Aerial Refueling Process Rescheduling Under Job Related Disruptions

The Aerial Refueling Scheduling Problem (ARSP) can be defined as determining the refueling completion times for each fighter aircraft (job) on the multiple tankers (machines) to minimize the total weighted tardiness. ARSP assumes that the jobs have different release times and due dates. The ARSP is dynamic environment and unexpected events may occur. In this paper, rescheduling in the aerial refueling process with a time set of jobs will be studied to deal with job related disruptions such as the arrival of new jobs, the departure of an existing job, high deviations in the release times and changes in job priorities. In order to keep the stability and to avoid excessive computation, partial schedule repair algorithm is developed and its preliminary results are presented

An Optimization Model for Scheduling Problems with Two-Dimensional Spatial Resource Constraint

Traditional scheduling problems involve determining temporal assignments for a set of jobs in order to optimize some objective. Some scheduling problems also require the use of limited resources, which adds another dimension of complexity. In this paper we introduce a spatial resource-constrained scheduling problem that can arise in assembly, warehousing, cross-docking, inventory management, and other areas of logistics and supply chain management. This scheduling problem involves a twodimensional rectangular area as a limited resource. Each job, in addition to having temporal requirements, has a width and a height and utilizes a certain amount of space inside the area. We propose an optimization model for scheduling the jobs while respecting all temporal and spatial constraints

Data Mining Based Hybridization of Meta-RaPS

Though metaheuristics have been frequently employed to improve the performance of data mining algorithms, the opposite is not true. This paper discusses the process of employing a data mining algorithm to improve the performance of a metaheuristic algorithm. The targeted algorithms to be hybridized are the Meta-heuristic for Randomized Priority Search (Meta-RaPS) and an algorithm used to create an Inductive Decision Tree. This hybridization focuses on using a decision tree to perform on-line tuning of the parameters in Meta-RaPS. The process makes use of the information collected during the iterative construction and improvement phases Meta-RaPS performs. The data mining algorithm is used to find a favorable parameter using the knowledge gained from previous Meta-RaPS iterations. This knowledge is then used in future Meta-RaPS iterations. The proposed concept is applied to benchmark instances of the Vehicle Routing Problem. 2014 The Authors

A Simulation-Based Approach to Risk Assessment and Mitigation in Supply Chain Networks

We present in this paper a simulation-based approach to evaluate the risk associated with supply chain disruptions caused by failures in some supply chains nodes and measure the impact of such disruptions on supply chain key performance measures (KPIs) of interest. The proposed framework enables analysts and managers to repeatedly assess the risk to their supply chains based on various simulated scenarios and identify the most critical nodes whose disruption will have the highest impact on the KPIs of interest. As a result, companies can focus on the most critical supply chain assets and develop targeted mitigation plans that minimize their risk. 2015 The Authors

Generic Environment for Simulating Launch Operations

GEM-FLO (A Generic Simulation Environment for Modeling Future Launch Operations) is a computer program that facilitates creation of discrete-event simulation models of ground processes in which reusable or expendable launch vehicles (RLVs) are prepared for flight. GEM-FLO includes a component, developed in Visual Basic, that generates a graphical user interface (GUI) and a component, developed in the Arena simulation language, that creates a generic discrete-event simulation model. Through the GUI, GEM-FLO elicits RLV design information from the user. The design information can include information on flight hardware elements, resources, and ground processes. GEM-FLO translates the user s responses into mathematical variables and expressions that populate the generic simulation model. The variables and expressions can represent processing times, resource capacities, status variables, and other process parameters needed to configure a simulation model that reflects the ground processing flow and requirements of a specific RLV. Upon execution of the model, GEMFLO puts out data on many measures of performance, including the flight rate, turnaround time, and utilization of resources. This information can serve as the basis for determining whether design goals can be met, and for comparing characteristics of competing RLV design

Meta-RaPS Algorithm for the Aerial Refueling Scheduling Problem

The Aerial Refueling Scheduling Problem (ARSP) can be defined as determining the refueling completion times for each fighter aircraft (job) on multiple tankers (machines). ARSP assumes that jobs have different release times and due dates, The total weighted tardiness is used to evaluate schedule's quality. Therefore, ARSP can be modeled as a parallel machine scheduling with release limes and due dates to minimize the total weighted tardiness. Since ARSP is NP-hard, it will be more appropriate to develop a ppro~imate or heuristic algorithm to obtain solutions in reasonable computation limes. In this paper, Meta-Raps-ATC algorithm is implemented to create high quality solutions. Meta-RaPS (Meta-heuristic for Randomized Priority Search) is a recent and promising meta heuristic that is applied by introducing randomness to a construction heuristic. The Apparent Tardiness Rule (ATC), which is a good rule for scheduling problems with tardiness objective, is used to construct initial solutions which are improved by an exchanging operation. Results are presented for generated instances

Simulation Modeling and Analysis of Complex Port Operations with Multimodal Transportation

AbstractWorld trade has been increasing dramatically in the past two decades, and as a result containers exchange has grown significantly. Accordingly, container terminals are expanding to meet this increase and new container ports have opened. Ports with one or more container terminals are considered complex systems in which many resources, entities and transporters interact to achieve the objective of safely moving containers delivered by ships inland as well as loading containers delivered by trucks and rail onto ships. Ports with multimodal transportation systems are in particular complex as they typically operate with ships arriving to one or more terminals, multiple quay cranes, rubber tyred gantry cranes, trains, and trucks delivering containers of different types to terminals.With several resources of different types working and interacting, the system can be so complex that it is not easy to predict the behavior of the system and its performance metrics without the use of simulation. In this paper, a generic discrete-event simulation that models port operations with different resource types including security gates, space, rubber tyred gantry cranes, trains, quay cranes, and arriving and departing ships, trucks, and trains is presented. The analysis will entail studying various scenarios motivated by changes in different inputs to measure their impact on the outputs that include throughput, resource utilization and waiting times

Tuning Parameters in Heuristics by Using Design of Experiments Methods

With the growing complexity of today's large scale problems, it has become more difficult to find optimal solutions by using exact mathematical methods. The need to find near-optimal solutions in an acceptable time frame requires heuristic approaches. In many cases, however, most heuristics have several parameters that need to be "tuned" before they can reach good results. The problem then turns into "finding best parameter setting" for the heuristics to solve the problems efficiently and timely. One-Factor-At-a-Time (OFAT) approach for parameter tuning neglects the interactions between parameters. Design of Experiments (DOE) tools can be instead employed to tune the parameters more effectively. In this paper, we seek the best parameter setting for a Genetic Algorithm (GA) to solve the single machine total weighted tardiness problem in which n jobs must be scheduled on a single machine without preemption, and the objective is to minimize the total weighted tardiness. Benchmark instances for the problem are available in the literature. To fine tune the GA parameters in the most efficient way, we compare multiple DOE models including 2-level (2k ) full factorial design, orthogonal array design, central composite design, D-optimal design and signal-to-noise (SIN) ratios. In each DOE method, a mathematical model is created using regression analysis, and solved to obtain the best parameter setting. After verification runs using the tuned parameter setting, the preliminary results for optimal solutions of multiple instances were found efficiently

Embedding Simulation Education into the Engineering Management Body of Knowledge

The American Society for Engineering Management (ASEM) established a Body of Knowledge (BoK). As simulation is of growing interest to engineers in general and to engineering managers in particular, simulation is part of this documentation of domains of interest that characterise the profession. The basis for the Body of Knowledge comprises of established and accredited curricula and additional input from practitioners of the field. As it is essential to cover the basic topics and core competences as well as application specific domain knowledge, the simulation education for engineers is categorised into topics on simulation theory and simulation application

An Integrated Framework for Modeling and Simulation of the U.S. Southern Border: A Border Patrol Perspective

Border Security is a complex system consisting of many interrelated components that must function as a whole in order to be effective. The efficacy of border security is dependent on several independent agencies; these include U.S. Customs and Border Patrol (CBP), Immigration and Customs Enforcement (ICE), the Department of Justice (DOJ), state and local law enforcement, and many others. Border security is not only a function of how well each of the agencies perform individually but also how well they interact to accomplish a goal. This paper attempts to model border security from a Border Patrol (BP) perspective using discrete event simulation in conjunction with Markovian analysis. The model will provide a baseline of the system\u27s current effectiveness as well as any interventions made to the system