Stowage Planning System for Ferry Ro-Ro Ships Using Particle Swarm Optimization Method

Stowage planning involves distributing cargo on board a ship, including quantity, weight, and destination details. It consists of collecting cargo manifest data, planning cargo location on decks, and calculating stability until the vessel is declared safe for sailing. Finding the ideal solution to real-world situations in this stowage planning problem is challenging and frequently requires a very long computing period. The Particle Swarm Optimization (PSO) algorithm is one of the evolutionary algorithms known for its efficient performance. PSO has been extended to complex optimization problems due to its fast convergence and easy implementation. In this study, the Particle Swarm Optimization (PSO) method is implemented to automate stowage arrangements on ships considering three factors (width, length, and weight of the vehicle). This system was evaluated with KMP Legundi vehicle manifest data and four load cases of 12 different vehicle types that can be loaded on Ferry / Ro-Ro Ships. It provides complete vehicle layouts and allows interactive changes for stowage planners, ensuring speed and accuracy in arranging ship cargo.Stowage planning involves distributing cargo on board a ship, including quantity, weight, and destination details. It consists of collecting cargo manifest data, planning cargo location on decks, and calculating stability until the vessel is declared safe for sailing. Finding the ideal solution to real-world situations in this stowage planning problem is challenging and frequently requires a very long computing period. The Particle Swarm Optimization (PSO) algorithm is one of the evolutionary algorithms known for its efficient performance. PSO has been extended to complex optimization problems due to its fast convergence and easy implementation. In this study, the Particle Swarm Optimization (PSO) method is implemented to automate stowage arrangements on ships considering three factors (width, length, and weight of the vehicle). This system was evaluated with KMP Legundi vehicle manifest data and four load cases of 12 different vehicle types that can be loaded on Ferry / Ro-Ro Ships. It provides complete vehicle layouts and allows interactive changes for stowage planners, ensuring speed and accuracy in arranging ship cargo

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

Hierarchical modeling and analysis of container terminal operations

After the breakdown of trade barriers among countries, the volume of international trade has grown significantly in the last decade. This explosive growth in international trade has increased the importance of marine transportation which constitutes the major part of the global logistics network. The utilization of containers and container ships in marine transportation has also increased after the eighties due to various advantages such as packaging, flexibility, and reliability. Parallel to the container throughput, the capacities of ships and sizes of fleets as well as the number of terminals have been increased considerably. Substantial pressure of competition on ship operators and terminal managers has forced them to consider the issues regarding operational efficiency more deeply. Thus, the operational efficiency at port container terminals has become the major concern of terminal managers to satisfy the rapid transshipment of goods. In this thesis, we focus on a set of decision problems regarding container terminal operations. Since these problems are interrelated hierarchically, we attempt to model and analyze them consecutively. First, we consider the storage space allocation problem over a rolling horizon as an aggregate planning model. Since the model has the minimum cost flow network structure there exist polynomial time solution procedures via linear programming models. Although ship turnaround time is the principal performance criteria for whole container terminal operations, the total distances traveled by containers in the terminal throughout the planning horizon is determined as the surrogate objective function for the allocation model. The output of the storage space allocation problem is used as the input for the next step of our methodology, namely the location matching model. With the location matching model, the routes of vehicles for each time period have been identified while minimizing the total distance traveled by the vehicles, which reveals the ship turnaround times. The routes that are found subject to the output of storage space allocation models are better than those of random allocation in terms of total distances traveled. Next, the vehicle scheduling problem is discussed for different levels of complexity. The solution procedures proposed for similar problems in the machine scheduling literature are provided. Finally, we discuss the problem of simultaneous vehicle dispatching with precedence constraints. We have modeled the problem as a nonlinear mixed integer programming model and proposed an iterative solution procedure to obtain reasonable solutions in considerable times. Moreover, we have presented the worst-case performance analysis for this heuristic

Discrete-Event Control and Optimization of Container Terminal Operations

This thesis discusses the dynamical modeling of complex container terminal operations. In the current literature, the systems are usually modeled in static way using linear programming techniques. This setting does not completely capture the dynamic aspects in the operations, where information about external factors such as ships and trucks arrivals or departures and also the availability of terminal's equipment can always change. We propose dynamical modeling of container terminal operations using discrete-event systems (DES) modeling framework. The basic framework in this thesis is the DES modeling for berth and quay crane allocation problem (BCAP) where the systems are not only dynamic, but also asynchronous. We propose a novel berth and QC allocation method, namely the model predictive allocation (MPA) which is based on model predictive control principle and rolling horizon implementation. The DES models with asynchronous event transition is mathematically analyzed to show the efficacy of our method. We study an optimal input allocation problem for a class of discrete-event systems with dynamic input sequence (DESDIS). We show that in particular, the control input can be obtained by the minimization/maximization of the present input sequence only. We have shown that the proposed approach performed better than the existing method used in the studied terminal and state-of-the-art methods in the literature

Optimal Planning of Container Terminal Operations

Due to globalization and international trade, moving goods using a mixture of transportation modes has become a norm; today, large vessels transport 95% of the international cargos. In the first part of this thesis, the emphasis is on the sea-land intermodal transport. The availability of different modes of transportation (rail/road/direct) in sea-land intermodal transport and container flows (import, export, transhipment) through the terminal are considered simultaneously within a given planning time horizon. We have also formulated this problem as an Integer Programming (IP) model and the objective is to minimise storage cost, loading and transportation cost from/to the customers. To further understand the computational complexity and performance of the model, we have randomly generated a large number of test instances for extensive experimentation of the algorithm. Since, CPLEX was unable to find the optimal solution for the large test problems; a heuristic algorithm has been devised based on the original IP model to find near „optimal‟ solutions with a relative error of less than 4%. Furthermore, we developed and implemented Lagrangian Relaxation (LR) of the IP formulation of the original problem. The bounds derived from LR were improved using sub-gradient optimisation and computational results are presented. In the second part of the thesis, we consider the combined problems of container assignment and yard crane (YC) deployment within the container terminal. A new IP formulation has been developed using a unified approach with the view to determining optimal container flows and YC requirements within a given planning time horizon. We designed a Branch and Cut (B&C) algorithm to solve the problem to optimality which was computationally evaluated. A novel heuristic approach based on the IP formulation was developed and implemented in C++. Detailed computational results are reported for both the exact and heuristic algorithms using a large number of randomly generated test problems. A practical application of the proposed model in the context of a real case-study is also presented. Finally, a simulation model of container terminal operations based on discrete-event simulation has been developed and implemented with the view of validating the above optimisation model and using it as a test bed for evaluating different operational scenarios