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We present a quadratic programming framework to address the problem of finding optimal maintenance policies for multifacility transportation systems. The proposed model provides a computationally-appealing framework to support decision making, while accounting for functional interdependencies that link the facilities that comprise these systems. In...
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... to capture functional relationships that link them. This, in turn, constitutes an obstacle to providing effective decision support because significant benefits and costs in the management pro- cess can be directly attributed to interdependencies that link a system's facilities. To further emphasize this point consider the following example: Fig. 1 represents a hypothetical transportation network with two paths between node 1 (origin) and node 4 (destination). We assume that the four links in the system are homogeneous with respect to their ...
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... this section, we consider additional numerical examples that are intended to illustrate how the proposed framework can be generalized to more complex networks. We also discuss some difficulties. In particular, we consider the systems pre- sented in Fig. 10 consisting of three ...
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... the analysis of the system presented in Fig. 10a we consider three situations. The results are presented in Fig. 11. In the first two situations (Fig. 11a and b), we ignore demand sensitivity to condition, and set the demand elasticities with respect to capacity as follows: k nn u ¼ 0:2; n ¼ 1; 2; 3 and k ni u ¼ À0:1; n-i ¼ 1; 2; 3. These elasticities represent a situation where ...
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... the analysis of the system presented in Fig. 10a we consider three situations. The results are presented in Fig. 11. In the first two situations (Fig. 11a and b), we ignore demand sensitivity to condition, and set the demand elasticities with respect to capacity as follows: k nn u ¼ 0:2; n ¼ 1; 2; 3 and k ni u ¼ À0:1; n-i ¼ 1; 2; 3. These elasticities represent a situation where traffic on a facility is less sensitive to capacity in alternative ...
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... the analysis of the system presented in Fig. 10a we consider three situations. The results are presented in Fig. 11. In the first two situations (Fig. 11a and b), we ignore demand sensitivity to condition, and set the demand elasticities with respect to capacity as follows: k nn u ¼ 0:2; n ¼ 1; 2; 3 and k ni u ¼ À0:1; n-i ¼ 1; 2; 3. These elasticities represent a situation where traffic on a facility is less sensitive to capacity in alternative facilities (than to its own capacity). Fig. 11a is ...
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... (Fig. 11a and b), we ignore demand sensitivity to condition, and set the demand elasticities with respect to capacity as follows: k nn u ¼ 0:2; n ¼ 1; 2; 3 and k ni u ¼ À0:1; n-i ¼ 1; 2; 3. These elasticities represent a situation where traffic on a facility is less sensitive to capacity in alternative facilities (than to its own capacity). Fig. 11a is for disruption costs where 5 6 c nn 6 7,n = 1, 2, 3. Fig. 11b is for c nn > 7, n = 1,2,3. The main observation is that higher disruption costs lead to an optimal maintenance policy where the interventions between the three facilities are coordinated. The upper bound on the magnitude of an intervention means that, when the ...
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... and set the demand elasticities with respect to capacity as follows: k nn u ¼ 0:2; n ¼ 1; 2; 3 and k ni u ¼ À0:1; n-i ¼ 1; 2; 3. These elasticities represent a situation where traffic on a facility is less sensitive to capacity in alternative facilities (than to its own capacity). Fig. 11a is for disruption costs where 5 6 c nn 6 7,n = 1, 2, 3. Fig. 11b is for c nn > 7, n = 1,2,3. The main observation is that higher disruption costs lead to an optimal maintenance policy where the interventions between the three facilities are coordinated. The upper bound on the magnitude of an intervention means that, when the steady-state is reached, facilities are maintained in 2 out of every three ...
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... Fig. 11b we observe that when an intervention is applied on a facility, the swing level traffic that it serves is diverted equally onto the other two facilities without regard to their condition. Fig. 11c presents the results for a case where demand is also sensitive to condition. In particular, we set k nn x ¼ À0:5; n ¼ 1; 2; 3 and k ni x ¼ ...
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... Fig. 11b we observe that when an intervention is applied on a facility, the swing level traffic that it serves is diverted equally onto the other two facilities without regard to their condition. Fig. 11c presents the results for a case where demand is also sensitive to condition. In particular, we set k nn x ¼ À0:5; n ¼ 1; 2; 3 and k ni x ¼ 0:25; n-i ¼ 1; 2; 3. In this case the state variables exhibit cycles that are symmetric for the three facilities that comprise the ...
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... the analysis of the system presented in Fig. 10b, we set the elasticities as shown in Table ...
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... In previous works, various forms of mathematical formulations were provided for roadway maintenance and pavement deterioration modeling (Abdullah, 2011;Cheu et al., 2004;Durango & Madanat, 2002;Durango-Cohen & Sarutipand, 2009;Labi, 2001;Ng et al., 2009;Ouyang, 2007). Numerical solutions algorithms using linear programming, dynamic programming, and genetic algorithms were proposed (Golabi et al., 1982;Jha et al., 2006;Madanat, 1991). ...
Roadway and highway agencies across the globe spend a sizable fraction of their annual budget for the upkeep and maintenance of roadways. Different road segments deteriorate at different rates owing to variable traffic flow along the segments. In previous works, various forms of mathematical formulations were provided for roadway maintenance and pavement deterioration modeling. Numerical solutions algorithms using linear programming, dynamic programming, and genetic algorithms were proposed. The solution algorithms, however, did not benefit from the prescriptive and predictive capabilities of machine learning (ML) algorithms (e.g., random forest classifier, support vector machine, and artificial neural networks). Furthermore, previous methods treated transition probabilities of condition states of a pavement in future years to be static. In this paper, a variable transition probability is introduced based on the deterioration rate of a pavement over time. A modified capacitated arc routing formulation is developed for a highway infrastructure management information system. Prescriptive and predictive analytics are performed using ML to analyze the road network in simulation studies and from Montgomery County, Maryland, USA. The pavement condition index (PCI) for the road network is predicted using ML algorithms. The results show a good promise for PCI prediction based on variable deterioration rate and for obtaining condition states in future years subject to varying transition probabilities.
... Extending on earlier advances, Ouyang (2007) and Durango-Cohen and Sarutipand (2009) incorporated traffic network effects by associating deterioration and maintenance actions with roadway conditions (e.g., pavement roughness) that affect driver experience and route choice. Ouyang (2007) modeled these traffic effects through a UE model that is embedded at the lower level of a bilevel modeling framework. ...
... Numerous studies provide methods for prioritizing roadway construction projects with a goal of minimizing total costs, including monetary costs of traffic delay, and safety from inaction (e.g., [11][12][13][14][15][16][17]). A few prior studies include the negative effects of improvement actions during their execution (i.e. ...
The execution of public roadway maintenance, rehabilitation, and restoration activities disturb normal traffic flows, resulting in roadway capacity reduction, inducing travel time delays, and promoting traffic safety concerns. While they improve public roadway performance once complete, the impacts endured in executing these actions is significant. This work seeks a deeper understanding of the effects of improvement actions on traffic by juxtaposing their effects against those arising from traffic incidents that cause similar capacity reductions and related negative externalities. This is accomplished through direct and reverse comparisons with traffic incident impacts. A measure of unit delay that uses observations to determine event location extent, duration, and propagation direction was computed at both facility and corridor-wide levels to establish the degree to which improvement actions and traffic incidents are similar or dissimilar. Alternative hybrid machine-learning methods are proposed to identify and contrast those traffic characteristics that contribute greatest to correct detection of each type of downtime event. These techniques can detect traffic events and accurately distinguish between event types (whether a collision or improvement activity). The techniques were applied on seven months of data obtained from 2019 along three corridors from northern, southern, and western regions of the Commonwealth of Virginia. Those traffic characteristics that contribute greatest to correct event detection of each event type were identified and their similarities and differences were studied. General linear, multivariate regression equations were also developed for more general application.
... In view of this, numerous studies in recent years have been conducted focusing on network-level maintenance optimization [2][3][4]. To our best knowledge, most approaches in the literature define the highway maintenance management as a problem of budget constrained maintenance strategy optimization [5], which can generate plans, e.g., types of maintenance actions [6], budget allocation scheme [7], maintenance sequence [8], and intensity of the maintenance actions [9], etc. over a planning horizon. Such maintenance plans are optimal, but cannot provide effective reference in the practice of highway maintenance implementation because they are not involved with a detailed maintenance activity scheduling in a very time period, i.e., they cannot identify the makespan of the entire maintenance project (a maintenance project includes a set of maintenance activities), the start time of each maintenance activity or the number of engineering teams. ...
... RCPSP can be classified into different variants by the number of projects and/or execution modes (multiple or single), the type of resources (renewable, unrenewable or doubly resource constrained), the precedence relationship among activities (finish-to-start, finish-to-finish, start-to-start or start-to-finish), the characteristics of scheduling (preemptive or non-preemptive), whether the duration of each activity is deterministic or stochastic, and whether the processing time and/or resource consumption is discrete or continuous [10]. To our best knowledge, most of the existing studies of RCPSP focus on the doubly resource constrained single project scheduling with finish-to-start precedence relationship [1,2,5,[13][14][15][16][17]. As reported in literature, the RCPSP can be modeled using two methods, i.e., activity-based method and event-based method [18]. ...
... Both Eqs (2) and (3) guarantee the WF maintenance strategy adopted in this study. Eq (4) ensures that each maintenance activity must be finished before the end of the given time period, i.e., by time T. Non-preemptive constraint (5) indicates that each maintenance activity must be implemented only once without interruption. P (6) is denoted as the ongoing maintenance activities at time t. ...
Although a wide range of literature has investigated the network-level highway maintenance plans and policies, few of them focused on the maintenance scheduling problem. This study proposes a methodology framework to model and compare two different maintenance scheduling strategies for highway networks, i.e., minimal makespan strategy (MMS) and minimal increased travel delay strategy (MITDS). We formulate MMS as a mixed integer linear programming model subject to the constraints of the quantity of manpower and the worst-first maintenance sequence. A bi-level programming model is proposed to quantify and optimize MITDS. The upper level model determines the optimal scheduling to minimize the increased traffic delays during the maintenance makespan. In the lower level, a modified day-to-day traffic assignment model is put forward to reflect the traffic evolution dynamics by simulating travelers' route choice behaviors. A simulated annealing algorithm and augmented Lagrange algorithm are employed to solve the two proposed models, respectively. Finally, a numerical example using a highway network is developed. The two proposed strategies are tested considering different traffic demands, numbers of engineering teams, and travelers' sensitivities to traffic congestion. The experiment results reveal that compared with MMS, MITDS extends makespan by 2 days though, it reduces the total increased travel delays by 4% and both MMS and MITDS can obtain the minimum total increased travel delays when the number of engineering teams is 6. The sensitivity analysis indicates that both the two strategies have the maximum and minimum total increased travel delays when the weight of prediction in travelers' perception is 0.3 and 0.7, respectively. The proposed framework has the potential to provide reference in implementing highway maintenance activities reasonably.
... In presence of a reduced number of impacted links, robustness is a good equivalence for network resilience. Numerous additional works consider the ordering of infrastructure repair operations and assignment of tasks to crews for civil infrastructure maintenance (Camahan et al., 1987;Chen and Tzeng, 1999;Childress and Durango-Cohen, 2005;Durango-Cohen and Sarutipand, 2009). ...
In addition to operating close to their maximum capacity, transport networks, and especially the urban ones, are subject to various disruptions induced by human, technical or natural factors, which often generate loss of performance, damages and high maintenance costs. Introduced in the 70's, the notion of resilience represents the ability of a system to maintain an acceptable level of performance in presence of a disruption. Modeling and quantifying the resilience of multimodal, large-scale, urban transport networks is expected to allow cities guaranteeing higher-quality of service and seamless mobility, even in the presence of disruptions and major, predictable events. The research presented in this dissertation is motivated by the need of proper defining the resilience of the transport network in order to understand their vulnerabilities. Such indication aims at improving the functioning of the network under disruption and anticipating the loss of performance by means of a resilient-oriented transport network design. In the literature, two major approaches aim at quantifying the network resilience. On the one hand, the topological approach, based on graph theory, which characterizes the static components of transport resilience, as issued from the redundancy of the network and its connectivity. On the other hand, the dynamic approach, which takes into account the traffic dynamics and leverages traffic theory for quantifying resilience induced by the network users behaviors and the transport network performances. The combination of the static and the dynamic approaches for resilience characterization is promising and provides deeper insights in the properties of a network, both in terms of its topology and performance. Centrality measures, aiming at ranking the importance of the graph components and issued from graph theory, are mainly analyzed to characterize the transport networks in static settings. By computing them on dynamic weighted graphs that capture traffic conditions and by adapting their formulation to consider the users’ demand, we are able to jointly consider network topology and traffic dynamics in resilience characterization. To emulate the impact of disruptions, both simulated and real data are considered. A stress test methodology, mostly used in the bank and nuclear sectors, which aims at simulating the worst scenarios in order to analyze the impact and the reaction of the network, is developed to observe the transport network behavior. Finally, we develop a methodology, quick-to-compute, which aims at prioritizing the construction of some new transport mode lines, by maximizing the performance improvement in a resilience context. We also propose an algorithm for the optimal deployment of a disruption-adapted park-and-ride system.
... Stochastic techniques (e.g., Markov chain) are useful for the insufficient data of pavement conditions [6]. While, a set of deterministic techniques were used to resolve maintenance problems, such as dynamic programming [7], goal programming [8], quadratic programming framework [9], and nonlinear mathematical program [10]. ...
... The ISA algorithm is applied to find the optimal solution of the constrained multiobjective function shown in (4) and (5). The input variable is the maintenance activity (X MA (s, j)) performed for each road section s and for treatment j. then the PCR of each section in a specific year is updated as in (9). The initialization of PCR, according to the constraints, is shown in the pseudo-code in Figure 2. The optimization procedure is applied for a number of sections and for a specified number of years, as shown in the pseudo-code in Figure 3. ...
Optimization techniques keep road performance at a good level using a cost-effective maintenance strategy. Thus, the trade-off between cost and road performance is a multi-objective function. This paper offers a new multi-objective stochastic algorithm for discrete variables, which is called the integer search algorithm (ISA). This algorithm is applied to an optimal pavement maintenance management system (PMMS), where the variables are discrete. The PMMS optimization can be achieved by maximizing the condition of pavement with a minimum cost at specified constraints, so the PMMS is a constrained multi-objective problem. The ISA and genetic algorithm (GA) are applied to improve the performance condition rating (PCR) of the pavement in developing countries, where the annual budget is limited, so a minimum cost for three years’ maintenance is scheduled. Study results revealed that the ISA produced an optimal solution for multi-function objectives better than the optimal solution of GA.
... Generally, the computational resources required by dynamic programming tend to grow rapidly with problem scale (Powell, 2011). To overcome the limitations of a Markov decision process, Durango-Cohen and co-workers have developed a quadratic programming formulation to address infrastructure management problems by using continuous decision variables (Durango-Cohen and Sarutipand, 2009;P. Durango-Cohen and Sarutipand, 2007;P. ...
This paper proposes a new probabilistic treatment path dependence (PTPD) model for budget allocation within pavement network management problems. During the evaluation of treatment alternatives for a segment in a pavement network, the model considers benefits of both the evaluated treatment and its following actions. It also incorporates the influence of treatment cost and deterioration uncertainties. Treatments are selected for each segment in the pavement network using a risk-based optimization model. Three case studies are presented to illustrate the application and benefits of this new model. Results of the first two cases show that the risk-aversion coefficient in the model influences segment-level treatment selections and pavement network performance. The third case shows that the PTPD model performs better than a benefit-cost ratio model due to the incorporation of uncertainties and treatment path dependence. To obtain a similar performance level, the conventional model requires a 10.4% higher annual budget for the given case study. The results presented here suggest that elements of this model – notably consideration of uncertainty in deterioration and cost, treatment path dependency, and explicit risk trade-offs – could be incorporated into asset management tools to improve the cost-effectiveness of pavement network planning.
... In view of this, many studies have been carried out focusing on pavement maintenance management, which is defined as a resource allocation strategy for pavement maintenance of road segments in one or multiple time periods [1]. The main purpose of the strategy is to reduce user costs, prolong pavement life span, and preserve the value of road assets [2]. The decision results are always based on the tradeoff between user costs and agency costs [3]. ...
... Traffic flow distribution varies dynamically on the basis of pavement performance loss in the service stage, while, in the maintenance stage, the bottleneck The existing approaches associated with pavement maintenance optimization can be classified into two categories, i.e., top-down and bottom-up. The former assumes that facilities have homogenous characteristics such as pavement type, performance deterioration, and so on [2,4], while the latter takes different attributes of all facilities into consideration [5,6]. However, the two groups of approaches both assume that the facilities are independent, and they often do not account for the dynamic traffic distribution during the life cycle. ...
... For example, Ouyang employed a nonlinear programming The existing approaches associated with pavement maintenance optimization can be classified into two categories, i.e., top-down and bottom-up. The former assumes that facilities have homogenous characteristics such as pavement type, performance deterioration, and so on [2,4], while the latter takes different attributes of all facilities into consideration [5,6]. However, the two groups of approaches both assume that the facilities are independent, and they often do not account for the dynamic traffic distribution during the life cycle. ...
An optimal pavement maintenance strategy can keep the pavement performance at a high level under budget constraint. However, the impact of changes in traffic distribution caused by maintenance actions on user costs is rarely investigated in existing approaches. This research aims to solve the optimization of pavement maintenance strategy using a multi-stage dynamic programming model combined with the stochastic user equilibrium model, which can simulate the dynamic traffic distribution in the life cycle. To deal with the proposed model, a heuristic iterative algorithm is put forward. Ultimately, a hypothetical network is established to test the model and algorithm. The testing results prove that the proposed framework has an advantage in assessing user costs comprehensively and can provide an effective and optimal pavement maintenance strategy in a 30-year life cycle, which improves the efficiency of budget and pavement conditions. Additionally, this research provides quantitative evidence of interdependency in a road network, i.e., pavement maintenance actions on links can interfere with the user costs and traffic flow distribution in the whole network, which should be taken into account in pavement maintenance decision-making.
... Stochastic programming techniques such as Markov decision process were used to overcome a deficiency of data availability [5]. To address particular optimisation problems, deterministic programming techniques such as linear or nonlinear programming [6] quadratic programming [7], integer programming [8], dynamic programming [9] and robust optimisation [10] were used. Many researchers use genetic algorithm for single and multiobjective optimization for pavement decision making problems: single objective GA [11], and Multi objective GA [12]. ...
Regular maintenance is paramount for a healthy road network, the arteries of any economy. As the resources for maintenance are limited, optimization is necessary. A number of conflicting objectives exist with many influencing variables. Although many methods have been proposed, the related research is very active, due to difficulties in adoption to the actual practice owing to reasons such high-dimensional problems even for small road networks. Literature survey tells that particle swarms have not been exploited much, mainly due to unavailability of many techniques in this domain for multi-objective discrete problems like this. In this work, a novel particle swarm algorithm is proposed for a general, discrete, multi-objective problem. In contrast to the standard particle swarm, the bare-bones technique has a clear advantage in that it is a parameter-free technique, hence the end users need not be optimization experts. However, the existing barebones algorithm is available only for continuous domains, sans any particle velocity terms. For discrete domains, the proposed method introduces a parameter-free velocity term to the standard bare-bones algorithm. Based on the peak velocities observed by the different dimensions of a particle, its new position is calculated. A number of benchmark test functions are also solved. The results show that the proposed algorithm is highly competitive and able to obtain much better spread of solutions compared to three other existing PSO and genetic algorithms. The method is benchmarked against a number of other algorithms on an actual pavement maintenance problem. When compared against another particle swarm algorithm, it not only shows better performance, but also significant reduction in run-time compared to other POS algorithm. Hence, for large road network maintenance, the proposed method shows a lot of promise in terms of analysis time, while improving on the quality of solutions.
... The objective is to choose a set of MR&I actions or policies, that minimises the expected discounted sum of agency and user costs incurred over planning horizon. A review and evolution of these models can be seen in Durango-Cohen and Madanat (2008) and Durango-Cohen and Sarutipand (2009). Seyedshohadaie, Damnjanovic, and Butenko (2010) used this framework and the Conditional Value at Risk to take into account risk associated with deterioration uncertainty in risk measurement in maintenance and rehabilitation planning. ...
This paper addresses one of the most common problems that a railway infrastructure manager has to face:
to prioritise a portfolio of maintenance, renewal and improvement (MR&I) projects in a railway network.
This decision-making problem is complex due to the large number of MR&I projects in the portfolio and
the different criteria to take into consideration, most of which are influenced and interrelated to each
other. To address this problem, the use of the analytic network process (ANP) is proposed. The method is
applied to a case study in which the Local Manager of the public company, who is responsible for the MR&I
of Spanish Rail Lines, has to select the MR&I projects which have to be executed first. Based on the results,
it becomes evident that, for this case study, the main factor of preference for a project is the location of
application rather than the type of project. The main contributions of this work are: the deep analysis
done to identify and weigh the decision criteria, how to assess the alternatives and provide a rigorous and
systematic decision-making process, based on an exhaustive revision of the literature and expertise.
http://www.tandfonline.com/eprint/bxB55K5ez9TmbuVwwngA/full
