Journal of Industrial Engineering and Management Studies
Volume:9 Issue: 2, Summer-Autumn 2022

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.

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.

Nowadays, not only improving service levels is not sufficient for consumer satisfaction, but also, the consumers themselves determine product or service quality. In other words, we can interpret quality as "the degree of accordance with the consumer's need." Therefore, we should look for solutions to identify consumers' needs and requirements for applying them in the design and development of the product or service. One of these methods is the Kano model. This model shows the decision maker if any of the consumers' requirements are in the product/service or not and how much it will affect their satisfaction. This tool classifies consumers' needs for converting them to design requirements. But, human mentality and behavior always are accompanied by uncertainties. Linguistic variables or fuzzy numbers have been used in the literature to overcome this defect. Researchers developed the fuzzy Kano's model using this method and enhanced the model's efficiency compared to the deterministic one. The efficiency of this model has increased compared with the deterministic one. However, the decision-makers are unsure how to classify customers' needs using this strategy. This research uses a Fuzzy Inference System (FIS) to tackle this challenge. The essential contribution is developing a fuzzy Kano's model based on FIS for consumer requirements analysis. A case study from the restaurant industry in Yazd city of Iran was considered to validate the proposed model. The results show the superior performance of the proposed model compared with fuzzy Kano's model in recognizing consumers' needs.

In the proposed study, an inventory model of a two-echelon green perishable supply chain which consists of one manufacturer and one retailer is investigated. The produced items have a deterministic shelf life and will be removed from the shelves when they reach to their expiration date. A novel formulation of the demand function is also presented, which is a multiplicative function of time after replenishment, retail price, and green improvement level. The formulated demand function increases with the time to expiration and the green improvement degree; it also decreases with the retail price. The mentioned characteristics in this supply chain are derived from the industries of selling fruits, vegetables, meat and poultry, as well as dairy products. The manufacturer is considered the leader of the Stackelberg game, and three approaches are proposed to solve the inventory model: Decentralized, Centralized, and Coordinated by the revenue and green technology investment cost sharing contract, which guarantees more profit for each member than the decentralized decision-making approach. The numerical results demonstrate that the proposed revenue-sharing contract could successfully help the supply chain members to achieve the potential supply chain profit in the centralized approach. A comparative study is also conducted to show the differences between implementing and not implementing green technology investments, which shows the profitability of making green technology investments when consumers have green preferences. It was observed that as the reservation cost increases, the importance of investments in green technology will increase for both parties. Also, with high potential market demand, it is more beneficial to invest in green technology. This study deals with a contribution to the formulation of the demand function, as a novel multiplicative function of time after replenishment in the form of power function, and retail price and green improvement level in the form of complementary linear function.

Environmental pollution and the deterioration of natural resources are now considered significant challenges in human societies. In fact, environmental pollution is mainly caused by manufacturing industries. Most industries (e.g., the cement industry) employ the green supply chain to overcome ecological problems, a goal that requires various techniques for quantifying the environmental impacts on the supply chain to improve processes. This study aimed to evaluate the green supply chain performance at 11 cement manufacturing factories through the hybrid BSC–DEA approach within the 2018–2020 period. After the principal indices were identified and placed in each perspective of the balanced scorecard (BSC), the DEMATEL technique was adopted to determine the relationships of perspectives. The multistage data envelopment analysis (DEA) model was then employed to measure the efficiency of each BSC perspective and the total network efficiency. Finally, reference units were introduced to improve the inefficient units. According to the results, managers focus mainly on the financial section and customers but pay less attention to growth and learning. The organization yielded the best efficiency in 2020 by following an upward trend. The energy consumption rate, clinker–cement ratio, and CO2 emission rate were analyzed in this study to better investigate the environmental problems in the cement industry. Most of the units followed upward trends in both CO2 emission and energy consumption but experienced a downward trend in clinker production.

In a data-driven decision-making process, there are various types of data that should be thoroughly processed and analyzed. Data mining is a well-recognized method to obtain such information by analyzing data and transforming it into actionable insights for further use. Among the various data mining techniques such as classification, clustering, and association rules, this research focused on classification techniques and presented an innovative regression-based learning approach in the decision tree (DT) models. DT algorithms are easy-to-understood and can work with different data types including continuous, discrete, and non-numerical. Despite a large number of existing studies, which attempt to enhance the performance of the DT models, there is still a gap in accurately extracting knowledge from databases. In this research, this issue is addressed by exploiting regression and coefficient of determination (R2) methods in a DT. The proposed tree provides new insights in the following aspects: split criterion, handling continuous and discrete variables, labeling leaf node, pruning process by stopping criteria and tree evaluation. The superiority of the proposed algorithm is demonstrated using a real-world hospital database and a comparison with existing approaches is provided. The results showed that the proposed algorithm outperforms the existing methods in terms of higher accuracy and lower complexity.

Balancing the production system’s resources like budget, equipment, and workers is one of the most important concerns of production managers. Managers seek to find an optimal way to balance their resources in production systems. By evaluating U-shaped assembly line papers, this investigation adds the literature on U-shaped assembly lines to the simultaneous examination of the balance ergonomic risks of human workers and current costs in the system when government offers tax benefits for using disabled workers. The mentioned outlook was not considered in previous papers. This study proposes a two-objective model to evaluate the effects of considering both robots and human workers in a U-shaped assembly line. The first objective is to minimize the system costs, and the second is to minimize the ergonomic risks. Human workers are divided into normal and disabled. The disabled workers are hired to enable tax benefits from the government. The constraint programming model for small and medium-sized problems and the grasshopper optimization algorithm (GOA) for big problems are developed to dissolve the problem. Numerical results show that two objective functions can also level system costs and ergonomic risks. The sensitivity analysis section analyzes three effective parameters (Production cycle time, Fatigue rate of human workers, and government tax benefit). It is shown that production cycle time directly affects using a robot or human workers (due to their mean time of speed), fatigue rate determines the allocation of tasks, and tax benefit helps to determine whether using disabled workers or not according objective functions. Also, it should be noticed the efficiency of GOA is shown by a comparison of several examples. Therefore, it is used for big-scale test problems.

The purpose of this paper is to optimize the integrated problem of lot-sizing and scheduling in a flexible job-shop environment considering energy efficiency. The main contribution of the paper is simultaneously considering lot-sizing and scheduling decisions, while accounting for energy efficiency.  In order to achieve this objective, a mathematical model has been developed for integrated optimization of scheduling and lot-sizing problems. The developed model uses a big bucket approach and is presented as a mixed integer nonlinear problem (MINLP). The BARON solver in GAMS software has been used to solve the proposed MINLP model. By defining the relative optimality limit (OPTCR) of 0.05 for the termination criterion in BARON solver, GAMS has not been able to solve large problems at a specified time to achieve relative optimality. Therefore, due to the NP-hard nature of the problem, a new genetic-based evolutionary algorithm has been developed to solve the problem of large scale. In the developed algorithm, a different approach (instead of cross-over and mutation operators) is used to generate a new solution. By presenting and solving various problems, the efficiency of this algorithm for solving big problems is shown. Comparing the values of the objective function obtained from the genetic algorithm and the exact method shows that, especially in large problems, the genetic algorithm has been able to achieve a better solution than GAMS software in a limited time. It has also been shown that energy efficiency has a significant effect on the solution of the problem.

This research focuses on the integrated production-inventory-routing planning (PIRP) problem, which persuades necessary decisions to study the supply chains (SCs). Previous research studies confirm that corporations coping with production, inventory, and routing problems, can remarkably decrease the total costs and meet the customers' demands efficaciously. Currently, because of severe obligations, corporations must consider environmental factors and cost optimization in their activities. Accordingly, in this article, a green PIRP (GPIRP) is addressed using mixed-integer linear programming (MILP), which simultaneously takes into account the economic and social decisions of the SCs. Furthermore, because the SCs routing-oriented problems belong to the NP-hard categories, we propose a two-phase heuristic solution method; in the first phase, the inventory and production decisions are determined using MILP formulation. The second phase seeks to find optimal vehicle routing and transportation decisions using a genetic algorithm (GA). Two main deals leading to this paper's unique position are to develop a bi-objective MILP model for the GPIRP and present a novel hybrid two-phase heuristic solution method that sequentially utilizes the CPLEX solver and the proposed GA. To validate the computational performance of the proposed solution method, we conduct a case study from the Ahvaz Sugar Refinery Company in Iran to demonstrate the advantages of the formulated model. Moreover, we handle sensitivity analyses to study the effectiveness of the suggested method for the large-sized examples

The high rate of outbreaks of the Coronavirus Disease 2019 (QUID-19) is a warning about health, economic, and social issues that are affecting the whole world. The COVID-19 pandemic has had many financial and economic implications worldwide. These economic turbulences, along with market uncertainty, can affect investors' confidence in firms' financial performance and, consequently, may lead to various financial crises. Audit quality can affect the auditors' ability to detect material misstatements. This study is conducted to investigate the effect of COVID-19 on audit quality during social distancing in companies listed on the Tehran Stock Exchange. This is an applied study in terms of purpose and a descriptive-comparative one in terms of method. In the study, one main hypothesis and five sub-hypotheses were developed, and the Kruskal-Wallis test with SPSS software was used. The statistical population included companies listed on the Tehran Stock Exchange from 2017 to 2020. The findings suggest that COVID-19 affects audit quality during social distancing in companies listed on the Tehran Stock Exchange.

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%.

Vendor-managed inventory (VMI) is a popular inventory management system that allows a vendor to access sales data and manage inventory levels for his retailers. The formulation of service level and pricing decisions are finite in the VMI model literature. The study examines how a manufacturer and its retailer communicate with one another to optimize their net profits through modifying service level, pricing, and inventory policy in a VMI system employing a Stackelberg game. The manufacturer produces a product and distributes it to several retailers at a similar wholesale price. The retailers subsequently offer the product at retail pricing in independent marketplaces. The Cobb-Douglas demand function could characterize the demand rate in every market, which is an enhancing function of the service level, however, a reducing function of retail prices. The manufacturer selects its wholesale pricing, replenishment cycles, backorder amount, and binary variable for production capacity to optimize profit. Retailers determine retail pricing and service levels so that they may optimize their profitability. Solution procedures are evolved for finding the Stackelberg game equilibrium from which no firm is inclined to deviate from maximizing its profit. The balance benefits the manufacturer while increasing the revenues of the retailers. If the retailers are prepared to engage with the manufacturer via a cooperative contract, the equilibrium could be enhanced to the advantage of both the manufacturer and his retailers. Ultimately, a numerical example is shown, along with the appropriate sensitivity analysis, to demonstrate that. 1) In certain circumstances, the manufacturer might benefit from his leadership and monopolize the additional profit generated by the VMI system. 2) The manufacturer's profit, and later the retailers' profit, could be increased more by the cooperative contract, in comparison to the Stackelberg equilibrium; 3) Market-related parameters have a substantial impact on the net profit of any enterprise.