Search Results (11)

Inland container depot (ICD) truck schedules must absorb new orders, service delays, appointment changes, congestion, and port cut-offs without destabilizing an already executed plan. This study asks whether event-triggered local repair can be separated into an explicit business-rule audit and a learned ranking of feasible task–vehicle actions. The proposed decision-support framework connects a static baseline, candidate task chains, six modeled hard-feasibility predicates, a Transformer encoder trained with proximal policy optimization (Transformer-PPO), and discrete-event execution logs. A five-seed, 120-episode confirmation gave Transformer-PPO a held-out online completion proxy (α_online) of 0.3226 and reward of 110.58, compared with 0.2581 and 61.87 for the matched multilayer perceptron (MLP); deterministic rules and search remained competitive. An independent audit of 4,968,000 action cells across 552 decision states found no disagreement with an independently coded oracle for the implemented hard predicates, while a reward-weight screen exposed the expected efficiency-stability trade-off. Together with a rolling-horizon comparator and a three-scale by three-disturbance stress test, the evidence supports an auditable system-integration contribution, not a new generic reinforcement learning (RL) algorithm or universal performance superiority. Claims are limited to synthetic simulation-based decision support. Full article

Static pre-scheduling in inland container depot (ICD)-centered drayage must coordinate tractors, detachable load units, factory loading, and port deadlines before next-day execution. Conventional order-based routing is too rigid for mixed direct haulage, drop-and-pull, relay pickup, street-turn, and buffering operations. This study proposes a task-pooling framework that decomposes logistics orders into atomic tasks and recombines them across tractors in a unified static planning space. A compact route-based MILP is used for reduced-scale calibration, and an enhanced adaptive large neighborhood search (E-ALNS) is developed around ICD-oriented relay recombination and temporal-slack shifting. On a realistic synthetic benchmark with 100 generated order records (90 active executable orders), 60 available tractors, and 330 executable tasks, the proposed method reduces the internal search-ledger value from 42,213.29 to 34,421.22 and the compact ex post blueprint value from 53,802.28 to 47,717.99 relative to the greedy construction baseline. The resulting blueprint preserves an average inter-task slack of 89.86 min and a 5th-percentile slack of 61.73 min. A generic adaptive-neighborhood baseline reaches a slightly lower ex post value of 46,722.48 only with a longer runtime and much lower temporal reserve. The results support a cost–reserve–runtime tradeoff interpretation rather than unconditional cost dominance. Full article

Cross-border e-commerce fulfillment depends on coordinated inland container movements across factories, inland container depots (ICDs), and port gateways, yet many container trucking operations still follow synchronous one-truck-one-order execution. This study models the fulfillment network as a platform-enabled socio-technical transportation system in which the ICD acts as a digital–physical coordination node for spatiotemporal decoupling. A drop–buffer–pick task architecture is developed to represent direct execution, relay execution, and delayed dispatch, and a mixed-integer linear programming (MILP) model optimizes task assignment and tractor sequencing under loading-time, port cutoff, inventory, and working-time constraints. In the certified-optimal 10-order instance, gross positive cost decreases from CNY 27,540 to CNY 19,915 (−27.7%); after applying the same post hoc coordination-credit accounting rule, net total fulfillment cost decreases to CNY 18,734 (−32.0%). The 10 orders are served with five tractors under the tested platform configuration, compared with 10 tractors under the restricted benchmark. To address sustainability explicitly, the analysis also reports distance-based emissions and energy-use proxies; the proposed schedule lowers cost and fleet deployment but increases total mileage, showing that economic efficiency and emissions performance do not automatically move together. The evidence is a deterministic baseline for later stochastic, mixed import/export, and collaborative-platform extensions. Full article

Inefficient inland repositioning of empty containers between depots remains a persistent challenge in container logistics, contributing significantly to unnecessary truck movements, elevated operational costs, and increased CO₂ emissions. Acknowledging the importance of this problem, a large amount of relevant literature has appeared. The objective of this paper is to track the empty container flow between ports, empty depots, inland terminals, and customer premises. Additionally, it aims to simulate and assess CO₂ emissions, capturing the dynamic interactions between different agents. In this study, agent-based modeling (ABM) was proposed to simulate the empty container movements with an emphasis on inland transportation. ABM is an emerging approach that is increasingly used to simulate complex economic systems and artificial market behaviours. NetLogo was used to incorporate real-world geographic data and quantify CO₂ emissions based on truckload status and to evaluate the other operational aspects. Behavior Space was also utilized to systematically conduct multiple simulation experiments, varying parameters to analyze different scenarios. The results of the study show that customer demand frequency plays a crucial role in system efficiency, affecting container availability and logistical tension. Full article

This study addresses the critical environmental challenge of increasing carbon emissions from Thailand’s freight transport sector, focusing on container movement in the strategic Lat Krabang ICD–Laem Chabang Port corridor. The research quantifies and compares CO₂ emissions between rail and road container transport modes to identify potential carbon reduction strategies. A comprehensive activity-based methodology was employed, incorporating fuel consumption testing across multiple load conditions, detailed transport activity mapping, and the application of locally relevant emission factors. The results demonstrate that rail transport produces 32.82 kgCO₂eq/TEU compared to 53.13 kgCO₂eq/TEU for road transport, representing a 38.23% emission advantage. Fuel consumption testing revealed a power relationship between train weight and fuel consumption (y = 0.1121x^0.5147, R² = 0.97), indicating improving efficiency with increased loading. Terminal operations contribute significantly to rail transport’s emission profile, accounting for 36% of total emissions. The current modal split presents substantial opportunities for emission reduction through increased rail utilization. This study identifies and evaluates practical carbon reduction strategies across operational, technological, and policy dimensions, with priority interventions including load factor optimization, terminal efficiency improvements, locomotive modernization, and differential road pricing. This research contributes empirical evidence to support sustainable freight transport development in Thailand while establishing a methodological framework applicable to emission assessments in similar corridors throughout developing economies. Full article

Container drayage, as a pivotal element of door-to-door intermodal transportation, has garnered increasing attention due to its significant influence on container logistics costs. Although various types of transport requests have been defined in the literature, no comprehensive study has addressed all of them together yet, due to the lack of an efficient model and corresponding algorithms. Furthermore, existing research on container drayage often neglects the simultaneous incorporation of two trucking operation modes, two empty container repositioning strategies, and the availability of empty containers across multiple depots. To address these issues, this study proposes a comprehensive container drayage problem (CDP) and mathematically formulates it as an innovative mixed integer linear programming (MILP) model, capturing the uncertainty and unpredictability inherent in empty container allocation, truck dispatching, and route planning. Given the problem’s complexity, obtaining an exact solution for large instances is not feasible. Therefore, an improved large neighborhood search (LNS) algorithm is tailored by incorporating the “Sequential insertion” and the “Solution re-optimization” operations. Extensive numerical experiments using randomly generated instances of varying scales validate the correctness of the proposed model and demonstrate the performance of the proposed algorithm. Additionally, sensitivity analysis on the number and distribution of depots and empty containers offers valuable managerial insights for the development of an effective container drayage system. Full article

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. Full article

This study investigates the impact of shareholder activism and board of directors (BOD) commitment on corporate agility and sustainability strategy within Indonesia’s inland container depot (ICD) industry. Data from 147 ICDs were collected using a census sampling method and a standardized questionnaire with a Likert scale. Using a quantitative explanatory research design, the data were analyzed using Structural Equation Modeling (SEM) via WarpPLS. The findings show that both shareholder activism and BOD commitment have a significant positive effect on corporate agility. Additionally, shareholder activism and corporate agility positively influence sustainability strategy, while BOD commitment has no direct significant impact on sustainability strategy. However, corporate agility mediates the relationship between BOD commitment and sustainability strategy, suggesting that BOD commitment enhances corporate agility, which, in turn, fosters the integration of sustainable practices. These results highlight the critical roles of shareholder activism and BOD commitment in enhancing corporate agility and driving sustainable practices within Indonesia’s ICD sector. Full article

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. Full article

Container drayage involves the transportation of containers by trucks. Although the distance is relatively short compared to maritime and rail transport, container drayage accounts for 25% to 40% of the total container transportation costs and significantly contributes to increased fuel consumption and carbon emissions. Thus, the modeling of the container drayage problem (CDP) has received a lot of attention in the last two decades. However, the three fundamental modeling factors, including the combination of trucking operation modes and empty container relocation strategies, as well as empty container constraints and multiple inland depots, have not been simultaneously investigated. Hence, this study addressed a comprehensive CDP that simultaneously incorporates the three modeling factors. The problem was formulated as a novel mixed integer linear programming (MILP) model based on the DAOV graph. Given the complexity of this problem, it was not realistic to find an exact solution for large instances. Therefore, an improved genetic algorithm (GA) was designed by integrating the “sequential insertion” method and “solution re-optimization” operation. The performance of Gurobi and GA was validated and evaluated through randomly generated instances. The results indicate that (1) the proposed algorithm can provide near-optimal solutions for large-scale instances within a reasonable running time, (2) the greatest cost savings from combining trucking operation modes and empty container relocation strategies range from 10.45% to 31.86%, and (3) the three modeling factors significantly influence the fuel consumption and carbon emissions, which can provide managerial insights for sustainable container drayage practices. Full article

Well-organized network configuration is the key to the success of inland container transportation systems. In this study, we firstly propose an integrated framework for the location selection of inland container depots (ICDs) and the scheduling of containers and trucks. The objective is to minimize the total cost of setting up the ICDs and transportation cost associated with trucks and containers. A mixed-integer linear programming (MILP) model is developed to solve the proposed problem. The computational studies show that the proposed decision approach is effective and can reduce the total operating costs of ICDs and transportation costs of containers. Sensitivity analysis on the impact of customer distributions and the number of ICDs on the total cost are conducted to reveal the characteristics of the problem. The utilization of ICDs can significantly improve the efficiency of the transportation network, i.e., the total cost is reduced by at least 27% for the proposed instances, and the transportation distance of empty containers is reduced by at least 4%. Finally, managerial insights and future research directions are provided. Full article