Makespan Minimization of Machines and Automated Guided Vehicles Schedule Using Binary Particle Swarm Optimization

An efficient and optimized Automated Guided Vehicles (AGVs) operation plays a critical role in improving the performance of a Flexible Manufacturing System (FMS). Among the main elements in the implementation of AGV is task scheduling. This is due to the fact that efficient scheduling would enable the increment of productivity and reducing delivery cost whilst optimally utilizes the entire fleet. In this research, Binary Particle Swarm Optimization (BPSO) is used to optimize simultaneous machines and AGVs scheduling process with makespan minimization function. It is proven that the method is capable to provide better solution compared to others

Practical solutions for a dock assignment problem with trailer transportation.

We study a distribution warehouse in which trailers need to be assigned to docks for loading or unloading. A parking lot is used as a buffer zone and transportation between the parking lot and the docks is performed by auxiliary resources called terminal tractors. Each incoming trailer has a known arrival time and each outgoing trailer a desired departure time. The primary objective is to produce a docking schedule such that the weighted sum of the number of late outgoing trailers and the tardiness of these trailers is minimized; the secondary objective is to minimize the weighted completion time of all trailers, both incoming and outgoing. The purpose of this paper is to produce high-quality solutions to large instances that are comparable to a real-life case. We implement several heuristic algorithms: truncated branch and bound, beam search and tabu search. Lagrangian relaxation is embedded in the algorithms for constructing an initial solution and for computing lower bounds. The different solution frameworks are compared via extensive computational experiments.Dock assignment; Multicriteria scheduling; Branch and bound; Beam search; Lagrangian relaxation; Tabu search;

Planning and Scheduling Optimization

Although planning and scheduling optimization have been explored in the literature for many years now, it still remains a hot topic in the current scientific research. The changing market trends, globalization, technical and technological progress, and sustainability considerations make it necessary to deal with new optimization challenges in modern manufacturing, engineering, and healthcare systems. This book provides an overview of the recent advances in different areas connected with operations research models and other applications of intelligent computing techniques used for planning and scheduling optimization. The wide range of theoretical and practical research findings reported in this book confirms that the planning and scheduling problem is a complex issue that is present in different industrial sectors and organizations and opens promising and dynamic perspectives of research and development

Sequence-Based Simulation-Optimization Framework With Application to Port Operations at Multimodal Container Terminals

It is evident in previous works that operations research and mathematical algorithms can provide optimal or near-optimal solutions, whereas simulation models can aid in predicting and studying the behavior of systems over time and monitor performance under stochastic and uncertain circumstances. Given the intensive computational effort that simulation optimization methods impose, especially for large and complex systems like container terminals, a favorable approach is to reduce the search space to decrease the amount of computation. A maritime port can consist of multiple terminals with specific functionalities and specialized equipment. A container terminal is one of several facilities in a port that involves numerous resources and entities. It is also where containers are stored and transported, making the container terminal a complex system. Problems such as berth allocation, quay and yard crane scheduling and assignment, storage yard layout configuration, container re-handling, customs and security, and risk analysis become particularly challenging. Discrete-event simulation (DES) models are typically developed for complex and stochastic systems such as container terminals to study their behavior under different scenarios and circumstances. Simulation-optimization methods have emerged as an approach to find optimal values for input variables that maximize certain output metric(s) of the simulation. Various traditional and nontraditional approaches of simulation-optimization continue to be used to aid in decision making. In this dissertation, a novel framework for simulation-optimization is developed, implemented, and validated to study the influence of using a sequence (ordering) of decision variables (resource levels) for simulation-based optimization in resource allocation problems. This approach aims to reduce the computational effort of optimizing large simulations by breaking the simulation-optimization problem into stages. Since container terminals are complex stochastic systems consisting of different areas with detailed and critical functions that may affect the output, a platform that accurately simulates such a system can be of significant analytical benefit. To implement and validate the developed framework, a large-scale complex container terminal discrete-event simulation model was developed and validated based on a real system and then used as a testing platform for various hypothesized algorithms studied in this work

Modelling of dual-cycle strategy for container storage and vehicle scheduling problems at automated container terminals

This study proposes a new approach to determine the dispatching rules of AGVs and container storage locations, considering both unloading and loading processes simultaneously. We formulate this problem as a mixed integer programming model, aiming to minimise the ship’s berth time. Optimal solutions can be obtained in small sizes, however, large-sized problems are hard to solve optimally in a reasonable time. Therefore, a heuristic method, i.e. genetic algorithm is designed to solve the problem in large sizes. A series of numerical experiments are carried out to evaluate the effectiveness of the integration approach and algorithm

Managing Advanced Synchronization Aspects in Logistics Systems

In this thesis, we model various complex logistics problems and develop appropriate techniques to solve them. We improve industrial practices by introducing synchronized solutions to problems that were previously solved independently. The first part of this thesis focuses on cross-docks. We simultaneously optimize supplier orders and cross-docking operations to either reduce the storage space required or evenly distribute workload over the week. The second part of this thesis is devoted to transport problems in which two types of vehicles are synchronized, one of which can be transported by the other. The areas of application range from home services to parcel delivery to customers. After analyzing the complexity associated with these synchronized solutions (i.e., largescale problems for which the decisions depend on each other), we design algorithms based on the "destroy-and-repair" principle to find efficient solutions. We also introduce mathematical programs for all the considered problems. The problems under study arose directly from collaborations with various industrial partners. In this respect, our achieved solutions have been benchmarked with current industrial practice. Depending on the problem, we have been able to reduce the environmental impact generated by the industrial activities, the overall cost, or the social impact. The achieved gains compared to current industrial practice range from 10 to 70%, depending on the application. -- Dans cette thèse, nous modélisons divers problèmes logistiques complexes et développons des techniques appropriées pour les résoudre. Nous cherchons à améliorer certaines pratiques industrielles en introduisant des solutions synchronisées à des problèmes qui étaient auparavant résolus indépendamment. La première partie de cette thèse porte sur les cross-docks. Nous optimisons simultanément les commandes fournisseurs et les opérations au sein de la plateforme de logistique pour réduire l’espace de stockage requis ou répartir uniformément la charge de travail sur la semaine. La deuxième partie de cette thèse est consacrée aux problèmes de transport dans lesquels deux types de véhicules sont synchronisés, l’un pouvant être transporté par l’autre. Les domaines d’application vont du service à domicile à la livraison de colis chez des clients. Après avoir analysé la complexité des solutions synchronisées (c’est-à-dire des problèmes de grandes dimensions pour lesquels les décisions dépendent les unes des autres), nous concevons des algorithmes basés sur le principe de "destruction / reconstruction" pour trouver des solutions efficaces. Nous modélisons également les problèmes considérés avec la programmation mathématique. Les problèmes à l’étude viennent de collaborations avec divers partenaires industriels. A cet égard, les solutions que nous présentons sont comparées aux pratiques industrielles actuelles. En fonction du problème, nous avons pu réduire l’impact environnemental généré par les activités industrielles, le coût global, ou l’impact social des solutions. Les gains obtenus par rapport aux pratiques industrielles actuelles varient de 10 à 70%, selon l’application. Mot-clefs: Logistique, Synchronisation, Problème de transport, Tournée de véhicules, Plateforme de Cross-dock (transbordement), Programmation Mathématiques, Métaheuristiques, Matheuristiques, Instances Réelle

Modelling of integrated vehicle scheduling and container storage problems in unloading process at an automated container terminal

Effectively scheduling vehicles and allocating storage locations for containers are two important problems in container terminal operations. Early research efforts, however, are devoted to study them separately. This paper investigates the integration of the two problems focusing on the unloading process in an automated container terminal, where all or part of the equipment are built in automation. We formulate the integrated problem as a mixed-integer programming (MIP) model to minimise ship’s berth time. We determine the detailed schedules for all vehicles to be used during the unloading process and the storage location to be assigned for all containers. A series of experiments are carried out for small-sized problems by using commercial software. A genetic algorithm (GA) is designed for solving large-sized problems. The solutions from the GA for the small-sized problems are compared with the optimal solutions obtained from the commercial software to verify the effectiveness of the GA. The computational results show that the model and solution methods proposed in this paper are efficient in solving the integrated unloading problem for the automated container terminal

Progress in AI Planning Research and Applications

Planning has made significant progress since its inception in the 1970s, in terms both of the efficiency and sophistication of its algorithms and representations and its potential for application to real problems. In this paper we sketch the foundations of planning as a sub-field of Artificial Intelligence and the history of its development over the past three decades. Then some of the recent achievements within the field are discussed and provided some experimental data demonstrating the progress that has been made in the application of general planners to realistic and complex problems. The paper concludes by identifying some of the open issues that remain as important challenges for future research in planning

A Simulation-Based Optimization Approach for Integrated Port Resource Allocation Problem

Todays, due to the rapid increase in shipping volumes, the container terminals are faced with the challenge to cope with these increasing demands. To handle this challenge, it is crucial to use flexible and efficient optimization approach in order to decrease operating cost. In this paper, a simulation-based optimization approach is proposed to construct a near-optimal berth allocation plan integrated with a plan for tug assignment and for resolution of the quay crane re-allocation problem. The research challenges involve dealing with the uncertainty in arrival times of vessels as well as tidal variations. The effectiveness of the proposed evolutionary algorithm is tested on RAJAEE Port as a real case. According to the simulation result, it can be concluded that the objective function value is affected significantly by the arrival disruptions. The result also demonstrates the effectiveness of the proposed simulation-based optimization approach. </span