Setting mode for precision systems of impact and laser engraving under conditions of uncertainty of characteristics

The Hertz impact interaction formula is extended to rigid bodies, the real physical characteristics of which a priori have a significant scatter and cannot be specified exactly. Fuzzy-functions are determined that characterize a micro-impact under conditions of indistinctly specified strength properties of the material of the processed halfspace and the geometry of the impact tool. It is shown that an effective solution of applied problems of precision engraving in conditions of this kind of uncertainty of parameters is the use of adaptive IT-technology based on the interactive pre-launch setting of the system. Keywords; impact engraving technology, micro impact, fuzzy parameters, critical parameters, fuzzy surfaces, gradation scale of shades.

A scenario-based mathematical approach to a robust project portfolio selection problem under fuzzy uncertainty

As a major concern of chief managers in each organization, project portfolio selection has a special place in their responsibilities. To assist managers in making decisions, applicable optimization models play an essential role in such processes. In this regard, this paper provides a stochastic optimization model for a project portfolio selection problem under different scenarios. Providing the novelty in the model along with making it closer to reality, the interdependency between revenue and cost of projects is considered. Due to the inherent uncertainty of parameters, the revenue and cost of each project, as well as contributed capital, follow triangular fuzzy parameters. Contrary to the previous model, the appreciation of assets is considered in the proposed model as the other novelty of the proposed model. To tackle the uncertainty of parameters, a robust possibilistic approach is used, which has been first-ever devised in such problems. Being both optimistic and pessimistic approaches available for decision-makers, a new measure is introduced to make the model inclusive. Moreover, by considering the confidence level as both parameter and decision variables, the robust possibilistic programming approach is adopted to solve the proposed model. Using the new proposed measure, the optimal average value of robust model are obtained under different confidence level. Finally, solving the optimization model, the results are provided by implementing the realization for uncertain parameters, and regarding the obtained results, discussions are made to provide some insights to the managers.

ARTIFICIAL INTELLIGENCE IN POWER INDUSTRY

Trends in the development of artificial intelligence and the use of neural networks as applied to the power industry are considered. It is revealed that the well-known forecasting systems based on artificial neural networks are difficult to formalize and get an unambiguous solution. There fore, this problem must be solved using a systematic approach that combines the capabilities of artifi cial neural networks and fuzzy logic under conditions of partial uncertainty of parameters

COGNITIVE IMAGES OF STATES OF UNCERTAINTY OF PARAMETERS IN THE SPACE OF CHARACTERISTICS OF ENTROPY POTENTIALS

The paper considers an approach to creating cognitive images of states of uncertainty of various parameters in the space of characteristics of entropy potentials. The resulting images are visual, convenient for perception and rapid assessment of states of uncertainty and trends in their evolution. The proposed cognitive models can be used to synthesize systems for operational monitoring and management of objects and processes of various complexity and nature, and, in particular, in conditions of a priori uncertainty. If it is necessary to clarify the results of monitoring changes in uncertainty states, the cognitive graph can be supplemented with appropriate analytical models of these changes, which are also given in the work. The implementation of the proposed solutions will contribute to improving the efficiency and efficiency of monitoring and managing states of uncertainty, which is relevant in various fields of activity.

Single machine earliness/tardiness scheduling problem with grey processing times and the grey common due date

PurposeThis paper presents a mixed-integer programming model for a single machine earliness/tardiness scheduling problem where the objective is to minimize total earliness/tardiness duration when the uncertainty of parameters such as processing times and due date is coded with grey numbers.Design/methodology/approachGrey theory and grey numbers are used for illustrating the uncertainty of parameters in processing times and common due date, where the objective is to minimize the total earliness/tardiness duration. The paper proposes a 0–1 mathematical model for the problem and an effective heuristic method for the problem by using expected processing times for ordering jobs.FindingsThe uncertainty of the processing times and common due date are encoded with grey numbers and a position-dependent mixed-integer mathematical programming model is proposed for the problem in order to minimize total grey earliness/tardiness duration of jobs having grey processing times and a common due date. By using expected processing times for ranking grey processing times, V-shaped property of the problem and an efficient heuristic method for the problem are proposed. Solutions obtained from the heuristic method show that the heuristic is effective. The experimental study also reveals that while differences between upper and lower bounds of grey processing times decrease, the proposed heuristic's performance decreases.Originality/valueThe grey theory and grey numbers have been rarely used as machine scheduling problems. Therefore, this study provides an important contribution to the literature.

A robust possibilistic programming model for water allocation problem

Over the past few years, water allocation problem has increasingly spotlighted by governments, researchers and practitioners. As water plays an important role in people’s life and business environment, the problem of water allocation should be considered carefully to properly satisfy demand of water consumers. In the real world applications, problems like water allocation are uncertain owing to long-term planning horizon of such problems. Therefore, employing efficient methods for tackling uncertainty of parameters should be regarded by field researchers. In this regard, this paper proposes a bi-objective mathematical programming model for water distribution network design. The extended model maximizes total profit of water distribution as well as maximizing priority of water transferring among water customer zones. Then, to cope effectively with uncertainty of parameters, a novel robust possibilistic programming method is applied. Then, fuzzy and robust fuzzy programming models are compared against each other and output results confirm superiority and effective performance of the robust fuzzy model in the water allocation problem. Also, output results of the extended model show its accurate performance that results in applicability of the model as a strong planning tool in real world cases.

A Possibilistic Reliable and Responsive Closed Loop Supply Chain Network Design Model under Uncertainty

Reliability of supply chain networks is an important issue affecting customer satisfaction and profitability of organizations. However, occurrence of disruptions such as flood, earthquake and fire could ruin performance of supply chains. Uncertainty of parameters is another important factor that could lower quality of long-term plans of companies. Hence, uncertainty of parameters and disruption strike are important issues adversely influencing reliability of networks. Also, responsiveness of supply chains is a significant matter that should be considered carefully while designing distribution networks. Responsiveness could increase customer loyalty and satisfaction that could result in increasing market share of companies and their long-term planned benefit. Regarding alluded matters, the aim of this paper is designing a reliable forward-reverse supply chain network that minimizes total costs of network design along with maximization of total responsiveness of distribution network. Extended closed-loop network is capable of considering environmental issues by caring about end-of-life products. Designing reverse supply chain network aside with forward ones could decrease bad environmental impact of end-of-life products. Notably, to cope with adverse effects of disruptions, a scenario-based approach is suggested that enables considering partial and complete disruption of capacity of facilities. Additionally, an effective possibilistic programming method is applied to appropriately control uncertainty of parameters. As quality of raw materials is important to produce high-quality products, minimum acceptable quality level of raw materials is considered in extended model to maximize customer satisfaction. Finally, it should be noted that designed test problems show appropriate performance of suggested model and its applicability in real world case studies. Extended model is solved regarding different risk-aversion levels and sensitivity analysis is performed for different parameters of network design that shows effectual performance of proposed model.