Min-Wook Kang

A Hybrid Methodology for Freeway Work-Zone Optimization With Time Constraints

To reduce the negative impacts of highway maintenance on traffic, lane-closure schedules, work-zone configurations, and traffic-control strategies should be carefully planned. In this article, optimization techniques are applied to determine the appropriate work-zone plans which can minimize the total costs including agency costs, road-user delay costs and accident costs, subject to working time constraints. A heuristic optimization algorithm, named two-stage modified simulated annealing (2SA), is developed to search for an optimized solution with a hybrid objective function evaluation approach (H2SA). After the decision variables are preoptimized analytically in the first stage, refined optimization is performed based on microscopic simulation models in the second stage. The results of a numerical experiment demonstrate that the H2SA can yield satisfactory solutions, which are close to the best optimization solutions based on simulation (S2SA) but obtained with much less computation time.

Empirical Model with Environmental Considerations in Highway Alignment Optimization

The highway alignment optimization (HAO) process is a complex combinatorial optimization problem in which several conflicting factors, such as highway costs, user preferences, and environmentally sensitive factors, must be simultaneously considered. In previous studies, single-level and bi-level optimization approaches were developed to optimize three-dimensional highway alignments. One drawback of previous approaches is that environmental factors, such as vehicular emissions, were not adequately considered in conjunction with other factors (such as user preferences and highway costs) in the optimization process. This paper builds on our previous studies and proposes two separate approaches for considering environmental emissions in the HAO process. The first approach involves a separate analysis of user and decision-maker preferences, in which a conceptual formulation of various environmental factors is presented. In the second approach, a novel tri-level optimization framework is proposed for optimizing highway alignments. At the upper level, optimization is performed using the traditional criteria of cost minimization. At the intermediate level, total systems emission is calculated. Finally, at the lower level, the user equilibrium traffic flow is optimized. The developed approaches are illustrated through case study examples. The proposed approaches will be beneficial for designing highway alignments when considering environmental emissions. Future studies may make additional refinements to the formulation and sensitivity analyses.

A Sensitivity Analysis of Critical Genetic Algorithm Parameters: Highway Alignment Optimization Case Study

Genetic Algorithms GAs have been applied in many complex combinatorial optimization problems and have been proven to yield reasonably good solutions due to their ability of searching in continuous spaces and avoiding local optima. However, one issue in GA application that needs to be carefully explored is to examine sensitivity of critical parameters that may affect the quality of solutions. The key critical GA parameters affecting solution quality include the number of genetic operators, the number of encoded decision variables, the parameter for selective pressure, and the parameter for non-uniform mutation. The effect of these parameters on solution quality is particularly significant for complex problems of combinatorial nature. In this paper the authors test the sensitivity of critical GA parameters in optimizing 3-dimensional highway alignments which has been proven to be a complex combinatorial optimization problem for which an exact solution is not possible warranting the application of heuristics procedures, such as GAs. If GAs are applied properly, similar optimal solutions should be expected at each replication. The authors perform several example studies in order to arrive at a general set of conclusions regarding the sensitivity of critical GA parameters on solution quality. The first study shows that the optimal solutions obtained for a range of scenarios consisting of different combinations of the critical parameters are quite close. The second study shows that different optimal solutions are obtained when the number of encoded decision variables is changed.

A Simulation Framework for the Path Planning of Unmanned Autonomous Systems

This paper presents a simulation framework designed to plan the movements of unmanned autonomous systems (UAS’s) in hazardous environments, to coordinate their actions, predict their behavior and evaluate their mission success in various combat situations. Current simulation methods do not predict the complex interrelations among vehicles, operating environments, and paths, thus providing inadequate tests. A family of methods for coordinating, positioning, routing and assessing diverse military units or “agents” (such as unmanned ground vehicles) is described in this paper. The methods are tested and evaluated through computer simulations to ensure suitability for operating unmanned autonomous systems (UAS’s). The path evaluation is performed using a dynamic GIS, distance transform, and genetic algorithms. The optimization algorithms for use in testing future unmanned systems are based on multiple objectives and criteria, including: (1) timeliness, (2) detectability & exposure time, (3) probabilities of survival & mission completion; (4) energy use; and (5) obstacle avoidance. A series of tests are presented which mimics real-world combat situation to test the effectiveness of the developed