Extended Framework for Preventive Maintenance Planning: Risk and Behaviour Analysis of a Proposed Optimization Model

<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>

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.

Optimisation of maintenance routing and scheduling for offshore wind farms

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.

Efficiency in public and freight transportation systems is of great importance for a society. Railways can other high capacity and relatively low environmental impact, but require that several technical systems are tuned and operate well. Specifically there is a tight interdependency between infrastructure and trains. The consequences are that all subsystems must be maintained and that the coordination of infrastructure activities and train operations is essential. Railway infrastructure maintenance and train services should ideally be planned together, but practice and research about railway scheduling has historically focused mainly on train operations and timetabling while maintenance planning has received less attention | and little research have considered the joint scheduling of both types of activities. Instead the traditional approach has been a sequential and iterative planning procedure, where train timetabling often has precedence over infrastructure maintenance. This thesis studies how maintenance windows, which are regular time windows reserved for maintenance work, can be dimensioned and jointly scheduled with train services in a balanced and efficient way for both maintenance contractors and train operators. Mathematical methods are used, with the aim of advancing the knowledge about quantitative methods for solving such coordination problems. The thesis contributes with new optimization models that jointly schedule maintenance windows and train services, investigates the solving efficiency of these models, and studies crucial extensions of the planning problem | primarily for the consideration of maintenance resources. Furthermore, the models are applied to, verified and validated on a demanding real-life problem instance. The main results are that integrated and optimal scheduling of maintenance windows and train services is viable for problems of practical size and importance, and that substantial maintenance cost savings can be achieved with such an integrated approach as compared to a traditional sequential planning process. The thesis consists of an introduction and overview of the research, followed by six papers which present: (1) A cost benefit model for assessment of competing capacity requests at a single location; (2) An optimization model for integrated scheduling of both maintenance windows and train services; (3) Mathematical reformulations that strengthen the optimization model; (4) Extensions for handling resource considerations and cyclic schedules; (5) A case study for a major single track line in Sweden; and (6) A mathematical study of length-restricted sequences under cyclic conditions.

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.

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.

The purpose of this study is to develop predictive and optimization models to reduce patients' waiting time for diagnostic tests and medical treatments. Long wait times for receiving medical care is a pressing issue in the Canadian healthcare system. These wait times are spread over different phases of treatment, such as diagnostic tests, physicians' treatments, and appointments with specialists and surgeons. An integrated system to monitor all the phases of treatment along with efficient workload distribution in each phase can reduce patients' waiting time at each of these phases. This study investigates the impact of efficient resource allocation and workload distribution in the medical system. This research also explores the effect of optimized resource allocation on the patients' waiting time in Medicare settings. Resource allocation planning is directly related to the number of patient-arrival, and it is hard to predict such uncertain parameters in the future time frame. The number of patient-arrival also varies across different medical departments and different timeframes which makes the patient-arrival prediction challenging. The goal of this study is to investigate the forecasting effect on patients' waiting time and physicians' workload. To achieve this goal, advanced machine learning technique is integrated with the optimization model. The machine learning technique is used to predict the uncertain parameters of the optimization model for a shorter time span. To predict time-dependent uncertain parameters, such as patient arrival is a major issue, as the prediction may suffer from the concept drift problem. Besides, real-time data are commonly prone to errors due to irregular fluctuations, seasonal biases, and missing values in the data. On the other hand, predicting for shorter intervals requires lower execution time combined with higher accuracy. The developed predictive ensemble model in this research has addressed these issues legitimately with four research contributions. In the first contribution (Chapter 2), we have investigated methodologies for predicting Radiologists’ workload in a short time interval by adopting a machine learning technique. An ensemble model is proposed with the fixed batch training method in this part. To excel in the execution time, a fixed batch training method is used. Secondly, in Chapter 3, an Adaptive Batched-Ranked Ensemble (ABRE) model that reduces the effect of fluctuation using the time-variant windowing technique. Besides, a data aggregation technique is developed and integrated with the offline training phase of the proposed model to tackle the concept drift problem. In the third contribution (Chapter 4), a novel Ensemble of Pruned Regressor Chain (EPRC) method is developed and trained offline to predict uncertain parameters, such as patients’ arrival. Finally, the fourth contribution in Chapter 4, the EPRC method is integrated with a novel multi-objective optimization model to reduce patients’ waiting time, and to determine workload allocation for future timespan. This research enables enhanced decision-making with effective resource allocation and workload scheduling, as well as assists in reducing healthcare expenditure.

More than ten billion tons of construction waste are generated every year in the world. The large volume of construction waste not only increases costs for contractors, but also poses a threat to the environment. A significant proportion of construction waste consists of off-cuts of raw materials. Therefore, to reduce construction waste, this study builds an optimization model to reduce the volume of off-cuts of raw materials. We then develop two solution methods—a mixed-integer linear programming method and a column generation method—to solve the proposed optimization model. We conduct numerical experiments to test the efficiency and applicability of our proposed model. The mixed-integer linear programming method obtains optimal solutions and is suitable for solving small-scale instances, whereas the column generation method gives high-quality solutions within seconds and is suitable for solving large-scale instances. In the large-scale instances, the column generation method reduces waste by over 10% compared to the use of two straightforward decisions rules. Our findings will help construction projects decrease material off-cuts, reduce costs, and achieve sustainable construction.

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.

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.

The importance of evaluating the complete automated knowledge-based planning pipeline

Novel reservoir system simulation procedure for gap minimization between water supply and demand