نشریه تصمیم گیری و تحقیق در عملیات
سال هشتم شماره 2 (تابستان 1402)

In this research due to the importance of the U-shaped assembly line balancing and, on the other hand, the importance of human factors and setup times, we want to develop a bi-objective mathematical model minimize the cycle time and the total cost.

Since the research problem is shown to be NP-hard, NSGA-II, which is a population-based algorithm, and also SPEA-II are used to solve the problem.

A mathematical model for the problem on hand is developed. We solve the problem using NSGA-II and SPEA-II. We use four criteria for analyzing the results of the mathematical model and evaluating the performance of the multi-objective evolutionary algorithms. The experimental results demonstrate that NSGA-II is superior to SPEA-II.

Originality/Value: 

A bi-objective mathematical model for the U-shaped assembly line balancing problem considering setup-times and workers' skill is developed, and the problem is solved using two algorithms.

Lot streaming, which has much attention in recent years, is an effective technique to increase production efficiency in a production system by splitting a job into several smaller parts in a multi-stage production system. But important assumptions that exist in the real-world scheduling environment are always ignored. Hence, in this paper, these assumptions are discussed and the results are reviewed. In this paper, the aim is solving a multi objective mathematical model for solving hybrid flow shop scheduling problem with lot-streaming, setup time and transportation time.

At first, a multi objective mathematical programming model is presented for solving the problem. Then, by wighting method, the multi objective model convert to single objective model and GAMS software is used to solve the small size problems to show the performance of the mathematical mothel. Inspired by previous studies, two multi objective metaheuristic algorithms based on the genetic algorithm is used to solve the large-scale problems. To illustrate the performance of the proposed metaheuristic algorithms, the obtained results of the algorithms compared with GAMS outputs in single mode.

To validate the proposed model, a sample is solved using GAMS software and compared with the genetic algorithm. The obtained results show the performance of the mathematical model. Then, two proposed algorithms are used to solve the large-scale problems. For this purpose, 30 instance problems are randomly generated and six indicators are used to compare the algorithms. After performing the experiments and comparing the algorithms with each other, the results show NRGA algorithm performs bether than NSGA-II.

Originality/Value:

 In this paper, for solving a multi objective hybrid flow shop scheduling problem with lot-streamingm mathematical model with the aim of minimizing the makespan and total tardiness, the sequence-dependent setup time and the transportation time constraints between consecutive stages are considered. Since the problem is NP-hard, NSGA-II and NRGA algorithms were used to solve the proposed problem.

Nowadays, robotic systems are widely used in advanced industrial operations. Therefore, making appropriate control decisions to ensure the efficiency of these systems is critical. Criteria such as operation time and response speed, control cost, and system error need to be controlled by providing appropriate methods to ensure the successful performance of industrial operations. Therefore, this article pursues two main objectives1) controlling the robotic system by presenting a method based on fractional-order calculus so that it can control the system despite its complexity and non-linearity, 2) presenting the meta-heuristic algorithm "Improved Grey Wolf" to optimize the system response.

First, the mathematical model of the robot is presented based on Lagrange rules, and then the fractional-order calculus is used to design the controller. In addition, the efficiency of the grey wolf algorithm is increased with the introduction of an improved method.

Different cost functions based on the main performance criteria of the robotic system are introduced, and an improved algorithm is applied to them. The comparison results of the proposed algorithm and other algorithms, indicate its satisfying performance. In addition, the efficiency of the fractional-order controller is compared with its integer-order counterpart, and the results show a significant improvement in system performance.

Originality/Value: 

The proposed controller can control the system well despite its complexity and non-linearity. In addition, inspired by the Grey Wolf algorithm, an improved optimization method is proposed that can increase the efficiency of the controlled system. Numerical results show the satisfying performances of the proposed controller and the improved optimization algorithm.

Mortality is a dynamic process that completes over time and is a fundamental issue in life insurance, pension fund, health insurance, and in general any issue related to financial planning that deals with the longevity of individuals. Therefore, the accuracy of mathematical models in predicting mortality rates is an important challenge. The purpose of this study is to generalize static stochastic mortality models to dynamic stochastic mortality models and to predict mortality rates based on the generalization of stochastic mortality models by the Cox-Ingersoll-Ross (CIR) process and to compare the results with each other.

In this research, two suggestions are presented: the first idea is to provide a dynamic correction method to increase the prediction accuracy using the CIR process and the second idea is to examine the out-of-sample validation method.

In this study, using the out-of-sample validation method, the force of mortality from the best models selected from the two famous mortality model families (Lee-Carter and Cairns, Blake and Dowd (CBD)) is compared with the results of the generalized model. After estimating the parameters of the studied models and calculating the prediction of the mortality rates, by calculating the mean absolute error and root mean squares error of prediction, it is determined that the generalization of stochastic mortality models by the CIR process performs much better than static mortality models. The Bayesian information criterion also indicates that the use of generalized stochastic mortality models is justified.

Originality/Value:

 In this study, stochastic mortality index models, which include Lee-Carter and Cairns-Blake-Dowd family models, are used and generalized by the CIR process. In this regard, Human Mortality Database (HMD) data is used. But there is no information about our country in this database. Because the French mortality pattern is very close to the Iranian pattern and the life tables of this country (TD 88-90) are used in Iranian insurance applications, the crude death rate of French men in the years 1900-2018 on the ages of 18, 40 and 65 years is used. Using these data and the backtesting method, static mortality models and generalized models with the CIR process are compared.

Location-Routing Problem (LRP) is a strategic supply chain design problem aimed at meeting customer demands. LRPs involve selecting one or more depot sites from a set of potential locations and determining the best routes to connect them to demand points. With the rising awareness about the environmental impacts of transportation over the past years, using green logistics to mitigate these impacts has become increasingly important.

To compensative a gap in the literature, this paper presents a robust bi-objective Mixed-Integer Linear Programming (MILP) model for the Green Capacitated Location-Routing Problem (G-CLRP) with demand uncertainty and the possibility of failure in depots and routes.

The final result of this robust multi-objective model is to set up the depots and select the routes that offer the highest reliability (maximizing network service) while imposing the lowest cost and environmental pollution. The paper also provides a numerical analysis and a sensitivity analysis of the solutions of the model.

Originality/Value: 

Determining backup depots and increasing network serviceability for LRPs.

The newsboy problem is one of the most widely used and important models in the field of inventory control. In fact, there are many industries whose products fall into the category of newosboy problem, such as seasonal goods, food products. But in practice there are more restrictions than the assumptions of the newsboy problem. In this article has tried, in order to make the problem more in line with the real world some limitations during production, such as outsourcing mode, capacity limit and returned goods, have been added to the problem.

This research is based on library studies and the development of mathematical modeling.

In the newsboy problem, there are important parameters such as outsourcing, capacity constraints and product referral which in this study, the effect of each of these parameters on profitability has been evaluated.

Originality/Value:

 Development of the newsboy problem in terms of outsourcing and returned products and capacity constraints.

In Islamic architecture, using arches to build dome-shaped buildings has been very common. So, the research on building the domes of shrines and mosques is undoubtedly directed at studying the arches of those buildings. In this article, we will investigate and geometrically model the domes from the perspective of differential geometry and as a rotating surface. We try to present the scientific connection between the art of architecture and differential geometry in a way that interests experts in both architectural and mathematical trends.

In architecture, the dome is the product of a productive cycle around the vertical axis. This interpretation is equivalent to the definition of the rotating procedure (generating curve) in the subject of differential geometry. Special methods can obtain the generator curve. At first, according to the drawing method, we parametrize half of the arch in the Euclidean xoz plane according to the length of the dome opening and then rotate the resulting curve (or the generating curve) around the vertical z-axis. The method of conducting this research is quantitative and includes calculations related to the types of domes, and its type can be considered descriptive research.

We found a significant link between the mathematics that governs domed buildings and the productive arch.

Originality/Value: 

Considering the multitude of types of arches in architecture, in this article, after stating some necessary definitions of differential geometry, in addition to presenting the method of drawing each arch, we will only bring the calculations related to the types of arches with legs, horned goats, five-o-seven and shamrocks. Ultimately, we will implement our calculations on the dome of Juma Mosque in Ardabil.

This paper aimed to design a robust blood supply chain model that includes the stages of collection, processing and distribution of blood and blood products taking into account the lifespan and age of demand, which seeks to reduce supply chain costs and reduce the shortage and waste of blood products.

In this paper, MINLP method is used to model the research problem and in order to face the uncertainty in the problem parameters, the MPFRP method based on fuzzy data is proposed. The designed model was first evaluated for validation with numerical examples in small and large size and using real data in a case study in GAMS software?

Using numerical examples and real data, the output indicates the performance of the proposed model. The output also had acceptable flexibility in the face of uncertainty in the parameters of the research model.

Originality/Value:

 In this study, in order to reduce the shortage of blood products in situations where blood product is not available in the same group as the requested blood product, the ABO-RH adaptability principle has been used to replace the received demand with a compatible inventory. And also, to deal with uncertainty in uncertain parameters in the supply chain a solution based onmixed possibilistic-flexible robust programming is proposed.

Finding the best situation for the expected profit by each of the participants (players) in the secondary auction market of financial congestion transmission rights.

From the point of view of game theory, finding at least one Nash equilibrium point for this competition takes into account the various constraints of the secondary auction market.

This paper study the issue of financial transmission congestion contracts then uses the game theory to analyze the short-term (weekly) secondary FTR markets, in which the players manage the transmission risk through optimal biddings.

Originality/Value: 

Congestion risk management is one of the most important topics of transmission network management in restructured power systems and electricity markets in the world, especially markets based on regional or local pricing, whose main tool is transmission financial contracts. In the other scientific articles published so far, mainly the modeling and analysis of the primary auction markets of transission congestion financial rights have been discussed, and the secondary auction markets of these contracts, which have a different pattern, have not been scientifically analyzed. The main innovation of this article is to pay attention to this issue using application of game theory.

The purpose of this paper is to consider products such as disposable tableware produced from plastic waste. In this setting, plastic waste is collected, recycled, and reused. In this research, a supply chain including waste depot, recycler, and manufacturer is considered in which plastic waste is reused in order to manufacture a product. Under this supply chain, the waste depot collects non-recycled plastic waste and the recycler recycles it. Then, the manufacturer uses recycled plastic waste to produce final product and meet customer demand. In this structure, the waste depot, recycler, and manufacturer set the price of non-recycled plastic waste, the price of recycled plastic waste, and the price of final product, respectively.

The game theory is used to make the decisions under the considered supply chain. It is assumed that the decision power of the manufacturer is more than of the waste depot and recycler. In this setting, Stackelberg game-theoretic model is established in order to specify the prices adopted by the members.

Results indicate that decision powers of the waste depot and recycler have no effect on the price and demand of final product. The profits allocated to the manufacturer are the same when decision powers of the waste depot and recycler are different. Moreover, more the price elasticity of the demand for final product leads to lower profits for the members.

Originality/Value: 

Using plastic waste is an effective approach to sustain environment and reduce pollution. In this research, plastic waste is used to produce products such as disposable tableware in a supply chain including manufacturer, recycler and waste depot. The game theory approach is also used to make decisions. To our knowledge, the idea of applying the game theory to use plastic waste in production of products under the considered supply chain has been raised for the first time in the literature.

High efficiency in the operating room may significantly improve the overall performance of the hospital and the service quality provided to patients. The operating room is the de facto financial hub of the hospital and maximizing its efficiency may lead to considerable improvements. To this end, we seek ways of accelerating the patient flow in order to save time and cost in healthcare facilities.

In this study, we use agent-based simulation to simulate patient care in the operating room. After performing the required validations, a number of improvement scenarios were developed and evaluated.

A hybrid scenario including modifications to the referral time of the patient by the surgeon, transfer time of the surgical set and supplies to the operating room, and the timing of anesthesia proved to have the most positive impact on the criteria i.e. activities, reducing the average Length of Stay (LOS) by 9.69 minutes. The second-most effective scenario involved modifying the referral time of the patient by the surgeon, reduced the LOS by 7.31 minutes.

Originality/Value:

 Through this research, it became apparent that minimizing the patients' LOS improves the efficiency of the operating room as it helps reduce the overall idle time and increases the number of operations carried out in each shift. Making time even for one additional operation per day significantly increases the operating room income. Moreover, a shorter LOS means less fatigue for the medical staff and reduces the total cost of running the operating room by reducing the staff's overtime hours.

This study seeks to assess the significant effects of blockchain, as a new decentralized technology, in waste reduction and performance improvement of Freight Transportation Management Systems (FTMS).

Methodology: 

The network analysis technique is used as one of the most valid multi-criteria decision-making models. We developed this model in four levels with four main criteria of performance wastes (including cost, time, trust and security and safety), and fourteen sub-criteria, considering the blockchain technologies in four areas of digital infrastructure, visibility, transparency, and smart contracts.

Findings indicate that each of these technologies, individually and collectively, has reduced the waste of road freight management systems. The digital infrastructure technology reduced time and cost waste by 46.44% and 53.56%, respectively. Visibility has reduced 23.99%, 22.30%, and 53.71% of the wastes of cost, time, and safety, respectively. The transparency technology has influenced time and trust and security by 22.63% and 77.37%, respectively, and smart contracts affected the two above waste categories by 88.81% and 11.19%, respectively. The most effective wastes were identified as transportation costs, control costs, transparency in providing information, control of product conditions, and driver monitoring.

Originality/Value:

 This study identified the most crucial road freight transport wastes and made it possible to determine priorities in various areas of blockchain technologies for future investment to improve the FTMS performance, and the findings of this study might be a starting point for future studies in this context.

Clustering algorithms are useful tools for understanding data structure and classifying them into different data sets. Due to the importance of using these algorithms in analyzing financial market data that have a high volume and scope, this study in order to select the best clustering algorithm for clustering companies listed on the Tehran Stock Exchange in the field of finance from It has used different clustering algorithms and evaluated the validity of these algorithms and selected the best algorithm.

This research is applied in terms of purpose and descriptive in terms of implementation method and is of quantitative type (mathematical modeling). The statistical population of the research includes 403 companies listed on the Tehran Stock Exchange in 2019, whose performance has been evaluated based on four financial criteria.

After clustering the surveyed companies by five clustering algorithms, namely K-means, EM, COBWEB, density-based algorithm and ward method, seven indicators RS, DB, Dun, SD, Purity, Entropy and Time were used to evaluate the algorithms. Finally, the total performance of the algorithms was analyzed based on TOPSIS, VICOR and DEA methods. Based on the results, K-means has a better performance in clustering based on the financial data sets.

Originality/Value:

 Since no clustering algorithm can have the best performance in all measurements for each data set, this study uses a combination of multiple criteria to analyze data clustering algorithms related to the field of financial performance appraisal. Companies have provided suggestions and the results of this study have been used effectively for investors in the field of finance, which leads to the optimal choice of investment portfolio.

Selection of the best stocks for the portfolio as well as allocating the optimal amount of capital per stock in the portfolio are serious challenges in investing in the stock market. The use of machine learning capacities in the process of optimal capital allocation among portfolio assets has received less attention and usually, the same weight is assigned to portfolio stocks or traditional risk assessment methods are used to divide capital between portfolio stocks. The common disadvantage of these methods is that they all use simple and inflexible mechanisms to estimate the performance of a set. The purpose of this paper is to show for the first time, that machine learning can be used to create a more effective mechanism for estimating performance, which leads to a more efficient allocation of capital to portfolio stocks.

Our proposed framework, uses two predictive models based on machine learning. In the first step, stocks historical information is used in a return forecasting model, then based on the predicted returns, the appropriate stocks of the portfolio are selected. In the second step, a separate forecasting model predicts portfolio returns by taking into account both the forecasted returns in the first model and the expected risk of the stocks. At the end based on the predicted return of the numerous random portfolios, the appropriate weight for each asset is selected.

Comparing the returns of adjusted portfolios with this model and adjusted portfolios with other portfolio optimization methods shows the superiority of the proposed model.

Originality/Value: 

In this paper, by using machine learning models, the process of selecting the appropriate stock of the portfolio and allocating capital among the candidate stocks is done optimally.

This study is conducted to design a model of mutual expectations in the relationship between the individual and the organization (Person as a client) in Isfahan's government organizations and service providers based on the phenomenological approach.

The purpose of this study is practical, and information collection is qualitative. The trio approach of Rao and Perry [1] is used for the reliability of the results and the validity of the research; the validity approaches of  Denzin and Lincoln [2] structures are used. In addition, to determine the validity of the questionnaire, the content validity ratio of Lawshe is used. The modified aesthetic-Klaizi method was also used to analyze the information. Seventeen experts and 19 managers of government and service organizations and 35 clients of these organizations were the statistical populations of the study. They were selected by purposive sampling method, and data were collected through semi-structured interviews. In quantitative research, the DEMATEL technique investigated the relationship between affecting and being affected between components. The existing gap between the organization's demands and the client's supply was also created through a researcher questionnaire. One hundred managers and experts of government and service organizations and 100 of their clients were purposefully selected using the pairwise comparison test method and identifying in the quantitative section.

The findings in DEMETEL technique showed that the most impact was related to the factor of capable and knowledgeable client and the most impact was related to the factor of respect for human dignity of employees. Also the findings show that among the components of the organizational questionnaire, the most gaps are in the component of the rule of law and avoidance of illegal methods, and the minor gaps are in the component of participation in client-related organizational systems by using the questionnaire to collect the required information. In the organization questionnaire, the respondents claim that the client's supply to the organization is much less than the organization's demand from the client.

Originality/Value:

 The results of this research can be effective in the theoretical development of the relationship between governmental organizations and service providers and clients, and policy makers and managers can create intelligent structures to reduce the expectations of clients and employees of governmental organizations and provide more favorable conditions for service and receiving services.