Geonwook Jeon

Forming part families by using genetic algorithm and designing machine cells under demand changes

This study develops a methodology which can be used to form manufacturing cells using both a new similarity coefficient based on the number of alternative routes during machine failure and demand changes for multiple periods. The methodology is divided into two phases. A new similarity coefficient, which considers the number of available alternative routes when available during machine failure, is suggested in Phase I. The primary objective of Phase I is to identify part families based on the new similarity coefficient by using a genetic algorithm. One of the major factors contributing to the success of cell implementation is flexibility for demand changes. It is difficult to reorganize the cells according to changes in demand, available machine capacity, and due date. Most of the suggested approaches in the literature tend to use a fixed demand for cellular manufacturing systems. Due to demand changes, cell design should include more than the one period that most researchers of cellular manufacturing systems consider. A new methodology for cell formation, which considers the scheduling and operational aspects in cell design under demand changes, is introduced in Phase II. Machines are assigned to part families by using an optimization technique. This optimization technique employs sequential and simultaneous mixed integer programming models for a given period to minimize the total costs which are related to the scheduling and operational aspects.

A vehicle routing problem solved by using a hybrid genetic algorithm

The main purpose of this study is to find out the best solution of the vehicle routing problem simultaneously considering heterogeneous vehicles, double trips, and multiple depots by using a hybrid genetic algorithm. This study suggested a mathematical programming model with a new numerical formula which presents the amount of delivery and sub-tour elimination. This model gives an optimal solution by using OPL-STUDIO(ILOG CPLEX). This study also suggests a hybrid genetic algorithm (HGA) which considers the improvement of generation for an initial solution, three different heuristic processes, and a float mutation rate for escaping from the local solution in order to find the best solution. The suggested HGA is also compared with the results of a general genetic algorithm and existing problems suggested by Eilon and Fisher. We found better solutions rather than the existing genetic algorithms.

A Requirement Assessment Algorithm for Anti-Ballistic Missile Considering Ballistic Missile`s Flight Characteristics

A Ballistic Missile(BM) is a missile that follows a sub-orbital ballistic flightpath with the objective of delivering one or more wardheads to a predetermined target and An Anti-Ballistic Missile(ABM) is a missile designed to destroy a ballistic missile before reaching its target. The main objective of this study is to assess the requirement of ABM by considering both flight characteristic of the SCUD-B/C, Nodong missiles and intercept performance of ABM in the Lower tier Ballistic Missile Defense(BMD). The Ballistic Missile`s flight characteristics, such as trajectory, velocity etc., are estimated by simulation using the physical motion equations. The requirement of ABM is calculated by evaluating whether the BMD forces can defend those when the ballistic missiles attack prime facilities.

A Reliability Optimization Problem of System with Mixed Redundancy Strategies

The reliability is defined as a probability that a system will operate properly for a specified period of time under the design operating conditions without failure and it has been considered as one of the major design parameters in the field of industries. Reliability-Redundancy Optimization Problem(RROP) involves selec tion of components with multiple choices and redundancy levels for maximizing system reliability with constraints such as cost, weight, etc. However, in practice both active and cold standby redundancies may be used within a particular system design. Therefore, a redundancy strategy(active, cold standby) for each subsystem in order to maximize system reliability is considered in this study. Due to the nature of RROP, i.e. NP-hard problem, A Parallel Particle Swarm Optimization(PPSO) algorithm is proposed to solve the mathematical programming model and it gives consistently better quality solutions than existing studies for benchmark problems.

A Path Planning to Maximize Survivability for Unmanned Aerial Vehicle based on 3-dimensional Environment

An Unmanned Aerial Vehicle(UAV) is a powered pilotless aircraft, which is controlled remotely or autonomously. UAVs are currently employed in many military missions(surveillance, reconnaissance, communication relay, targeting, strike etc.) and a number of civilian applications(communication service, broadcast service, traffic control support, monitoring, measurement etc.). For accomplishing the UAVʼs missions, guarantee of survivability should be preceded. The main objective of this study is the path planning to maximize survivability for UAV based on 3-dimensional environment. A mathematical programming model is suggested by using MRPP(Most Reliable Path Problem) and solved by transforming MRPP into SPP(Shortest Path Problem). This study also suggests a A*PS algorithm based on 3-dimensional environment to UAVʼs path planning. According to comparison result of the suggested algorithm and SPP algorithms (Dijkstra, A* algorithm), the suggested algorithm gives better solution than SPP algorithms. 1) Keyword: unmanned aerial vehicle, path planning, most reliable path problem, shortest path problem, survivability

A vehicle routing problem considering traffic situation with time windows by using hybrid genetic algorithm

The vehicle travel time between demand points at downtown is greatly influenced by both complex road condition and traffic situation that change real time to various external environments. Customers are often needed service time windows that products are delivered on time. Most of vehicle routing problem suggested a vehicle route considering average vehicle speed between the demand points, however did not consider dynamic external environments such as traffic situation. A realistic vehicle routing problem with time windows which considers traffic situations (smooth, going slowly, delaying, and stagnating) will be suggested in this study. A mathematical programming model and a hybrid genetic algorithm will be suggested to minimize the total spending time.

Mission Path Planning to Maximize Survivability for Multiple Unmanned Aerial Vehicles based on 3-dimensional Grid Map

An Unmanned Aerial Vehicle (UAV) is a powered pilotless aircraft, which is controlled remotely or autono-mously. UAVs are an attractive alternative for many scientific and military organizations. UAVs can perform operations that are considered to be risky or uninhabitable for humans. UAVs are currently employed in many military missions and a number of civilian applications. For accomplishing the UAV’s missions, guarantee of survivability should be preceded. The main objective of this study is to suggest a mathematical programming model and a

A Hybrid Parallel Genetic Algorithm for Reliability Optimization

Reliability engineering is known to have been first applied to communication and transportation systems in the late 1940s and early 1950s. Reliability is the probability that an item will perform a required function without failure under stated conditions for a stated period of time. Therefore a system with high reliability can be likened to a system which has a superior quality. Reliability is one of the most important design factors in the successful and effective operation of complex technological systems. As explained by Tzafestas (1980), one of the essential steps in the design of multiple component systems is the problem of using the available resources in the most effective way so as to maximize the system reliability, or so as to minimize the consumption of resources while achieving specific reliability goals. The improvement of system reliability can be accomplished using the following methods: reduction of the system complexity, the allocation highly reliable components, and the allocation of component redundancy alone or combined with high component reliability, and the practice of a planned maintenance and repair schedule. This study deals with reliability optimization that maximizes the system reliability subject to resource constraints.