Purpose: The purpose of this research is to design a perishable crops supply chain network. Furthermore, the disruption risk in the production sector and the protection strategy are considered to increase the reliability of the network. The goal of the proposed model is to maximize the total profit of the supply chain while considering the time to send the products to the consumers.Methodology: In this research, a mixed-integer non-linear programming model is proposed to optimize crops supply chain network design. Moreover, a real case study has been implemented in Iran to prove the applicability of the addressed model.Findings: The results and sensitivity analysis of the mathematical model indicate that by planning the planting and harvesting While optimizing the chain's profit, it is possible to respond well to society's product demand. Moreover, considering the disruptions and protection of products brings us closer to real-world conditions and reduces the amount of waste and related adverse environmental effects.Originality/Value: Agriculture is one of the most important and influential sectors in the economy of any country, it plays a vital role in its political and economic independence. The growing global demand to provide food for the human population has caused a significant increase in cultivation. However, the amount and time of production should be consistent with the amount of product demand and avoid a shortage or excess of products. Considering that there is currently no comprehensive planning for the production of perishable crops in the country, the necessity of studying supply chain models in the agricultural sector and specifically the production and distribution of perishable crops is created. In this connection, the design of the supply chain network of the mentioned products from planting to distribution is presented in this study.
Purpose: This study examines the accuracy of Heterogeneous Autoregressive (HAR) models in forecasting the Conditional Value-at-Risk (CVaR) of Exchange-Traded Funds (ETFs) on the Tehran Stock Exchange. The significance of this study stems from the need for better risk management in financial markets, where volatility and jumps significantly affect investment decisions.Methodology: Data from nine equity, index, and fixed-income funds were analyzed intraday with high frequency (daily and fifteen-minute intervals) from 2019 to 2022. Three main families of HAR models were evaluated by considering the relevant variables.Findings: The results revealed that models based on second-order variations outperformed others in forecasting Realized Volatility (RV). Additionally, CVaR prediction was more accurate for index funds than for equity and fixed-income funds, with the HARQ model demonstrating superior performance.Originality/Value: This study investigates the application of HAR models in predicting ETF risks and provides a novel framework for risk management and investment decision making, particularly in the Iranian financial market.
Purpose: This study aims to investigate the potential of chaotic optimization algorithms in improving performance compared to other optimization methods, focusing on determining the appropriate shape parameter of radial basis functions for solving partial differential equations.Methodology: In this research, a two-stage process is employed where the Kansa method, based on meshless local techniques, is combined with the FCW method. In the first stage, the FCW algorithm is utilized to obtain the optimal shape parameter for radial basis functions, followed by the Kansa method in the second stage to estimate the Root Mean Square (RMS) error for approximate solutions.Findings: Numerical results indicate that approximately 95% of the results obtained from two partial differential equations using PSO and FCW algorithms are similar. These results demonstrate the effectiveness and efficiency of this approach in estimating appropriate shape parameters for solving differential equations.Originality/Value: This study confirms the importance of chaos-based optimization algorithms in solving partial differential equations, which can contribute to future research in this field.
Purpose: Governments today are increasingly focused on managing environmental and economic challenges to safeguard the environment. Consequently, companies are striving to remain competitive in society. This paper introduces a revenue-sharing agreement between the retailer and the manufacturer. The contract is designed to alleviate the research and development costs incurred by the manufacturer in greening their products. Additionally, the paper incorporates consumer surplus as a consideration for the social dimension, which is a key pillar of sustainability.Methodology: Government interventions in this paper are modeled as subsidies that influence the final price of the product. Notably, this study, for the first time, integrates all three dimensions of sustainability—social, economic, and environmental—within a game-theoretic framework that includes government intervention and a revenue-sharing agreement between the retailer and the manufacturer.Findings: The modeling results demonstrated that incorporating consumer surplus enhances the profitability of both the government and the manufacturer. Although consumer surplus reduces wholesale prices and revenue-sharing proportions, it leads to increased product sales. This boosts the benefits for consumers and members of the supply chain, contributing to improved greenness and higher levels of product sustainability. Consumers show a preference for purchasing products with greater environmental benefits; however, higher levels of greenness require increased production costs. Manufacturers can boost their sales through research and development efforts and by raising public awareness about green products. As a result, manufacturers, retailers, and the government adjust prices and subsidies to maximize their respective profits, altering the overall profitability of the supply chain. The findings highlight that social welfare is a crucial consideration in supply chains, as it significantly enhances the profitability of chain members and promotes higher levels of green production.Originality/Value: This paper is the first to examine all dimensions of sustainability—social, economic, and environmental—within a theoretical framework that incorporates government intervention and the impact of consumer surplus on pricing policies and the greening of products. The scientific contribution of this research lies in providing new insights to managers and policymakers to enhance strategic decision-making aimed at improving sustainability and social welfare within green supply chains.
Purpose: Making decisions to choose stocks and forming an accurate portfolio are always anxieties for investors. The primary purpose of this study is to evaluate and compare portfolios based on the fundamental strategies PE, PEG, PERG-SD, and PERG-Beta and find the best strategy for investing. Another goal of this research is to determine whether the strategies above can be superior to the TEDPIX index in the long term.Methodology: Our evidence includes 362 companies listed on the Tehran Stock Exchange from 2016 to 2023, and 110 have been selected as a research sample. The investment horizon in this study is three years (2021–2023), and investing is seasonal in the form of 12 seasons according to the mentioned strategies. The variable's value (strategy) is calculated for each strategy at the beginning of each season. All the companies are sorted from top to bottom based on the calculated variable (strategy) and divided into five equal categories. Then, for each portfolio category, investing was done at the beginning of the season. At the end of each season, the return and systematic risk of each category of portfolios were calculated. Finally, based on the risk and return of each portfolio, research hypotheses were tested using statistical tests (Wilcoxon and Friedman) and SPSS software.Findings: The results showed that the return of portfolios formed by low coefficients (LOW) is higher than the return of portfolios formed by high coefficients (HIGH) and the TEDPX index in all four strategies: PE, PEG, PERG-SD, and PERG-Beta. Also, based on Friedman's test, the low PE strategy has the best performance compared to other strategies.Originality/Value: This research fills a gap in the existing literature by organizing companies into five categories for each strategy, balancing portfolios seasonally, and analyzing the PERG strategy based on the nature of risk adjustment (systematic risk or total risk). These aspects have not been explored in previous internal research. Calculating the growth rate and systematic risk to evaluate these strategies differs from previous research. Moreover, no internal research has recently been conducted on these strategies, particularly during the research investment horizon (2021-2023). Given our country's ongoing political and economic changes (such as inflation, interest rates, the Corona pandemic, and the stock market crash in 2020), this research is crucial to confirm or challenge past findings in the highly dynamic investment environment.
Purpose: In this research, a modular hub location problem has been investigated where the objective is to reduce the transportation costs in the hub network. The proposed model determines the location of hubs, allocation of the non-hub nodes to the hubs, and the optimal vehicle traffic, i.e., the number of flights or the number of trucks traveling in the network, considering the appropriate capacity for each vehicle. Also, decisions regarding the percentage of the traffic volume sent via multiple network routes are made by the presented model.Methodology: The mathematical model, including the objective function and constraints, is constructed and solved by GAMS software. The effect of different parameters on the results is investigated. Due to long solution times for the MIP model, a heuristic solution method based on LP relaxation of the integer variables is developed for the proposed problem, which is able to obtain near-optimal solutions in less time.Findings: The developed mathematical model is implemented on the air passenger transportation data for the airports of the United States of America, which is known as the CAB data set. The results give the optimal number of hubs, as well as the optimal number of transportation units on each arc of the network, which depend on the capacity of the means of transportation.Originality/Value: In this research, a mixed integer programming model is developed for the multiple allocation modular hub location problem. Numerical experiments are conducted with the use of GAMS software and the results are discussed.
Purpose: This paper aims to investigate the proposed mathematical model using the optimal control strategy to prevent the spread of the disease on the model. For this purpose, an optimal control problem with the objective function is introduced, and vaccination and treatment are considered control variables.Methodology: In this study, the necessary control conditions and the existence of optimal control are expressed by applying the control to the SIR-differential equation system. The experimental results are compared with those obtained from the fourth-order runge-kutta scheme. It should be noted that the proposed model is a public model suggested for contagious diseases and can be used as a method to prevent the spread of diseases such as influenza, coronavirus, and other infectious diseases.Findings: Numerical simulation, considered in a 90-day period, shows that using appropriate optimal control of vaccination and treatment will limit disease transmission and reduce the number of infected and infected people. The number of improved people also increases.Originality/Value: Vaccination and treatment are two controls that may be used to control the spread of disease in society. Therefore, the results are analyzed from a mathematical point of view by applying control over the model, which is considered in two cases of vaccination and treatment.
Purpose: Generally, selecting an investment portfolio with appropriate returns that is also secure and auditable has been one of the issues raised in recent decades. For this purpose, the present research proposes an appropriate approach using ideal and anti-ideal values, ideal values, as well as maximum deviations of each objective, considering the sample in the examined market, fuzzy goals, interval fuzzy values for each asset, and their combination with satisfaction functions, fuzzy ideal planning, and weighting objectives using expert decision-makers' opinions, as well as the development of fuzzy basic weighting method. It seeks to select an investment portfolio in the digital currency market.Methodology: In this research, a new approach to selecting an investment portfolio based on uncertain data and multi-objective uncertain planning is proposed, and ultimately, the proposed approach is implemented in the digital currency market for portfolio selection.Findings: The results of the present study show that the proposed model of investment portfolio compared to the base model not only led to higher returns but also had higher audibility and better risk control. In other words, the proposed model outperformed the base model in all the objectives under study.Originality/Value: As distinguishing features of the proposed model of this research, one can mention: 1) constructing and using fuzzy distribution functions and calculating ideal values and expected ranges for all desired objectives considering the conditions of the examined market research using simple mathematical modeling, 2) utilizing the experience of financial market experts in planning model for selecting suitable investment portfolios in emerging financial markets, 3) presenting an approach to calculating portfolio risk in conditions of information scarcity in the problem environment using fuzzy theory, 4) development of the fuzzy benchmark-criterion method for weighting the objectives under study in the problem considering the expertise of financial market experts, and 5) simple modeling, considering interval fuzzy values in the model, and being usable for all individuals with different levels of investment knowledge.
Purpose: The cost-time tradeoff in project scheduling is a significant challenge that has garnered substantial attention. This research aims to establish a balance between time compression and delays in activity execution to optimize resource utilization and facilitate activity scheduling based on existing constraints. To achieve this, a bi-objective mathematical model is developed to support decision-makers in selecting the optimal schedule for project execution.Methodology: In this study, a bi-objective mathematical model is proposed to balance cost and time, incorporating a nonlinear cost function and the time value of money. The model is then solved using the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm to analyze the effects of time compression and activity delays on the outcomes.Findings: The proposed model's results demonstrate its ability to optimize project resource usage by considering current constraints and capacities. This enables decision-makers to adjust activity scheduling to achieve the best balance between cost and time. Furthermore, the model assumes costs are nonlinear and calculated based on the time value of money. This approach allows for scheduling decisions that account for the time value, reducing delay-related costs and enhancing overall project efficiency.Originality/Value: This research presents a novel approach by introducing a bi-objective decision-making model that integrates the time value of money in project scheduling, marking a new step in optimizing the cost-time tradeoff. Unlike previous studies that focus solely on reducing either cost or time, the proposed model provides a comprehensive solution by accounting for nonlinear complexities and the time value of money. This model assists project managers in gaining better insight into cost-time impacts, optimizing resource allocation, and ultimately improving project performance by reducing delays.
Purpose: This research aims to construct a portfolio based on risk-adjusted performance and distribution-based returns and determine the efficiency using the Data Envelopment Analysis (DEA) approach. In this study, the role of return distribution in the efficiency of risky assets is also examined to form a diversified portfolio consisting of assets with varying degrees of performance.Methodology: In this study, the diversified portfolio performance of 28 firms during 1398-1402, based on the risk-adjusted value and conditional risk-adjusted value obtained from the probability distributions of returns, was compared with the minimum-variance Markowitz portfolio performance in terms of the Sharpe ratio. After estimating the maximum likelihood parameters of the model, the risk values for each stock were calculated based on the empirical return distribution, the Cauchy distribution, and the normal distribution. These risk values were then used in the data envelopment analysis to calculate the efficiency scores of each company.Findings: The diversified portfolio with stock performance degrees outperforms the minimum-variance Markowitz portfolio in terms of risk-adjusted and conditional risk-adjusted values. The probability distribution of returns leads to different results in calculating stock risk-adjusted value/conditional value, with the empirical return distribution and normal distribution providing a more desirable performance (in terms of the Sharpe ratio) compared to the Cauchy distribution and sample ratios.Originality/Value: In the literature, an efficient portfolio is usually formed by calculating asset weights in the stock basket so that the Sharpe ratio reaches its maximum value. In the current study, this hypothesis is challenged in favor of the proposed method, which estimates portfolio weights based on the efficiency of risky assets.
Purpose: Like other sub-disciplines of humanities, scientometrics is influenced by significant factors leading to Belief in Favorable Future (BFF). Education in the field of scientometrics should always strive to prolong its survival and guarantee its evolution in the coming years. Hence, the excellence of scientometrics requires constant and undivided attention to both external and internal contributing factors. The principal objective of this study is to recognize the underlying trends and driving forces influencing the future of scientometrics education in Iran.Methodology: A meta-analysis of contingent surveys is conducted to serve the purpose of practicality. The expert panel is comprised of 15 professionals in knowledge and information science. Subsequently, a researcher-constructed questionnaire was distributed among professionals.Findings: The research findings demonstrated that the driving forces influencing the future of scientometrics education in Iran could be categorized into eleven general indicators. Seven internal indicators include philosophy and the model of higher education for scientometrics, educational forces, curriculum, services and facilities, information and communication technology, academic level of universities, and specific field challenges. Four external indicators include sociological, economic, information technology, policy-making, and higher education system management. Altogether, both internal and external factors are sub-categorized into 57 items.Originality/Value: Due to inconvenient circumstances of scientometrics education in Iran, policymakers and managers of the Iranian higher education system are duly required to address the need to transform the ongoing trends and functional driving forces in the field of scientometrics.
Purpose: Iranian universities have undergone many changes in recent years, which have led to more attention being paid to innovative development and changes in the main function of universities. In an era when university managers face new challenges such as fleeting changes and opportunities, uncertainty and disorder, having a strategic approach can help managers focus on distant horizons and identify opportunities and advantages as well as coherence. The activities of the university helped to clear the path of collective goals. For this reason, most universities have prepared a strategic plan to recognize and solve problems, identify strengths and weaknesses, make optimal use of opportunities and situations, and master the threats that endanger the university's existence. One of the main sources for realizing the strategic plan is financial resources, which the university often faces with a lack of funds. In addition, sudden economic shocks add to the instability and make it necessary to use the university's financial resources effectively.Methodology: A mathematical optimization model of the type of integers is presented for the management and optimal allocation of financial resources according to the performance and effectiveness of universities' scientific activities.Findings: This model has been implemented to realize nine different activities in the strategic plan of Allameh Tabatabai University, separated by faculties. These nine activities include 1) publishing a book, 2) publishing an internal article, 3) publishing an external article, 4) holding a theory session, 5) holding a meeting to solve problems and a promotional session, 6) implementing educational plans, 7) holding internal and external conferences, 8) holding internal and external workshops and meetings, and 9) other activities.Originality/Value: Implementing this model and performing sensitivity analysis on its important parameters will bring acceptable and effective results for the support of faculty officials as well as managers of the university's strategic plan.
Purpose: The present study aims to identify the most important variables affecting the fluctuations of gold prices. It is the first Iranian research in which the fluctuations in this market are modeled using non-linear Bayesian Model Averaging (BMA) and deep neural network approaches.Methodology: It is applied research where monthly data collected from 2010 to 2022 were used. It evaluates 35 factors playing a role in gold price fluctuations. GARCH and random fluctuation models are used to extract gold price fluctuations. TVPDMA, TVPDMS, and BMA models are used to identify the most important variables causing gold price fluctuations. Furthermore, the deep learning approach is used to investigate how effective the selected variables are in gold price fluctuations.Findings: The results indicated that Support Vector (SV) models were more accurate than GARCH models in capturing fluctuations and that BMA outperformed TVPDMA and TVPDMS. Additionally, 12 variables were identified as influential in gold price fluctuations, with in-market factors playing a more significant role than out-of-market factors. The study also employed Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Multi-Layer Perceptron (MLP) neural network models in deep learning mode to predict gold price fluctuations. It was concluded that global interest rates had the most significant impact on fluctuations in the gold price, with the Pivot Point DeMark's Index making the greatest contribution.Originality/Value: The gold market is known for its volatility, and accurate predictions about its future can significantly impact decision-making. Understanding the gold price and making correct forecasts can help inform decisions about buying and selling gold in global markets, and determine the most favorable times for transactions and investments. Therefore, it is crucial to accurately predict the gold price from various perspectives. This research attempted to develop an intelligent model for forecasting fluctuations in the gold price.
Purpose: Most research in the field of designing and planning bioethanol supply chains has been based on deterministic models, which do not consider dynamic environmental conditions and thus do not provide reliable outputs. Classic robust models did not have this weakness, but due to their excessive conservatism, they increased supply chain costs, making them unattractive to investors. Therefore, the aim of this study is to design and optimize the biomass-to-bioethanol supply chain network using data-driven robust optimization methods and disjunctive uncertainty sets.Methodology: The methodology of this study is a multi-methodology approach based on mathematical modeling and machine learning algorithms. Initially, uncertainty sets for the non-deterministic model parameter were created using K-means and SVC methods. Then, a data-driven optimization model was designed to optimize the biomass-to-bioethanol supply chain network, addressing the issues of previous classic approaches.Findings: The findings of this study are presented in two categories: strategic and operational decisions. The strategic section focuses on determining the optimal locations for biomass cultivation, preprocessing centers, and refineries. In the operational section, the optimal amounts of biomass sent to preprocessing centers and refineries were determined.Originality/Value: This study, by producing robust solutions without the conservatism of traditional robust optimization approaches, can significantly attract public and private sector investors. Additionally, using a three-objective model based on a sustainable development approach that simultaneously considers economic, social, and environmental components, enhances the comprehensiveness of this research, providing more realistic and detailed results.
Purpose: One of the most effective methods for solving unconstrained optimization problems is the trust region method. The strategy of determining the radius of the trust region has a significant effect on the efficiency of this method. On the other hand, imposing the monotonicity condition will decrease the convergence speed of this method. Therefore, improving and increasing the efficiency of this method is one of the most important issues and the attention of researchers.Methodology: Establishing a new adaptive trust region radius as well as combining the trust region method with a non-monotone strategy to avoid the adverse effects of monotonocity.Findings: A new adaptive trust region radius converged to zero is provided, and then a trust region combination is performed using a non-monotone strategy. Running the algorithm on a set of test functions shows that the new adaptive radius, along with the non-monotone strategy used, significantly improves the efficiency of the trust region method.Originality/Value: The presented non-monotone adaptive algorithm has a second-order convergence rate. In addition, it significantly reduces computational costs compared to traditional algorithms. On the other hand, the new adaptive radius avoids the ineffectiveness of the trust region close to the solution.
Purpose: This research presents an application-oriented approach for developing machine learning models that consider the trade-off between model accuracy, processing speed, and efficient resource utilization, focusing on applications such as wearable smart systems.Methodology: A set of models is developed based on the Abstraction and Decision Fusion Architecture (ADFA), and then, using a multi-criteria decision-making approach, the appropriate models for the intended application are identified. The proposed methodology has three main phases: 1) developing models based on the ADFA, 2) defining evaluation criteria, and 3) selecting models using the Fuzzy Analytic Hierarchy Process (FAHP).Findings: The experimental results of this research demonstrate the effectiveness of this approach in developing suitable machine learning models for applications related to wearable devices, such as smart glasses.Originality/Value: This research introduces three innovations: 1) the use of ADFA for developing models for the classification of Persian handwritten characters, 2) defining a new abstraction for summarizing handwritten character images, and 3) developing a fuzzy multi-criteria decision-making approach for mapping the developed models in the ADFA to real-world applications.
Purpose: This research aims to optimize humanitarian logistics to increase coordination between actors in the phase during and after the disaster and aims to minimize the cost of relief, minimize the time of relief and minimize the cost of rebuilding infrastructure and housing for the affected people.Methodology: This research, in terms of the research direction types, is developmental because it is trying to expand the existing models in the design of the humanitarian logistics network and consider the optimization of two phases during the post-disaster phase. The proposed model has been solved using two metaheuristic algorithms named multi-objective genetic algorithm and multi-objective particle swarm optimization.Findings: The implementation of this study will lead to a reduction in the costs of locating, routing and reconstruction in the humanitarian supply chain, as well as reducing the time of providing aid to the affected people and increasing their satisfaction. It is also possible to reduce the inventory of relief products with the help of this issue. Appropriate planning in humanitarian logistics processes, especially in the coordination phase of reconstruction, will be done according to the limited budget of governments and the appropriate use of resources.Originality/Value: One of the innovations of this study is reducing the cost of reconstruction after an earthquake. Several studies were conducted in order to recover from the disaster. Over the past two decades, response phase relief operations have been the focus of a significant number of researchers. However, the issue of post-disaster recovery and reconstruction programs has not been sufficiently discussed in scientific and practical forums.
Purpose: The COVID-19 pandemic has led to a significant crisis in society's health, industries, and businesses. In this regard, the medical devices industry has played an important role in crisis management and providing healthcare and has faced several major challenges in supplying raw materials, production activities, and distribution activities due to the disruptions caused by the pandemic. One of the critically important issues in the medical devices supply chain is supplier selection. Hence, this research investigates the supplier selection problem considering the emerging concepts that have dramatically attracted the attention of researchers after the COVID-19 pandemic, namely viability and Industry 5.0.Methodology: In this study, a hybrid fuzzy decision-making approach is developed to investigate the viable supplier selection problem considering the Industry 5.0 dimensions. In this regard, in the first stage, according to the literature and experts, the main indicators of the research problem are extracted, and their weights are calculated using the fuzzy best-worst method. In the next stage, the feasible suppliers are evaluated by employing the fuzzy VIKOR method. Also, to show the robustness and validation of the proposed approach, its results are compared with those of traditional approaches.Findings: In this study, a list of indicators, including six aspects and 34 criteria, is provided for the research problem based on its nature, and their importance has been computed. Based on the outputs, the general metric is the most important aspect, and the human-centricity metric is the least significant. Also, the results show that in addition to the general criteria, such as cost and quality, other criteria, such as reliability, technical capability, pollution control, risk reduction, and service, also play a significant role in the process of selecting suppliers. The results indicate that managers in today's competitive and industrial markets should redirect their attention from traditional criteria to those contributing to the sustainability and improvement of their systems' performance.Originality/Value: Reading the results of this research can help Industrial and organizational managers evaluate the potential suppliers of their companies based on the viability and Industry 5.0 dimensions and select the best ones, which can significantly improve the performance and efficiency of their businesses.
Purpose: In this paper, we present optimal single acceptance sampling inspection plans for Inverted Nadarajah-Haghighi distribution so that the consumer’s and producer’s risks are controlled simultaneously.Methodology: Nonlinear optimization program is used to obtain the optimal sample size and acceptance number as well as the associated consumer’s and producer’s risks.Findings: Optimal sample size and acceptance number are obtained in generalized half-normal distribution. Two real data sets are given for illustrating the results.Originality/Value: Two-point method is used to find the best plan by using nonlinear programing optimization problem. All computations are performed by the R software.
Purpose: One of the issues faced by the managers of production and commercial organizations, which generally requires making challenging decisions with high risk and fast, is removing the product. Removal of a product for various reasons such as its life cycle status, entry of similar products and loss of market, lack of technical and economic justification for continuing its production and supply or the decision to enter newer products, product diversity, competition, activities of companies in the innovative environment. The weakness of the product is placed on the agenda of an organization. This research aims to "investigate the factors that determine the decision-making speed in the process (adoption and implementation) of the decision to remove the product in the cosmetics industry.Methodology: This was applied research using a descriptive-analytical design, and a survey method; the study was causal in nature. The questionnaire validity using validity and factorAnalysis and reliability was approved by reported of total Cronbach's alpha coefficient (0/834). The study population included the managers and administrators of Zarrin Cellulose Saveh Company in 2016. Suitable descriptive and inferential statistics, and SPSS and PLS, were used for data analysis.Findings: The findings suggest that structural-temporal characteristics, decision-specific factors and environmental have a significant effect on the speed of Product Elimination Decision-Making (PEDM) process. The PEDM decentralization at the tactical level, centralization of PEDM power, polychronicity, and environmental turbulence have a positive and significant effect on the speed of PEDM process. The polychronicity reinforces the positive effect of decentralization at the tactical level on the speed of PEDM process. The product significance in the company's image and history and environmental complexity has a negative and significant effect on its speed. The speed of PE decision process for products in their birth and growth stage is more than those in their maturity and decline stage. The environmental complexity weakens the positive effect of polychronicity on the speed of PEDM process. Environmental turbulence weakens the negative effect of environmental complexity on the PE decision-reaching speed, but has no effects on the PE implementation speed.Originality/Value: The output of this research is to determine the effective factors in the speed of decision-making in the process (adoption and implementation) of the product removal decision in the cosmetics industry.
