A Two‐Stage Stochastic Model for Maintenance and Rehabilitation Planning of Pavements

Ameri, Mahmoud; Jarrahi, Armin; Haddadi, Farshad; Mirabimoghaddam, Mohammad Hasan

doi:10.1155/2019/3971791

Cited by 11 publications

(4 citation statements)

References 40 publications

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“…Two-stage stochastic optimization models are used in a wide array of transportation planning problems, including disaster response (Barbarosoǧlu and Arda 2004), supply chain management (Marufuzzaman, Eksioglu, and Huang 2014), and pavement maintenance and rehabilitation (Ameri et al 2019). Capacity, supply, demand, technology advancement, and budget are widely used stochastic parameters.…”

Section: Two-stage Stochastic Optimization Models For Transportation ...mentioning

confidence: 99%

A Two Stage Stochastic Optimization Model for Port Infrastructure Planning

Bhurtyal

Hernandez

Ekşioğlu

et al. 2023

Preprint

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This paper investigates inland port infrastructure investment planning under uncertain commodity demand conditions. A two-stage stochastic optimization is developed to model the impact of demand uncertainty on infrastructure planning and transportation decisions. The two-stage stochastic model minimizes the total expected costs, including the capacity expansion investment costs associated with handling equipment and storage, and the expected transportation costs. To solve the problem, an accelerated Benders decomposition algorithm is implemented. The Arkansas section of the McClellan -Kerr Arkansas River Navigation System (MKARNS) is used as a testing ground for the model. Results show that commodity volume and, as expected, the percent of that volume that moves via waterways (in ton-miles) increases with increasing investment in port infrastructure. The model is able to identify a cluster of ports that should receive investment in port capacity under any investment scenario. The use of a stochastic approach is justified by calculating the value of the stochastic solution (VSS).

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Section: Two-stage Stochastic Optimization Models For Transportation ...mentioning

confidence: 99%

A Two Stage Stochastic Optimization Model for Port Infrastructure Planning

Bhurtyal

Hernandez

Ekşioğlu

et al. 2023

Preprint

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“…Due to the importance, budgets are widely involved in M&R decision-making process [23][24][25][26]. However, their values were often artificially given based on experience, and the corresponding problems arise when allocating the assigned funding rather than explaining how to determine the appropriate values of annual budgets [27][28][29]. Some researchers defined several budget scenarios and generated a M&R strategy or strategies subject to each scenario [30][31][32][33][34].…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Network-level pavement maintenance and rehabilitation decision-making with the optimized annual budget

Chen

2023

PLoS ONE

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Maintenance and rehabilitation (M&R) is necessary to keep pavement networks in good condition. Due to the capital intensity, M&R funding is always insufficient. The annual budget, determining the available funding, is a critical criterion when planning M&R treatments. However, its values are often given, and the determination of the values is seldom discussed. To fill the gap, this paper focuses on both the determination of annual budgets and the budget allocations, and therefore enhances the network-level decision-making on M&R by developing a Multi-Objective Optimization (MOO) method. This method does not only optimize and trade off the annual budgets and their consequences, but also allocates the funding across the entire network through generating the optimized M&R decisions. According to a case study with 50 segments, the developed method successfully and effectively identified non-linear discrete relationship between the minimized annual budgets and the maximized M&R benefits subject to all the constraints, and generated the optimized annual budget allocation for each M&R decision. The achievements of this paper can be used to enhance the efficiency of M&R decisions and contribute to informed pavement management.

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“…e proposed results organize the best and optimal scheduling framework with the minimum expected cost per unit time of the production [9]. Two objectives such as addressing the uncertainty in maintenance budgeting and rehabilitation problems in the pavement have been analyzed with an application of the two stages of stochastic models in integer programming [10].…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Tyre Manufacturing System in the SMEs Using RAMD Approach

Velmurugan

Venkumar

Sudhakarapandian

2021

Mathematical Problems in Engineering

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In the recent trends, production plants in the automobile industries all over the world are facing a lot of challenges to achieve better productivity and customer satisfaction due to increasing the passenger’s necessity and demand for transportation. In this direction, the belt, tyre, and tube manufacturing plants act as vital roles in the day-to-day life of the automobile industries. Tyre production plant comprises five major units, namely, raw material selection, preparation, tyre components, finishing, and inspection. The main purpose of this research is to implement the new method to predict the most critical subsystems in the tyre manufacturing system of the rubber industry. As mathematically, any one maintenance parameter among reliability, availability, maintainability, and dependability (RAMD) parameters is evaluated to identify the critical subsystems and their effect on the effectiveness of the tyre production system. In this research, the effect of variation in maintenance indices, RAMD, is measured to identify the critical subsystem of the tyre production system based on the mathematical modeling Markov birth-death approach (MBDA), and the equations of the subsystems are derived by using the Chapman–Kolmogorov method. Besides, it also calculates the performance of certain maintenance parameters concerning time such as mean time between failures (MTBF), mean time to repair (MTTR), and dependability ratio for each subsystem of the tyre production system. Finally, RAMD analysis of the tyre production systems has been executed for predicting the most critical subsystem by changing the rates of failure and repair of individual subsystems with the utilization of MATLAB software. RAMD analysis reveals that the subsystem bias cutting is most critical with the minimum availability of 0.8387, dependability 5.19, dependability ratio 0.8701, and maximum MTTR 38.46 hours of the subsystem. In this implementation of the proposed method, a real-time case study of the industrial repairable system of tyre manufacturing system has been taken for evaluating RAMD indices of the production plant of rubber industry cited in the southern region of Tamil Nadu, India.

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A Two‐Stage Stochastic Model for Maintenance and Rehabilitation Planning of Pavements

Cited by 11 publications

References 40 publications

A Two Stage Stochastic Optimization Model for Port Infrastructure Planning

A Two Stage Stochastic Optimization Model for Port Infrastructure Planning

Network-level pavement maintenance and rehabilitation decision-making with the optimized annual budget

Performance Analysis of Tyre Manufacturing System in the SMEs Using RAMD Approach

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