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Register a new userA Simulation-Optimization Technique for Service Level Analysis in Conjunction with Reorder Point Estimation and Lead-Time consideration: A Case Study in Sea Port
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This study offers a step-by-step practical procedure from the analysis of the current status of the spare parts inventory system to advanced service-level analysis by virtue of simulation-optimization technique for a real-world case study associated with a seaport. The remarkable variety and immense diversity on one hand, and extreme complexities not only in consumption patterns but in the supply of spare parts in an international port with technically advance port operator machinery, on the other hand, have convinced the managers to deal with this issue in a structural framework. The huge available data require cleaning and classification to properly process them and derive reorder point (ROP) estimation, reorder quantity (ROQ) estimation, and associated service level analysis. Finally, from 247000 items used in 9 years long, 1416 inventory items are elected as a result of ABC analysis integrating with the Analytic Hierarchy Process (AHP), which led to the main items that need to be kept under strict inventory control. The ROPs and the pertinent quantities are simulated by Arena software for all the main items, each of which took approximately 30 minutes run-time on a personal computer to determine near-optimal estimations.
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We present a data-driven optimization framework that aims to address online adaptation of the flight path shape for an airborne wind energy system (AWE) that follows a repetitive path to generate power. Specifically, Bayesian optimization, which is a data-driven algorithm for finding the optimum of an unknown objective function, is utilized to solve the waypoint adaptation. To form a computationally efficient optimization framework, we describe each figure-$8$ flight via a compact set of parameters, termed as basis parameters. We model the underlying objective function by a Gaussian Process (GP). Bayesian optimization utilizes the predictive uncertainty information from the GP to determine the best subsequent basis parameters. Once a path is generated using Bayesian optimization, a path following mechanism is used to track the generated figure-$8$ flight. The proposed framework is validated on a simplified $2$-dimensional model that mimics the key behaviors of a $3$-dimensional AWE system. We demonstrate the capability of the proposed framework in a simulation environment for a simplified $2$-dimensional AWE system model.
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This work deals with the problem of estimating the turnaround time in the early stages of aircraft design. The turnaround time has a significant impact in terms of marketability and value creation potential of an aircraft and, for this reason, it should be considered as an important driver of fuselage and cabin design decisions. Estimating the turnaround time during the early stages of aircraft design is therefore an essential task. This task becomes even more decisive when designers explore unconventional aircraft architectures or, in general, are still evaluating the fuselage design and its internal layout. In particular, it is of paramount importance to properly estimate the boarding and deboarding times, which contribute for up the 40% to the overall turnaround time. For this purpose, a tool, called SimBaD, has been developed and validated with publicly available data for existing aircraft of different classes. In order to demonstrate SimBaD capability of evaluating the influence of fuselage and cabin features on the turnaround time, its application to an unconventional box-wing aircraft architecture, known as PrandtlPlane, is presented as case study. Finally, considering standard scenarios provided by aircraft manufacturers, a comparison between the turnaround time of the PrandtlPlane and the turnaround time of a conventional competitor aircraft is presented.
In this paper, we study the problem of consensus-based distributed optimization where a network of agents, abstracted as a directed graph, aims to minimize the sum of all agents cost functions collaboratively. In existing distributed optimization approaches (Push-Pull/AB) for directed graphs, all agents exchange their states with neighbors to achieve the optimal solution with a constant stepsize, which may lead to the disclosure of sensitive and private information. For privacy preservation, we propose a novel state-decomposition based gradient tracking approach (SD-Push-Pull) for distributed optimzation over directed networks that preserves differential privacy, which is a strong notion that protects agents privacy against an adversary with arbitrary auxiliary information. The main idea of the proposed approach is to decompose the gradient state of each agent into two sub-states. Only one substate is exchanged by the agent with its neighbours over time, and the other one is kept private. That is to say, only one substate is visible to an adversary, protecting the privacy from being leaked. It is proved that under certain decomposition principles, a bound for the sub-optimality of the proposed algorithm can be derived and the differential privacy is achieved simultaneously. Moreover, the trade-off between differential privacy and the optimization accuracy is also characterized. Finally, a numerical simulation is provided to illustrate the effectiveness of the proposed approach.
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