Development Of Model For Supplier Selection And Order Allocation With Discount Pricing And Expected Quality Loss

This paper discusses the development of optimization model for supplier selection and order allocation considering price discounts and quality of the components that are measured based on expectation of quality loss cost. The approach which was used quadratic loss function to estimated quality loss. The development of model is based on the drawback of previous research; where quality was measured only by defective components without considered to any loss of quality due to deviation from quality characteristics target. In the section of results and discussion of this paper is presented a numerical example in order to illustrate the implementation of proposed model. This numerical experiment performed by optimization software has indicated that the model able to generated optimal solution. Keywords- optimization model, supplier selection, price, discount, quality los

Advanced Techniques for Assets Maintenance Management

16th IFAC Symposium on Information Control Problems in Manufacturing INCOM 2018 Bergamo, Italy, 11–13 June 2018. Edited by Marco Macchi, László Monostori, Roberto PintoThe aim of this paper is to remark the importance of new and advanced techniques supporting decision making in different business processes for maintenance and assets management, as well as the basic need of adopting a certain management framework with a clear processes map and the corresponding IT supporting systems. Framework processes and systems will be the key fundamental enablers for success and for continuous improvement. The suggested framework will help to define and improve business policies and work procedures for the assets operation and maintenance along their life cycle. The following sections present some achievements on this focus, proposing finally possible future lines for a research agenda within this field of assets management

Estimation of component redundancy in optimal age maintenance

The classical Optimal Age-Replacement defines the maintenance strategy based on the equipment failure consequences. For severe consequences an early equipment replacement is recommended. For minor consequences the repair after failure is proposed. One way of reducing the failure consequences is the use of redundancies, especially if the equipment failure rate is decreasing over time, since in this case the preventive replacement does not reduce the risk of failure. The estimation of an active component redundancy degree is very important in order to minimize the life-cycle cost. If it is possible to make these estimations in the early phase of system design, the implementation is easier and the amortization faster. This work proposes an adaptation of the Optimal Age-Replacement method in order to simultaneously optimize the equipment redundancy allocation and the maintenance plan. The main goal is to provide a simple methodology, requiring the fewer data possible. A set of examples are presented illustrating that this methodology covers a wide variety of operating conditions. The optimization of the number of repairs between each replacement, in the cases of imperfect repairs, is another feature of this methodology

Taming Uncertainty in the Assurance Process of Self-Adaptive Systems: a Goal-Oriented Approach

Goals are first-class entities in a self-adaptive system (SAS) as they guide the self-adaptation. A SAS often operates in dynamic and partially unknown environments, which cause uncertainty that the SAS has to address to achieve its goals. Moreover, besides the environment, other classes of uncertainty have been identified. However, these various classes and their sources are not systematically addressed by current approaches throughout the life cycle of the SAS. In general, uncertainty typically makes the assurance provision of SAS goals exclusively at design time not viable. This calls for an assurance process that spans the whole life cycle of the SAS. In this work, we propose a goal-oriented assurance process that supports taming different sources (within different classes) of uncertainty from defining the goals at design time to performing self-adaptation at runtime. Based on a goal model augmented with uncertainty annotations, we automatically generate parametric symbolic formulae with parameterized uncertainties at design time using symbolic model checking. These formulae and the goal model guide the synthesis of adaptation policies by engineers. At runtime, the generated formulae are evaluated to resolve the uncertainty and to steer the self-adaptation using the policies. In this paper, we focus on reliability and cost properties, for which we evaluate our approach on the Body Sensor Network (BSN) implemented in OpenDaVINCI. The results of the validation are promising and show that our approach is able to systematically tame multiple classes of uncertainty, and that it is effective and efficient in providing assurances for the goals of self-adaptive systems