A Tabu Search Method for a Bi‐Objective Urban Waste Collection Problem

A bi‐objective model for the collection of waste bins in rural areas within a planning period is analyzed. This model is a real problem raised by local authorities in a rural region of northwestern Spain. The two objectives under consideration are: minimization of transport costs and improvements to the level of service. Specifically, the level of service depends on the frequency of waste collection at each point over the planning period. A solution method for this problem was developed by applying tabu search within the framework of Multiobjective Adaptive Memory Programming (MOAMP) and the results were compared with an implementation of NSGA‐II, a well‐known approach to multiobjective optimization.     

A Linear Model for the Continuous Network Design Problem

Abstract:   This article is concerned with the continuous network design problem on traffic networks, assuming system optimum traffic flow conditions and time-dependent demand. A linear programming formulation is introduced based on a dynamic traffic assignment (DTA) model that propagates traffic according to the cell transmission model. The introduced approach is limited to continuous link improvements and does not provide for new link additions. The main contribution of the article is to provide an analytical formulation for network design that accounts for DTA conditions that can be used for further analysis and extensions. The model is tested on a single destination example network, resembling a freeway corridor, for various congestion levels, loading patterns and budget sizes, to demonstrate the simplicity and effectiveness of the approach.     

High‐Order Computational Scheme for a Dynamic Continuum Model for Bi‐Directional Pedestrian Flows

Abstract: In this article, we present a high‐order weighted essentially non‐oscillatory (WENO) scheme, coupled with a high‐order fast sweeping method, for solving a dynamic continuum model for bi‐directional pedestrian flows. We first review the dynamic continuum model for bi‐directional pedestrian flows. This model is composed of a coupled system of a conservation law and an Eikonal equation. Then we present the first‐order Lax–Friedrichs difference scheme with first‐order Euler forward time discretization, the third‐order WENO scheme with third‐order total variation diminishing (TVD) Runge–Kutta time discretization, and the fast sweeping method, and demonstrate how to apply them to the model under study. We present a comparison of the numerical results of the model from the first‐order and high‐order methods, and conclude that the high‐order method is more efficient than the first‐order one, and they both converge to the same solution of the physical model.     

A Clustering Algorithm for Bi‐Criteria Stop Location Design with Elastic Demand

This article proposes a bi‐criteria formulation to find the optimal location of light rapid transit stations in a network where demand is elastic and budget is constrained. Our model is composed of two competing objective functions seeking to maximize the total ridership and minimize the total budget allocated. In this research, demand is formulated using the random utility maximization method with variables including access time and travel time. The transit station location problem of this study is formulated using mixed integer programming and we propose a heuristic solution algorithm to solve large‐scale instances which is inspired by the problem context. The elastic demand is integrated with the optimization problem in an innovative way which facilitates the solution process. The performance of our model is evaluated on two test problems and we carry out its implementation on a real‐world instance. Due to the special shape of the Pareto front function, significant practical policy implications, in particular budget allocation, are discussed to emphasize the fact that the trade‐off between cost and benefit may result in large investments with little outcomes and vice versa.     

A Hybrid Branch‐and‐Bound and Benders Decomposition Algorithm for the Network Design Problem

Given a set of candidate road projects associated with costs, finding the best subset with respect to a limited budget is known as the network design problem (NDP). The NDP is often cast in a bilevel programming problem which is known to be NP‐hard. In this study, we tackle a special case of the NDP where the decision variables are integers. A variety of exact solutions has been proposed for the discrete NDP, but due to the combinatorial complexity, the literature has yet to address the problem for large‐size networks, and accounting for the multimodal and multiclass traffic flows. To this end, the bilevel problem is solved by branch‐and‐bound. At each node of the search tree, a valid lower bound based on system optimal (SO) traffic flow is calculated. The SO traffic flow is formulated as a mixed integer, non‐linear programming (MINLP) problem for which the Benders decomposition method is used. The algorithm is coded on a hybrid and synchronized platform consisting of MATLAB (optimization engine), EMME 3 (transport planning module), MS Access (database), and MS Excel (user interface). The proposed methodology is applied to three examples including Gao's network, the Sioux‐Falls network, and a real size network representing the city of Winnipeg, Canada. Numerical tests on the network of Winnipeg at various budget levels have shown promising results.     

A Bi‐Level Model for Planning Signalized and Uninterrupted Flow Intersections in an Evacuation Network

Abstract: The problem to be addressed in this paper is the lack of an advanced model in the literature to locate the optimal set of intersections in the evacuation network for implementing uninterrupted flow and signal control strategies, respectively, which can yield the maximum evacuation operational efficiency and the best use of available budgets. An optimization model, proposed in response to such needs, contributes to addressing the following critical questions that have long challenged transportation authorities during emergency planning, namely: given the topology of an evacuation network, evacuation demand distribution, and a limited budget, (1) how many intersections should be implemented with the signals and uninterrupted flow strategies; (2) what are their most appropriate locations; and (3) how should turning restriction plans be properly designed for those uninterrupted flow intersections? The proposed model features a bi‐level framework. The upper level determines the best locations for uninterrupted flow and signalized intersections as well as the corresponding turning restriction plans by minimizing the total evacuation time, while the lower level handles routing assignments of evacuation traffic based on the stochastic user equilibrium (SUE) principle. The proposed model is solved by a genetic algorithm (GA) ‐based heuristic. Extensive analyses under various evacuation demand and budget levels have indicated that the location selection of uninterrupted flow and signalized intersections plays a key role in emergency traffic management. The proposed model substantially outperforms existing practices in prioritizing limited resources to the most appropriate control points by significantly reducing the total evacuation time (up to 39%).     

A Two‐Dimension Dynamic Bayesian Network for Large‐Scale Degradation Modeling with an Application to a Bridges Network

Modeling the stochastic evolution of a large‐scale fleet or network generally proves to be challenging. This difficulty may be compounded through complex relationships between various assets in the network. Although a great number of probabilistic graph‐based models (e.g., Bayesian networks) have been developed recently to describe the behavior of single assets, one can find significantly fewer approaches addressing a fully integrated network. It is proposed an extension to the standard dynamic Bayesian network (DBN) by introducing an additional dimension for multiple elements. These elements are then linked through a set of covariates that translate the probabilistic dependencies. A Markov chain is utilized to model the elements and develop a distribution‐free mathematical framework to parameterize the transition probabilities without previous data. This is achieved by borrowing from Cooke's method for structured expert judgment and also applied to the quantification of the covariate relationships. Some metrics are also presented for evaluating the sensitivity of information inserted into the covariate DBN where the focus is given on two specific types of configurations. The model is applied to a real‐world example of steel bridge network in the Netherlands. Numerical examples highlight the inference mechanism and show the sensitivity of information inserted in various ways. It is shown that information is most valuable very early and decreases substantially over time. Resulting observations entail the reduction of inference combinations and by extension a computational gain to select the most sensitive pieces of information.     

Simulation‐Optimization Models for the Dynamic Berth Allocation Problem

Container terminals (CTs) are designed to provide support for the continuous changes in container ships. The most common schemes used for dock management are based on discrete and continuous locations. In view of the steadily growing trend in increasing container ship size, more flexible berth allocation planning is mandatory. The consideration of continuous location in the CT is a good option. This article addresses the berth allocation problem with continuous dock, which is called dynamic berth allocation problem. We propose a mathematical model and develop a heuristic procedure, based on a genetic algorithm, to solve the corresponding mixed integer problem. Allocation planning aims to minimize distances traveled by the forklifts and the quay crane, for container loading and unloading operations for each ship, according to the quay crane scheduling. Simulations are undertaken using Arena software, and experimental analysis is carried out for the most important CT in Spain.     

A Cell‐Based Model for Multi‐class Doubly Stochastic Dynamic Traffic Assignment

Abstract: This article proposes a cell‐based multi‐class dynamic traffic assignment problem that considers the random evolution of traffic states. Travelers are assumed to select routes based on perceived effective travel time, where effective travel time is the sum of mean travel time and safety margin. The proposed problem is formulated as a fixed point problem, which includes a Monte–Carlo‐based stochastic cell transmission model to capture the effect of physical queues and the random evolution of traffic states during flow propagation. The fixed point problem is solved by the self‐regulated averaging method. The results illustrate the properties of the problem and the effectiveness of the solution method. The key findings include the following: (1) Reducing perception errors on traffic conditions may not be able to reduce the uncertainty of estimating system performance, (2) Using the self‐regulated averaging method can give a much faster rate of convergence in most test cases compared with using the method of successive averages, (3) The combination of the values of the step size parameters highly affects the speed of convergence, (4) A higher demand, a better information quality, or a higher degree of the risk aversion of drivers can lead to a higher computation time, (5) More driver classes do not necessarily result in a longer computation time, and (6) Computation time can be significantly reduced by using small sample sizes in the early stage of solution processes.     

基于动刚度法的复杂索缆体系的动态分析方法

索缆结构是一种重要的结构承载系统。随着现代工程结构跨度和高度的增加,土木工程中使用的索缆长度也在增加,其结构形式也越来越复杂。在这种情况下,索缆结构的动态分析已成为结构设计、使用性能监测和维护以及振动控制的关键。现有的索缆动态分析理论只能考虑部分设计参数的影响,或仅适用于结构形式相对简单的索缆系统,而对于复杂索缆结构的精确动态分析,难以兼顾分析效率和精度,因而不太适用。本文在动态刚度法（DSM）的基础上,建立了一套复杂索缆系统的精确动态分析方法;以裸索系统、复合索缆系统和带侧向支撑的多段索缆系统为代表,说明了该方法的有效性和适用性。主要的贡献和成果如下： 提出了考虑弯曲刚度、内部阻尼和电缆附加力的小下垂索缆频率方程的精确解;给出了带双护套防腐系统的拉索的动态模型和解法,探讨了填充材料对系统阻尼和频率的影响;基于DSM建立了多段索缆系统的动态模型,并提出了相应的动态解法方案。据此,分析了某悬索桥主缆的动态特性。理论分析结果与实测值和有限元求解结果有很好的一致性,这表明所提出的方法的准确性。本文提出的索缆动态分析方法具有较高的计算精度,且易于实现,可为工程中索缆及索结构的初步设计和健康监测提供理论依据。     

Antithetic Method‐Based Particle Swarm Optimization for a Queuing Network Problem with Fuzzy Data in Concrete Transportation Systems

The aim of this article is to develop an antithetic method‐based particle swarm optimization to solve a queuing network problem with fuzzy data for concrete transportation systems. The concrete transportation system at the Jinping‐I Hydropower Project is considered the prototype and is extended to a generalized queuing network problem. The decision maker needs to allocate a limited number of vehicles and unloading equipment in multiple stages to the different queuing network transportation paths to improve construction efficiency by minimizing both the total operational costs and the construction duration. A multiple objective decision‐making model is established which takes into account the constraints and the fuzzy data. To deal with the fuzzy variables in the model, a fuzzy expected value operator, which uses an optimistic–pessimistic index, is introduced to reflect the decision maker's attitude. The particular nature of this model requires the development of an antithetic method‐based particle swarm optimization algorithm. Instead of using a traditional updating method, an antithetic particle‐updating mechanism is designed to automatically control the particle‐updating in the feasible solution space. Results and a sensitivity analysis for the Jinping‐I Hydropower Project are presented to demonstrate the performance of our optimization method, which was proved to be very effective and efficient compared to the actual data from the project and other metaheuristic algorithms.     

A Dantzig‐Wolfe Decomposition‐Based Heuristic for Off‐line Capacity Calibration of Dynamic Traffic Assignment

Abstract: One of the critical elements in considering any real‐time traffic management strategy requires assessing network traffic dynamics. Traffic is inherently dynamic, since it features congestion patterns that evolve over time and queues that form and dissipate over a planning horizon. Dynamic traffic assignment (DTA) is therefore gaining wider acceptance among agencies and practitioners as a more realistic representation of traffic phenomena than static traffic assignment. Though it is imperative to calibrate the DTA model such that it can accurately reproduce field observations and avoid erroneous flow predictions when evaluating traffic management strategies, DTA calibration is an onerous task due to the large number of variables that can be modified and the intensive computational resources required. To compliment other research on behavioral and trip table issues, this work focuses on DTA capacity calibration and presents an efficient Dantzig‐Wolfe decomposition‐based heuristic that decomposes the problem into a restricted master problem and a series of pricing problems. The restricted master problem is a capacity manipulation problem, which can be solved by a linear programming solver. The pricing problem is the user optimal DTA which can be optimally solved by an existing combinatorial algorithm. In addition, the proposed set of dual variable approximation techniques is one of a very limited number of approaches that can be used to estimate network‐wide dual information in facilitating algorithmic designs while maintaining scalability. Two networks of various sizes are empirically tested to demonstrate the efficiency and efficacy of the proposed heuristic. Based on the results, the proposed heuristic can calibrate the network capacity and match the counts within a 1% optimality gap.     

土岩组合地基变刚度调平设计新方法

使用plaxis有限元软件对土岩组合地基上设置变形调节器的桩筏基础进行了三维模拟,分析其优化调平效果。结果表明：桩基及变形调节器联合变刚度设置可以显著减小基础的差异沉降,降低基础不均匀沉降导致的筏板内力,同时充分发挥了筏板下地基土的承载能力,实现了优化调平设计。     

基于概率的结构抗震设计方法

本文分析了结构抗震可靠度的特点,着重分析了结构在地震作用下承载能力的可靠度。在校准现行抗震规范可靠指标的基础上,运用优化方法导出了设计表达式中的地震作用系数,抗力的抗震调整系数,以及荷载组合系数;同时对以概率为基础的多系数表达式和现行抗震规范设计表达式的构件荷载效应计算上进行了分析比较。     

A Wavelet‐Based Adaptive Pole Assignment Method for Structural Control

This article presents a time varying wavelet‐based pole assignment (WPA) method to control seismic vibrations in multi‐degree of freedom (MDOF) structural systems. The discrete wavelet transform is used to determine the energy content over the frequency band of the response in real time. The frequency content was implemented in the Big Bang–Big Crunch algorithm to update the optimum values of the closed‐loop poles of the structural system adaptively. To calculate optimum gain matrix, a robust pole placement algorithm was used. The gain matrix is calculated online based on response characteristic in real time and must not be calculated a priori (offline) choice. The WPA is tested on a 10‐story structural system subject to several historical ground motions. It is observed that the WPA has advantages in some design problems. Numerical examples illustrate that the proposed approach reduces the displacement response of the structure in real time more than conventional linear quadratic regulator (LQR) controller.     

Nature as a Model for Large-scale Planting Design: Variable Classification Method

Two methods, the Regression Method and the Least Difference Method, for large-scale planting design resembling native plant communities had previously been developed. They involved selection of a plant palette from a native (model) plant community suitable for the site's climate and overall soil characteristics; followed by plant placement based on topo-edafic variables. The respective articles provided a basis for the development of software to be used by landscape designers. The Variable Classification Method proposed in the present paper is a simplified version that can be readily applied without requiring the development of special software. It involves: a) site analysis of the project site using the available site analysis software, b) classifying the topo-edaphic data of the grid cells on the project site along with those of the sample plots in the model community, and c) assigning species composition of the sample plots to the grid cells with matching topo-edaphic classes.     

Fractal Dimension‐Based Damage Detection Method for Beams with a Uniform Cross‐Section

Abstract: For civil structures, damage usually occurs in localized areas. As fractal dimension (FD) analysis can provide insight to local complexity in geometry, a damage detection approach based on Katz's estimation of the FD measure of displacement mode shape for homogeneous, uniform cross‐sectional beam structures is proposed in this study. An FD‐based index for damage localization (FDIDL) is developed utilizing the difference of angles of sliding windows between two successive points, which is expressed in FD. To improve robustness against noise, FDIDL is calculated using multisliding windows. The influence of the spatial sampling interval length and the number of 2‐sampling sliding windows on sensitivity to damage and robustness against noise is investigated. The relationship between the angle expressed in FD and the modal strain energy is established and thereby an FD‐based index for the estimation of damage extent (FDIDE) is presented. The two damage indices are applied to a simply supported beam to detect the simulated damage in the beam. The results indicate that the proposed FDIDL can locate the single or multiple damages, and FDIDE can reliably quantify the damage extent. The optimal spatial sampling interval and the number of sliding windows are investigated. Furthermore, the simulation with measurement noise is carried out to demonstrate the effectiveness and robustness of the two defined FD‐based damage indices. Finally, experiments are conducted on simply supported steel beams damaged at different locations. It is demonstrated that the proposed approach can locate the damages to a satisfactory precision.     

GA‐Based Multi‐Objective Optimization for Retrofit Design on a Multi‐Core PC Cluster

This article presents a distributed nondominated sorting genetic algorithm II (NSGA‐II) for optimal seismic retrofit design using buckling restrained braces (BRBs) on a cluster of multi‐core PCs. In the formulation, two conflicting objective functions of the initial BRB installation cost required for seismic retrofitting and damage cost that can be incurred by earthquakes expected during the life cycle of the structure were minimized. Because time‐consuming nonlinear structural analyses are required for fitness evaluations of individuals in every generation, parallelism at candidate design level or individual level is exploited by assigning fitness evaluations for individuals to slave core processors evenly. The distributed algorithm is applied to seismic retrofit design of 2D steel frame structure and 3D irregular reinforced concrete structure. The performance of the distributed NSGA‐II was assessed based on three criteria: convergence of the distributed algorithm, efficiency of distributed computing, and quality of optimal solutions. Implementation of the distributed algorithm on the multi‐core cluster consisting of up to 64 core processors resulted in relatively high speedups or efficiencies of the distributed optimization without deteriorating the quality of the optimal solutions.     

A Quantum‐Inspired Genetic Algorithm‐Based Optimization Method for Mobile Impact Test Data Integration

The traditional impact test method needs a large number of sensors deployed on the entire structure, which cannot meet the requirements of rapid bridge testing. A new mobile impact test method is proposed by sequentially testing the substructures then integrating the test data of all substructures for flexibility identification of the entire structure. The novelty of the proposed method is that the quantum‐inspired genetic algorithm (QIGA) is proposed to improve computational efficiency by transforming the scaling factor sign determination problem to an optimization problem. Experimental example of a steel–concrete composite slab and numerical example of a three‐span continuous rigid‐frame bridge are studied which successfully verify the effectiveness of the proposed method.     

A two‐level mixed‐integer programming model for bridge replacement prioritization

A bridge network is an essential part of the transportation system. Therefore, the restoration and replacement activities of aging bridges result in severe traffic delays and disruptions that heavily impact the daily traffic. Accelerated bridge construction (ABC) techniques are rapidly gaining acceptance as an alternative to conventional construction due to reduced construction duration and minimum closure impact at the network level. The limitations and completion rates vary depending on types of ABC. There is a trade‐off between a faster ABC technique with higher investment and a faster construction of a critical bridge in the network resulting large savings to users. To provide a balanced portfolio of ABC techniques on bridge sites and the prioritization of bridges for replacement, this paper develops a mixed‐integer programming (MIP) model with two levels. In this model, a network‐level scheme is used to select bridges for rapid replacement based on their criticality to the network, and a project‐level scheme is used to optimize the choice of ABC techniques for each selected bridge. To account for the effects of different construction strategies for bridge replacement, the costs associated with each replacement activity are calculated, including direct costs from the actual replacement of bridges and indirect costs experienced by network users due to bridge closures during maintenance. Using the MIP model and based on investment, outcomes are estimated for the enhanced serviceability, efficient ABC techniques, an optimal bridge replacement strategy, and minimized total cost during the entire process. These outcomes could provide decision makers and stakeholders with a complete understanding of the prioritization process at both the network and project levels.