Development of Fuzzy Applications for High Performance Induction Motor Drive

This chapter develops a sliding mode and fuzzy logic-based speed controller, which is named adaptive fuzzy sliding-mode controller (AFSMC) for an indirect field-oriented control (IFOC) of an induction motor (IM) drive. Essentially, the boundary layer approach is the most popular method to reduce the chattering phenomena, which leads to trade-off between control performances, and chattering elimination for uncertain nonlinear systems. For the proposed AFSMC, a fuzzy system is assigned as the reaching control part of the fuzzy sliding-mode controller so that it improves the control performances and eliminates the chattering completely despite large and small uncertainties in the system. A nonlinear adaptive law is also implemented to adjust the control gain with uncertainties of the system. The adaptive law is developed in the sense of Lyapunov stability theorem to minimize the control effort. The applied adaptive fuzzy controller acts like a saturation function in the thin boundary layer near the sliding surface to guarantee the stability of the system. The proposed AFSMC-based IM drive is implemented in real-time using digital signal processor (DSP) board TI TMS320F28335. The experimental and simulation results show the effectiveness of the proposed AFSMC-based IM drive at different operating conditions such as load disturbance, parameter variations, etc

Development of an integrated FIS-DEA method for sustainable supplier selection in manufacturing / Atefeh Amindoust

Supplier selection is an important area of decision making in manufacturing, especially for large and medium companies – either multinational (MNCs) or local. As sustainability in terms of preserving physical environment and developing long-term relationships between the partners in carrying out of manufacturing activities has gained world-wide focus, this dimension deserves due attention in selecting the competent suppliers in today’s companies. Literatures show that the past researches done in this area didn’t adequately discern and put the sustainable issues in a form of generic model. In real life applications, the importance of the various sustainable supplier selection criteria differ from one company to another and that depends on the circumstances where each organization may consider their relative importance for supplier selection criteria. The relative importance of the selection criteria and also the suppliers’ performance with respect to these given criteria is to be established by the pertinent decision makers. Decision makers, however, normally prefer to answer these two scenarios (the weights of criteria and the suppliers’ rating with respect to the criteria) in linguistic terms instead of being compared them numerically. So, the conventional supplier selection decision process involves a high degree of vagueness and ambiguity in practice. This research takes the aforesaid issues into account, proposes a conceptual sustainable supplier selection model, and develops an integrated method based on Fuzzy Inference System (FIS) and Data Envelopment Analysis (DEA) theories for such supplier selection under uncertainty considering the relative importance of the performance indicators. The FIS-DEA method is designed so that the shortcomings of the conventional DEA approach (not being able to handle imprecise data, decision makers can freely choose the weights to be assigned to each input and output in a way that maximizes the efficiency, limitation on the number of inputs and outputs (criteria) iii in accordance with the number of suppliers) could be eliminated. To handle the subjectivity of decision makers’ preferences, the related data including the relative importance of criteria and the suppliers’ performance with respect to these criteria are processed through fuzzy set theories. The processed data of suppliers’ performance are then passed into modular FIS system to achieve the sustainability affinity indices of suppliers. Moreover, to get the supplier ranking results, these indices are fed into a DEA approach. The applicability and feasibility of the proposed FIS-DEA method is tested through two test beds, which have been designed based on experts’ knowledge in two large companies from two different countries. The performance of the proposed FIS-DEA method is also assessed by comparing the results obtained with the existing supplier selection FIS-based method through error measurement criteria. The results show that the amounts of all error measurement criteria (such as mean squared error (MSE), root mean square error (RMSE), and mean absolute error (MAE)) are found to be very small. Among all, the biggest errors are found under RMSE calculations and these are 9.55 and 7.12 percent for the first and second test beds respectively. These are less than 10 percent (acceptable range is 0-10%) and that show the validity on acceptance of the proposed method. The proposed method is an open-ended approach to adapt any number of candidate suppliers as well as their selection criteria that might suit today’s flexible manufacturing needs

Supplier selection considering sustainability measures: an application of weight restriction fuzzy-DEA approach

With the growing of consumer awareness in environmental and social issues sustainable development has become an essential element in supply chain management. Supplier evaluation and selection is one of the main strategic decisions for purchasing management in supply chain. This paper use Data Envelopment Analysis (DEA) to propose a new model for evaluation and ranking of a given set of suppliers from sustainable point of view. The proposed model integrates the fuzzy set theory and DEA to consider the decision makers’ preferences and handle the ambiguity and uncertainty in supplier selection process. For this purpose, linguistic values in the form of triangular fuzzy numbers are used to assess the weights of criteria, sub-criteria, and the ratings of suppliers’ performance with respect to sub-criteria. Then, a fuzzy-DEA model, using α-cut approach, is developed considering weight constraints. An application from Supplying Automotive Parts Company (SAPCO) Company, which is one of the largest suppliers of automotive parts in the Middle-East, is presented to show the applicability of the proposed model. Finally, the proposed weight restriction fuzzy-DEA model is validated through comparing with one of the recent supplier selection methods

Investigating the problem of pollution in cities and providing the solutions using system dynamics method

The greatest environmental problem of cities is air pollution that natural and human factors are involved in creating it. Natural factors influencing it include city surrounding by mountains, lack of constant winds with proper speed, and low rainfall. Among the human factors, we can refer to existence of large numbers of vehicles and cars, introduction of excessive new cars to urban traffic cycle, insufficient or reluctance to use public transport systems and the cheap price of fuel. The concentration of atmospheric pollutants in the cities in many cases is several times more than the allowed level, followed by short and long term adverse effects. The first step to control air pollution of the cities is the identification of effective resources and sharing the relative pollution production rate. Sources of pollution include stationary sources (units with different land uses including residential units, factories, and industrial workshops, and refinery) and mobile sources (including public and private light and heavy vehicles). Methods to determine the relative contribution of the sources include the use of emission factors and calculating emissions at the source of pollution production that the focus of this method is on emission source. Another approach is to focus on the current situation by measuring the concentration in pollution monitoring stations or reverse modeling to detect emission source. In this research, the transport system of cities and its impact on air pollution, the economy, and the population are studied. Effective variables and parameters were identified and causal loops were designed and the equations and model were calibrated and validated using information and Vensim software that showed very good accuracy. Using this model, the effect of different scenarios to reduce air pollution caused by transport was investigated.

A Hybrid Genetic Algorithm for Nurse Scheduling Problem considering the Fatigue Factor

Nowadays and due to the pandemic of COVID-19, nurses are working under the highest pressure benevolently all over the world. This urgent situation can cause more fatigue for nurses who are responsible for taking care of COVID-19 patients 24 hours a day. Therefore, nurse scheduling should be modified with respect to this new situation. The purpose of the present research is to propose a new mathematical model for Nurse Scheduling Problem (NSP) considering the fatigue factor. To solve the proposed model, a hybrid Genetic Algorithm (GA) has been developed to provide a nurse schedule for all three shifts of a day. To validate the proposed approach, a randomly generated problem has been solved. In addition, to show the applicability of the proposed approach in real situations, the model has been solved for a real case study, a department in one of the hospitals in Esfahan, Iran, where COVID-19 patients are hospitalized. Consequently, a nurse schedule for May has been provided applying the proposed model, and the results approve its superiority in comparison with the manual schedule that is currently used in the department. To the best of our knowledge, it is the first study in which the proposed model takes the fatigue of nurses into account and provides a schedule based on it

Nonlinear genetic-based model for supplier selection: A comparative study

Evaluation and selection of candidate suppliers has become a major decision in business activities around the world. In this paper, a new hybrid approach based on integration of Gene Expression Programming (GEP) with Data Envelopment Analysis (DEA) (DEA-GEP) is presented to overcome the supplier selection problem. First, suppliers’ efficiencies are obtained through applying DEA. Then, the suppliers’ related data are utilized to train GEP to find the best trained DEA-GEP algorithm for predicting efficiency score of Decision Making Units (DMUs). The aforementioned data is also used to train Artificial Neural Network (ANN) to predict efficiency scores of DMUs. The proposed hybrid DEA-GEP is compared to integrated approach of Artificial Neural Network with Data Envelopment Analysis (DEA-ANN) to support the validity of the proposed model. The obtained results clearly show that the model based on GEP not only is more accurate than the DEA-ANN model, but also presents a mathematical function for efficiency score based on input and output data set. Finally, a real-life supplier selection problem is presented to show the applicability of the proposed hybrid DEA-GEP model.Sin financiación3.244 JCR (2017) Q1, 31/353 EconomicsUE

Przedstawienie matematycznego modelu programowania do wyznaczania tras i harmonogramowania między dokami i transportem

Cross-docking is the practice of unloading goods from inbound delivery vehicles and loading them directly onto outbound vehicles. Cross-docking can streamline supply chains and help them move goods to markets faster and more efficiently by eliminating or minimizing warehouse storage costs, space requirements, and inventory handling. Regarding their short shelf-life, the movement of perishable commodities to cross-dock and their freight scheduling is of great importance. Accordingly, the present study developed a Mixed-Integer linear Programming (MILP) model for routing and scheduling, cross-docking, and transportation in a reverse logistics network. The model was multi-product and multi-level and focused on minimizing the logistics network costs to increase efficiency and maximizing the consumption value of delivered products. Considering cost minimization (i.e., earliness and tardiness penalty costs of customer order delivery, the inventory holding costs at the temporary storage area of the cross-dock and costs of crossover and use of outbound vehicles) as well as uncertainty in customer demand for perishable products, the model was an NP-hard problem. In this model, the problem-solving time increased exponentially according to the problem dimensions; hence, this study proposed an efficient method using the NSGA II algorithm.Cross-docking to praktyka polegająca na rozładowywaniu towarów z przychodzących samochodów dostawczych i ładowaniu ich bezpośrednio na pojazdy wyjeżdżające. Cross-docking może usprawnić łańcuchy dostaw i pomóc im szybciej i wydajniej przemieszczać towary na rynki, eliminując lub minimalizując koszty magazynowania, wymagania przestrzenne i obsługę zapasów. Ze względu na ich krótki okres przydatności do spożycia ogromne znaczenie ma przemieszczanie łatwo psujących się towarów do cross-dockingu i planowanie ich przewozu. W związku z tym w niniejszym badaniu opracowano model programowania liniowego (MILP) z mieszaną liczbą całkowitą do wyznaczania tras i harmonogramów, przeładunku towarów i transportu w sieci logistyki zwrotów. Model był wieloproduktowy i wielopoziomowy oraz koncentrował się na minimalizacji kosztów sieci logistycznej w celu zwiększenia wydajności i maksymalizacji wartości konsumpcyjnej dostarczanych produktów. Uwzględniając minimalizację kosztów (tj. Koszty karne za wczesne i spóźnione dostawy zamówień do klienta, koszty magazynowania w tymczasowej strefie składowania cross-docku oraz koszty crossovera i wykorzystania pojazdów wychodzących), a także niepewność dotyczącą zapotrzebowania klientów na łatwo psujące się produkty model był problemem NP-trudnym. W tym modelu czas rozwiązywania problemów wzrastał wykładniczo zgodnie z wymiarami problemu; stąd w badaniu zaproponowano skuteczną metodę wykorzystującą algorytm NSGA II

Integration of the TRIZ Matrix and ANP to Select the Reactive Maintenance Tactics Using the Meta-Synthesis Approach

So far, numerous studies have been published on the selection of appropriate maintenance tactics based on some factors affecting them such as time, cost, and risk. This paper aims to develop the TRIZ contradiction matrix by explaining the dimensions and components of each of the following Reactive maintenance tactics. The related findings of previous studies were analyzed by adopting “Rousseau and Sandoski” seven-step method to identify and extract the relationships between TRIZ principles and Reactive maintenance tactics. Thereafter, 5 Reactive maintenance tactics were replaced TRIZ’s 40 principles in the TRIZ contradiction matrix. Finally, the ANP method were used to extract and prioritize the appropriate Reactive maintenance tactics. The proposed matrix in this research was used in the desalination section of one of the oil companies to select on the appropriate Reactive maintenance tactics. The results of this research is useful for managers and maintenance specialists of units in making decisions to provide appropriate Reactive maintenance tactics for the desired equipment