container terminals involves the pickup or delivery of containers at customer locations. The attractiveness of intermodal transport can be increased by organizing the road segment in the intermodal transport chain more efficiently. In this paper the drayage of containers in the service area of an intermodal terminal is modelled as a full truckload pickup and delivery problem with time windows (FTPDPTW). A two-phase insertion heuristic is proposed to construct an initial solution. This solution is improved with a local search heuristic based on three neighbourhoods. Numerical experiments are described to demonstrate the mechanisms of the heuristics.

Look-ahead strategies for dynamic pickup and delivery problems

In this paper we consider a dynamic full truckload pickup and delivery problem with time-windows. Jobs arrive over time and are offered in a second-price auction. Individual vehicles bid on these jobs and maintain a schedule of the jobs they have won. We propose a pricing and scheduling strategy based on dynamic programming where not only the direct costs of a job insertion are taken into account, but also the impact on future opportunities. Simulation is used to evaluate the benefits of pricing opportunities compared to simple pricing strategies in various market settings. Numerical results show that the proposed approach provides high quality solutions, in terms of profits, capacity utilization, and delivery reliability.

Real-Time Multivehicle Truckload Pickup and Delivery Problems

In this paper we formally introduce a generic real-time multivehicle truckload pickup and delivery problem. The problem includes the consideration of various costs associated with trucks' empty travel distances, jobs' delayed completion times, and job rejections. Although very simple, the problem captures most features of the operational problem of a real-world trucking fleet that dynamically moves truckloads between different sites according to customer requests that arrive continuously. We propose a mixed-integer programming formulation for the offline version of the problem. We then consider and compare five rolling horizon strategies for the real-time version. Two of the policies are based on a repeated reoptimization of various instances of the offline problem, while the others use simpler local (heuristic) rules. One of the reoptimization strategies is new, while the other strategies have recently been tested for similar real-time fleet management problems. The comparison of the policies is done under a general simulation framework. The analysis is systematic and considers varying traffic intensities, varying degrees of advance information, and varying degrees of flexibility for job-rejection decisions. The new reoptimization policy is shown to systematically outperform the others under all these conditions.

Local truckload pickup and delivery with hard time window constraints

This paper describes a solution method for a multiple traveling salesman problem with time window constraints (m-TSPTW). The method was developed for local truckload pickup and delivery problems such as those supporting rail or maritime intermodal operations but is suitable for application in other problems in which the number of tasks assigned to each server at any time is relatively small. We present a model and describe an iterative solution technique in which explicit time constraints are replaced by binary flow variables. At each iteration two versions of the problem, one over-constrained and the other under-constrained are solved. The solution to the over-constrained problem provides a feasible solution, while the optimality gap provided by the two solutions informs the decision of whether to continue searching or to implement the best solution found so far. A specific time window partitioning scheme is used to ensure that the cost of solutions found are monotonically non-increasing. The method developed is suitable for real-time or quasi real-time implementation.