Journal of Industrial and Systems Engineering
Volume:14 Issue: 2, Spring 2022

In recent years, the existence of some challenges in the water industry has led organizations to design and implement various solutions. This paper seeks to propose a methodology to address some of the most important challenges such as water sustainable supply and allocation (WSSA) problem, type of decision-making approach, coordination, sustainability, and uncertainty. The proposed methodology focuses on solving the WSSA problem, by considering these challenges in the problem. Concerning the conflict between sectors benefits of water resources and consumption and the need for coordinating between them, in this paper the type of decision-making approach is based on coordination and because of the existence of conflicting goals in important areas of water management decision making, a multi-objective bi-level programming model is presented. At the model leader level, the water supply management problem and the follower level, the water allocation management problem with multiple objectives is formulated, so that some of the parameters are assumed to be random and normally distributed. Also, a hybrid model based on chance-constrained programming (CCP) and nadir compromise programming (NCP) models as a deterministic transformation to bi-level stochastic programming model is proposed and a bi-level genetic algorithm is used to solve it. The proposed model is illustrated to solve a real problem in water resources and consumption management of Tehran city and based on several scenarios, the results are analyzed. The results show that the proposed methodology presents a suitable solution for addressing the mentioned challenges in the decision-making and planning process in the water management.

The importance of order promising process has led manufacturers to use more productive production systems. Optimizing the production system is one of the ways to increase productivity. This issue becomes even more significant when some of the raw materials needed to produce different final products are homogenous. In this paper, a decision structure for the order promising process with product homogeneity and product substitution in a Hybrid Make-To-Stock and Make-To Order environment is studied. For this purpose, a bi-objective mathematical model has been designed and solved by the Lagrangian Relaxation solution method. Despite the extensive studies that have been done in this area, there are few articles that have studied the possibility of substituting the final products by the manufacturer. In order to investigate this gap, product substitution has been studied in this article. Two different types of customers are considered in this model. A case study is also conducted to evaluate the applicability of the proposed model. The results of this article show that the possibility of products substitution will reduce rejected orders and increase system profits. Also, fulfilling orders that are more flexible in terms of product delivery time is a higher priority for the manufacturer than other orders.

Nowadays, given the competitive environment of business world, designing a supply chain (SC) that is compatible with the needs of the consumer market seems crucial. Due to its long-term impact on the company's performance, making decisions related to fulfilling the customer demand is an important issue in SC design and management. The present research tries to design a closed-loop supply chain network (SCN) with possible partial disruption in distribution centers during servicing. The objectives of this model are to minimize the total cost of SCN and maximize the system reliability, which is, in turn, dependent on the strategy chosen to cope with the partial disruption. Thus, in case of a partial disruption, some centers should be selected to compensate for disruption that, in addition to reducing costs, will be able to increase the system reliability. A weighted goal programming approach is used for solving the proposed multi-objective model, and a non-dominated sorting genetic meta-heuristic algorithm along with the exact method are developed in order to solve the problem. The results indicated that the proposed algorithm has appropriate performance in achieving near-exact solutions in large scales problems.

The outbreak of COVID-19 sparked a massive movement among the world's people to control this dangerous and unknown disease. So many nutritionists have made many medical recommendations to control this disease by using special nutrients. In this regard, we decided to examine the effect of two nutrients, protein and fat, which are the main ingredient in many nutrients, on the rate of death and recovery of patients covid-19. Available data from 170 countries worldwide have been examined to discover this effect. Linear and non-linear relationships and the correlation coefficient between response variables and different nutrients have been calculated and analyzed in detail. According to the results, these two elements cannot be considered influential in predicting the current rate with high reliability. Protein and fat have a high nutritional value and play an essential role in human health, but the amount of this need for humans is different, which in turn contradicts the results obtained from patients. Although correlation coefficients are not high, the existence of this correlation still requires further studies in this field. We have also used models such as Decision tree, Rule introduction, and Naive Bayes in our research to predict future results, which will give us an understanding of the results obtained.

Evaluating the performance of the groups of decision-making units (DMUs) in an organization and ranking them is very important because the groups are the basic components of organizations. Also, assessing the performance of groups and finding their strengths and weaknesses affect the future decisions of the organization. Most previous research has been done on the Malmquist productivity index of decision-making units to evaluate productivity changes of them. In practical evaluation problems, although decision units are homogeneous in many organizations, in the sense that they produce similar outputs by receiving the same input sources; However, these units are classified into different groups based on management strategies and environmental conditions. In this paper, we want to develop the Malmquist productivity index for a group of DMU’s and examine the productivity changes of groups. We also identify local and global factors that affect changes in group productivity. Finally, to explain the applicability of the proposed index, a case study is presented on the evaluation of bank branches in different regions.

Data analysis in competitive sports has increased significantly in recent years, and a significant number of studies have been done during the last decades. In the sports Data analysis field, bibliometric analysis and maps have not yet been used to analyze the production and visualize evolution and trends. Therefore, the primary purpose of this article is to review data science analysis in sports activities with network embedding-based visualization on a large-scale dataset.805 articles were published between 1997 and 2020 and written by 3141 different authors from 1181 institutions, and 60 different countries were extracted from WOS by using R, Cite Space, and VOS viewer. Articles, journals, authors, countries, and universities that have played a significant role in developing this field are identified. Following that, meaningful knowledge of the communication networks among articles, authors, and keywords are illustrated by scientific paper mining. Moreover, articles have been divided into six groups based on the subject and methodology, which provide a comprehensive sight for researchers in this emerging field of sports.

Due to the necessity of electronic transactions with credit cards in this modern era and that fraudulent activity with credit cards are on the rise, the development of automated systems that can prevent such financial fraud is considered vital. This study presents a method for detecting credit card fraud by deploying a neural network that distinguishes between legitimate and illegitimate transactions and detects fraudulent activities with stolen physical credit cards. For this purpose, after collecting data in the preprocessing stage, cleaning and normalizing the data, the feature selection operation is performed using fisher discriminant analysis. After that, a multilayer perceptron (MLP) neural network is trained during the post-processing period using the teaching learning-based optimization algorithm (TLBO) to optimize credit card fraud detection. In this algorithm, local search (exploitation) is done using the teacher phase, and global searching(exploration) is done using the student phase. Moreover, the fisher discriminant analysis algorithm reduces within-class scattering. It increases between-class diffusion to increase classification accuracy and decrease the CPU time of the algorithm in the training phase. The latest available algorithms such as AdaBoost, Random Forest, CNN, and RNN are also compared with the proposed method. The results show that the proposed algorithm outperforms the mentioned algorithms regarding some standards criteria and CPU time.

Evaluating the efficiency of industrial units has long been an important issue to find the position of each unit in comparison with others. In this paper, a model for evaluating the efficiency using data envelopment analysis approach is explained in such a way that due to the breadth of input and output criteria, using the principal component analysis approach, data dimensions can also be reduced and the power to distinguish between efficient and inefficient units be increased. Due to lack of attention to the internal structure and also not considering the effective criteria in each department, it is tried to determine the most important criteria involved in each part in the purchasing, production, support and sales sectors. To calculate the efficiency, all the components have been examined as a model of network data envelopment analysis to take into account the effect of all departments and criteria in industrial units' efficiency. In this network, by considering the criteria involved in each of the sub-networks, all effective factors were identified. These criteria are selected based on the SCOR model and the balanced scorecard and also include sustainability criteria. To implement the model, 26 stone factories have been considered. The supply chain network was determined and dimensions of the data were reduced by implementing the principal component analysis approach. Then, by modeling the data envelopment analysis in each of the subnets in GAMS software, the efficiency was calculated. The results show an acceptable difference among industrial units to evaluate those units.

In recent years, and after further studies on the effects of natural disasters and catastrophes that threaten human life, a new concept called secondary disasters has been proposed. The fact that all areas affected by natural disasters may be affected by secondary disasters is generally overlooked, and this has exacerbated the effects of disasters. Therefore, to reduce the human and economic effects of natural disasters, this article examines the issue of designing a supply chain for relief resources and providing optimal rescue operations, considering the possibility of primary and secondary disasters. Due to the dynamic nature of the secondary effects and the need for continuous updating of the relief management process, this paper presents a one-objective model of mixed nonlinear integer programming to meet the demand for relief items, rescue the injured and evacuate the affected peoples concerning the prioritization of demand points under the conditions of primary and secondary crises, minimizes transportation time, transportation costs and unsatisfied demand; also defines the priority of demand points based on the amount of unmet needs and duration of deprivation of relief items and services; in this view, demand points are prioritized and unmet needs is minimized. Since the problem of the current study falls into the category of Np-hard problems, in order to solve the model, a combined approach of genetic algorithm (GA) and rolling horizon planning is introduced, finally the proposed algorithm is based on a case study has been implemented on the existing data set, which shows the high quality of this solution method in terms of the quality of the solution and the computational time.

Sustainability is more like a mandatory requirement in today’s business world. This study investigates coordination decisions in a dyadic supply chain with a socially responsible manufacturer. Social aspect of sustainability is getting more attention in addressing quantitative mathematical models. With use of participative pricing applications, an extension of this mechanism is applied to resolve the conflict of interests in a retailing channel. Cause-related marketing is proved to be an effective approach in satisfying both the economic and social concerns in a business practice. Our findings prove the applicability of the proposed model. Pricing decisions of the supply chain members are successfully coordinated via a revenue-sharing contract. Sensitivity analysis shows that the channel members gain more profit under the new pricing scheme while benefiting the society in terms of social responsibility.

The majority of scheduling research considers a deterministic environment with pre-known and fixed data. However, under the tools conditions and worker skill levels in assembly work stations, there is uncertainty in the assembling times of the products. This study aims to address a two-stage assembly flow shop scheduling problem with uncertain assembling times of the products which is assumed to follow a normal distribution. The problem is formulated as an MIP model in general form and under deterministic condition. Since the problem is strongly NP-hard, genetic algorithm is adopted with a new solution structure and fitness function to solve the problem on the practical scales. The presented robust procedure aims to maximize the probability of ensuring that makespan will not exceed the expected completion time. In addition, Johnson’s rule is extended and simulated annealing algorithm is tuned for the problem at hand. The computational results indicate that the obtained robust schedules hedge effectively against uncertain assembling times. The results also show that the proposed genetic algorithm gets better robust schedules than Johnson’s rule and outperforms simulated annealing algorithm in terms of deviation percentage ( ) of the expected makespan from the optimal schedule.

The title of the present study is designing and explaining the decision-making model of shareholders with a comparative approach to classical finance and behavioral finance in the capital market. The traditional finance viewpoint assumes that people make rational decisions to maximize wealth at a certain level of risk or minimize risk at a certain level of wealth. Such an approach, which states "how people should behave," is called norm. In this study, in the first stage, based on the literature review and using the previous related studies, a complete list of fifty-seven factors affecting the decision-making model of shareholders was provided to the members of the experts’ panel in the form of a questionnaire for the sake of weighting. The DANP technique was then used. The results show that political factors, economic factors, market psychological factors, cognitive factors, emotional factors, and finally financial factors have the highest effect at the company level.

Multi-criteria decision-making (MCDM) challenges are expanding rapidly. Introducing a comprehensive decision model under uncertainty that considers the weight of criteria, the importance of experts, and the ranking of alternatives is essential for solving the MCDM problems. This paper aims to introduce a comprehensive decision model. For this purpose, a developed version of the grey relational analysis (GRA) method by using reference point approach is presented for ranking of alternatives. Moreover, an extension of the best-worst method (BWM), namely G-BWM, is applied for criteria weight determination. Furthermore, the multi-attributive border approximation area comparison (MABAC) method is enhanced by the average ideal concept to specify the weight of experts. The comprehensive model is enriched by employing grey numbers to cope with the uncertainty. To represent the usability of the proposed method, an illustrative example is solved. The outcomes illustrate the reliability of the comprehensive approach, and it can be applied to various MCDM problems.

Despite the passing of more than 30 years from introducing the UTilitès-Additives (UTA) method and its extensive presentation in academic communities, this method is still not very popular among portfolio managers. Many portfolio managers still question the usefulness of the UTA method and prefer to rely on other multi-criteria decision making (MCDM) approaches. Therefore in this study, we examined the features of one of the most popular variants of the UTA methods, called UTASTAR, and on this basis, we have been developed this traditional approach in such a way that it would have more ability to meet the expectations of portfolio managers. In this way, to demonstrate how the proposed method can be applied in practice it is implemented in Tehran stock exchange (TSE) and to validate its efficiency, we designed an experiment, which is a novel approach in operations research but common in psychology and experimental economics. From the experimental results, we can extract that the outstanding features of the proposed method, compared to the original UTASTAR method are as follows: (1) it can provide a more accurate estimation of the portfolio managers’ attitude because in addition to the sequential preferences of the alternatives it also considers the relative preferences; (2) it has always feasible solutions although it requires more comparison data and (3) it allows portfolio managers to observe the inconsistency of their decisions and take corrective action if desired.0

Regulation changes affect pollutions tax of industry, labor union problems such as insurance and retirement health plan. Although environmental and economic performance is significant, safety and inherent risk are important to the supply chain. The paper proposes a multi-objective optimization model to minimize the inherent risk, carbon emissions, and economic cost. There is uncertainty in the risk consequence of facilities and transportation accidents between facilities, whose distribution function is unknown. Therefore, robust optimization is applied to resolve the uncertainty. The weighted sum utility method also combines some functions having different measurement units. Three functions of risk, carbon emissions, and cost are converted into one. The paper presents a case study to prove the proposed model and discusses constraints for more improvement.