Search Results (9)

The four-way shuttle storage and retrieval system (FWSS/RS) is an advanced automated warehousing solution for achieving green and intelligent logistics, and task allocation is crucial to its logistics efficiency. However, current research on task allocation in three-dimensional storage environments is mostly conducted in the single-operation mode that handles inbound or outbound tasks individually, with limited attention paid to the more prevalent composite operation mode where inbound and outbound tasks coexist. To bridge this gap, this study investigates the task allocation problem in an FWSS/RS under the composite operation mode, and deep reinforcement learning (DRL) is introduced to solve it. Initially, the FWSS/RS operational workflows and equipment motion characteristics are analyzed, and a task allocation model with the total task completion time as the optimization objective is established. Furthermore, the task allocation problem is transformed into a partially observable Markov decision process corresponding to reinforcement learning. Each shuttle is regarded as an independent agent that receives localized observations, including shuttle position information and task completion status, as inputs, and a deep neural network is employed to fit value functions to output action selections. Correspondingly, all agents are trained within an independent deep Q-network (IDQN) framework that facilitates collaborative learning through experience sharing while maintaining decentralized decision-making based on individual observations. Moreover, to validate the efficiency and effectiveness of the proposed model and method, experiments were conducted across various problem scales and transport resource configurations. The experimental results demonstrate that the DRL-based approach outperforms conventional task allocation methods, including the auction algorithm and the genetic algorithm. Specifically, the proposed IDQN-based method reduces the task completion time by up to 12.88% compared to the auction algorithm, and up to 8.64% compared to the genetic algorithm across multiple scenarios. Moreover, task-related factors are found to have a more significant impact on the optimization objectives of task allocation than transport resource-related factors. Full article

Efficient material handling is crucial in the logistics operations of modern salt warehouses, where Rail Guided Vehicles (RGVs) and Air Sorting Robots (ASRs) are often deployed to manage inbound and outbound tasks. However, as the number of tasks increases within a given period, conflicts and deadlocks between simultaneously operating RGVs and ASRs become more frequent, reducing efficiency and increasing energy consumption during transportation. To address this, the research frames the inbound and outbound problem as a task allocation issue for the RGV/ASR system with a finite buffer, and proposes a collision avoidance strategy and a zero-wait strategy for loaded machines to reallocate tasks. To improve computational efficiency, we introduce an adaptive multi-neighborhood hybrid search (AMHS) algorithm, which integrates a dual-sequence coding scheme and an elite solution initialization strategy. A dedicated global search operator is designed to expand the search landscape, while an adaptive local search operator, inspired by biological hormone regulation mechanisms, along with a perturbation strategy, is used to refine the local search. In a case study on packaged salt storage, the proposed AMHS algorithm reduced the total makespan by 30.1% compared to the original task queue. Additionally, in 15 randomized test scenarios, AMHS demonstrated superior performance over three benchmark algorithms—Genetic Algorithm (GA), Discrete Imperialist Competitive Algorithm (DICA), and Improved Whale Optimization Algorithm (IWOA)—achieving an average makespan reduction of 12.6% relative to GA. Full article

This paper analyzes how a robust and dynamic forecasting system was designed and implemented to predict material volumes for the inbound logistics network of an international automotive company. The system aims to reduce transportation logistics costs and improve demand capacity planning for freight forwarders. The forecasting horizon is set for 4 months and 12 months ahead in the future. To solve this problem, a time series modeling approach was carried out by using different time series forecasting methods like ARIMA, Neural Networks, Exponential Smoothing, Prophet, Automated Simple Moving Average, Multivariate Time Series, and Ensemble Forecast. Additionally, important data preprocessing methods and a robust model selection framework were used to train the models and select the best-performing one. This is known as Forward Chaining Nested Cross Validation with origin recalibration. The system performance was assessed using the Symmetric Mean Absolute Error (SMAPE). The final version of the forecasting system can deliver 4-month-ahead forecasts with a SMAPE lower than 10% for 86% of all material flow connections. The system’s forecast output is updated on a monthly basis and was integrated into the inbound logistics network system of the company. Full article

It is now widely accepted that the locations of intermediate facilities, such as logistics platforms or inland ports, are key elements of multimodal freight distribution networks and heavily influence their effectiveness. This crucial role of localization decisions is even more significant if we consider their impact on the external costs of the entire logistic corridor, with reference to the cost components associated with environmental sustainability. This paper faces a facility location problem concerning a port system network serving inbound container flows arriving by sea and travelling via road and/or rail towards the hinterland. The aim is to evaluate the impact of externalities on the overall management of the distribution network, including location decisions, flow routing and transport mode choice. We present a Mixed Integer Linear Programming (MILP) model having the goal of minimizing both the location and shipping costs, while accounting for external cost components. In particular, as a novel environmental issue, we propose three different objective functions including congestion, air pollution, and, incidentally, noise and infrastructure deterioration. We allow the containerized flows to be split among several capacitated facilities and road and rail transport modalities. The reported computational experimentation refers to different intermodal freight logistic networks through real data derived from the logistic network departing from the maritime terminals associated with the port of the Ligurian region towards their main destinations in the north-west side of Italy. Finally, we evaluate the impact on both flows and total costs due to a closure or a capacity reduction on some links of the network. The evidence of the impact of sustainability external costs on the design and management of the multimodal logistic network under analysis is emphasized. Full article

In recent years, policymakers of urban agglomerations in various regions of the world have been striving to reduce environmental pollution from harmful exhaust and noise emissions. Restrictions on conventional vehicles entering the inner city are being introduced and the introduction of low-emission measures, including electric ones, is being promoted. This paper presents a method for scenario analysis applied to study the reduction of exhaust emissions by introducing electric vehicles in a selected city. The original scenario analyses relating to real problems faced by contemporary metropolitan areas are based on the VISUM tool (PTV Headquarters for Europe: PTV Planung Transport Verkehr AG, 76131 Karlsruhe, Germany). For the case study, the transport model of the city of Bielsko-Biala (Poland) was used to conduct experiments with different forms of participation of electric vehicles on the one hand and traffic restrictions for high emission vehicles on the other hand. Scenario analyses were conducted for various constraint options including inbound, outbound, and through traffic. Travel time for specific transport relations and the volume of harmful emissions were used as criteria for evaluating scenarios of limited accessibility to city zones for selected types of vehicles. The comparative analyses carried out showed that the introduction of electric vehicles in the inner city resulted in a significant reduction in the emission of harmful exhaust compounds and, consequently, in an increase in the area of clean air in the city. The case study and its results provide some valuable insights and may guide decision-makers in their actions to introduce both driving ban restrictions for high-emission vehicles and incentives for the use of electric vehicles for city residents. Full article

Recently, in the context of “The Belt and Road” Initiative, the China Railway Express, which has a high volume and spans a long distance has greatly facilitated the construction of international freight transport corridors between developed and developing countries. To ensure sustainable development, this paper introduces an optimization problem of a container distribution organization scheme for the China Railway Express resulting from the major existing problems arising in railway port stations, which is a special and crucial link in transportation organization of the China Railway Express. The problem of a long dwell time of inbound trains is typically concerned with the operation process in railway port stations. Taking various real-world influencing factors of efficiency into account, this paper formulates a distribution organization optimization model to minimize the total container-hours of inbound China Railway Express at Alataw Pass railway station. Subsequently, a solution method based on the main idea of a genetic algorithm is developed to solve the problem, and two examples of different modes of transportation organization are given for validating the effectiveness of the model. Finally, we compare the results between two modes under different orders of magnitude according to the characteristics of sustainability to discuss the possible change and development of the China Railway Express in the future. Full article

This research developed a solution approach that is a combination of a web application and the modified differential evolution (MDE) algorithm, aimed at solving a real-time transportation problem. A case study involving an inbound transportation problem in a company that has to plan the direct shipping of a finished product to be collected at the depot where the vehicles are located is presented. In the newly designed transportation plan, a vehicle will go to pick up the raw material required by a certain production plant from the supplier to deliver to the production plant in a manner that aims to reduce the transportation costs for the whole system. The reoptimized routing is executed when new information is found. The information that is updated is obtained from the web application and the reoptimization process is executed using the MDE algorithm developed to provide the solution to the problem. Generally, the original DE comprises of four steps: (1) randomly building the initial set of the solution, (2) executing the mutation process, (3) executing the recombination process, and (4) executing the selection process. Originally, for the selection process in DE, the algorithm accepted only the better solution, but in this paper, four new selection formulas are presented that can accept a solution that is worse than the current best solution. The formula is used to increase the possibility of escaping from the local optimal solution. The computational results show that the MDE outperformed the original DE in all tested instances. The benefit of using real-time decision-making is that it can increase the company’s profit by 5.90% to 6.42%. Full article

Recent trends in the management of supply chains have witnessed an increasing implementation of the cross-docking strategy. The cross-docking strategy, being the one that can potentially improve supply chain operations, has received a lot of attention from researchers in recent years, especially over the last decade. Cross-docking involves the reception of inbound products, deconsolidation, sorting, consolidation, and shipping of the consolidated products to the end customers. The number of research efforts, aiming to study and improve the cross-docking operations, increases every year. While some studies discuss cross-docking as an integral part of a supply chain, other studies focus on the ways of making cross-docking terminals more efficient and propose different operations research techniques for various decision problems at cross-docking terminals. In order to identify the recent cross-docking trends, this study performs a state-of-the-art review with a particular focus on the truck scheduling problem at cross-docking terminals. A comprehensive evaluation of the reviewed studies is conducted, focusing on the major attributes of the cross-docking operations. These attributes include terminal shape considered, doors considered, door service mode considered, preemption, internal transportation mode used, temporary storage capacity, resource capacity, objectives considered, and solution methods adopted. Based on findings from the review of studies, some common issues are outlined and future research directions are proposed. Full article

The rapid development of cities has brought new challenges and opportunities to traditional traffic management. The usage of smart cards promotes the upgrading of intelligent transportation systems, and also produces considerable big data. As an important part of the urban comprehensive transportation system, Nanjing metro has more than 1 million inbound and outbound records of traffic smart cards used by residents every day. How to process these traffic data and present them visually is an urgent problem in modern traffic management. In this study, five working days with normal weather conditions in Nanjing were selected, and the swiping records of the smart cards were extracted, and the space–time characteristics were analyzed. In terms of time analysis, this research analyzed the 24-h fluctuation of daily average passenger flow, peak hour coefficient of passenger flow, 24-h fluctuation of passenger flow on different metro lines, passenger flow intensity on different metro lines and passenger flow comparison at different stations. In spatial analysis, this study uses thermodynamic charts to represent the inflow and outflow of passengers at different stations during early and evening peak periods. The analysis results and visualized images directly reflect the area where Nanjing metro congestion is located, and also shows the commuting characteristics of residents. It can solve the problem of urban congestion, carry out the rational layout of urban functional areas, and promote the sustainable development of people and cities. Full article