abdollah arasteh

The purpose of this research is to predict the daily demand for seven yogurt products from the Kale Amol Dairy Products Company, leveraging external influencing factors such as weather conditions, specific calendar days, and product prices. This forecasting is particularly crucial due to the perishable nature of the products, which have a high rate of deterioration.

This study employs a public dataset comprising 12 months of demand history from various food retail stores. Initially, the research uses the classic time series forecasting technique, specifically the Seasonal Auto-Regressive Integrated Moving Average with eXogenous factors (SARIMAX). Subsequently, it implements more advanced machine learning methods, including Long Short-Term Memory (LSTM) neural networks and Convolutional Neural Networks (CNN). The performance of these models is evaluated and compared using accuracy measurement methods such as Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE).

The findings reveal that the CNN method outperforms other forecasting methods in terms of accuracy. Additionally, the LSTM model also demonstrates good performance, although it is not as superior as the CNN method.

Originality/Value:

 This research contributes to the existing literature by focusing on the demand forecasting of perishable goods, which has double importance due to their high deterioration rate. It also highlights the growing interest and effectiveness of advanced machine learning techniques, particularly CNN and LSTM, in improving the accuracy of demand forecasts. This study provides valuable insights for businesses in the dairy industry aiming to enhance their economic profit and competitiveness through better demand forecasting.

E-commerce has transformed global retail, increasing demand for faster deliveries and straining urban logistics systems. This study examines e-commerce's growing challenges, including high operational costs, delivery delays, traffic congestion, air pollution, and electronic waste. Alternative delivery methods are one of the best ways to address these issues. Smart lockers are becoming popular for convenient delivery. If the right locations are chosen, this method can improve customer satisfaction, delivery times, and network performance. This paper seeks to design a smart locker network by locating the best facilities to solve last-mile delivery issues.

This study uses MCDM to determine smart locker installation locations. The Hierarchical Best-Worst Method (HBWM) weights criteria and sub-criteria, and the Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) ranks candidate locations. This method evaluates candidate locations based on convenience, accessibility, resilience, social, environmental, and physical factors. Validate and implement the approach with a Babol, Iran, case study.

The hybrid approach found the best Babol smart locker locations. The results also showed that convenience and accessibility are more important than physical characteristics when choosing locations. Installing smart lockers in optimal locations reduces delivery times and improves urban sustainability. Finally, shahriar shopping center was chosen as the best location for installing a smart locker network in Babol after evaluating criteria, analysis results, and candidate locations.

Originality/Value:

 This study uses the MARCOS method and the HBWM to calculate the weight of criteria and sub-criteria to select optimal smart locker locations, a novel approach in the literature. It addresses Iran's smart locker location issue for the first time. This research provides a practical solution to improve last-mile delivery logistics efficiency by considering optimal placement factors.

In recent years, there has been a very increasing trend in energy consumption worldwide, especially in the electricity sector, which, due to global sensitivities to protecting the environment and reducing greenhouse gas emissions, leads to the use of other non-fossil sources of electricity generation, such as renewable energy (wind, hydropower and solar) as well as nuclear energy. Nuclear power has been re-evaluated as a viable alternative to fossil fuels due to growing energy costs, climate change, and concerns about supply security. The present implementation of economies of scale has led to the development of a Light Water Reactor (LWR) design that lacks the ability to assess the strategic advantage and economic potential of contemporary nuclear systems. Real options analysis and portfolio theory are used to diversify and modify nuclear investments. According to this study, a single investor may make better selections for efficient and mindful nuclear energy development. The nuclear power industry received special attention. Multiple commercial and technology problems face huge, irrevocable investments. The fuel cycle's front and rear ends provide versatility. By leveraging the fuel cycle, many nuclear energy alternatives have the potential to provide economic benefits, hence expediting the shift towards more environmentally friendly energy usage. A wide range of operational and strategic considerations need the expanding use of mathematical models to help decision-making.

We aimed to identify the factors contributing to human error in hospital emergency departments using scientific methods.

We used the Fuzzy Analytical Network Process (FANP) and Success Likelihood Index Method (SLIM) to investigate human reliability in 54 hospital emergency departments in 15 provinces of Iran from 2021 to 2022.

The study classified 17 general factors affecting human errors in hospital emergency departments. Organizational (0.349), occupational (0.330), and personal factors (0.320) had the most significant impact on human error. Based on a matrix of paired comparisons for nine emergency tasks using the probability of success index method, "checking test results and diagnosis" had the highest probability of error when referring patients to intensive care or discharge. Although the study prioritized patients, there was still a cumulative probability of human error before disease diagnosis at 0.01332, highlighting the need for further training to minimize these risks.

The FANP and SLIM were effective in identifying the factors contributing to human error in hospital emergency departments. Doctors and nurses working in these departments require more knowledge, experience, and responsibility to avoid errors. By identifying factors influencing the occurrence of human error and finding solutions to reduce risks, hospitals can improve the quality of their care and prevent errors.

In today’s growing world, the Green Supply Chain (GSC) is a new approach to include environmental impacts and economic goals in a supply chain network. This paper continues previous research studies by designing a new green supply chain network considering different social, economic, environmental, service level, and shortage aspects. This study introduces a fresh, comprehensive tradeoff model that considers factors such as overall expenses, quality of service, environmental pollution levels, and societal impacts within a sustainable supply chain. The proposed model is formulated as a multi-product multi-objective mixed-integer programming model to assist in planning a green supply chain. The suggested model has three objective functions: maximizing social responsibility, minimizing the cost of carbon dioxide (CO2) emissions, and minimizing economic costs. The model allows for shortages in the form of backorders and seeks to maximize service level in addition to the mentioned objective functions. Robust Possibilistic Programming (RPP) was employed to deal with the problem's uncertain input parameters in the solution approach. Also, a multi-objective model of the problem was solved using Fuzzy Goal Programming (FGP). To examine and evaluate the model in a simple framework, the proposed mathematical model of the problem was implemented in an industrial unit in the real world, and the results obtained from it were analyzed. Among the results that the output of the model provides to managers and decision-makers, it is possible to mention the determination of the optimal amount of production of each product in the manufacturing plants, quantity of products and parts transported between facilities, and also the determination of the of network's carbon emissions which is equal to 51.59 tons.

Selection of the appropriate performance shaping factors (PSFs) is a vital challenge encountered by all experts in human reliability analysis (HRA) and plays a significant role in achieving reliable results. The main purpose of this research is to present a comprehensive set of staff PSFs that affect the emergence of errors and incidents in the healthcare system, referred to as healthcare- (H)-PSFs.

In this study, the set of H-PSFs was extracted using the PSFs presented for other fields and through consultation with HRA and healthcare experts. For investigation of whether the set of H-PSFs was comprehensive and appropriate, 318 reports of errors and incidents that had arisen at 14 hospitals during 12 months were examined, and the frequency values of each factor were obtained in two modes: where a PSF could and where it could not be repeated in an incident. The most significant H-PSFs were identified using Pareto charts. Also, using Minitab software, the Chi-square goodness of fit test was used to validate the proposed set of PSFs and demonstrate the appropriate accuracy of their frequency of occurrence.

According to experts, 43 PSFs were identified for the healthcare system. Using the medical error reports, the number of times that errors and mistakes were related to each PSF was calculated in two cases with and without repetition. To identify the most important PSFs, the Pareto principle has been used. According to the Pareto principle, 14 out of the 43 presented PSFs affected 80% of the errors and incidents that had arisen where PSF repetition was allowed, which amounted to 15 where repetition was not allowed.

The Chi-square goodness of fit test result showed that the proposed H-PSFs have sufficient validity to be generalized to other healthcare systems. The results of this article can be of great help to healthcare system managers so that they can make better decisions with the help of the results of this article in formulating general and healthcare system policies. Also, the findings of this study can be used in the healthcare system to analyze and improve human reliability, direct resources more efficiently to improve the performance of safety management systems, and reduce errors and incidents.

Many nations’ quick development and progress during the last century may be directly attributed to the widespread use of fossil fuels. Particularly, oil has stood out as a defining feature of human civilization. However, the increasing use of fossil fuels like oil and coal has led to serious problems for the world’s ecosystems, national security, and economic prosperity. This article uses actual options to determine the best time to invest in renewable energy based on diesel price volatility, electricity price volatility, and oil consumption externalities. Different actual choice approaches for discretion assessment are addressed and compared, as well as the usage of devolution for decision making. Finally, Monte Carlo simulations are used to compare these techniques to conventional approaches. The findings show that investments in renewable energy have a positive net present value. The timelessness of investing choices is emphasized by the real options method. Under the present energy system in Iran, switching to renewable energy sources is preferable than maintaining reliance on oil to provide power. Switching to renewable energy sources can help Iran reduce its reliance on oil and promote sustainable economic growth. Furthermore, can help to address the negative externalities associated with fossil fuel use, such as air pollution and climate change. Therefore, it is essential to continue to evaluate and promote the development of renewable energy sources in Iran and around the world. By increasing the cost of using oil or reducing the cost of electricity, policies should encourage investment in renewable energy sources.

In this paper, we offer an analysis and model of a manufacturing line that uses a priority mechanism to process various types of parts in faulty machinery. The manufacturing line comprises machines separated in a set order by storage rooms where components are fluxed. When it is possible, a machine works on the most important part first and only switches to less important parts if it is unable to produce the most important ones. Only one sort of function is required for each section. Because it is expected that the processing line machinery can handle a range of part types, switching from one kind of component to another will not result in any setup penalty. Only when unable to process higher priority parts owing to obstruction or hunger can the machines work on the lower priority parts. The machines function according to a fixed priority rule. The purpose of this study is to develop mathematical formulations and procedures for each kind of component in a flexible production line. In a variety of supply and demand scenarios, the multipart line's qualitative behavior is described.. To better understand the line, we devise decomposition equations and a solution technique to put them to use. With suitable line parameters, the method converges consistently. The findings of the decomposition were verified using simulations. The line's fascinating behavior may be seen in the system's study of many parameters.

This study proposes a multi-stage supply chain model with direct and reverse flows of goods to assess the effects of risk on the profit of a supply chain network and the realization of demand. The studied network aims to maximize profit, minimize unmet demand, reduce delivery time, alleviate disruption risks in facilities and transportation, and decrease supply chain visibility. We created a system for quantifying the disruption risk ratings of supply chain components. To help the company better understand its suppliers, address essential network components, and prioritize risk management initiatives, the evaluation may be useful. For our supply chain optimization models, we rely on the predicted disruption risk ratings as a basis. Goal programming is used to solve the multi-criteria model. The resiliency of the supply chain network is shown numerically. In order to build the model, the designer had to make strategic judgments. Risk mitigation methods such as extra inventory and backup suppliers are adopted to increase the supply chain network’s resiliency. Short-term disruptions may be mitigated by stockpiling additional raw materials to avoid component shortages. A cost-benefit analysis shows that every risk reduction strategy is worthwhile.

Supply chain management is one of the requirements for achieving economic growth in any supply chain. If managers' decisions are optimally allocated, it will be possible for companies and industries with a competitive and profitable advantage to grow and develop. The main desire of any company for survival is to minimize costs and maximize profitability. Due to the increasing complexity and dynamics of the situation, decision-making in this area requires more advanced analytical methods. Accordingly, the Real options theory has emerged, which introduces a new way of thinking about investing, especially in conditions of uncertainty. In this paper, a multi-period model is considered that examines the demand uncertainty in each period and also uses the Real options theory to seek the optimal strategy for investors in conditions of uncertainty and the effect of investors’ discretion on it. Using a decision tree to estimate the probable demand in each period and using Monte Carlo simulations to identify the lowest cost scenario in each period, the model has been solved in this research. In the case of the uncertainty parameter, sensitivity analysis is performed, and under different values ​​of this parameter, the obtained result is evaluated and validated. And the extension of outsourcing will increase the company’s profitability and meet higher demand and lower costs.

For a production business, the measurement of safety inventory to be maintained during each step of a supply chain is a key concern and requires providing the clients an irregular state of management. The stock held should be small to reduce holding costs and capacity while maintaining the capacity to service customers in time to satisfy much, if not all, the demand. This paper discusses this issue by using a deterministic time structure and provides a measure of the security position of stock in supply chains for the overall network. First, prove that the overall problem is NP-hard. Then set up a couple of parameters that characterize an optimal overall network structure. To take care of these problems, a polynomial approximation is considered. An arrangement of computational tests to survey the execution of the general-network calculation and to decide how to set different parameters for the calculation is selected. In addition to the general network case, the two-layer network issues are considered. Also, a nonlinear model for determining the level of safety stock in different components of the supply chain to minimize the related safety stock costs is developed.

The investment interests in the electricity industry are transmitted through various mechanisms to other economic activities. This paper considers methods for esteeming the adaptability of demand-side response (DSR) in its capacity to react to future uncertainties. The capacity to evaluate this adaptability is particularly critical for vitality frameworks speculations given their extensive and irreversible capital expenses. The primary result of this exploration is a broad survey of current real options (RO) strategies that elucidate the suppositions and use of RO for basic leadership in engineering applications. The second result is the structure of a probabilistic RO framework and operational model for DSR that evaluates its advantages as a vitality benefit for supporting diverse market price risks. The third result of this work is the improvement of a total, general and viable apparatus for making long haul multi-arranged speculation choices in future power organizes under numerous vulnerabilities.

This paper presents a structure that empower designing supervisory groups to survey the estimation of real options in projects of enormous scale, incompletely standardized frameworks actualized a couple of times over the medium term. Specific options writing is done using a methodology of planning the design and making prior decisions regarding the arrangements of specific options, with a recreation-based value measure designed to be near-current construction rehearsals and to resolve financial problems in particular cases. To study the case and demonstrate the actual application of this method, drug chain modeling at the tactical level was investigated. The physical and financial flow and their disturbance are simultaneously modulated. In order to complete the financial flow, financial ratios are also entered into the model. Problem uncertainty has been modeled using one of the most recent robust optimization approaches called Robust Possibilistic Programming (RPP) in combination with real options theory. The model was applied to a case study and its results were analyzed and validated by GAMS software. The results show that without violating the limitations of the problem, it shows appropriate decisions to deal with the problem.

The investment interests in the electricity industry are transmitted through various mechanisms to other economic activities. This paper considers methods for esteeming the adaptability of demand-side response (DSR) in its capacity to react to future uncertainties. The capacity to evaluate this adaptability is particularly critical for vitality frameworks speculations given their extensive and irreversible capital expenses. The primary result of this exploration is a broad survey of current real options (RO) strategies that elucidate the suppositions and use of RO for basic leadership in engineering applications. The second result is the structure of a probabilistic RO framework and operational model for DSR that evaluates its advantages as a vitality benefit for supporting diverse market price risks. The third result of this work is the improvement of a total, general and viable apparatus for making long haul multi-arranged speculation choices in future power organizes under numerous vulnerabilities.

The Department of Medical Services has a long-standing goal to improve the quality and operation of clinical management by efficient use of materials. The present undertaking structures and aspects of the continuing critical evaluation of current support operations in the Medical service department (MSD) -constraint semi-governmental emergency centers, including the re-appropriation and eventual management of temporary workers and services. The key test is to see how maintenance activities can be managed even more easily and conducted more cleverly. In this paper, a mechanism was developed to answer these questions: only with correcting the processes and at the lowest cost possible, how can we improve the maintenance operations?

For this purpose, current procedures and circumstances in 2 general hospitals in Mazandaran province in Iran are studied and data were gathered via questionnaire, interviews, and observation from 66 people including managers, supervisors, and employees. 56 questions were posed about 5 factors affecting maintenance management and their reliability was confirmed through the Cronbach's alpha method. To analyze these questions, a wide range of statistical techniques such as Spearman's rank correlation coefficient, Chi-squared test, Cramer’s V and Factor Analysis were used by SPSS software. Then for a more accurate survey, tools like Critical Success Factors, Key Performance Indicator, the theory of constraints & the Current Reality Tree are utilized.

Results have shown that effective factors in the improvement of maintenance management can be divided into 4 important principles called continuous learning, clarity in policies and procedures, encouraging innovation and balancing the number of maintenance employees with the work volume.

These four principles can form a useful framework with the lowest cost to improve the current maintenance management procedure in the hospitals which are under review in this paper’s case study.

This paper considers a popular problem in the investment, the best time and size of investment, using methods of real options in a cooperative game setting. Moreover, it shows a combination of real option theory to invest, combined with a competitive game between two movers in the growth of a general-use asset and cooperative game theory between two movers to catch a network effect. In the model, two firms have similar and interacting investment opportunities. There is a real option for both firms to postpone the investment until they have proper price and production states. There are benefits to a first mover who can create a facility to its own conditions. Also, there is a useful network effect of operating synergy if the first mover successfully motivates the second mover to start production instantaneously by sharing the production facility. So, the first mover has to discover when to create, what capacity to create and what the best economic rent is for using the facility. The second mover has to discover whether to use the first mover’s facility or create its own facility, and if it discovers to create it owns, what better time and size are.

In this paper, real options theory is utilized to evaluate the effect of uncertain electricity and CO2 costs on speculation conduct. Methodologically, the allegiance of the newspaper in this appreciation is that uncertainty is not just stopped down as far as stochastic processes and their fluctuation, additionally as far as expected and acknowledged procedures, i.e. the procedures, which are used as a constituent of the progression system, and the processes that the speculator really confronts when picking the choices as per his ideal methodology. We utilize the components of portfolio theory and consolidate them in a vintage setting, keeping in mind the end goal to conquer the lack of it and advantage from that focal point, while as yet having the capacity to think about element portfolios. The idea is to not just discover portfolios that augment returns subject to a predefined level of danger or the other way around keeping in mind the end goal to place the ideal system of innovations at a period in time, yet to decide the ideal means of advancement of such a portfolio after some time, given changing information costs and continuous mechanical advancement and exposure about these processes. In other words, we locate the ideal portfolio over advancements, as well as crosswise over time and quality.

Investments in technology create a large amount of capital investments by major companies. Assessing such investment projects is identified as critical to the efficient assignment of resources. Viewing investment projects as real options, this paper expands a method for assessing technology investment decisions in the linkage existence of uncertainty and competition. It combines the game-theoretic models of strategic market interactions with a real options approach. Several key characteristics underlie the model. First, our study shows how investment strategies rely on competitive interactions. Under the force of competition, firms hurry to exercise their options early. The resulting “hurry equilibrium” destroys the option value of waiting and involves violent investment behavior. Second, we get best investment policies and critical investment entrances. This suggests that integrating will be unavoidable in some information product markets. The model creates some new intuitions into the forces that shape market behavior as noticed in the information technology industry. It can be used to specify best investment policies for technology innovations and adoptions, multistage R&D, and investment projects in information technology.

Decision making plays an important role in economics, psychology, philosophy, mathematics, statistics and many other fields. In each field, decision making consists of identifying the values, uncertainties and other issues that define the decision. In any field, the nature of the decisions is affected by environmental characteristics. In this paper, we are considered the production planning problem in a stochastic and complex environment, i.e. the environment of electronic equipment production, where planning is done with variable results and changeable requests. In this complicated environment we are encountered with joint replenishment, variable yields and exchanging demands and many other complex variables related to inventory management and control systems that have complicated and unpredictable behavior and could not be simply modelled. We are trying to modelling this environment as a stochastic problem. The aim of mathematical model is managing and controlling inventories in such a complex production environment. We also try to solve this proposed stochastic problem by estimation procedures. The planning problem is devised as a gain-maximizing stochastic program. Also we use Arena and Matlab softwares to predict the behavior of production system in various situations. The results of these simulations are mentioned in the paper.

Acute hypotension episodes (AHEs) are one of the hemodynamic instabilities with high mortality rate that is frequent among many groups of patients. Prognosis of acute hypotension episodes can help clinicians to diagnose the cause of this physiological disorder and select proper treatment based on this diagnosis. In this study two groups of features, physiological and chaotic features, were extracted from the physiological time series to be applied for prediction of AHEs in the future 1 hour time interval. The best set of the features from the extracted features were selected using Genetic Algorithm (GA) and were classified by SVM. The prediction accuracy for physiological features was 87.5% and for chaotic features was 85%. In order to improve prediction accuracy, physiological and chaotic features were employed simultaneously in feature selection and the best combination of these features was selected by GA and classified by SVM. The best prognosis accuracy, which was achieved in this study by classification of the selected features, was 95% that was better than other previously studies on the same database.

One of an important factor in the success of organizations is the efficiency of knowledge flow. The knowledge flow is a comprehensive concept and in recent studies of organizational analysis broadly considered in the areas of strategic management, organizational analysis and economics. In this paper, we consider knowledge flows from an Information Technology (IT) viewpoint. We usually have two sets of technological challenges that prevent the knowledge flow efficiency in the organizations: the passive kind of present knowledge management technologies and the information excess problem. To get the efficient flow of knowledge, we need high exactness recommender systems and dynamic knowledge management technologies that automate knowledge transportation and permit the management and control of knowledge flow. In this paper, we combine and make upon the information management systems and workflows presented in literature to generate technologies that address the serious gap between current knowledge management systems. Also, we propose a knowledge management framework for educational organizations and use this framework in a real situation and analyze the results. The weakness of knowledge flow infrastructure is one of the most important barriers to knowledge sharing through an organization. The proposed technology in this paper provides a new generation of knowledge management systems that will permit the efficient flow of knowledge and conquest to the technological constraints in knowledge sharing across an organization.