Optimal Multiasset Maintenance Budget Allocation in Highway Asset Management

Highway asset management is a systematic process that aims to preserve, expand, and operate highway assets in the most cost-effective manner. It is an analytical tool that facilitates organized, logical, and integrated decision-making in asset management practice. This study proposes a methodology for the development of a highway asset management system that addresses asset valuation, performance modeling, marginal benefit analysis, and multicriteria decision-making, including tradeoff analysis as well as project selection and programming. While most existing management systems deal with individual physical highway assets or system usage only under certainty or risk, this research focuses on the management of an entire highway network that also incorporates tradeoff decisions involving uncertainty. Systemwide multiattribute utility functions and standardized focus gain-over-loss ratio functions based on utility theory and Shackle’s model, respectively, are calibrated using data collected through a series of questionnaire surveys. A system optimization model, along with a solution algorithm, is formulated to facilitate project selection and programming. A Highway Asset Management System software program is developed and utilized in a case study for systemwide project selection based on information for candidate projects proposed for state highway programming in Indiana during 1998-2001. For all given years and regardless of the tradeoff decision under certainty, risk, or uncertainty, the software outputs match with the results of actual highway programming at least 85 percent of the time. The case study results validate the proposed methodology and research findings and also reveal the advantages of using the algorithm for overall highway asset management practice.

ABSTRACTCommercial plant breeders improve economically important traits by selectively mating individuals from a given breeding population. Potential pairings are evaluated before the growing season using Monte Carlo simulation, and a mating design is created to allocate a fixed breeding budget across the parent pairs to achieve desired population outcomes. We introduce a novel objective function for this mating design problem that accurately models the goals of a certain class of breeding experiments. The resulting mating design problem is a computationally burdensome simulation optimization problem on a combinatorially large set of feasible points. We propose a two-step solution to this problem: (i) simulate to estimate the performance of each parent pair and (ii) solve an estimated version of the mating design problem, which is an integer program, using the simulation output. To reduce the computational burden when implementing steps (i) and (ii), we analytically identify a Pareto set of parent pairs that will receive the entire breeding budget at optimality. Since we wish to estimate the Pareto set in step (i) as input to step (ii), we derive an asymptotically optimal simulation budget allocation to estimate the Pareto set that, in our numerical experiments, out-performs Multi-objective Optimal Computing Budget Allocation in reducing misclassifications. Given the estimated Pareto set, we provide a branch-and-bound algorithm to solve the estimated mating design problem. Our approach dramatically reduces the computational effort required to solve the mating design problem when compared with naïve methods.

In this paper, a simulation-based optimization approach, named NHOCBA, for a typical resource allocation problem is presented. The hybrid algorithm based on neighborhood algorithm is applied to explore toward optimal direction in design space. For increasing efficiency, an optimal computing budget allocation (OCBA) is adopted to compute the optimal number of replications and to provide reliable evaluation of variance. In addition, we deal with the resource allocation problem which exist multiple global optima in design space. Therefore, a trim procedure which prevents to allocate extra numbers of replication to local optima has been proposed to enhance efficiency. Then, we use confidence interval at the end of algorithm procedure to find an optimal set instead of an optimal solution from design space. Finally, we compare the NHOCBA with different algorithms by experimentation study which shows that NHOCBA approach can perform better than the other algorithms under certain conditions.

Existing bridge performance metrics are limited in their ability to objectively reflect the safety and serviceability of bridges and identify effective Maintenance, Repair and Replacement (MR&R) actions for a large number of bridges in the face of budget constraints. This study presents a comprehensive optimal budget allocation framework with an integer linear programing formulation for a portfolio of bridges based on element-level inspection data. Considering budget constraints, this method determines optimal MR&R actions at element-level such that agency and user costs of repair actions required to have bridges in their like-new state at the next budget allocation year are minimized. The framework incorporates structural safety risks via the probability that the designated structural functionality of a bridge is or deemed to be compromised. The approach is applied to 484 National Highway Systems bridges in district 3 of Ohio. Results indicate that the proposed method can identify work plans comprising the optimal set of MR&R actions that maximize the network-level performance of bridge portfolios, while satisfying budgetary constraints. It is observed that this method consistently assigns higher priority to work plans that reduce structural safety risks of bridges, and to bridges with high traffic demands and long detour lengths.

Many applications in the areas of finance, environments, service systems, and statistical learning require the computation for a general function of the expected performances associated with one or many random objects. When complicated stochastic systems are involved, such computation needs to be done by stochastic simulation and the computational cost can be expensive. We design simulation algorithms that exploit the common random structure shared by all random objects, referred to as stratified splitting. We discuss the optimal simulation budget allocation problem for the proposed algorithms. A brief numerical experiment is conducted to illustrate the performance of the proposed algorithm with various budget allocation rules.

사회기반시설의 노후화로 유지보수 비용이 급격하게 증가하고 있는 선진국뿐만 아니라 국내에서도 시설물을 체계적으로 관리하여 안전사고를 예방하고 유지보수 예산을 절감해야 할 필요성이 날로 증대하고 있다. 뉴질랜드, 미국, 영국 등은 시설물의 공학적 상태와 성능에 따라 시설물을 유지보수하는 업무에 자산관리 개념을 도입하여 공공시설물을 관리하고 있다. 이들 나라들은 공공시설물 자산관리에 적용할 표준화된 자산관리 정보모델을 제시하고 시스템으로 개발하여 운용하고 있다. 국내에서도 공공시설물 자산관리 연구가 진행 중에 있으며, KTAM-40 자산관리 업무절차가 제시되었다. 본 논문에서는 공공시설물 중에서 도로자산관리시스템의 업무아키텍처 설계를 위하여 뉴질랜드 IIMM과 국내에서 개발된 KTAM-40 업무절차를 조사 분석하였다. 그리고 KTAM-40 업무절차를 참조하여 도로 자산관리시스템의 업무아키텍처를 설계하였다. 업무아키텍처는 첫째, 도로자산관리를 효율적으로 수행하기 위한 조직구조와 역할을 설정하고, 둘째로 도로자산관리 업무기능을 세분화하고, 셋째로 자산관리 업무기능들 간의 관계와 업무 기능들간 정보와 흐름을 정의하였다. 그리고 설계된 업무아키텍처가 유사한 업무 시스템의 다른 아키텍처와의 차별성을 검토하였다. As the social infrastructure becomes deteriorated, its maintenance cost has been increased rapidly. There are growing needs to manage facilities systematically to reduce this kind of maintenance budget, and furthermore to avoid safety accidents in Korea. Asset management is widely used in New Zealand, the United States, Great Britain to manage their social facilities. In these countries, standardized information model and its system are developed and in service for asset management. There are several researches on asset management of social infrastructure in Korea, and the KTAM-40 work procedure is one of the main results for these researches. In this paper, the IIMM of New Zealand and the KTAM-40 of Korea, were surveyed to develop a business architecture of the highway asset management system. A new business architecture for the highway asset management system is proposed based on the KTAM-40 work procedure: First, the organization and its role for the highway asset management are defined. Second, business functions of the highway asset management are classified. Third, the highway asset management functions and the flow of information are defined. In the last section, differences between the newly designed architecture and the other systems' architecture is reviewed.

Trade-off Analysis for Infrastructure Management: New Approaches to Cross-asset Challenges

In this paper, we study a strategic model of marketing and product consumption in social networks. We consider two competing firms in a market providing two substitutable products with preset qualities. Agents choose their consumptions following a myopic best response dynamics which results in a local, linear update for the consumptions. At some point in time, firms receive a limited budget which they can use to trigger a larger consumption of their products in the network. Firms have to decide between marginally improving the quality of their products and giving free offers to a chosen set of agents in the network in order to better facilitate spreading their products. We derive a simple threshold rule for the optimal allocation of the budget and describe the resulting Nash equilibrium. It is shown that the optimal allocation of the budget depends on the entire distribution of centralities in the network, quality of products and the model parameters. In particular, we show that in a graph with a higher number of agents with centralities above a certain threshold, firms spend more budget on seeding in the optimal allocation. Furthermore, if seeding budget is nonzero for a balanced graph, it will also be nonzero for any other graph, and if seeding budget is zero for a star graph, it will be zero for any other graph too. We also show that firms allocate more budget to quality improvement when their qualities are close, in order to distance themselves from the rival firm. However, as the gap between qualities widens, competition in qualities becomes less effective and firms spend more budget on seeding.

Optimal Level and Allocation of Cybersecurity Spending: Model and Formula

In recent years, the issue of maximizing submodular functions has attracted much interest from research communities. However, most submodular functions are specified in a set function. Meanwhile, recent advancements have been studied for maximizing a diminishing return submodular (DR-submodular) function on the integer lattice. Because plenty of publications show that the DR-submodular function has wide applications in optimization problems such as sensor placement impose problems, optimal budget allocation, social network, and especially machine learning. In this research, we propose two main streaming algorithms for the problem of maximizing a monotone DR-submodular function under cardinality constraints. Our two algorithms, which are called StrDRS1 and StrDRS2, have (1/2−ϵ), (1−1/e−ϵ) of approximation ratios and O(nϵlog(logBϵ)logk), O(nϵlogB), respectively. We conducted several experiments to investigate the performance of our algorithms based on the budget allocation problem over the bipartite influence model, an instance of the monotone submodular function maximization problem over the integer lattice. The experimental results indicate that our proposed algorithms not only provide solutions with a high value of the objective function, but also outperform the state-of-the-art algorithms in terms of both the number of queries and the running time.

We analyze optimal strategies for the allocation of a finite budget that can be invested in different advertising channels over time with the objective of influencing social opinions in a network of individuals. In our analysis, we consider both exogenous influence mechanisms, such as advertising campaigns, as well as endogenous mechanisms of social influence, such as word-of-mouth and peer-pressure, which are modeled using diffusion dynamics. We show that for a broad family of objective functions, the optimal influence strategy at every time uses all channels at either their maximum rate or not at all, i.e., a bang-bang strategy. Furthermore, we prove that the number of switches between these extremes is bounded above by a term that is typically much smaller than the number of agents. This means that the optimal influence strategy is to exert maximum effort in waves for every channel, and then cease effort and let the effects propagate. We also show that, at the beginning of the campaign, the total cost-adjusted reach of an exogenous advertising channel determines its relative value. In contrast, as we approach our investment horizon (e.g., election day), the optimal strategy is to invest in channels able to target individuals instead of broad-reaching channels. We demonstrate that the optimal influence strategies are easily computable in several practical cases, and explicitly characterize the optimal controls for the case of linear objective functions in closed form. Finally, we see that, in the canonical example of designing an election campaign, identifying late-deciders is a critical component in the optimal design.

Efficient Global Optimization (EGO) is a popular method that searches sequentially for the global optimum of a simulated system. EGO treats the simulation model as a black-box, and balances local and global searches. In deterministic simulation, EGO uses ordinary Kriging (OK), which is a special case of universal Kriging (UK). In our EGO variant we use intrinsic Kriging (IK), which eliminates the need to estimate the parameters that quantify the trend in UK. In random simulation, EGO uses stochastic Kriging (SK), but we use stochastic IK (SIK). Moreover, in random simulation, EGO needs to select the number of replications per simulated input combination, accounting for the heteroscedastic variances of the simulation outputs. A popular selection method uses optimal computer budget allocation (OCBA), which allocates the available total number of replications over simulated combinations. We derive a new allocation algorithm. We perform several numerical experiments with deterministic simulations and random simulations. These experiments suggest that (1) in deterministic simulations, EGO with IK outperforms classic EGO; (2) in random simulations, EGO with SIK and our allocation rule does not differ significantly from EGO with SK combined with the OCBA allocation rule.

Simulation-based Optimization (SbO) assumes that the simulation model is valid, and that the probability distributions used therein are accurate. However, in practice, the input probability distributions (input models) are estimated by sampling data from the real system. The errors in such estimates can have a profound impact on the optimal solution obtained by SbO. The existing two-stage framework for SbO under computational budget constraint considers only the stochastic uncertainty. In our variant, we consider the input model parameter uncertainty as well. Our algorithmic procedure is based on the stochastic kriging metamodel-assisted bootstrapping with an efficient global optimization technique which sequentially searches the optimum and incorporates Optimal Computational Budget Allocation (OCBA). This framework is also used for determining tighter worst-case bounds of the SbO with input uncertainty. The proposed framework is illustrated with the M/M/1 queuing model.

This article considers fixed‐budget ranking and selection (R&S) problems where the performance of alternative designs can only be assessed through pairwise comparisons, a setting encountered in many applications, including player ranking in games, sports tournaments, recommender systems, image‐based search, public choice models such as voting schemes or decision rules in committees, and market research. Assuming Gaussian sampling noise, we successfully extend the knowledge gradient (KG) procedure to adaptively allocate the sampling budget for identifying the best design or the top‐ designs. The proposed algorithms inherit several attractive features of KG such as enabling exact computation and achieving asymptotic mean‐variance trade‐offs. Additionally, we investigate the asymptotically optimal sampling budget allocation ratio for the pairwise R&S problem within a large deviations framework. The derived balance conditions for the optimal ratio resemble those of traditional R&S but include specific features pertinent to pairwise comparisons. Lastly, we perform extensive numerical experiments to demonstrate that the proposed algorithms deliver competitive and robust finite‐budget performance compared with several other state‐of‐the‐art procedures.

Optimal project selection and budget allocation for R&D portfolios