Journal of Industrial Engineering and Management Studies

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 Human errors are a growing threat to EHR technology adoption and information sharing. Healthcare data breaches and criminal attacks continue to increase in volume and complexity. To achieve the full benefits of EHR technology, the industry must place the protection of health information as its highest priority. This paper presents the results of a systematic literature review of socio-technical system (STS) factors that influence human error in EHR data breaches. We present a conceptual framework of the STS factors that are hypothesized to reduce human error data breaches in the healthcare sector. The existing literature highlights a research gap in terms of understanding and modeling of human-computer interactions and the consideration of STS factors when developing solutions, signifying a need for further research in this domain. Hence, we recommend future research into the formulation and implementation of a STS approach to mitigate human error in information security, aiming to enhance the resilience of EHR and make them less attractive to cybercriminals.

Abstract A major disruption in the supply chain causes a shutdown or reduction in capacity. A disruption at any point in the supply chain spreads as a ripple effect to other members, causes a reduction or interruption in production and makes distributors unable to respond to customers with a lack of inventory or affects the lead time. The present research introduces and quantitatively shows the ripple effect of the disruption of the supply chain. To describe supply chain recovery and vulnerability, we integrated a discrete-time Markov chain with a Bayesian network model for simulating the propagation behaviour of supply chain interruptions. After that, we provided a set of standards for estimating the knock-on effects of a supply disruption on the supplier,manufacturer and distributor in terms of delivery lead time and revenue loss. We made a comparison between the results of the manufacturer disruption and the supplier disruption. Results indicate that this model can show the result of disruption and high-risk paths in the supply chain so that we can estimate the supply chain vulnerability before disruption. If a disruption occurs at one point in the supply chain, other members will be subject to disruption, and each member closer to the disruption point will receive a larger blast wave. The novelty of the present study is the quantitative estimation of the risk of a long-time disruption ripple effect based on the lead time and lost sales for a three-tier supply chain and compared the ripple effect of the manufacturer disruption with the ripple effect of the supplier disruption.

Abstract By providing timely transportation and dispatch of raw materials and finished goods, freight transport plays an essential role in industries, commercial activities, and trade war industries. It also has a significant impact on the overall performance of associated organizations and the ultimate costs of their products. Therefore, freight transport providers are under pressure to decrease costs and increase their service levels and should overcome these pressures by redesigning and improving their logistics processes on strategic, tactical, and operational levels. In this research, a multi-objective model is proposed for hub location in the field of war equipment under uncertainty. The first objective is to minimize costs, the second objective is to maximize the fulfillment of demands, and the third objective is to minimize congestion on the routes. Taking into account the parameters in the state of uncertainty, the mathematical model is modeled in a robust state and a robust counterpart model of the problem is proposed. In order to solve the problem on a small scale, the exact epsilon constraint method is used in GAMS software. Also, meta-heuristic approaches of grey wolf optimizer (GWO) and non-dominated sorting genetic algorithm (NSGA-II) are used to solve the model in medium and large dimensions. Next, the solution time of two algorithms was compared. 10 numerical experiments with different dimensions were designed and implemented through GWO and NSGAII algorithms. The results showed that the time to solve the GWO problem is less than the other algorithm. Finally, proper performance indicators are used to compare the performance of the used algorithms, and as a result of solving several numerical examples and calculating their performance indicator, it is concluded that the GWO algorithm has a better performance in solving the model.

Abstract Maintenance management along with production planning and control are two major components of production systems and operation management. In this paper, a production system that has two operational states plus a failure state is considered. In this system, maintenance actions are carried out based on the age-based policy which means the maintenance is performed after passing a specified time from the age of the system or after a system failure whichever occurs first. According to the random variables of the system failure, i.e., transition among the states of the system, two approaches are proposed to model the age-based policy considering different scenarios and their respective probabilities that may occur in the age-based policy. Both models aim to optimize the expected cost of the system per time unit, while the optimal time to terminate the production cycle and conduct preventive maintenance is determined as the main decision variable of the models. Some numerical examples employing different statistical distributions for the system failure mechanism, e.g., Weibull, gamma, normal, are also provided.

Abstract In today's world, international exchanges have become more prosperous due to trade and globalization. In this competitive environment, availability of products is as important as price, quality of materials, and construction. This issue doubles the importance of logistics in today's era. In this paper, a new hybrid multi-criteria decision-making MCDM technique for choosing third-party logistics service providers is developed. With having the necessary capacities and facilities, third-party logistics, or 3PLs, can take on logistic activities in a specialized manner so that manufacturers can focus on the important issues related to production optimization. To outsource logistics activities, evaluation, ranking, and selection of 3PLs are strategic decisions. By using the BWM, Best-Worst Method, the identified criteria were weighted to evaluate the financial and operational performance of third-party logistics companies. After that, the EDAS technique was used to rank 3PLs listed on Tehran Stock Exchange. According to the calculations made in this research, Tidewater Co was ranked first, Iran Shipping Co was ranked second, and Tuka Transport, Persian Gulf Transportation, and Rail Seyr Co was ranked third to fifth.

Abstract The current research is a descriptive survey in terms of its purpose in the field of applied research and based on its nature and method. In this research, Delphi, Fuzzy Dimetal, and Fuzzy Network Analysis methods have been used. The results of this research showed that the most important indicators of selecting an innovative supplier from the perspective of cooperation are: the supplier's ability, supplier's willingness, and supply risk. In the first step, the fuzzy Dimetal technique was used to reflect the mutual relationships between the criteria. In this way, at first, the matrix of the direct relationship of indicators was formed. According to the results, the willingness of the supplier has the most influence and has the most interaction with other criteria. In the next step, the main criteria of the research were prioritized, and it was found that the ability of the supplier and the willingness of the supplier and the supply risk are the first to third priority respectively. Finally, based on the calculations and the limit supermatrix, it was determined that the geographic proximity index is the priority. The ability of the supplier with a normal weight of 0.366 has the highest priority, the willingness of the supplier with a normal weight of 0.365 is the second priority, and the supply risk with a normal weight of 0.269 has the lowest priority. Finally, based on the calculations and the limit supermatrix, it was determined that "geographical proximity" with a weight of 0.1739 is the priority. "Organizational closeness" with a weight of 0.0936 is the second priority. "Commitment to continuous improvement in product and process" with a weight of 0.0833 is the third most important index.

Abstract The facility layout problem (FLP) aims to find the location of the facilities so that the departments do not overlap and the desired goals are optimized. The feasibility of proposed solutions in actual conditions is rarely considered in previous studies. This research proposes a two-objective mathematical programming model for solving the FLP to minimize the material handling cost and maximize the total closeness rating between departments, considering the limitations of space and allocable area. The objective functions are aggregated using the fuzzy goal programming approach. Due to the nonlinearity of the proposed model, an algorithm based on the Artificial Bee Colony (ABC) has also been developed to solve the model. The proposed method has been simulated in MATLAB on small, medium, and large samples containing 15, 50, and 100 departments respectively, and the results were compared with that of the PSO. The related aggregated objective function value was obtained as 0.845, 0.837, and 0.836 by the proposed method, and 0.809, 0.789, and 0.839 by the PSO algorithm. Respectively, the computation times were calculated as 15.21, 24.37, and 36.32 seconds in the proposed method, where the PSO obtained the best solutions in 18.22, 32.08, and 46.17 seconds for solving the sample problems. Hence, the calculation results show that the proposed method has a faster calculation time than the PSO and performs better in small and medium examples. Besides, a small variance of obtained solutions in 50 different runs, revealed a high stability of the proposed method.

Abstract Today, due to the instability in the various markets, the modeling of price shock becomes the challenging task due to continuous price changes caused by numerous external factors. Therefore, in this paper, a novel pricing mechanism for manufacturers who produces substitution products in a duopoly is proposed considering price shock, market prediction and customer behavior. Consequently, a game theory approach based on the Cournot model is designed to determine the equilibrium decisions. To extract managerial insights, Nash and Stackelberg approaches investigated in two scenarios before and after occurring price shock, considering the behavior of manufacturers and consumers. Moreover, the government policies are investigated in these two scenarios. The obtained results from parametric analysis indicated that the market forecasting parameter plays a significant role in the profitability and production quantity of manufacturers in both scenarios. Besides, the tax deduction policy provides better conditions for the government only before occurring price shock.

Abstract The new challenge for business managers is to model and simulate an efficient and effective perishable foods supply chain network that is resilient enough to deal with different disruptions. Therefore, this research aims to model a resilient supply chain for unnecessary perishable foods using an agent-based simulation to deal with future disruptions. To confirm the strategies and model, the statistical population and sample include 7 prominent university professors and 11 managers of various departments of companies producing perishable foods (sales department; production department; planning and warehouse department; laboratory and quality control department; and commercial department). NetLogo software has been utilized to test the agent-based model. The simulation environment in this study includes the behavior and interactions between the members of the supply chain of unnecessary perishable foods and the consumers in Shiraz City. The simulation results indicate that the use of strategies such as consumer behavior tracking, discount, awareness of product safety, robotics, the use of blockchain among the levels of distributors and retailers, and the activation of several supporting suppliers, leads to a resilience supply chain of unnecessary perishable foods under different disruptions. In addition, among the different scenarios, the 30% discount and 40% robotics have been the most effective in the resilience of the supply chain of unnecessary perishable goods under different disruptions.

Abstract Rework is one of the factors that always affects and jeopardizes the productivity of construction projects. With the increasing growth of urbanization in recent decades, provision of housing has become one of the most significant problems in Iran. The construction industry is faced with substantial problems, such as high costs of project delivery, poor financial performance, and inability to provide value to customers ahead of schedule. This paper aims to prioritize the reasons behind rework delay in terms of contractor, employer, and third party using the framework proposed in a real case study on the freeway projects in Iran. First, a number of well-known delays in freeway construction projects are considered using available theoretical resources, and then the importance of each of these criteria are identified using the stepwise weight assessment ratio analysis (SWARA) method. Based on the importance obtained by the SWARA method, the contract suspension index with the importance of 0.110 and the weather index with the importance of 0.043 are the most and least important factors in delays due to rework, respectively. Subsequently, by the importance identified, the reasons behind delays due to rework are determined using the weighted aggregated sum product assessment (WASPAS) method. According to the obtained results, the reasons for delays due to rework are prioritized. Based on the obtained results, employer ranks first, contractor ranks second, and the third party ranks third. Finally, a sensitivity analysis is carried out on the importance weights of the factors, and it can be seen that the prioritization of the causes of delays due to rework does not change with the increase in weights of the indicators.

Abstract Identifying and evaluating the key parameters of resilience on the evaluating of suppliers in order to select the best resource supplier in the industry is very important. For this purpose, in this study, after a comprehensive review of the literature and the application of fuzzy Delphi technique and using the opinions of petrochemical upstream industry experts, six key and general parameters of supplier resilience (including key performance factors, supplier responsiveness, supplier risk reduction, supplier technical support, supplier stability, information technology management) were identified in eighteen factors. Then the weight of the parameters was determined using the best-worst method (BWM). The output of this method indicates the extraction of "supplier risk mitigation systems" and "key performance factors" as the most important parameters, respectively. Then, the five suppliers of the mentioned industry were evaluated and ranked using TOPSIS (technique for order performance by similarity to ideal solution) technique and based on the extracted weight of the parameters. The output showed that the fourth supplier was in the first place and the second supplier was in the last place. Thus, the proposed model of this research can be a good guide for upstream petrochemical industries in the successful and future evaluation of potential suppliers in order to improve supply and achieve competitive advantage and further satisfy customer needs.