Journal of Industrial Engineering and Management Studies

Abstract Time is one of the most valuable assets in industry, and cost is another highly regarded factor. Optimal utilization of these resources can increase efficiency and profit. The parallel machine scheduling problem is a fundamental issue in industry and services. This research proposes a two-objective mathematical model for parallel machine scheduling. The first objective function is defined as the makespan, which is the completion time of the last job. The second objective function is defined as the maximum cost incurred by any single machine, which is a function of the sum of the processing costs of each operation and the fixed cost of purchasing and maintaining the machines. Each job consists of multiple operations, and all operations must be completed to finish the job. Additionally, it is assumed that jobs have priorities, and precedence constraints between operations must be satisfied. Due to the model's non-linearity and the problem's complexity, a metaheuristic algorithm based on the particle swarm optimization (PSO) approach is developed to solve the proposed model by aggregating the objective functions. The proposed method is simulated in MATLAB on three sample instances in small, medium, and large scales. The computational results demonstrate the robustness and efficiency of the proposed method.

Abstract Customer equity, the potential profit that all of the company's customers can generate throughout the business-customer relationship, is a critical business management concern for companies. In the face of intelligent customers and high market competition, the survival of any brand depends on its ability to increase the specific value of the customer, the management of customer relations, and retention. However, studying customer equity management under dynamic situations and analyzing factors affecting social media marketing has not been fully explored. A dynamic approach is presented in this manuscript to cover this gap. This research proposes a model using the system dynamics approach to analyze and predict the influence of social media marketing activities on customer equity. The model, simulated in Vensim under two possible scenarios, provides practical insights. The results show that if the policy of commercial companies leads to a decrease in customer equity, the customer's loyalty will not last for more than ten months. Conversely, increasing customer equity leads to a reliable, steady state of customer loyalty after the fourth month, demonstrating the practical implications of the research that should be noticed by the department of marketing management.

Abstract Social trust is defined as an individual's reasonable opinion towards other members of society, which leads to expanding and facilitating social relations. Trust is vital as a social mechanism with diverse social, political, economic, and psychological functions. Many studies have been conducted on social trust, and several factors have been introduced. Most of these studies are primarily static and focus on the structural investigation of social trust and do not consider the inter-relation effects among essential parameters. To cover this gap, a dynamic approach is presented in this manuscript. This research identifies and models factors affecting social trust using the system dynamics approach and aims to analyze the behavioral equations of the subject under dynamic conditions which is addressed as the main contribution of this paper. For this purpose, an online questionnaire is designed, data are collected from 1238 Iranian social network users, and the cause-effect model is presented. The proposed model is simulated in Vensim under three scenarios, and the results revealed that having a 0.68% population growth rate, social trust will reach a maximum of 56% over 35 years and begin to decrease afterward. More simulations showed that a 1% population growth rate leads to a 52.5% equilibrium in the long term. Besides, a slightly higher growth rate (1.2%) does not lead to balance, and social trust will continue to experience a declining situation.

Abstract In recent years, the significant increase in energy consumption and global warming have raised international concerns. Given the interconnectedness of economics, energy, and environmental concerns, energy consumption is critical in planning various systems. Optimizing production operations in various industries is a significant and complex challenge. Given the increasing global market competition and the importance of cost reduction, production process optimization has become increasingly important. One critical issue in this area is job scheduling in production systems with parallel machines. These systems' machine performance and energy consumption differences can significantly impact operating costs and job delivery times. These differences lead to machine heterogeneity, which is observed in many modern industries. Considering the challenges in managing energy consumption and the negative impacts of delays in product delivery, optimizing production processes to increase system efficiency and reduce energy consumption has become increasingly important. This research investigated the job scheduling problem in production systems with a heterogeneous parallel machine environment to minimize energy consumption and total job tardiness. In this research, a two-objective mathematical model for job scheduling was first designed, and a multi-objective meta-heuristic algorithm based on decomposition was used to solve this model. It was simulated in MATLAB software on several small, medium, and large sample examples. Comparing the results of the proposed method with those of previous methods shows the efficiency and superiority of the proposed method.

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 In this research, a goal programming model is proposed for optimizing the production of Boehmite in the Iranian West Minerals Applied Research Center (IWMARC). This product can be produced using internal or external methods and currently is produced traditionally, and the production process is not optimal. This research optimizes the production process using the linear goal programming technique. A multi-objective model is proposed containing 20 goal constraints of effective parameters concerning production, sales, raw materials usage, water and energy consumption, customer needs, and workforce components. The main objectives are ranked using the AHP method, and the model is implemented in Lingo 11 software. The computational results show that due to the impact of the price of foreign raw materials and the limitations caused by its use, as well as the good efficiency of the gasification method in the internal(domestic) method, the domestic method can effectively tackle the major and minor objectives of the production system of in IWMARC and achieve 16 goals out of 20 goals with zero or positive (more than the expected level) deviations. Besides, changing the technical and production specifications according to customer needs can increase profitability up to 3.75 times the current amount (375%) and decrease inventory cost by 32%.

Abstract The Cellular Manufacturing System (CMS) is one of the most efficient systems for production environments with high volume and product variety which takes advantage of group technology. In the cellular production system, similar parts called part families are assigned to a production cell having similar production methods, and the needed machines are dedicated to cells. Determining part families and allocating the necessary machines to the production cell is known as the Cell Formation Problem (CFP) which is known as an NP-Hard problem. Safaei and Tavakkoli-Moghaddam (2009a) proposed a model that is widely used in literature which suffers some killer weaknesses highly affecting subsequent researches. In this paper, the mentioned model is modified and revised to fix these major issues.  Besides, due to the NP-Hard nature of the problem, a meta-heuristic algorithm based on Gray Wolf Optimization (GWO) approach is also developed for solving the revised model on the sample examples and the results are compared. Simulation results indicated that the proposed method can reduce the total cost of the manufacturing system by 3% in comparison with the base model. Furthermore, simulation results of five sample problems indicate the better performance of the proposed method comparing with Lingo and PSO.