A study of maintenance contribution to joint production and preventive maintenance scheduling problems in the robustness framework.

International audienceIn this paper, we deal with a joint production and Preventive Maintenance (PM) scheduling problem in the robustness framework. The contributions of this paper are twofold. First, we will establish that the insertion of maintenance activities during production scheduling can hedge against some changes in the shop environment. Furthermore, we will check if respecting the optimal intervals of maintenance activities guarantees a minimal robustness threshold. Then, we will try to identify from the used optimisation criteria those that allow making predictive schedules more robust. The computational experiments in a flowshop show that joint production and PM schedules are more robust than production schedules and maintenance provides an acceptable tradeoff between equipment reliability and performance loss under disruption

Meta-Heuristic Solution in RCPSP

Tato práce se zabývá popisem stavu resource-constrained project scheduling problem. Definuje základní problém a jeho složitost. Také popisuje varianty tohoto problému. Jsou představeny algoritmy pro řešení RCPSP. V práci je důkladně rozebrán heuristický genetický algoritmus GARTH. Je také naznačena implementace dvou prototypů řešících RCPSP pomocí algoritmu GARTH. Je navrhnuto několik vylepšení originálního algoritmu a ty jsou vyhodnoceny.This thesis deals with the description of the state of resource-constrained project scheduling problem. It defines the formal problem and its complexity. It also describes variants of this problem. Algorithms for solving RCPSP are presented. Heuristic genetic algorithm GARTH is analyzed in depth. The implementation of prototypes solving RCPSP using GARTH is outlined. Several improvements to the original algorithm are designed and evaluated.

Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning

The symposium presented issues involved in the development of scheduling systems that can deal with resource and time limitations. To qualify, a system must be implemented and tested to some degree on non-trivial problems (ideally, on real-world problems). However, a system need not be fully deployed to qualify. Systems that schedule actions in terms of metric time constraints typically represent and reason about an external numeric clock or calendar and can be contrasted with those systems that represent time purely symbolically. The following topics are discussed: integrating planning and scheduling; integrating symbolic goals and numerical utilities; managing uncertainty; incremental rescheduling; managing limited computation time; anytime scheduling and planning algorithms, systems; dependency analysis and schedule reuse; management of schedule and plan execution; and incorporation of discrete event techniques

Coordinated rescheduling of precast production

Ph.DDOCTOR OF PHILOSOPH

Willingness towards cognitive engagement: a preliminary study based on a behavioural entropy approach

Faced with a novel task some people enthusiastically embark in it and work with determination, while others soon lose interest and progressively reduce their efforts. Although cognitive neuroscience has explored the behavioural and neural features of apathy, the why’s and how’s of positive engagement are only starting to be understood. Stemming from the observation that the left hemisphere is commonly associated to a proactive (‘do something’) disposition, we run a preliminary study exploring the possibility that individual variability in eagerness to engage in cognitive tasks could reflect a preferred left- or right-hemisphere functioning mode. We adapted a task based on response-independent reinforcement and used entropy to characterize the degree of involvement, diversification, and predictability of responses. Entropy was higher in women, who were overall more active, less dependent on instructions, and never reduced their engagement during the task. Conversely, men showed lower entropy, took longer pauses, and became significantly less active by the end of the allotted time, renewing their efforts mainly in response to negative incentives. These findings are discussed in the light of neurobiological data on gender differences in behaviour

Willingness towards cognitive engagement: a preliminary study based on a behavioural entropy approach

Faced with a novel task some people enthusiastically embark in it and work with determination, while others soon lose interest and progressively reduce their efforts. Although cognitive neuroscience has explored the behavioural and neural features of apathy, the why’s and how’s of positive engagement are only starting to be understood. Stemming from the observation that the left hemisphere is commonly associated to a proactive (‘do something’) disposition, we run a preliminary study exploring the possibility that individual variability in eagerness to engage in cognitive tasks could reflect a preferred left- or right-hemisphere functioning mode. We adapted a task based on response-independent reinforcement and used entropy to characterize the degree of involvement, diversification, and predictability of responses. Entropy was higher in women, who were overall more active, less dependent on instructions, and never reduced their engagement during the task. Conversely, men showed lower entropy, took longer pauses, and became significantly less active by the end of the allotted time, renewing their efforts mainly in response to negative incentives. These findings are discussed in the light of neurobiological data on gender differences in behaviour

Using Decision Analysis to Select Facility Maintenance Management Information Systems

Maintenance organizations, charged with preserving the built environment, are receiving a shrinking portion of an organization’s operational budget to do its job. It has been demonstrated through various studies that efficiencies can be gained by implementing a maintenance management information system (MMIS). However, with so many choices available, maintenance organizations often select the wrong system. This research effort used value-focused thinking decision analysis to create a model based on values from the Air Force Civil Engineer career field. Data for values and weights were collected from official documents and interviews. The resulting model is highly flexible, allowing the ultimate decision-maker to easily modify weights and value functions related to MMISs. The values and evaluation measures were used to score systems that were selected as alternatives. Sensitivity analyses were conducted to study the influence of evaluation measure weights on the final alternative rankings. The sensitivity analyses displayed alterations in rankings for each alternative based on changes in value weighing. Results indicate that commercially available systems may not be appropriate for Air Force use. The resulting model provides a readily modifiable decision model for the Air Force, as well as other maintenance organizations, to use when selecting a MMIS

Dynamic scheduling in a multi-product manufacturing system

To remain competitive in global marketplace, manufacturing companies need to improve their operational practices. One of the methods to increase competitiveness in manufacturing is by implementing proper scheduling system. This is important to enable job orders to be completed on time, minimize waiting time and maximize utilization of equipment and machineries. The dynamics of real manufacturing system are very complex in nature. Schedules developed based on deterministic algorithms are unable to effectively deal with uncertainties in demand and capacity. Significant differences can be found between planned schedules and actual schedule implementation. This study attempted to develop a scheduling system that is able to react quickly and reliably for accommodating changes in product demand and manufacturing capacity. A case study, 6 by 6 job shop scheduling problem was adapted with uncertainty elements added to the data sets. A simulation model was designed and implemented using ARENA simulation package to generate various job shop scheduling scenarios. Their performances were evaluated using scheduling rules, namely, first-in-first-out (FIFO), earliest due date (EDD), and shortest processing time (SPT). An artificial neural network (ANN) model was developed and trained using various scheduling scenarios generated by ARENA simulation. The experimental results suggest that the ANN scheduling model can provided moderately reliable prediction results for limited scenarios when predicting the number completed jobs, maximum flowtime, average machine utilization, and average length of queue. This study has provided better understanding on the effects of changes in demand and capacity on the job shop schedules. Areas for further study includes: (i) Fine tune the proposed ANN scheduling model (ii) Consider more variety of job shop environment (iii) Incorporate an expert system for interpretation of results. The theoretical framework proposed in this study can be used as a basis for further investigation

Modeling and Communicating Flexibility in Smart Grids Using Artificial Neural Networks as Surrogate Models

Increasing shares of renewable energies and the transition towards electric vehicles pose major challenges to the energy system. In order to tackle these in an economically sensible way, the flexibility of distributed energy resources (DERs), such as battery energy storage systems, combined heat and power plants, and heat pumps, needs to be exploited. Modeling and communicating this flexibility is a fundamental step when trying to achieve control over DERs. The literature proposes and makes use of many different approaches, not only for the exploitation itself, but also in terms of models. In the first step, this thesis presents an extensive literature review and a general framework for classifying exploitation approaches and the communicated models. Often, the employed models only apply to specific types of DERs, or the models are so abstract that they neglect constraints and only roughly outline the true flexibility. Surrogate models, which are learned from data, can pose as generic DER models and may potentially be trained in a fully automated process. In this thesis, the idea of encoding the flexibility of DERs into ANNs is systematically investigated. Based on the presented framework, a set of ANN-based surrogate modeling approaches is derived and outlined, of which some are only applicable for specific use cases. In order to establish a baseline for the approximation quality, one of the most versatile identified approaches is evaluated in order to assess how well a set of reference models is approximated. If this versatile model is able to capture the flexibility well, a more specific model can be expected to do so even better. The results show that simple DERs are very closely approximated, and for more complex DERs and combinations of multiple DERs, a high approximation quality can be achieved by introducing buffers. Additionally, the investigated approach has been tested in scheduling tasks for multiple different DERs, showing that it is indeed possible to use ANN-based surrogates for the flexibility of DERs to derive load schedules. Finally, the computational complexity of utilizing the different approaches for controlling DERs is compared

A control strategy for promoting shop-floor stability

This research aimed to study real-time shop floor control problem in a manufacturing environment with dual resource (machine and labour), under impact of machine breakdowns. In this study, a multiperspective (order and resource perspectives) control strategy is proposed to improve effectiveness of dispatching procedure for promoting shop floor stability. In this control strategy, both order and resource related factors have been taken into account according to information on direct upstream and succeeding workcentres. A simulated manufacturing environment has been developed as a platform for testing and analysing performances of the proposed control strategy. A series of experiments have been carried out in a variety of system settings and conditions in the simulated manufacturing environment. The experiments have shown that the proposed control strategy outperformed the ODD (Earliest Operation Due Date) rule in hostile environments, which have been described by high level of shop load and/or high intensity of machine breakdowns. In hostile environments, the proposed control strategy has given best performance when overtime was not used, and given promising results in reduction of overtime cost when overtime was used to compensate for capacity loss. Further direction of research is also suggested