Journal of Industrial Engineering and Management Studies

Abstract Make-to-order is a production strategy in which manufacturing starts only after a customer's order is received; in other words, it is a pull-type supply chain operation since manufacturing is carried out as soon as the demand is confirmed. This paper studies the order acceptance problem with weighted tardiness penalties in permutation flow shop scheduling with MTO production strategy, the objective function of which is to maximize the total net profit of the accepted orders. The problem is formulated as an integer-programming (IP) model, and a cloud-based simulated annealing (CSA) algorithm is developed to solve the problem. Based on the number of candidate orders the firm receives, fifteen problems are generated. Each problem is regarded as an experiment, which is conducted five times to compare the efficiency of the proposed CSA algorithm to the one of simulated annealing (SA) algorithm previously suggested for the problem. The experimental results testify to the improvement in objective function values yielded by CSA algorithm in comparison with the ones produced by the formerly proposed SA algorithm.

Abstract Fuzzy reliability is often used in analyzing the reliability in the large industrial systems. In this paper, a relatively new method is presented to analyze Neishabour (also called Nishapur, a city in Iran) train disaster. In this regards, by using the certain and uncertain propositions, unreliability circuit of the system is depicted .Due to the inability to provide exact values for the unreliability of each subsystem, regarding the opinion of experts, fuzzy logic is applied and triangular and Gaussian membership functions are attributed depending to the type of each subsystem and the fuzzy unreliability value of the system is calculated. Finally, by defuzzification and comparing the obtained value with the classification table of linguistic variables, unreliability of the system is identified.

Abstract Nowadays, logistics and supply chain management (SCM) is critical to compete in the current turbulent markets. In addition, in the global context, there are many uncertainties which affect on the market. One of the most important risks is supplier disruption. The first step to cope with these uncertainties is quantifying them. In this regard many researches have focused on the problem but measurement of the risk in the global SCM is yet a challenge. In the uncertain conditions, simulation is a good tool to study the system. This paper aims to study a global supply chain with related risks and measurement of the risks using simulation. Global aspects considered in the paper are: 1- currency exchange rate, 2- extended leadtime for abroad supplies, 3- regional and local uncertainties. In this regard, two popular risk measurement approaches (VaR and CVaR) are used in the simulation of uncertainties in the global supply chain. Results showed that adopting risk averse behavior to cope with the uncertainties leads to the lower stockouts and also higher costs.

Abstract In the recent years, renewable energy sources are an important component of world energy consumption. GDP is one of the main measures of a country’s economic activity. Most of the studies examine the impact of renewable energy consumption on GDP with single equation model and the others use dynamic panel data. Since the Granger causality analysis’s findings of this paper establish bidirectional causality between GDP and renewable energy consumption, the purpose of this study is to develop a simultaneous-equations model to explore the interaction between GDP and renewable energy consumption in a dynamic panel data. This model uses GDP and renewable energy consumption as endogenous variables and seven factors as exogenous variables. By using a dynamic panel data of 34 OECD countries from 1990 to 2012, the model is estimated by using the two-stage least-squares method. The results confirm the important influence of renewables and non-renewables as well as capital and labor force on GDP in OECD countries. Based on the results, both GDP and real oil price play an important role in renewable energy consumption. Our findings suggest that energy planners and policy makers need to increase renewable energy investment to ensure sustainable economic development in future.

Abstract CCC-r chart extended approach of CCC charts, is a technique applied when nonconforming items are rarely observed. However, it is usually assumed that the inspection process is perfect in the implementation control charts imperfect inspections may have a significant impact on the performance of the control chart and setting the control limits. This paper first investigates the effect of inspection errors on the formulation of CCC-r chart, then an economic model is presented in the presence of inspection errors to design control chart so that the average cost per item minimized. The r parameter in the chart is optimized with respect to the economic objective function, Modified Consumer Risk, and Modified Producer Risk. 

Abstract The theory of constraints is an approach to production planning and control that emphasizes on the constraints to increase throughput by effectively managing constraint resources. One application in theory of constraints is product mix decision. Product mix influences the performance measures in multi-product manufacturing system. This paper presents an alternative approach by using of goal programming to determine the product mix of the manufacturing system. The objective of paper is to provide a methodology in order to make product mix decision. Key point of the proposed methodology is considering decision maker idea to determine the weights of objective functions that are throughput and bottleneck exploitation. Therefore the weights of the objective functions are determined by the information get from decision maker. Through an example, inefficiency of theory of constraints in multiple bottleneck problems has been showed. Comparison of theory of constraints, linear programming and other methods to product mix problem has also discussed to show the advantages of the proposed method.

Abstract This study considers a reliable location – inventory problem for a supply chain system comprising one supplier, multiple distribution centers (DCs), and multiple retailers in which we determine DCs location, inventory replenishment decisions and assignment retailers to DCs, simultaneously. Each DC is managed through a continuous review (S, Q) inventory policy. For tackling real world conditions, we consider the risk of probabilistic distribution center disruptions, and also uncertain demand and lead times, which follow Poisson and Exponential distributions, respectively. A new mathematical formulation is proposed and we model the proposed problem in two steps, in the first step, a queuing system is applied to calculate mean inventory and mean reorder rate of steady-state condition for each DC. Next, regarding the results obtained from the first step, we formulate a mixed integer nonlinear programming model which minimizes the total expected cost of inventory, location and transportation and can be solved efficiently by means of LINGO software. Finally, several test problems and sensitivity analysis of key parameters are conducted in order to illustrate the effectiveness of the proposed model.

Abstract One of the main challenges of strategic management is implementing the strategies. Designing the strategy map in Balanced Scorecard framework to determine the causality between strategic objectives is one of the most important issues in implementing the strategies. In designing the strategy map with intuition and judgment, the link between strategic objectives is not clear and it is not obvious which strategic objectives are related and influenced each other. Hence, it is essential to offer a quantitative and accurate method to design the strategy map and clarify these relationships. In this paper, after reviewing the methods for determining the causal relationships among BSC perspectives in the literature, a framework on the basis of historical data analysis and multi-response surface regression analysis is offered to determine causal relationships among strategic objectives with respect to data of key performance measures of past years in order to obtain the coefficients and equations that can be used in the prediction of the responses. Using statistically significant models, the correlations between the factors and several responses were acquired. The presented quantitative approach is useful for determining the causal relationships resulting in an accurate strategy map and is a supporting approach for improving decision makers’ opinions and enabling them to reach a more accurate picture of the relationships. This research also presents a case study to demonstrate the applicability of the proposed approach. The application and implication of the proposed method in a real case show that the contributions of the research are not only theoretical but practical as well. The strategy map constructed in this study can also serve as a reference point for similar businesses.

Abstract Credit risk management is a process in which banks estimate probability of default (PD) for each loan applicant. Data sets of previous loan applicants are built by gathering their data, and these internal data sets are usually completed using external credit bureau’s data and finally used for estimating PD in banks. There is also a continuous interest for bank to use rule based classifiers to build their default prediction models. However, in practice the data records are usually incomplete and have some missing values and this make problems for banks, especially in credit risk portfolios which are low default and makes model rule based building complex. Several strategies could be used in order to handle the missing data issue. This paper used five missing value handling strategies including; ignoring, replacing with random, mean, C&R tree induced values and elimination strategies in a real credit scoring dataset. Experimental results show that ignoring strategy consistently outperforms other methods on test data set, and suggest that the CHAID is a useful classifier for handling low default portfolios with missing value. 

Abstract The nature of input materials is changed as long as the product reaches the consumer in many types of manufacturing processes. In designing and improving multi-stage systems, the study of the steps separately may not lead to the greatest possible improvement in the whole system, therefore the study of inputs and outputs of each stage can be effective in improving the output quality characteristics. In this study, the double sampling method is applied for inspection where decision variables are the sample size per sampling time and the maximum amount of defective items in the first and second samples in each stage. Furthermore, uncertainty in parameters such as production, inspection, and replacement costs are included in the objective function and handled by a Monte-Carlo based optimization method. In order to show the efficacies of the proposed method, a numerical example has been designed, and further analyses on solutions have been conducted.

Abstract Iranian electronic products supplier industries are developing day by day and modern techniques and facilities are assigning as well as many promotions about green products supply chain as input materials introduced into the generation cycle of industries. Current cluster study of Iranian Electronic Products Manufacturing Industries (IEPMI) comprised a technical and hierarchical evaluation carried out as the objective of current research. It was used SPSS and Excel Software to classify and analysis about 33 IEPMI via an unsupervised model, Additive Ratio Assessment (ARAS), Simple Additive Weighting (SAW) and Data Envelopment Analysis (DEA) models. Finally, a hierarchical cluster classification has developed for the 33 industries pertaining to 5 main criteria as well as the total inventory of input, output materials and facilities employed. It was found that the ranking systems based on ARAS and SAW presented the same results for IEPMI. DEA model was also classified IEPMI in terms of efficiency score.

Abstract Today, outsourcing is recognized as one of the most effective strategies in the business world. In this regard, outsourcing of business processes is considered to be one of the most common forms of outsourcing. The purpose of this study is to provide In-depth and quantitative analysis of the benefits of BPO in a dairy plant in Iran and how these benefits affect the willingness of senior plant managers to increase the levels of outsourcing of business processes. Therefore, Structural Equation Modeling (SEM) based on BPO Benefit Analysis is used. The population of the study consisted of 50 managers who all answered a questionnaire containing 20 questions. Responses were analyzed using the Partial Least Squares (PLS) method. The research method is a quantitative experimental one. The findings of this study show that cost planning has a higher value than real cost savings and this is one of the benefits of BPO.

Abstract This research extends a two-phase algorithm for parallel job scheduling problem by considering earliness and tardiness as multi-objective functions. Here, it is also assumed that the jobs may use more than one machine at the same time, which is known as parallel job scheduling. In the first phase, jobs are grouped into job sets according to their machine requirements. For this, here, a heuristic algorithm is proposed for coloring the associated graph. In the second phase, job sets will be sequenced as a single machine scheduling problem. In this stage, for sequencing the job sets which are obtained from the first phase, a discrete algorithm is proposed, which comprises two well-known metaheuristics. In the proposed hybrid algorithm, the genetic algorithm operators are used to discretize the particle swarm optimization algorithm. An extensive numerical study shows that the algorithm is very efficient for the instances which have different structures so that the proposed algorithm could balance exploration and exploitation and improve the quality of the solutions, especially for large-sized test problems.

Abstract With the development and widespread use of social networks among people, high-volume data is produced and the analysis of this data can be useful in many areas, including people's daily lives. Classification of this volume of data using traditional methods is a very difficult, time-consuming, and low-accuracy task, therefore, using sentiment analysis techniques, people's opinions can be effectively summarized and categorized. To this end, we propose an algorithm that combines Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA). The reason for combining the two algorithms is that the GSA has a good ability to search overall, but in the last iterations, it has a low speed in exploiting the search space. Since the PSO algorithm has a special ability to exploit the search space, this algorithm is used in the exploitation phase to solve the problem. The accuracy obtained from our proposed algorithm (PSO-GSA) shows an improvement in the accuracy of the GSA algorithm.

Abstract The purpose of this research is to optimize the use of water resources in dams in Khuzestan province. For this purpose, in this research, we seek to optimize the cost and time of sending water to each of the cities from the total dams in Khuzestan province. The model is solved using the deterministic epsilon constraint method and NSGA-II and MOPSO algorithms meta-heuristically. According to the results presented in this research, the water supply from the Balaroud dam to the cities of Ahvaz, Izeh, Abadan, Baghmolk, and Bandar Imam Khomeini has not been determined to be optimal. The same dam sends a certain amount of water to the cities of Andimeshk, Dezful, Shush, Shushtar and Gotvand. The results showed that NSGA-II has a more acceptable performance than the MOPSO algorithm from the point of view of three criteria, and the MOPSO algorithm has a better condition than the NSGA-II algorithm only in terms of the distance to the ideal point. In addition, according to the sensitivity analysis, it has been determined that the increase in water demand can increase the shipping time by 1.9% and the shipping cost by 60%. Therefore, the effect of water demand is more on time and not on cost. Increasing the budget can have an effect on cost and time, which of course has more effect on time than cost.

Abstract In recent decades, the world has focused on a new concept that emphasizes the effective protection of the environment and careful use of natural resources; these emphases bring the goals of sustainable economic, environmental and social growth. Sustainable development (SD) means the development and progress of the current generation while preserving resources for the development of the future generation. The main purpose of this research is evaluation of key cement production industries based on SD factors with a combined approach of FAHP-FVIKOR. The statistical population includes 55 experts and specialists familiar with the concept of SD in 5 key industries of Iran's cement industry. Due to the limited community and lack of access to them, the opinions of 33people were finally used. The research method was practical in terms of its purpose, and it was a descriptive survey type using two questionnaires in terms of the data collection method. For this purpose, to evaluate the importance of three key factors in SD, FAHP (Analytic Hierarchy Process) approach was used and the weight of the factors was calculated. The results showed that the economic sustainability factor is more important than the environmental and social sustainability factors. Next, in order to evaluate the mentioned 5 industries based on SD factors, the fuzzy VIKOR (VlseKriter ijumskaOptimizacija I KompromisnoResenje) technique was used and the final ranking of the industries was extracted. The present research is the first applied research in the field of evaluating the SD factors of the country's cement industry and ranking the key cement industries with multi-criteria decision-making techniques; So that its results can be used in the evaluation of other cement production industries and related industries of the country.

Abstract This paper investigates the Stackelberg equilibrium for pricing and ordering decisions in a multi-channel supply chain. We study a situation where a manufacturer is going to open a direct online channel in addition to n existing traditional retail channels. It is assumed that the manufacturer is the leader and the retailers are the followers. The situation has a hierarchical nature and is formulated as a bi-level programming problem. The upper level problem is a mathematical model dealing with decisions of the manufacturer, while the lower level is a Nash equilibrium model determining the retail prices and order quantities by formulating the competition between the physical retailers. We consider a price-sensitive linear demand model with an additive uncertain part and analyze the optimal decisions for each sales channel. To enable supply chain coordination, we propose a particular revenue-sharing contract. This contract enables the retailers to set pricing and ordering policies that are equivalent to those in an integrated supply chain.  Finally, we examine the impact of the model parameters on the equilibrium with a comprehensive numerical study. 

Abstract The traveling salesman problem (TSP) is a well-known optimization problem in graph theory, as well as in operations research that has nowadays received much attention because of its practical applications in industrial and service problems. In this problem, a salesman starts to move from an arbitrary place called depot and after visits all of the nodes, finally comes back to the depot. The objective is to minimize the total distance traveled by the salesman.  Because this problem is a non-deterministic polynomial (NP-hard) problem in nature, it requires a non-polynomial time complexity at runtime to produce a solution. Therefore, a reactive bone route algorithm called RBRA is used for solving the TSP in which several local search algorithms as an improved procedure are applied. This process avoids the premature convergence and makes better solutions. Computational results on several standard instances of TSP show the efficiency of the proposed algorithm compared to other meta-heuristic algorithms. 

Abstract Today, HSE (health, safety, and environment) systems play a vital role in green and sustainable aspects of the companies. However, performance evaluation of HSE systems is a crucial issue in industry and academia. This paper tries to identify and prioritize the effective factors in HSE performance in Iran Khodro (the largest automotive company in Iran) and Tabriz Petrochemical (one of the biggest Iranian petrochemical company). The factors are achieved through the literature and recent publications and then they are customized by the expert's opinions. Finally, a hybrid Fuzzy DEMATEL ANP approach is developed for prioritization of the factors. Indeed, Fuzzy DEMATEL is used in order to determine the relations among factors and sub factors and to help in providing ANP super matrix. Afterward, the Fuzzy ANP is proposed to find the final weights of the factors and sub factors. The weights are used in order to prioritize the factors for two selected companies.

Abstract This paper presents a common set of weights (CSWs) method for multi-stage or network structured decision-making units (DMUs). The decision-making approaches proposed here consist of three stages. In the first step, a hybrid dynamic network data envelopment analysis (DNDEA) model is used to determine the efficiency values of the supply chain. Next, a CSW model is developed using the range-adjusted measure (RAM). In the third step, the extracted CSWs are used to compute a separate weight for each component of each DMU.  the extracted CSWs are then used in the third step to calculate DMUs weights separately for each component. Then the overall efficiency is obtained by weighted averaging of the efficiency of individual components. Thus, this model evaluates the overall efficiency of a network process as well as the contribution of individual network components. The results of this study demonstrate the model’s capability to evaluate the efficiency of dynamic network structures with very high discriminatory power. In an implementation of the model in a case study, only one supplier (KARAN) earned the maximum efficiency value, and the efficiency scores of other suppliers were in the range of 0.6409-0.9983. After applying the CSWs, KARAN remained the most efficient supplier, and the efficiency scores of other suppliers moved to the range of 0.5002-0.9349. The range shifted to 0.4823-0.9921 after applying the stages weights. This weighting method should be considered an integral part of such modeling procedures, Given the enhancement observed in the results of CSW after incorporating the component weights.

Abstract This study aims to develop a comprehensive framework for integrating blockchain technology into the supply chain of Golrang Industrial Group. Employing a qualitative research approach, the study follows a data-driven theoretical methodology based on the Strauss and Corbin paradigm model. The research population consists of food industry factories affiliated with Golrang Industrial Group. Data collection was conducted through open interviews with ten industry experts and university professors, selected purposefully. The gathered data underwent analysis using the grounded theory method, comprising open coding, axial coding, and selective coding. The proposed model includes 54 indicators categorized into 19 concepts. The findings reveal that causal conditions for blockchain integration include strategic planning, blockchain structure design, inter-company collaboration, and financial infrastructure development. Industrial transformation, IoT, and artificial intelligence are key enablers, while employee training, continuous data updates, and skill-based selection of blockchain technology play essential roles in implementation. Effective background conditions involve transformational leadership, regulatory frameworks, and scaling mechanisms. The strategies identified include identity and access management, encryption, and secure data transmission. The study highlights blockchain’s potential to enhance production security, corporate transparency, product traceability, and cost efficiency in transportation and maintenance. Path coefficient analysis indicates “intervening factors” have the highest impact on “strategies” (0.819), followed by “contextual conditions” (0.625) and “strategies” on “implications” (0.570). These findings provide valuable insights into both the opportunities and challenges of blockchain implementation in supply chain management.

Abstract One of the most important extensions of the capacitated vehicle routing problem (CVRP) is the vehicle routing problem with simultaneous pickup and delivery (VRPSPD) where customers require simultaneous delivery and pick-up service. In this paper, we propose an effective ant colony optimization (EACO) which includes insert, swap and 2-Opt moves for solving VRPSPD that is different with common ant colony optimization (ACO). ACO is a meta-heuristic algorithm inspired by the foraging behavior of real ants. Artificial ants are used to build a solution for the problem by using the pheromone information from previously generated solutions. An extensive numerical experiment is performed on 68 benchmark problem instances involving up to 200 customers available in the literature. The computational result shows that EACO not only presented a very satisfying scalability, but also was competitive with other meta-heuristic algorithms such as tabu search, large neighborhood search, particle swarm optimization and genetic algorithm for solving VRPSPD problems.

Abstract This paper, considers Network Failure Manufacturing System (NFPMS) and production control policy of unreliable multi-machines, multi-products with perishable items. The production control policy is based on the Hedging Point Policy (HPP). The important point in the simulation of this system is assumed that the customers who receive perishable item are placed in priority queue of the customers who are faced with shortage. The main goal of this paper is determining of optimal production rates that minimizes average total cost consist of shortage, production, holding and perishable costs. Because of uncertainly and complexity of this system, simulation optimization of this system using ARENA software has been done. A numerical example will show the efficiency of the proposed approach.

Abstract This paper discusses the modeling and solution of a flexible flow shop scheduling problem with forward and reverse flow (FFSP-FR). The purpose of presenting this mathematical model is to achieve a suitable solution to reduce the completion time (Cmax) in forward flow (such as assembling parts to deliver jobs to the customer) and reverse flow (such as disassembling parts to reproduce parts). Other important decisions taken in this model are the optimal assignment of jobs to each machine in the forward and reverse flow and the sequence of processing jobs by each machine. Due to the uncertainty of the important parameters of the problem, the Fuzzy Jiménez method has been used. The results of the analysis with CPLEX solver show that with the increase in the uncertainty rate, due to the increase in the processing time, the Cmax in the forward and reverse flow has increased. GA, ICA and RDA algorithms have been used in the analysis of numerical examples with a larger size due to the inability of the CPLEX solver. These algorithms are highly efficient in achieving near-optimal solutions in a shorter time. Therefore, a suitable initial solution has been designed to solve the problem and the findings show that the ICA with an average of 273.37 has the best performance in achieving the near-optimal solution and the RDA with an average of 31.098 has performed the best in solving the problem. Also, the results of the T-Test statistical test with a confidence level of 95% show that there is no significant difference between the averages of the objective function index and the calculation time. As a result, the algorithms were prioritized using the TOPSIS method and the results showed that the RDA is the most efficient solution algorithm with a utility weight of 0.9959, and the GA and ICA are in the next ranks. Based on the findings, it can be said that industrial managers who have assembly and disassembly departments at the same time in their units can use the results of this research to minimize the maximum delivery time due to the reduction of costs and energy consumption, even though there are conditions of uncertainty

Abstract Despite several individual forecasting models that have been proposed in the literature, accurate forecasting is yet one of the major challenging problems facing decision makers in various fields, especially financial markets. This is the main reason that numerous researchers have been devoted to develop strategies to improve forecasting accuracy. One of the most well established and widely used solutions is hybrid methodologies that combine linear statistical and nonlinear intelligent models. The main idea of these methods is based on this fact that real time series often contain complex patterns. So single models are inadequate to model and process all kinds of existing relationships in the data, comprehensively. In this paper, the auto regressive integrated moving average (ARIMA) and artificial neural networks (ANNs), which respectively are the most important linear statistical and nonlinear intelligent models, are selected to construct a set of hybrid models. In this way, three combination architectures of the ARIMA and ANN models are presented in order to lift their limitations and improve forecasting accuracy in financial markets. Empirical results of forecasting the benchmark data sets including the opening of the Dow Jones Industrial Average Index (DJIAI), closing of the Shenzhen Integrated Index (SZII) and closing of standard and poor’s (S&P 500) indicates that hybrid models can generate superior results in comparison with both ARIMA and ANN models in forecasting stock prices.