Solving synchromodal container transportation problem using a genetic algorithm

This paper investigates the Inland Container Transportation (ICT) problem with carbon dioxide emissions. The separation mode that the tractor and semi-trailer could be detached and it permits multitasking to reduce fuel and carbon emission costs. A mixed-integer programming model with Full-Empty container integration has been built. An improved ant colony optimization with two-dimensional variable matrix encoding and Infeasible-Arc filtration strategy has been proposed. Numerical experiments with different scales and characteristics are simulated and validated in order to demonstrate the effectiveness of the proposed algorithm. The comparison result indicates the excellent stability for our approach with different task characteristic distribution.

This paper investigates the inland container transportation problem with a focus on multi-size containers, fuel consumption, and carbon emissions. To reflect a more realistic situation, the depot’s initial inventory of empty containers is also taken into consideration. To linearly model the constraints imposed by the multiple container sizes and the limited number of empty containers, a novel graphical representation is presented for the problem. Based on the graphical representation, a mixed-integer programming model is presented to minimize the total transportation cost, which includes fixed, fuel, and carbon emission costs. To efficiently solve the model, a tailored branch-and-price algorithm is designed, which is enhanced by improvement schemes including a heuristic label-setting algorithm, decremental state-space relaxation, and the introduction of a high-quality upper bound. Results from a series of computational experiments on randomly generated instances demonstrate that (1) the proposed branch-and-price algorithm demonstrates a superior performance compared to the tabu search algorithm and the genetic algorithm; (2) each additional empty container in the depot reduces the total transportation cost by less than 1%, with a diminishing marginal effect; (3) the rational configuration of different types of trucks improves scheduling flexibility and reduces fuel and carbon emission costs as well as the overall transportation cost; and (4) extending customer time windows also contributes to lower the total transportation cost. These findings not only deepen the theoretical understanding of inland container transportation optimization but also provide valuable insights for logistics companies and policymakers to improve efficiency and implement more sustainable operational practices. Additionally, our research paves the way for future investigations into the integration of dynamic factors and emerging technologies in this field.

As an effective solution to the first- and last-mile logistics of door-to-door intermodal container transportation, inland container transportation involves transporting containers by truck between terminals, depots, and customers within a local area. This paper is the first to focus specifically on the inland container transportation problem with limited depot capacity, where the storage of empty containers is constrained by physical space limitations. To reflect a more realistic scenario, we also consider the initial stock levels of empty containers at the depot. The objective of this problem is to schedule trucks to fulfill inland container transportation orders such that the overall cost is minimum and the depot is neither out of stock or over stocked at any time. A novel graphical representation is introduced to model the constraints of empty containers and depot capacity in a linear form. This problem is then mathematically modeled as a mixed-integer linear programming formulation. To avoid discretizing the time horizon and effectively achieve the optimal solution, we design a tailored branch-and-price-and-cut algorithm where violated empty container constraints for critical times are dynamically integrated into the restricted master problem. The efficiency of the proposed algorithm is enhanced through the implementation of several techniques, such as a heuristic label-setting method, decremental state-space relaxation, and the utilization of high-quality upper bounds. Extensive computational studies are performed to assess the performance of the proposed algorithm and justify the introduction of enhancement strategies. Sensitivity analysis is additionally conducted to investigate the implications of significant influential factors, offering meaningful managerial guidance for decision-makers.

When shipping ports are colocated with major population centers, the exclusive use of road transport for moving shipping containers across the metropolitan area is undesirable from both social and economic perspectives. Port shuttles, an integrated road and short-haul rail transport modality, are thereby gaining significant interest from governments and industry alike, especially in the Australian context. In “A Simultaneous Magnanti-Wong Method to Accelerate Benders Decomposition for the Metropolitan Container Transportation Problem,” Perrykkad, Ernst, and Krishnamoorthy explore the mathematics behind the optimal integration of road and port shuttle modalities for container transportation in metropolitan areas, including proofs of NP harness, a Benders decomposition, and an extensive computational study. Critically, to accelerate their Benders decomposition the authors develop the simultaneous Magnanti-Wong method: an extension of the classical Magnanti-Wong acceleration that preserves this problem's important network substructure. In addition to the problem at hand, this technique shows promise more generally for Benders decompositions with special subproblem structure.

A model for a multi-size inland container transportation problem

The Inland Container Transportation Problem defines the movement of fully loaded and empty containers among terminals, depots, and customers within the same inland area. All kinds of customer requests are organized by one trucking company that owns depots containing a homogeneous fleet of trucks and a sufficiently large set of empty containers. The objective of this study is to minimize the total distance the trucks travel. We present a two-stage iterative solution approach that is capable to optimize around 300 requests. In the first step, the set of requests is divided into subsets, a tabu list prevents returns to recently considered subsets. In the second step, a mathematical problem is solved for each subset. These steps are then repeated and the best known solution is updated so long as certain stopping criteria are not met. The approach is implemented in C++ using IBM ILOG CPLEX. The quality was verified by several computational experiments.

Combined strip and discharge delivery of containers in heterogeneous fleets with time windows

Due to the obvious difference of resources distribution and economic development between different regions, the railway container flow in China is seriously unbalanced in space and time, which leads to the problem of empty railway container transportation. Therefore, this paper will focus on the optimization of railway empty container transportation scheme. Firstly, the multi-objective optimization model is determined by considering the cost of railway companies and the satisfaction of shippers, then, the master objective method is adopted and Gurobi is combined to solve the actual case, finally, the conclusion is drawn, example verification proves that the model and algorithm are feasible. In addition, taking into account the interests of both buyers and sellers and using the main objective method to solve the model are not involved in the previous similar papers. In general, the starting point and the algorithm have a certain innovation.

A Neighborhood Search for a Multi-size Container Transportation Problem

A Physical Internet (PI) based inland container transportation problem with selective non-containerized shipping requests

Optimised Use of Fixed-Scheduled Container Trains in Collaborative Logistics

Planning connections between underground logistics system and container ports

In the inland container transportation problem, one trucking company operating a homogeneous fleet of trucks has to move 40-ft. containers. The recent extension to this problem is to introduce two different commodities, namely 20- and 40-ft. containers, instead of only 40-ft. containers. Our objective is to minimize the total travel distance of the trucks for the extended problem. A model is shown and implemented in C++ using IBM ILOG CPLEX solver. Fifteen test instances are created to obtain computational results using our implementation.

Scheduling for inland container truck and train transportation

In this paper, a Mixed-Shift Vehicle Routing Problem is proposed based on a real-life container transportation problem. In a long planning horizon of multiple shifts, transport tasks are completed satisfying the time constraints. Due to the different travel distances and time of tasks, there are two types of shifts (long shift and short shift) in this problem. The unit driver cost for long shifts is higher than that of short shifts. A mathematical model of this Mixed-Shift Vehicle Routing Problem with Time Windows (MS-VRPTW) is established in this paper, with two objectives of minimizing the total driver payment and the total travel distance. Due to the large scale and nonlinear constraints, the exact search showed is not suitable to MS-VRPTW. An initial solution construction heuristic (EBIH) and a selective perturbation Hyper-Heuristic (GIHH) are thus developed. In GIHH, five heuristics with different extents of perturbation at the low level are adaptively selected by a high level selection scheme with the Hill Climbing acceptance criterion. Two guidance indicators are devised at the high level to adaptively adjust the selection of the low level heuristics for this bi-objective problem. The two indicators estimate the objective value improvement and the improvement direction over the Pareto Front, respectively. To evaluate the generality of the proposed algorithms, a set of benchmark instances with various features is extracted from real-life historical datasets. The experiment results show that GIHH significantly improves the quality of the final Pareto Solution Set, outperforming the state-of-the-art algorithms for similar problems. Its application on VRPTW also obtains promising results.