Pavement maintenance optimization model using Markov Decision Processes

The considerable increase in the complexity associated with the formulation of maintenance plans has enabled the development of new techniques to bring maintenance scheduling optimization models to more realistic environments. In this sense, a previous optimization model was proposed considering the use of time windows for the formation of grouping schemes under an opportunistic strategy for maintenance activities considering non-negligible execution times, thus offering the possibility of analysing scenarios with limited resources. This article proposes a risk analysis based on the failure probability of each component involved in the maintenance scheduling optimization model, which has the particularity of enabling a greater number of combinations of grouped PM activities. Moreover, it seeks to identify the general behaviour of the optimization model against different scenarios of periodicities and execution times of each maintenance activity. The proposed optimization model is formulated under a mixed integer linear programming (MILP) paradigm and its objective function seeks to minimize the unavailability of the system associated with the execution times of the activities developed, generating different experimental cases, and varying the start time scheduling under a tolerance factor from 0% up to a maximum of 25% for advance or delay. Results show in contrast with the base optimization model, an 8% less unavailability when the tolerance factor is 10%. Finally, it was possible to quantify the risk present in each maintenance schedule, at the same time a behaviour towards advancing PM activities is evidenced by the optimization model proposed over the delay.

The purpose of this section is to introduce the notation that will be used in the subsequent parts and the most essential facts that we will need from the theory of Markov Decision Processes (MDPs) in the rest of the book. Readers familiar with MDPs should skim through this section to familiarize themselves with the notation. Readers unfamiliar with MDPs are suggested to spend enough time with this section to understand the details. Proofs of most of the results (with some simplifications) are included in Appendix A. The reader who is interested in learning more about MDPs is suggested to consult one of the many excellent books on the subject, such as the books of Bertsekas and Shreve (1978), Puterman (1994), or the two-volume book by Bertsekas (2007a,b).

The research described in this paper aimed to develop a cost optimisation model for determining the optimal approach to expanding airport capacity in metropolitan areas, taking into account demand uncertainties. The study began by analysing airport capacity expansion cases from diverse global regions to identify potential metropolitan-level solutions and key cost functions associated with airport capacity issues. A deterministic optimisation model was then developed using mixed-integer nonlinear programming (MINLP), incorporating six cost functions: capital cost, operation cost, delay cost, noise cost, operational readiness airport transfer (ORAT) cost and passenger access cost. The model was validated through a case study of the Sydney metropolitan area in Australia over a 50-year horizon and further tested for reliability using six additional experimental models. Subsequently, the deterministic model was adapted into a stochastic optimisation model employing Monte Carlo simulation to address the uncertainties in future traffic demand. This stochastic model was evaluated under three different demand scenarios, including the impact of the COVID-19 pandemic. The findings highlight the model’s reliability and reveal the trade-offs among the six cost functions over time, as well as the influence of demand uncertainty on identifying optimal solutions. The research underscores the effectiveness of integrating MINLP and Monte Carlo simulation methods for long-term airport capacity planning in metropolitan areas and addressing the diverse needs of airport stakeholders. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.

Software-defined networking provides a promising solution for traffic engineering (TE) by utilizing a centralized controller to remotely configure programmable network switches. In order to achieve optimal traffic distribution and meet the Quality of Service (QoS) requirements, the SDN controller must frequently solve complex optimization models. Applying an optimization model in ISP (Internet Service Provider) networks implies abundant routing reconfigurations, which adversely impact the network stability and QoS parameters. Namely, since routing updates usually cannot be applied on all switches at the same time, transient congestion and loops often happen as a result. Here, in this paper, we propose a new control framework that strives to maximize the network throughput and provide QoS with minimal reconfiguration costs. In contrast to the conventional TE approaches, which perform the network optimizations periodically and control the side effects of reconfigurations by carefully choosing the period length between the optimization cycles, we propose a multi-objective optimization model which jointly minimizes the routing cost function and the reconfiguration overhead. The Pareto frontier of the optimization model is generated by the augmented ∈-constrained method, whereas a Lyapunov drift-plus-penalty function is used to select the best compromise solution from the Pareto set. Since the reconfiguration overhead is reduced, the network controller could be allowed to optimize resource allocation more frequently, in order to respond quickly and efficiently to the network changes. Considering that the proposed optimization model is computationally intractable in large-scale networks, we also propose a heuristic algorithm to efficiently solve large instances of the problem. In simulations and Mininet experiments, we show that our solution brings performance improvement over the conventional periodic TE techniques. Moreover, the proposed heuristic achieves better QoS request acceptance ratio than the state-of-the-art multi-objective optimization solution, despite the significantly reduced computational complexity.

<p>In practice, manufacturing systems are highly dynamic and continuously facing unexpected events and interruptions. This puts production managers in charge of making frequent updates to the ongoing plans and schedules to cope with these changes. This is done by adopting different scheduling strategies and optimization models. Despite several attempts in the literature, the need for models that can minimize the effect of changes (i.e., stochastic and robust models) or react to them using real-time information (i.e., real- time optimization models) is still felt. </p> <p>This dissertation includes three main contributions with different optimization models and heuristic algorithms that can help managers deal with different unexpected events and interruptions on the shop floor. In the first contribution (Chapter 2), the case of stochastic deterioration-based failures in a single machine production system is considered. The machine’s degradation is modeled as a multi-state system. The obtained formulations are then integrated into an optimization model that jointly optimizes the production sequences, machine inspections, and condition-based maintenance actions. The results showed an average improvement of about 35% in total expected costs when information about the machine’s degradation level was used. </p> <p>In the second contribution (Chapter 3), the case of unexpected new job arrivals and random machine breakdowns in a flexible job shop production environment is considered. The effect of random machine breakdowns on the processing durations is formulated and then integrated into a dynamic optimization model. The proposed model investigates how real-time updates can be utilized to improve scheduling decisions based on unexpected arrivals, the availability of machines (downtimes and recovery times), and the completion times of operations. </p> <p>Finally, in Chapter 4, the case of integrating production scheduling and condition-based preventive maintenance (PM) planning in a flexible job shop production system is addressed. The study considers the case of stochastic machine degradation, random machine breakdowns, minimal repairs, condition-based PM, due date changes, and new job arrivals. The reliability of machines is modeled as a multi-state system, in which the obtained formulations are incorporated into an integrated dynamic optimization model. The developed model aims to study the effects of different RTS policies on each of the considered cases and empirically quantify the potential benefits of using real-time information to enhance scheduling decisions. </p>

During the last two decades, there has been a growing interest in component-based software engineering CBSE both in academia and industry. In component-based system development, it is common to identify software modules first. Once they are identified, we need to select appropriate software components for each module. These components can either be bought as commercial off-the-shelf COTS components and probably adapted to work in the software system or can be developed in-house. This is a 'build-or-buy' decision. This paper discusses a framework that helps a developer to decide whether to buy or to build software components while designing a fault-tolerant modular software system. This paper proposes optimisation models for optimal component selection for a fault-tolerant modular software system under the consensus recovery block scheme. It is necessary to identify critical modules in the design of a fault-tolerant modular software system and also to develop a system with a built in redundancy for critical modules. Therefore, the first optimisation model is developed for optimal component selection with the dual objective of reliability maximisation and cost minimisation of the overall system under the constraints on the delivery time and criticality of modules. The second optimisation model is an extension of the first optimisation model and discusses the issue of compatibility of components of modules. In practice, it is not possible for management to obtain precise value of reliability, cost, delivery time, etc., therefore both the models are formulated as fuzzy multi-objective optimisation models. A case study of developing a manufacturing system for medium-size enterprise is used to illustrate the proposed methodology.

In this paper, we propose an optimal video streaming framework using retransmissions based on the theory of Markov decision processes (MDP). Using MDPs, we calculate average reward optimal policies so as to maximize the on time delivery probability of video frames in the transmission window while conforming to network-imposed available bandwidth constraints. The effectiveness of the proposed approach is validated by comparing the results with theoretically obtained rate-distortion curves corresponding to the optimal policies by using the distortion model in K. Stuhlmuller et al., (2000)

We present a framework for capacity allocation decisions for assembly-test (A-T) facilities that is comprised of an optimization model and a simulation model. The optimization and simulation models are used iteratively until a feasible and profitable capacity plan is generated. The models communicate using an automated feedback loop and at each iteration the model parameters are adjusted. We describe the role of the optimization model, the simulation model and the feedback loop. Once the capacity plan is generated, it is passed down to the shop-floor for implementation. Hence, decision makers can develop accurate and more profitable execution level capacity plans using the integrated model which utilizes both optimization and simulation models. In this paper, we focus on the optimization model for capacity planning for the entire A-T facility at the individual equipment (resource) level for a two-week planning period and briefly discuss the simulation and the adjustment model.

This paper studies an integrated optimization problem on production and procurement for a manufacturer who faces stochastic demands of multiple products made by multiple components. This paper first proposes a deterministic model for the problem when the demands of products are known. Then the model is extended to the case of uncertain demands. A stochastic programming model and a robust optimization model are proposed to handle the production and procurement decisions under uncertain demands. The stochastic programming model can cope with arbitrary probability distributions of products' random demands; while the robust optimization model applies to situations in which limited information about probability distributions is available. Some numerical experiments are performed to investigate the efficiency of the proposed models. We also compare the above two models and evaluate the potential benefits in practice.

This study aims to develop an integrated optimization model for water resources management in the Jatigede Reservoir by incorporating the influence of land cover dynamics on hydrological processes. The objectives are to improve the reliability of water allocation for irrigation, raw water supply, and hydropower while simultaneously enhancing ecological sustainability through land use planning. The methodology combines hydrological analysis using rainfall–runoff and dependable flow estimation, spatial assessment of land cover change based on the satellite imagery, and optimization modeling with multi-objective programming. Simulation scenarios were constructed to represent the existing conditions, spatial planning policies (RTRW), and optimized land cover management. The findings reveal that incorporating land cover factors into the optimization model increases water supply reliability by 5–10% and reduces the risk of sedimentation, thereby extending the effective lifetime of the reservoir. Moreover, the optimization scenario demonstrates that expansion of dry land forest cover contributes positively to water regulation, although it requires trade-offs with plantation forest areas. The novelty of this research lies in bridging the gap between technical water allocation models and spatial ecological considerations, offering a more comprehensive decision-support tool. This improvement provides valuable insights for policymakers in integrating reservoir operation strategies with land use planning to achieve sustainable water resources management.

The objective of this paper is the development and solution of nonlinear and mixed-integer (MIP) optimization models for real-world planning and scheduling problems in petroleum refineries. Firstly, we present a nonlinear planning model that represents a general refinery topology and allows implementation of nonlinear process models as well as blending relations. The optimization model is able to define new operating points, thus increasing the production of the more valuable products and simultaneously satisfying all specification constraints. The second part addresses scheduling problems in oil refineries, which are formulated as MIP optimization models and rely on both continuous and discrete time representations. Three practical applications closely related to the current refinery scenario are presented. The first one addresses the problem of crude oil inventory management of a refinery that receives several types of crude oil delivered exclusively by a single oil pipeline. Subsequently, two optimization models intended to define the optimal production policy, inventory control and distribution are proposed and solved for the fuel oil and asphalt plant. Finally, the planning model of Moro et al. (1998) is extended in order to sequence decisions at the scheduling level in the liquefied petroleum gas (LPG) area for maximization of the production of petrochemical-grade propane and product delivery.

Bolted connections are important components of steel structures and they must be based on good design, with analytical descriptions of failure mechanisms that are thoroughly researched. A robust and reliable analytical description of a failure mode requires a large base of results, collected experimentally or numerically, usually via FEA. The modelling procedure in FEM simulations can significantly influence result accuracy, and one of the most important parameters is spatial discretization. Mesh size presents a delicate balance between accuracy, efficiency and convergence issues, especially in bolted connection analysis with contact, material and geometric nonlinearity. Due to increased complexity, sensitivity studies are difficult to interpret and time expensive, which is why in this study a mesh size optimization procedure is presented. This optimization is based on a previously verified and validated numerical model parametric study of a single-bolt lap connection. The first step included forming of an optimization model based on an artificial neural network, through which results were extrapolated and compared to experimental. However, estimated solving wall time was significantly longer. In the second step, a nonlinear optimization model was structured to determine a mesh size for which the difference between experimental and optimization results would be equal, whilst having a minimal solving wall time. The optimization model provided insights into mesh size quality to efficiency ratios and rendered that the minimal difference between experimental and numerical results is 1.57%, while having a solving wall time of 1.3 h, instead of the step one optimization modelling estimated 36 h.

Aiming at the insufficiency of the logistics distribution model of traditional agricultural products, this paper puts forward the optimization modeling analysis of the intelligent logistics distribution route of agricultural products under the Internet of Things. According to the logistics distribution model of agricultural products under the Internet of Things environment, the intelligent logistics distribution path of agricultural products is optimized and modeled and analyzed. The objective function of the shortest path of the model is calculated, and constraint conditions are set, thereby completing the intelligent logistics distribution path optimization modeling of agricultural products. Experimental parameters are set and traditional methods with path optimization modeling analysis methods are compared. From the comparison results, When the time is 10:00, the difference between the accuracy of the traditional method and the accuracy of the intelligent logistics distribution route optimization model is the largest, with a difference of 80%. It can be seen that the use of intelligent logistics distribution route optimization modeling and analysis has higher accuracy. It can be seen that the path optimization modeling and analysis method has higher precision in the analysis of agricultural products intelligent logistics distribution route, and provides an effective solution to ensure freshness of agricultural products.

Little research has considered fuzzy scheduling and sequencing problem in operating rooms. Multiple-period fuzzy scheduling and sequencing of patients in operating rooms optimization models are proposed in this research taking into consideration patient‘s preference. The objective of the scheduling optimization model is obtaining minimal undertime and overtime and maximum patients' satisfaction about the assigned date. The objective of sequencing the optimization model is both to minimize overtime and to maximize patients' satisfaction about the assigned time. A real-life case study from a hospital that offers comprehensive surgical procedures for all surgical specialties is considered for illustration. Research results showed that the proposed models efficiently scheduled and sequenced patients while considering their preferences and hospitals operating costs. In conclusion, the proposed optimization models may result in improving patient satisfaction, utilizing hospital's resources efficiently, and providing assistance to decision makers and planners in solving effectively fuzzy scheduling and sequencing problems of operating rooms.

This paper helps cyclists obtain optimal pacing strategies. This paper establishes objective optimization models under various conditions and obtain optimal power output distribution which provides reference for cyclists. This paper uses hyperbolic model, which fits well on collected data, as an illustrative definition of power profiles for cyclists. Based on that, this paper builds singular-objective optimization model for straight stages and dual-objective optimization model for bends considering different road conditions. Then this paper takes into account the impact of weather conditions and make adjustments to the original singular-objective optimization model. This paper uses 8 random disturbances to simulate rider deviations from target output and examine the sensitivity of results. It concludes that the total length of time will increase by 3.3% if a rider deviates from his target power output. For the team time trial, this paper adopts the strategy of uniform speed throughout the whole process according to previous research. Different types of cyclists take turns to lead the team and this paper applies the singular-objective optimization model to them respectively. In this way, this paper extends our model to the fields of team work and the generality of this model is confirmed. Finally, this paper draw the conclusion that team work can significantly decrease the impact of wind resistance on every individual.