Integrating Messy Genetic Algorithms and Simulation to Optimize Resource Utilization

Abstract:   This article presents a mechanism for integrating messy genetic algorithms (MGAs) and a discrete event simulation technique to facilitate the simulation of optimal resource utilization to enhance system performance, such as in relation to the production rate or unit cost. Various resource distribution modeling scenarios were tested in simulation to determine their system performances. MGA operations were then applied in the selection of the best resource utilization schemes based on those performances. A case study showed that this new modeling mechanism, along with the implemented computer program, could not only ease the process of developing optimal resource utilization, but could also improve the system performance of the simulation model.     

基于交通特性的住区道路结构模式分析

在国内外规划设计理论和范式的影响下,我国住区空间形态设计日渐丰富,住区道路系统灵活多变。实质上,形态各异的住区道路结构是由格网模式、内环模式和外环模式这三种基本道路模式通过排列、叠加、变形等变异衍生而来。这三种道路模式在交通可达性、交通效率、安全性、私密性四个方面存在不同的特性,应在理性分析的基础上,分别针对不同模式存在的不利因素进行可行性优化设计。     

Aggregate Blending Using Genetic Algorithms

Abstract:   Aggregate blending consists of finding the proportions of fractions to form a final blend satisfying predefined specifications. It is a problem which is posed in many ways, and solved by using different techniques. These techniques range from simple graphical methods to advanced computer methods such as nonlinear programming or dynamic programming. In this article, an aggregate-blending problem is formulated as a multiobjective optimization problem and solved by using genetic algorithms (GAs). It is shown that in this way all existing formulations of an aggregate-blending problem can be covered and solved. The effectiveness of this new application is demonstrated through numerical examples. The technique is shown to be quite versatile in tackling multiple objectives including cost minimization, and approaching at best a given target curve. Linear and nonlinear cost functions can be dealt with equal ease; additional objectives may be inserted into the problem with no difficulty. The user has the possibility of defining and finding the best solutions with Pareto optimality considerations.     

Improving Pareto Optimal Designs Using Genetic Algorithms

Abstract: Pareto optimal designs are the best designs that can be produced for a given problem formulation for a given set of criteria when the criteria are not combined in any way. If the goal is to improve the performance in those criteria, then it is possible to manipulate the problem formulation to achieve an improvement. The approach adopted is to encode the formulation in a genetic algorithm and to allow the formulation to evolve in the direction of improving Pareto optimal designs. A set of rules (in the form of a shape grammar), the execution of which produces a design, is encoded as the genes in a genetic algorithm. However, the rule set is allowed to evolve, not just the order of execution of rules. We present an example demonstrating both the approach and its utility in improving Pareto optimal designs.     

成渝客运专线路基填筑技术分析

结合成渝客运专线路基技术工艺标准与现场施工角度,对用细圆砾土作为路基填料的路基施工工艺进行了研究,重点分析了施工过程中的质量控制与施工经验,具有较强的实际工程意义。     

基于交通特性的居住区空间模式比较研究

为研究不同居住区空间模式对居住区内外交通运行以及居民出行方式选择的影响,选取南京市封闭和半开放两种空间模式下的居住区进行对比研究.采取现场交通流量观测和居民问卷调查的方法,从居住区内部、出入口和外部交通运行状况及居民出行方式比例等4个方面,对不同空间模式下居住区的交通特性进行比较.研究结果表明,相较于封闭式居住模式,半开放空间模式下的居住区内外交通运行更为顺畅,并能通过引入常规公交线路,提高居民公交出行比例和便捷程度.     

Optimum Design of Grillage Systems Using Genetic Algorithms

Genetic algorithms have attracted great attention due to their ability to provide a solution to discrete optimum design problems. In this study, a genetic algorithm is presented for the optimum design of grillage systems to decide the cross-sectional properties of members from a standard set of universal beam sections. The deflection limitations and the allowable stress constraints are considered in the formulation of the design problem. Furthermore, in obtaining the response of grillage systems, the effect of warping and shear is also taken into account. The algorithm starts with an initial population of designs and carries out basic genetic operations of selection, mating, crossover, and mutation that yield to a new generation. It continues the generation of populations until the same individual dominates the population. An improvement is also suggested to the general steps of the genetic algorithm to prevent the destruction of good individuals during the generation of new populations. The algorithm is applied to the optimum design of a 40-member grillage system to investigate the effect of warping.     

Developing Crew Allocation System for the Precast Industry Using Genetic Algorithms

Abstract: The Precast Concrete Industry (PCI) is one of the major contributors to the national economy and can be categorized as labor‐intensive industry. It is currently experiencing shortcomings in terms of delivery products at a competitive cost and time. This is mainly due to the inefficiencies associate with planning and scheduling of skilled operators within crew configurations. This article presents a new strategy for efficient allocation of crews of workers in the precast concrete industry using Genetic Algorithms‐based simulation modeling. The aim of this study is to develop a crew allocation system that can efficiently allocate possible crews of workers to precast concrete labor‐intensive repetitive processes. Genetic algorithms (GAs) have been developed to solve this type of problem. Process mapping methodologies were used to identify and document the processes involved in producing precast components. Then process simulation was used to model and simulate all these processes and GAs were tailored to be embedded with the simulation model for a better search of promising solutions. GA operators were designed to suit this type of allocation problem. “Class Interval” selection strategy was developed to give a greater opportunity for the promising chromosomes to be chosen for further investigation. Dynamic crossover and mutation operators were developed to add more randomness to the search mechanism. The results showed that adopting different combinations of crews of workers had a substantial impact on reducing the process throughput time, minimizing resources cost, and achieving the required operatives utilization.     

Estimating a Rail Passenger Trip Origin-Destination Matrix Using Automatic Data Collection Systems

Abstract:   Automatic data collection (ADC) systems are becoming increasingly common in transit systems throughout the world. Although these ADC systems are often designed to support specific fairly narrow functions, the resulting data can have wide-ranging application, well beyond their design purpose. This article illustrates the potential that ADC systems can provide transit agencies with new rich data sources at low marginal cost, as well as the critical gap between what ADC systems directly offer and what is needed in practice in transit agencies. To close this gap requires data processing and analysis methods with support of technologies such as database management systems (DBMS) and geographic information systems (GIS). This research presents a case study of the automatic fare collection (AFC) system of the Chicago Transit Authority (CTA) rail system and develops a method for inferring rail passenger trip origin-destination (OD) matrices from an origin-only AFC system to replace expensive passenger OD surveys. A software tool is developed to facilitate the method implementation and the results of the application in CTA are reported.     

A Decision–Support Tool for Airline Yield Management Using Genetic Algorithms

Airline yield management has gained widespread acceptance in recent years. Yield management is used to estimate the number of passengers belonging to each fare class in order to maximize revenue—to increase profits. Forecasts of future booking for different classes and the fare for each class are assumed fixed and known. The complexity of the problem increases as additional attributes such as the effect of continued flights on pricing, ticket cancellations, and overbooking are considered. In addition, demand varies with time. Hence, to make the problem realistic, the dynamic nature of demand has to be accounted for in the model. The focus of this article is to develop a decision–support tool to estimate the number of seats allocated to each fare class in the yield management problem. This problem is formulated as a linear integer programming model. Genetic algorithms (GAs) are used as a solution technique. The model is coded using C programming language. The decision–support tool considers the effect of time–dependent demand, ticket cancellations, and overbooking policy. The results are consistent with expectations.     

Network-Wide Optimal Scheduling of Transit Systems Using Genetic Algorithms

The primary objective of any transit system is to provide a better level of service to its passengers. One of the good measures of level of service is the waiting time of passengers during their journey. The waiting time consists of an initial waiting time (the time a passenger waits to board a vehicle at his or her point of origin) and a transfer time (the time a passenger waits at a transfer station while transferring from one vehicle to another). An efficient schedule minimizes the overall transfer time (TT) of passengers transferring between different routes as well as the initial waiting time (IWT) of the passengers waiting to board the vehicle at their point of origin. This paper uses genetic algorithm (GA)—a search and optimization procedure—to find optimal/near-optimal schedules of vehicles in a transit network. The main advantage of using GA is that the transit network scheduling problem can be reformulated in a manner that is computationally more efficient than the original problem. Further, the coding aspect of GA inherently takes care of most of the constraints associated with the scheduling problem. Results from a number of test problems show that GAs are able to find optimal/near-optimal schedules with minimal computational resources.     

Optimal Design of Water Distribution Systems Using Genetic Algorithms

This article proposes an optimal design methodology for the design of water distribution systems based on genetic algorithms. The objective of the optimization is to minimize the capital cost, subject to ensuring adequate pressures at all nodes. The proposed method differs from those of previous workers who have applied genetic algorithms in that the strings in the genetic algorithm model are coded using real variables, and this avoids the problem of redundant states often found when using binary (and Gray) coding schemes. A fitness function is also proposed that incorporates a variable penalty coefficient that depends on the degree of violation of the pressure constraints. The method also differs from those of previous workers in that it does not require solution of the nonlinear equations governing the flows and pressures in the distribution system for each individual member within the population. Hence this method shows a significant advantage compared with previously published techniques in terms of computational efficiency. The method has been tested on several networks, including networks used for benchmark testing least-cost design algorithms, and has been shown to be very efficient and robust.     

A Fuzzy Expert System for Damage Assessment Using Genetic Algorithms and Neural Networks

Abstract: An efficient knowledge-acquisition support method is required for improvement and maintenance of the knowledge base in durability evaluation of an RC bridge deck. Such a method is proposed in this paper to automatically acquire fuzzy production rules. This method makes joint use of genetic algorithms and a neural network. Using a neural network as a subsystem, the evaluation function of genetic algorithms can be provided with the weights of the neural network. Introducing a neural network into genetic algorithms, it is possible to acquire new knowledge so that the method is useful when it is difficult to acquire knowledge in the field.     

盐城五星客运站综合布线解决方案

本文以盐城五星客运站为例,介绍综合布线系统的构成、各个系统的功能以及元器件的参数。     

A Computerized Feature Selection Method Using Genetic Algorithms to Forecast Freeway Accident Duration Times

Abstract: This study presents a feature selection method that uses genetic algorithms to create two artificial neural network‐based models that provide a sequential forecast of accident duration from the time of accident notification to the accident site clearance. These two models can provide the estimated duration time by plugging in relevant traffic data as soon as an accident is notified. To select data feature, the genetic algorithm is designed to decrease the number of model inputs while preserving the relevant traffic characteristics. Using the proposed feature selection method, the mean absolute percentage error for forecasting accident duration at each time point is mostly under 29%, which indicates that these models have a reasonable forecasting ability. Thanks to this model, travelers and traffic management units can better understand the impact of accidents. This study shows that the proposed models are feasible in the Intelligent Transportation Systems context.     

Generating Future Land-Use and Transportation Plans for High-Growth Cities Using a Genetic Algorithm

Abstract:   An elitist genetic algorithm was used to find a diverse non-dominated set of optimal future zoning and street plans for two high-growth cities in the United States of America. Plans were judged with regard to housing capacity, employment capacity, greenspace, traffic congestion, and change from the status quo. A multiobjective fitness function was used. The genetic algorithm offers the possibility of efficiently searching over tens of thousands of plans for a trade-off set of non-dominated plans. The trade-off set ranged from a minimum change plan, where undeveloped farmland was rezoned as commercial or residential land, to a minimum traffic congestion plan where commercial and residential usage were spread throughout the cities rather than concentrated in one or two areas. The algorithm is general enough to be applied to other cities and metropolitan regions.     

精细管理,合理优化铁路客站建设工期

以用科学换时间,用空间换时间的工作思路,认真贯彻六位一体,坚持四化建设,合理优化铁路客站建设工期。确保客运专线在开通之时,现代化的铁路客站能够同时运营。服务出行旅客,让人民群众满意。     

Reliability-Based Optimal Design of Electrical Transmission Towers Using Multi-Objective Genetic Algorithms

Abstract:   A hybrid methodology for performing reliability-based structural optimization of three-dimensional trusses is presented. This hybrid methodology links the search and optimization capabilities of multi-objective genetic algorithms (MOGA) with structural performance information provided by finite element reliability analysis. To highlight the strengths of the proposed methodology, a practical example is presented that concerns optimizing the topology, geometry, and member sizes of electrical transmission towers. The weight and reliability index of a tower are defined as the two objectives used by MOGA to perform Pareto ranking of tower designs. The truss deformation and the member stresses are compared to threshold values to assess the reliability of each tower under wind loading. Importance sampling is used for the reliability analysis. Both the wind pressure and the wind direction are considered as random variables in the analysis. The research results presented demonstrate the benefit of implementing MOGA optimization as an integral part of a reliability-based optimization procedure for three-dimensional trusses.     

Using GIS, Genetic Algorithms, and Visualization in Highway Development

A model for highway development is presented, which uses geographic information systems (GIS), genetic algorithms (GA), and computer visualization (CV). GIS serves as a repository of geographic information and enables spatial manipulations and database management. GAs are used to optimize highway alignments in a complex search space. CV is a technique used to convey the characteristics of alternative solutions, which can be the basis of decisions. The proposed model implements GIS and GA to find an optimized alignment based on the minimization of highway costs. CV is implemented to investigate the effects of intangible parameters, such as unusual land and environmental characteristics not considered in optimization. Constrained optimization using GAs may be performed at subsequent stages if necessary using feedback received from CVs. Implementation of the model in a real highway project from Maryland indicates that integration of GIS, GAs, and CV greatly enhances the highway development process.     

Dynamic Optimal Traffic Assignment and Signal Time Optimization Using Genetic Algorithms

Abstract:   In this article a dynamic system-optimal traffic assignment model is formulated for a congested urban road network with a number of signalized intersections. A simulation-based approach is employed for the case of multiple-origin-multiple-destination traffic flows. The artificial intelligence technique of genetic algorithms (GAs) is used to minimize the overall travel cost in the network with fixed signal timings and optimization of signal timings. The proposed method is applied to the example network and results are discussed. It is concluded that GAs allow the relaxation of many of the assumptions that may be needed to solve the problem analytically by traditional methods.