Comparison of Alternative Pulpwood Inventory Strategies and Machine Systems at a Log-Yard Using Simulations

The rising throughput of log-yards imposes new constraints on existing equipment and increases the complexity of delivering an optimal and uninterrupted supply of pulpwood to pulp mills. To find ways of addressing these problems by reducing log cycle times, this work uses a discrete-event mathematics model to simulate operations at a log-yard and study the impact of three different log-yard inventory strategies and two alternative machine systems for log transportation between main log-yard and buffer storage. The yard's existing inventory strategy of last load in and first out limits access to older logs at the main storage site. By allocating space for 89,000 m(3) and 99,000 m(3) of pulpwood at the buffer storage it is possible to keep the log cycle time at the main storage to a maximum of 12 and 6 months. Additionally, the use of an alternative log transportation machine system comprising a material handler with a trailer increased the work time capacity utilization relative to the yard's current machine system of two shuttle trucks and a material handler for transporting logs between the main and buffer storage areas. Compared to the currently-used last in first out inventory strategy and purposely emptying the main storage area once or twice per year did reduce the total work time of both machine systems by 14% and 30%. Consequently, the volume delivered from the buffer to the log-yard decreased on average by 17% and 37% when emptying the main storage area once and twice per year. Even with reduced work time when emptying the main storage area, both machine systems could fulfil given work load for transporting logs from the buffer storage to the main log-yard without interrupting operations of the log-yard

Impact of Sugarcane Delivery Schedule on Product Value at Raw Sugar Factories

Conversion to combine harvesters has resulted in Louisiana sugarcane growers delivering a more perishable product to raw sugar factories. Dextran formation increases as the time between harvest and milling is extended. Milling of freshly cut sugarcane reduces the formation of dextran and associated economic losses. One approach available to factories to reduce dextran formation is to extend the harvested sugarcane delivery schedule to the mill. A simulation model was developed to evaluate alternative delivery schedules at raw sugar factories. Economic losses in product value associated with dextran formation were estimated and compared for various extended delivery schedules.dextran, milling, product value, raw sugar factories, scheduling, sugarcane industry, Crop Production/Industries, Marketing, Production Economics,

Research into container reshuffling and stacking problems in container terminal yards

Container stacking and reshuffling are important issues in the management of operations in a container terminal. Minimizing the number of reshuffles can increase productivity of the yard cranes and the efficiency of the terminal. In this research, the authors improve the existing static reshuffling model, develop five effective heuristics, and analyze the performance of these algorithms. A discrete-event simulation model is developed to animate the stacking, retrieving, and reshuffling operations and to test the performance of the proposed heuristics and their extended versions in a dynamic environment with arrivals and retrievals of containers. The experimental results for the static problem show that the improved model can solve the reshuffling problem more quickly than the existing model and the proposed extended heuristics are superior to the existing ones. The experimental results for the dynamic problem show that the results of the extended versions of the five proposed heuristics are superior or similar to the best results of the existing heuristics and consume very little time

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

Toward digital twins for sawmill production planning and control : benefits, opportunities and challenges

Sawmills are key elements of the forest product industry supply chain, and they play important economic, social, and environmental roles. Sawmill production planning and control are, however, challenging owing to severalfactors, including, but not limited to, the heterogeneity of the raw material. The emerging concept of digital twins introduced in the context of Industry 4.0 has generated high interest and has been studied in a variety of domains, including production planning and control. In this paper, we investigate the benefits digital twins would bring to the sawmill industry via a literature review on the wider subject of sawmill production planning and control. Opportunities facilitating their implementation, as well as ongoing challenges from both academic and industrial perspectives, are also studied

Modeling and Analysis of the Rotor Blade Refurbishment Process at the Corpus Christi Army Depot

Much of the Army’s equipment is coming to the end of its planned life cycle.  At the same time, the Department of Defense and the Army are facing severe budget reductions for the foreseeable future.  As a result, the planned modernization and acquisition of new equipment will be delayed.  The Army is now forced to keep and maintain current equipment as opposed to retiring old systems and buying new ones.  With the increased investment in the current systems, the organizations and depots that maintain and refurbish the Army’s equipment are becoming increasingly valuable assets.  Corpus Christi Army Depot (CCAD) is the Army’s only facility for repair and overhaul of rotary wing aircraft.  CCAD receives approximately 10 rotor blades per day for the Black Hawk helicopter.  Each blade is routed through a detailed inspection and rework process consisting of approximately 67 sequential operations which take approximately 45 days per blade.  Recently CCAD has expanded and reorganized the rotor blade refurbishment facility which provides an opportunity to re-examine processes, adjust positioning of work stations, and improve efficiency.  In this research we develop a discrete-event simulation model of the CCAD rotor blade refurbishment process in order to identify inefficiencies and examine “what if” scenarios to improve key performance metrics.  The key performance metrics used to analyze model input include throughput, work in progress, mean queue time, mean queue size, and workstation utilization.  The baseline model revealed that there were two crucial bottlenecks that severely limited the throughput and overall performance of the refurbishment process.  Adjusting the capacities of these workstations was very effective in reducing the number of blades in WIP and reducing the impact of the queues in front of these stations, but failed to increase the throughput to the desired amount.  Additionally, we found that the loss of one whirl tower’s production would not be a significant factor for CCAD’s performance in terms of throughput since operating with only one whirl tower did not significantly impact metrics of interest for the process

A Data-driven and multi-agent decision support system for time slot management at container terminals: A case study for the Port of Rotterdam

Controlling the departure time of the trucks from a container hub is important to both the traffic and the logistics systems. This, however, requires an intelligent decision support system that can control and manage truck arrival times at terminal gates. This paper introduces an integrated model that can be used to understand, predict, and control logistics and traffic interactions in the port-hinterland ecosystem. This approach is context-aware and makes use of big historical data to predict system states and apply control policies accordingly, on truck inflow and outflow. The control policies ensure multiple stakeholders satisfaction including those of trucking companies, terminal operators, and road traffic agencies. The proposed method consists of five integrated modules orchestrated to systematically steer truckers toward choosing those time slots that are expected to result in lower gate waiting times and more cost-effective schedules. The simulation is supported by real-world data and shows that significant gains can be obtained in the system

Modelling the supply chain impact of a digital terminal appointment systems parameters and user behaviours. A discrete event simulation approach

This research-in-progress paper is part of an ongoing investigation that explores the role of information and digital systems for understanding congestion challenges and management approaches in bulk cargo marine terminals and supply chains. This paper contributes to the broader investigation by developing a discrete-event simulation model to improve understanding of the impact of driver behaviors and scheduling parameters in the use of a digital terminal appointment system on truck flows in the supply chain and turnaround times at the terminal. The data supporting the simulation model was collected from an RFID-enabled weigh-bridge system of an Australian terminal operator and GPS units mounted on trucks. Simulation results indicate that even low levels of system use can reduce truck turnaround times and reduce service time uncertainty. Interestingly, the truck turnaround time benefits resulting from the use of the appointment system are particularly significant when the terminal operates at high capacity