فهرست مطالب

Journal Of Industrial Engineering International
Volume:19 Issue: 4, Autumn 2023
- تاریخ انتشار: 1403/08/24
- تعداد عناوین: 6
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Pages 1-17
Supply chain coordination is one of the most recently studied fields. One of the coordination contracts is the wholesale contract that aims to balance the order quantity by offering a lower wholesale price to the retailer. The primary indeterminate player in this contract is the demand. The goal is to find the order amount so that both parties in the channel are satisfied with a reasonable profit concerning the proposed price. When a new product is presented to the market, the main challenge would be a sensible demand prediction such that either stock over or shortage stays under a moderate level. In such a new product, either there is insufficient sample space to be dealt with in probability theory, or the existing data for similar products does not apply to this new case. Referring to an expert would be an appropriate approach in this situation. Uncertainty theory is one of the mathematical paradigms which deals with this problem well. Using this paradigm, we assume that the demand is an uncertain variable with known uncertainty distribution. We mainly consider the linear uncertain demand and investigate optimal policy for both parties with and without coordination. An illustrative example verifies the proposed approach.
Keywords: Supply Chain, Coordinating Contract, Wholesale Contract, Uncertainty Theory -
Pages 18-32
Selecting systems configuration is a critical step in the safe design of systems. Optimizing systems configuration means maximizing their availability and minimizing their overall cost. In this regard, this paper aims to present a novel binary non-linear fuzzy goal programming (FGP) model to choose parts suppliers of multistate parallel series systems based on availability and manufacturing costs. Quantity-based discounts, components purchase cost, and penalties for delaying system construction were also considered. In addition, a fuzzy target programming model was applied to minimize deviations from goal values of expenses. A system reliability block diagram illustrates the system's status. The Markov chain model describes a sequence of possible events in which the probability of each event depends on the reliability of system parts. The other effect of ordering several pieces from the same supplier is considered (reducing the unit price of elements and increasing their delivery lead time). Model results indicate the practical application of this method to optimize system parts reliability, taking into account life cycle parameters, including system construction cost and operational reliability.
Keywords: Goal Programming, Reliability (RBD), Concurrent Engineering, Markov Model, Fuzzy Theory -
Pages 33-54
This study examines the digital banking system using a cross-sectional research method that incorporates qualitative and quantitative exploratory data. Fuzzy logic is used to address the complexity and ambiguity of digital banking characteristics, particularly quality indicators and linguistic variables. The successful adoption of new technologies in digital banking depends on continuous use and habit formation. Previous models of electronic and digital banking have their strengths and weaknesses. Therefore, it is important to develop a dynamic model of the D- Banking Ecosystem that aligns with the specific characteristics of the Iranian bank under study. The research aims to analyze existing models and their strengths and weaknesses to present the boundaries, main players, and relationships within the digital banking ecosystem. Proposed models for digital banking were examined, and a new classification of dimensions and components was presented based on a systematic review method and interviews with subject matter experts. A fuzzy Delphi panel of digital banking experts was formed in three stages to determine the components. The developed model for the studied bank was simulated using dynamic analysis. Based on the findings of the simulation and examination of different scenarios, it is concluded that for this bank to enter the digital banking ecosystem, the first step should be an innovative approach to the use of new technologies and their benefits, along with special attention to cooperation and attraction of startups and Fintechs to satisfy and persuade customers and employees. Other important factors in the next stages include banking industry structure, digital transformation infrastructure, security, environmental-social factors, and laws and policies.
Keywords: Ecosystem, Digital Banking, Fintech, Dynamic Simulation, Bibliometrics -
Pages 55-69
With the development of computer systems in recent years, transactions in financial markets have been made available for investors. Artificial intelligence (AI) -based models have also used in the financial markets due to the development of information systems and their ability to store and retrieve the large volumes of financial data. This research presents a new approach to modeling the buying and selling process in the stock market based on deep learning, LSTM, and CNN methods. In the proposed method, the forecast of the future value of stock indicators obtained using the LSTM algorithm is used as the input features of a CNN network. The CNN network as a classification model provides the buy/sell signal for the algorithmic trading system. In addition, the EC-FS model has been used to determine the most appropriate input indicators for the classification model. The proposed model has been evaluated on the Tehran Stock Exchange market and five selected stocks. The implementation results of this model have been compared with other models such as LSTM-MLP, RNN-CNN, and RNN-MLP. As a result, it can be concluded that the LSTM algorithm performs better in forecasting the indicators of selected stocks that will be used as the features of the classification model. It is a general statement that the collaborative LSTM-CNN method is more effective than other methods at training the buying and selling process. This study can aid the stock market of Iran participants for designing the most effective trading strategy.
Keywords: Deep Learning, LSTM-CNN Method, Stock Market, Tehran Stock Exchange, Trading System -
Pages 70-85
The impact of server unavailability during complete vacation (CV) and reduced service pace during working vacation (WV) periods is a significant concern for the service and manufacturing organizations. This study presents a performance model for a queueing system functioning under CV, WV, and impatience behavior of the customers. The key contribution of the present study lies in the feature that the server has options to choose either CV or WV, immediately after finishing the jobs of all customers. Due to slow service during the WV period, the customers may renege from the system without getting the service. This paper derives transient analytical expressions for the queue size distribution by using continued fraction (CF) and probability generating function (PGF) approaches. Furthermore, the formulae for various performance indices viz. expectation and variance of the queue length, throughput, cost function etc. are obtained. The practical applicability of the concern queueing model is explored by numerical analysis. Additionally, a cost optimization is conducted to determine the least cost associated with the optimal service rate.
Keywords: Transient Queue, Vacation, Reneging, Continued Fractions, Cost Optimization -
Pages 86-107
This study introduces a novel Copula-Driven Framework (CDF) specifically designed to enhance the reliability and performance of serial systems, which play vital roles in various industrial processes. Effective operation of serial systems is paramount for overall system performance. However, traditional methods of reliability assessment and optimization tend to treat system components as independent entities, disregarding potential synergies and correlations among them. The CDF proposed in this research harnesses Copula theory to accurately model and analyze these interdependencies. It initiates by establishing a Copula-based dependency structure among the critical components of the serial system, considering both the strength and directionality of correlations. This comprehensive modelling approach enables the CDF to unveil hidden relationships that significantly impact system reliability and performance, often overlooked in conventional methodologies. By incorporating copula-derived dependencies, the framework facilitates the identification of the most effective repair policies aimed at optimizing system performance. This innovative approach offers a more holistic perspective, ensuring that critical interconnections within the system are accounted for, thus enabling more accurate reliability assessments and enhanced performance optimization strategies.
Keywords: Reliability, Maintenance, Serial, Redundancy