International Journal of Industrial Engineering: Theory, Applications and Practice
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SENSOR FAILURE IDENTIFICATION AND SEGREGATION USING WAVELET PERFORMANCE ANALYSIS FOR WSN BASED STATUS SURVEILLANCE SYSTEM OF A WIND TURBINE
No full textOne of the most useful renewable energy sources is wind, from which electrical power can be generated using a turbine system for long periods. The reliability of a wind turbine mainly depends on the maintenance work carried out at the site. The Status Surveillance System (SSS) is an important factor for wind turbines to guarantee uninterrupted power supply to the end user. However, the condition monitoring system based on the Wireless Sensor Node (WSN), housed with the current sensor node, is more vulnerable to failure due to circumstantial faults. Due to sensor faults, the data used for decision-making on maintenance are corrupted. This paper devises a robust and reliable mechanism called Sensor Failure Identification and Segregation (SFIS) to detect and detach corrupted data to effectively perform work related to wind turbine failure detection. The short-circuit fault is addressed by a wavelet transient approach to restore the corrupted data, while the invariable anomaly fault is analyzed with the help of the cross-correlation method. Hence, the interference fault can be analyzed using a dynamic time-warping approach. The proposed mechanism is compared with the existing Adaptive Neuro-Fuzzy Inference System (ANFIS) method that uses Supervisory Control and Data Acquisition (SCADA) to prove its reliability and robustness. SFIS offers a reliable and cost-effective solution for wind turbine maintenance work
GREEN VEHICLE ROUTING MODEL VIA LINEAR FRACTIONAL PROGRAMMING: A RETAIL CASE STUDY FOR MARMARA REGION, TÜRKİYE
No full textThe Vehicle Routing Problem (VRP) is a crucial subject in the discipline of logistics and transportation management since it connects the distribution centers to the arrival points of services and goods in the most efficient way possible. Considering the VRP in the framework of operational research, the optimization quality of the VRP mainly depends on an efficient model built for the network and the successful choice for the objective of the model. The optimization problem, which involves the objective function as the ratio of two functions, is described as Fractional Programming (FP), which has attracted noteworthy attention in the previous five decades because of its usefulness in modeling several decision processes in operations research, management science, and economics. In this research, we have studied a Linear Fractional Vehicle Routing Problem (LFVRP) regarding the contribution of the mathematical modeling of VRP to both the optimization literature and the commercial market, which is incontrovertible. For this purpose, we propose an iterative method for LFVRP that aims to optimize the delivery of goods and services while minimizing the rate of total cost/load without any variable transformation technique proposed for the first time. By this methodology of iterative optimization, unlike the literature, the mathematical complexity of the model will be facilitated as a Linear Programming Problem (LPP) and, meanwhile, provide the capability of considering multiple objectives (both cost and load) as one. Furthermore, by this advantage, it has been able to express a green-based approach by presenting an objective that minimizes fuel consumption which constructs transportation expenses, and hence it lowers its carbon footprint for our world while keeping the aim of maximum load. In order to illustrate the effectiveness of our approach, we have built a real-life model with real data in the retail sector in Türkiye and provided a comparative analysis
AN APPROACH BASED ON MACHINE LEARNING AND DISCRETE EVENT SIMULATION FOR SUPPLY CHAIN OPTIMIZATION: THE CASE OF ON-STOCK CHAINS
Get PDFThe complexity of supply chain problems, more specifically the case of on-stock chains, is due to performance indicators variety, antagonism, and the difficulty of understanding the effects and interactions of different performance drivers with regard to these indicators. As mathematical formalization is essential to optimize the performance of these chains, this paper generally aims to study the contribution of Machine Learning to mathematically link the evaluation parameters of an on-stock supply chain to its action parameters. This work is based on an academic case study that seeks to mathematically formalize the problem of delivery delay in an on-stock supply chain. To this end, several Machine Learning algorithms have been tested and compared. This experience highlighted the impossibility of obtaining a labeled dataset through data collection from the real system. It thus demonstrates the necessity to use a simulation system, in particular, discrete event simulation, to generate this dataset
COMPUTATIONAL PERFORMANCE COMPARISON OF MATHEMATICAL MODELS FOR MULTI-LEVEL REDUNDANCY ALLOCATION PROBLEM
No full textThe pursuit of high system reliability while dealing with limited resources such as cost, weight, and size is a crucial concern in various industrial sectors, particularly in military weapon systems. Redundancy of units is a widely recognized method for increasing reliability. In this research, we focus on the multiple-level redundancy allocation problem, which revolves around a series-parallel tree structure system. The main objective of the problem is to maximize the system's reliability while adhering to budget and path restrictions. Prior research has proposed integer non-linear programming formulations and heuristic approaches to address the problem. However, we propose an alternative solution using integer linear programming formulation to ensure the attainment of an optimal solution. To validate our proposed approach, we conducted performance comparison tests using 3-level and 4-level hierarchy tree data sets provided by previous research. The results demonstrate that our integer linear programming formulation surpasses the existing integer non-linear programming formulation in terms of the obtained system reliability and run time. Moreover, we observe that our formulation is capable of handling real-size instances of the problem effectively. By employing the integer linear programming approach, we can achieve better results in terms of system reliability and computational efficiency, making it a promising solution for tackling the multiple-level redundancy allocation problem in various practical applications
PATH PLANNING FUSION ALGORITHM BASED ON IMPROVED A-STAR AND ADAPTIVE DYNAMIC WINDOW APPROACH FOR MOBILE ROBOT
No full textTo achieve the path planning of mobile robots in a complex, unknown environment, the conventional A-star (A∗) algorithm has been enhanced for global path optimization, and the dynamic window approach (DWA) is incorporated for dynamic, real-time obstacle avoidance. Addressing issues of inadequate real-time performance and adaptability of the path planning method, we propose a fusion path planning method for mobile robots based on real-time positioning and map construction by vision sensors, which includes a local potential field A* algorithm (LPF-A*) and an adaptive dynamic window approach (ADWA). Initially, the proportion of obstacles in the grid environment is assessed, and this ratio is incorporated into the traditional A* algorithm to optimize the heuristic function, augment the evaluation function and boost its search efficiency in varying environments. Subsequently, in view of the intersection issue between the path optimized by the conventional A* algorithm and obstacle vertices in a complex grid environment, the search neighborhood of the A* algorithm is expanded to decrease the number of child nodes for path planning, remove superfluous nodes in the path planning, and enhance path smoothness. Ultimately, the ADWA is integrated to facilitate dynamic real-time obstacle avoidance for mobile robots in a complex setting. Compared to the traditional A* algorithm and the conventional DWA, the proposed fusion path planning algorithm improves track length, track smoothness, and search efficiency, meeting the needs for a globally optimal path and enabling dynamic real-time obstacle avoidance
PRIORITIZING LEAN TOOLS FOR INTEGRATION WITH INDUSTRY 4.0 TECHNOLOGIES USING A HYBRID FUZZY MOORA APPROACH
No full textThe fourth industrial revolution, or Industry 4.0 (I4.0), relies on the all-pervasive capability of the internet and digitalization. Key technologies and tools under this are bringing a sea change in manufacturing practices. Lean manufacturing, which has a proven acceptance by manufacturers, is also undergoing this transition marked as Lean 4.0. This paper presents a study on prioritizing various groups of lean tools for this transition based on their basic features and compatibility with I4.0 technologies. A systematic literature review (SLR) is carried out, followed by a thematic analysis to understand the status of this integration based on seven I4.0 technology themes. Priority weightage for six selected I4.0 capabilities is established by mapping with seven major I4.0 enabling technologies based on inputs from twelve industry experts. Four groups of lean tools are formed on various criteria like operation level, data-driven analysis, process design, and system improvement. Using a fuzzy MOORA (Multi-objective Optimization On the basis of Ratio Analysis approach), the rankings for these lean tool groups are found considering the I4.0 capabilities as an attribute along with their priority weightings. A consistency test and sensitivity analysis were carried out to ensure the data's validity and the robustness of the results. Based on the results of this study, compatibility and priority for the potential integration of lean tools and I4.0 technologies are suggested
CONTRACT CHOICE FOR A BRAND-LED HYBRID COMPETING SUPPLY CHAIN CONSIDERING LOYAL CONSUMERS
No full textFrom the perspective of cooperation between brands and manufacturers, this paper constructs a hybrid channel supply chain with brands as the dominant players in the game, considering the different market structure factors of traditional and direct sales channels and introducing channel transfer coefficients and loyal consumers. Based on this model, we compare the optimal solutions of each variable and optimal returns under different decisions and study the wholesale price discount model and revenue compensation coordination mechanism for the hybrid channel. The effectiveness of the coordination mechanism is verified through numerical analysis of the wholesale price discount rate and the revenue compensation coefficient. Moreover, the effect of the parameters on the supply chain’s profitability before and after coordination is also analyzed. The study shows that the smaller the channel transfer coefficient and the larger the demand of loyal consumers is, the more beneficial the increase in the supply chain system’s profitability
TRANSMISSION-CONSTRAINED GENERATION EXPANSION PLANNING FOR A REAL-WORLD POWER INDUSTRY
Get PDFTransmission-constrained generation expansion planning (TC-GEP) problem for new generating units significantly involves location, capacity and type of fuel. This problem can be solved by adding Optimal Power Flow (OPF) constraints. This study renders an application of the Self-adaptive Differential Evolution (SaDE) algorithm to the TC-GEP problem for multiple horizons, at least cost, for the power generating system of Tamil Nadu, India. TC-GEP problem has been solved for 6-year (till 2022) and 12-year (till 2028) planning horizon by considering the least cost and reliable supply. The problem is solved for six different scenarios on an Indian utility 62 bus test system, and the results are validated with Dynamic Programming (DP). The results of the TC-GEP problem for the year 2028 are compared with the solutions of the GEP problem without transmission constraint. Finally, a comparison is made between the proposed solution and the practically implemented expansion plan for the year 2017 by the Tamil Nadu electricity sector
AN INTEGRATED FRAMEWORK FOR QUALITY EVALUATION OF FRUITS AND VEGETABLE STORE LOCATED IN THE SUPERMARKET UNDER UTOPIAN ENVIRONMENT
Get PDFCustomer satisfaction depends on the availability of different varieties of fruits and vegetables in a supermarket store as well as the quality of this supermarket store for fruits and vegetables. The store may contain different variety of fruits and vegetables in a utopian environment. Apart from this, there are several quality parameters of a fruits and vegetable store. The quality evaluation of fruits and vegetable stores located in a supermarket is a big challenge for managerial personnel. Here, a quality evaluation framework is proposed for the fruits and vegetable store. The committee of experts identifies and finalizes the quality evaluation parameters through a brainstorming session. Fuzzy AHP is used to calculate the weights of evaluation parameters. A fuzzy TOPSIS generally ranks for the alternative stores. An improved fuzzy TOPSIS, which is named fuzzy k-TOPSIS, is proposed here to evaluate the quality of fruits and vegetable stores located in a supermarket. The fuzzy k-TOPSIS will provide rank as well as classification of the alternatives. A numerical example is demonstrated for a better understanding of the proposed framework
CONTEMPLATION OF OPERATIONS PERSPECTIVES ENCUMBERING THE PRODUCTION-CONSUMPTION AVENUES OF THE PROCESSED FOOD SUPPLY CHAIN
Get PDFIn developing nations, the changing lifestyle of the masses and the winds of globalization have increased the consumption rate of processed food items. However, the network of processed food supply chains is confronting various challenges impacting the production-consumption avenues. The presented work aims to identify and analyze the challenges encumbering the processed food supply chain dynamics. To meet this goal, thirty-two distinct challenges are identified from the research literature and refined by seeding inputs from field experts into an exploratory factor analysis. Challenges of high field relevance are further analyzed for the contextual interrelationships and severity by the novel hybrid approach Interval Valued Neutrosophic Vague Set Decision Making Trial and Evaluation Laboratory. The outcomes of the presented work find their implication in formulating the decisional framework and strategic and tactical roadmaps to mitigate the impact of these challenges to mapping demand and supply patterns