Truck And Trailer Routing Problem Research Articles

A Radial Hybrid Estimation of Distribution Algorithm for the Truck and Trailer Routing Problem

The truck and trailer routing problem (TTRP) has been widely studied under different approaches. This is due to its practical characteristic that makes its research interesting. The TTRP continues to be attractive to developing new evolutionary algorithms. This research details a new estimation of the distribution algorithm coupled with a radial probability function from hydrogen. Continuous values are used in the solution representation, and every value indicates, in a hydrogen atom, the distance between the electron and the core. The key point is to exploit the radial probability distribution to construct offspring and to tackle the drawbacks of the estimation of distribution algorithms. Various instances and numerical experiments are presented to illustrate and validate this novel research. Based on the performance of the proposed scheme, we can make the conclusion that incorporating radial probability distributions helps to improve the estimation of distribution algorithms.

A self-adaptive bat algorithm for the truck and trailer routing problem

PurposeThe purpose of this paper is to provide an effective solution method for the truck and trailer routing problem (TTRP) which is one of the important NP-hard combinatorial optimization problems owing to its multiple real-world applications. It is a generalization of the famous vehicle routing problem (VRP), involving a group of geographically scattered customers served by the vehicle fleet including trucks and trailers.Design/methodology/approachThe meta-heuristic solution approach based on bat algorithm (BA) in which a local search procedure performed by five different neighborhood structures is developed. Moreover, a self-adaptive (SA) tuning strategy to preserve the swarm diversity is implemented. The effectiveness of the proposed SA-BA is investigated by an experiment conducted on 21 benchmark problems that are well known in the literature.FindingsComputational results indicate that the proposed SA-BA algorithm is computationally efficient through comparison with other existing algorithms found from the literature according to solution quality. As for the actual computational time, the SA-BA algorithm outperforms others. However, the scaled computational time of the SA-BA algorithm underperforms the other algorithms.Originality/valueIn this work the authors show that the proposed SA-BA is effective as a method for the TTRP problem. To the authors’ knowledge, the BA has not been applied previously, as in this work, to solve the TTRP problem.

The two-echelon time-constrained vehicle routing problem in linehaul-delivery systems considering carbon dioxide emissions

Multi-echelon distribution strategy is primarily to alleviate the environmental (e.g., energy consumption and emissions) consequence of logistics operations. Differing from the long-term strategic problems (e.g., the two-echelon vehicle routing problem (2E-VRP), the two-echelon location routing problem (2E-LRP) and the truck and trailer routing problem (TTRP)) that make location decisions in depots or satellites, the paper introduces a short-term tactical problem named the two-echelon time-constrained vehicle routing problem in linehaul-delivery systems (2E-TVRP) considering carbon dioxide (CO2) emissions. The linehaul level and the delivery level are linked through city distribution centers (CDCs). The 2E-TVRP, which takes CO2 emissions per ton-kilometer as the objective, has inter-CDC linehaul on the 1st level and delivery from CDCs to satellites on the 2nd level. The Clarke and Wright savings heuristic algorithm (CW) improved by a local search phase is put forward. The case study shows the applicability of the model to real-life problems. The results suggest that the vehicle scheduling provided by the 2E-TVRP is promising to reduce the CO2 emissions per ton-kilometer of the linehaul-delivery system. Adjusting the central depot location or developing the loaded-semitrailer demand among O-D pairs to eliminate empty-running of tractors will contribute to reduce the CO2 emission factor.

The two-echelon time-constrained vehicle routing problem in linehaul-delivery systems

Most of the studies address issues relating to the delivery from satellites to customers, which is throughout the end part of the linehaul-delivery system. Differing from the long-term strategic problems including the two-echelon vehicle routing problem (2E-VRP), the two-echelon location routing problem (2E-LRP) and the truck and trailer routing problem (TTRP) which make location decisions in depots or satellites, the paper introduces a short-term tactical problem named the two-echelon time-constrained vehicle routing problem in linehaul-delivery systems (2E-TVRP) that does not involve location decisions. The linehaul level and the delivery level are linked through city distribution centers (CDCs) located on the outskirts of cities. The 2E-TVRP has inter-CDC linehaul on the first level and urban delivery from CDCs to satellites on the second level. Vehicle routes on different levels are interacted by time constraints. A mixed integer nonlinear programming model for the 2E-TVRP is put forward, and a mixed integer linear programming model is used as the benchmark model. The Clarke and Wright savings heuristic algorithm (CW) improved by a local search phase is adopted. The 2E-TVRP formulations and the heuristic algorithm are tested by using 140 randomly-generated instances with up to 10 CDCs and 500 satellites. The computational results indicate that the heuristic can effectively solve various instances of the 2E-TVRP.

The Combination Truck Routing Problem: A Survey

The combination-vehicle attributes of vehicle routing problems are additional characteristics that aim to consider more effectively the specificities of real logistics applications. Because various combination truck situations exist, the combination truck routing problem (CTRP) is supported by well-developed literature, especially with respect to the truck and trailer routing problem (TTRP), the rollon-rolloff vehicle routing problem (RRVRP), the tractor and semitrailer routing problem (TSRP), and a variety of heuristics. This article first reviews the three primary forms of the CTRP, providing a survey of problem foundations and heuristics for the TTRP, the RRVRP and the TSRP. Next, this report takes a closer look at comparing the three forms of the CTRP. The TTRP aims to efficiently apply trailers that can attach/detach trucks easily to serve less-than-truckload shipping, and the RRVRP and the TSRP aim to attain high use rates for tractors in different full truckload shipping practices. The three forms of the CTRP share a number of common features. In particular, most of the formulations and heuristic strategies developed for specific problems share many similar characteristics. The CTRP is an extremely rich and promising operations research field. More general formulations and more general-purpose solvers are necessary to address practical combination truck routing applications efficiently and in a timely manner.

Memetic Heuristic Approach for Solving Truck and Trailer Routing Problems with Stochastic Demands and Time Windows

Manufacturers and service providers often encounter stochastic demand scenarios. Researchers have, thus far, considered that the deterministic truck and trailer routing problem (TTRP) cannot address the prevailing demand uncertainties and/or other complexities. The purpose of this study is to expand the deterministic TTRP model by introducing stochastic demand and time window constraints to bring the TTRP model closer to reality and solve the model in a reasonable timeframe by administering the memetic algorithm (MA). This paper presents a model that can be applied in stochastic demand conditions. To employ the MA, various crossovers, mutations and local search approaches were applied. First, two experimental tests were carried out to show the validity and consistency of the MA for solving stochastic TTRPs. The results can be compared with the VRPSDTW (vehicle routing problem with stochastic demands (VRSPD) with time windows) solution obtained using large neighbourhood search (LNS) of earlier research. The average results from Tests 1 and 2 achieved by MA are 514.927 and 516.451. However, the average result obtained by LNS is 516.877. Therefore, the MA can generate results. Thus, MA is found to be suitable for solving truck and trailer routing problem(s) under stochastic demand with a time window (TTRPSDTW). Moreover, 54 benchmark instances were modified for this case and the initial feasible solutions were generated for this purpose. The solutions were significantly improved by the MA. Also, the problems were tested using sensitivity analysis to understand the effects of the parameters and to make a comparison between the best results obtained by MA and sensitivity analysis. Since the differences between the results are small, the MA was found to be appropriate and better for solving TTRPSDTW. The paper also gives some suggestions for further research.

Metaheuristic Approaches for Solving Truck and Trailer Routing Problems with Stochastic Demands: A Case Study in Dairy Industry

Manufacturers and service providers often encounter stochastic demand scenarios. Researchers have, thus far, considered the deterministic truck and trailer routing problem (TTRP) that cannot address ubiquitous demand uncertainties and/or other complexities. The purpose of this study is to model the TTRP with stochastic demand (TTRPSD) constraints to bring the TTRP model closer to a reality. The model is solved in a reasonable timeframe using data from a large dairy service by administering the multipoint simulated annealing (M-SA), memetic algorithm (MA), and tabu search (TS). A sizeable number of customers whose demands follow the Poisson probability distribution are considered to model and solve the problem. To make the solutions relevant, first, 21 special TTRPSD benchmark instances are modified for this case and then these benchmarks are used in order to increase the validity and efficiency of the aforementioned algorithms and to show the consistency of the results. Also, the solutions have been tested using sensitivity analysis to understand the effects of the parameters and to make a comparison between the best results obtained by three algorithms and sensitivity analysis. Since the differences between the results are insignificant, the algorithms are found to be appropriate and relevant for solving real-world TTRPSD problem.

Truck and trailer routing—Problems, heuristics and computational experience

In the truck and trailer routing problem (TTRP) the vehicle fleet consists of truck units and trailer units with some customers only accessible by truck. For that purpose trailers can be uncoupled en-route at customers where truck sub-tours are built. We discuss several variants of this specific rich vehicle routing problem (RVRP): the TTRP with and without the option of load transfer between truck and trailer as well as the requirement of time windows for delivery. We present computational experience with a simple and flexible hybrid approach which is based on local search and large neighborhood search as well as standard metaheuristic control strategies. This approach which has shown to be rather effective on several other RVRP-classes before can compete with complex state-of-the-art approaches with respect to speed and accuracy on the TTRP too.

A simulated annealing heuristic for the truck and trailer routing problem with time windows

European firms have been using a combination of trucks and trailers in the delivery/collection of food products for years. Thus, some previous studies had been devoted to improving the efficiency of the resulting truck and trailer routing problem (TTRP). Since time window constraints are present in many real-world routing applications, in this study, we introduce the truck and trailer routing problem with time windows (TTRPTW) to bring the TTRP model closer to the reality. A simulated annealing (SA) heuristic is proposed for solving the TTRPTW. Two computational experiments are conducted to test the performance of the proposed SA heuristic. The results indicate that the proposed SA heuristic is capable of consistently producing quality solutions to the TTRPTW within a reasonable time.

A GRASP with evolutionary path relinking for the truck and trailer routing problem

In the truck and trailer routing problem (TTRP) a heterogeneous fleet composed of trucks and trailers has to serve a set of customers, some only accessible by truck and others accessible with a truck pulling a trailer. This problem is solved using a route-first, cluster-second procedure embedded within a hybrid metaheuristic based on a greedy randomized adaptive search procedure (GRASP), a variable neighborhood search (VNS) and a path relinking (PR). We test PR as a post-optimization procedure, as an intensification mechanism, and within evolutionary path relinking (EvPR). Numerical experiments show that all the variants of the proposed GRASP with path relinking outperform all previously published methods. Remarkably, GRASP with EvPR obtains average gaps to best-known solutions of less than 1% and provides several new best solutions.

A note on the truck and trailer routing problem

This study considers the relaxed truck and trailer routing problem (RTTRP), a relaxation of the truck and trailer routing problem (TTRP). TTRP is a variant of the well studied vehicle routing problem (VRP). In TTRP, a fleet of trucks and trailers are used to service a set of customers with known demands. Some customers may be serviced by a truck pulling a trailer, while the others may only be serviced by a single truck. This is the main difference between TTRP and VRP. The number of available trucks and available trailers is limited in the original TTRP but there are no fixed costs associated with the use of trucks or trailers. Therefore, it is reasonable to relax this fleet size constraint to see if it is possible to further reduce the total routing cost (distance). In addition, the resulting RTTRP can also be used to determine a better fleet mix. We developed a simulated annealing heuristic for solving RTTRP and tested it on 21 existing TTRP benchmark problems and 36 newly generated TTRP instances. Computational results indicate that the solutions for RTTRP are generally better than the best solutions in the literature for TTRP. The proposed SA heuristic is able to find better solutions to 18 of the 21 existing benchmark TTRP instances. The solutions for the remaining three problems are tied with the best so far solutions in the literature. For the 36 newly generated problems, the average percentage improvement of RTTRP solutions over TTRP solutions is about 5%. Considering the ever rising crude oil price, even small reduction in the route length is significant.

Solving the truck and trailer routing problem based on a simulated annealing heuristic

In this study, we consider the application of a simulated annealing (SA) heuristic to the truck and trailer routing problem (TTRP), a variant of the vehicle routing problem (VRP). In the TTRP, some customers can be serviced by either a complete vehicle (that is, a truck pulling a trailer) or a single truck, while others can only be serviced by a single truck for various reasons. SA has seen widespread applications to various combinatorial optimization problems, including the VRP. However, to our best knowledge, it has not been applied to the TTRP. So far, all the best known results for benchmark TTRP instances were obtained using tabu search (TS). We applied SA to the TTRP and obtained 17 best solutions to the 21 benchmark TTRP benchmark problems, including 11 new best solutions. Moreover, the computational time required by the proposed SA heuristic is less than those reported in prior studies. The results suggest that SA is competitive with TS on solving the TTRP.