Journal of Industrial Engineering and Management Studies
Volume:9 Issue: 1, Winter-Spring 2022

Regarding contractors are one of the fundamental features of construction and industrial projects, therefore the selection of contractors is one of the major decisions of managers and decision-makers. This paper uses the multi-criteria decision-making method Analytic Hierarchy Process (AHP) to incorporate the weightings of input and output variables into Data Envelopment Analysis (DEA) for evaluation and ranking of contractors (Zarand Iranian Steel Company). At first, according to previous research, the most effective and important evaluation indicators of contractors are selected, then in the proposed model with the AHP approach, seven input indicators and three output indicators are weighted and ranked, and the performance of 20 contractors from one of the company's projects is determined and ranked with the input-oriented CCR model. By applying this approach, decision-makers and practitioners can effectively compare operational efficiency between contractors, and therefore generate more informed and they can provide appropriate solutions to increase the efficiency of other contractors.

Competitive advantage in features, number of branches, or location of any company enables it to provide better services to customers than competitors. In this article, the issue of location in a situation where competitors can decide based on competitor conditions to maximize their profits is examined. First, based on the conditions and characteristics of each competitor, including the number of branches and budget limit, the performance range of each competitor is determined as the radius of effect. Two mathematical formulas are presented for the player and using the concepts of game theory, each player's market share in the competitive environment is determined to earn maximum profit. To solve the problem, first, the initial answers were obtained through the ant colony algorithm, then these answers were entered as input to the Simulated Annealing algorithm, which has a high speed to obtain the answer. The models developed for the two supermarkets have been evaluated and the results have been approved by experts.

Social networks provide marketing managers and businesses with opportunity to target their customers. By understanding the demographics of users, marketing managers can offer suitable products and services. Although direct questioning can be drawn upon to solicit users’ demographics such as age, some customers due to privacy concerns do not like to reveal their personal information and, it cannot come in handy for potential customer identification. The huge amount of data social networks generate can solve this problem. Previous studies in the prediction of demographic characteristics suffer some limitations because they were mainly text based and hence, language-bound. This study investigates how some interactive data can predict users’ age. Further, it examines if classification methods can be used for age prediction. The results revealed that the number of friends, number of opposite sex friends, number of comments received, and number of photos which users share can predict users’ age. Also, a linear relationship between interactive data and users’ age was found.

Railways are considered the efficient transport system that provides the possibility of transportation through a rail network. Railway stations are the major part of the rail transport system and evaluating its performance is of particular importance, since various activities such as passenger transport, and welfare and commercial services are provided in this part of the system. In this research, the efficiency of Iranian railway stations in 19 zones is measured by data envelopment analysis (DEA), and the efficient centers and reference units for inefficient centers have been identified by analyzing the efficiency of stations. Railway stations are analyzed using an output-oriented slack-based measure (SBM) model with a constant returns to scale. The performance of station was evaluated by the input index of total station area, number of platforms, number of staff, number of available seats, total cost of station, output index of number of passengers transported, number of trains stopped, and total revenue of the station. The ranking results showed that Tehran, Mashhad, Shahroud, Zanjan, Qom, and Kerman stations had the highest level of efficiency. Finally, for inefficient stations, the surplus values of inputs and slack values of outputs were provided to improve the efficiency.

Nowadays, supply chains have been facing significant economic forfeitures because of unpredicted disruptions. Furthermore, managers try to design sustainable and reliable supply chains. In this paper, we present an inventory-location model to propound a reliable three echelon supply chain which includes a production plant, distribution centers, and retailers. The production plant distributes a single product to retailers through distribution centers that are at risk of disruption. We considered reactive (consider backup distribution center for each retailer) and proactive (distribution center fortification) activates to enhance the supply chain's reliability. The proposed model indicates the location of distribution centers (DCs), the DCs that must be fortified, the allocation of retailers to DCs, and the inventory policy of DCs. The problem is formulated as a nonlinear integer programming model. Since our model is an NP-hard problem, we provide a Lagrangian relaxation algorithm to solve it. Numerical examples demonstrate the computational efficiency of the proposed solution algorithm. Results show that, with increasing the budget of fortification, the total expected cost will decrease. A higher inventory cost leads to an increase in the number of opened DCs, while higher ordering cost and the transportation cost from production plant to DCs decrease the number of opened DCs. Among other results, the number of opened DCs is positively affected by the cost of transporting from DCs to retailers.

In this paper, a new model for hybrid flow shop scheduling is presented in which after the production is completed, each job is held in the warehouse until it is sent by the vehicle. Jobs are charged according to the storage time in the warehouse. Then they are delivered to customers by means of routing vehicles with limited and equal capacities. The problem’s goal is finding an integrated schedule that minimizes the total costs, including transportation, holding, and tardiness costs. At first, a mixed-integer linear programming (MILP) model is presented for this problem. Due to the fact that the problem is NP-hard, a hybrid metaheuristic algorithm based on PSO algorithm and GA algorithm is suggested to solve the large-size instances. In this algorithm, genetic algorithm operators are used to update the particle swarm positions. The algorithm represents the initial solution by using dispatching rules. Also, some lemma and characteristics of the optimal solution are extracted as the dominance rules and are integrated with the proposed algorithm. Numerical studies with random problems have been performed to evaluate the effectiveness and efficiency of the suggested algorithm. According to the computational results, the algorithm performs well for large-scale instances and can generate relatively good solutions for the sample of investigated problems. On average, PGR performs better than the other three algorithms with an average of 0.883. To significantly evaluate the differences between the algorithms’ solutions, statistical paired sample t-tests have been performed, and the results have been described for the paired algorithms.

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.

Scheduling is a vital part of daily life that has been the focus of attention since the 1950s. Knowledge of scheduling is a very important and applicable category in industrial engineering and planning of human life. In the field of education, scheduling, and timetabling for best results in classroom teaching is one of the most challenging issues in university programming. As each university has its own rules, policies, resources, and restrictions a unique model of scheduling and timetabling cannot implement. This can cause more complexity and challenging point which needs to be considered scientifically. This study presents a sound scientific model of timetabling and classroom scheduling to improve faculties’ desirability based on days, times, and contents preferences. A sample in Parand branch of Islamic Azad university   chooses using the Bat metaheuristic algorithm. By considering the limitations, some unchangeable constraints regarding the specific rules and minimal linear delimitation of the soft constraints of the model, using the appropriate meta-heuristic algorithm to reduce the model run time to a minimum. The results show that the algorithm achieves better results in many test data compared to other algorithms due to meeting many limitations in the problem coding structure. The Bat algorithm is compared with four other algorithms while comparing the results of solving the proposed mathematical model with five metaheuristic algorithms to evaluate the performance. In this research, a multi-objective model is presented to maximize the desirability of professors and to solve the model using Bat, Cuckoo Search, Artificial bee colony, firefly, and Genetic algorithms. In this research 40 different runs of each algorithm were compared, and conclusions were drawn. Modeling has been solved with GAMS and MATLAB software and using the bat meta-heuristic algorithm. It is concluded that in this model, the bat algorithm is the most appropriate algorithm with the shortest time, which has caused the satisfaction of the professors of the educational departments of this academy.

The purpose of this study is to design a post-purchase regret model and determine online business strategies. Regret is a state of mind in which the customer is hesitant to buy a product or service. This hesitation can be due to paying a high price for the quality received, comparing the quality of the goods or services received with competing companies, or the result of various risks that may arise in online shopping. To design the regret model, the qualitative research method was used utilizing the grounded theory strategy and the Strauss-Corbin systematic design. The sampling method was judgmental and to collect information and achieve theoretical saturation, 14 semi-structured interviews were conducted with university professors and managers of online commerce and web-based businesses. The key points of the interviews were analyzed during the three stages of open, axial, and selective coding. For the validity and reliability of the research, the members` review, participatory, triangulation, and retest methods were used. The results were extracted in a paradigm model with 20 categories and 76 concepts. The Delphi method was used to prioritize the constructive factors of the model and the opinion of experts was determined in 2 stages and converged with a standard deviation of less than 0.05. The results of the research help online business activists to gain an accurate understanding of post-purchase regrets in online shopping behavior.

The management and design of supply chain networks in various dimensions are so critical today that managers' decisions significantly impact the configuration and flow of material in the network. Above all, supply chain management intends to reduce costs. The inability to accurately predict certain features, such as demand, can complicate the cost estimation process. To that end, an essential parameter is the reliability of supply chain networks. Considering the reliability of the supply chain network brings the model closer to reality, and the wellness or failure of its elements under different scenarios increases the enthusiasm to face unpredictable events in managers and helps network performance. Furthermore, appropriate management and design of the supply chain network can increase customer satisfaction and reduce costs in the long term. In this research, a four-tier supply chain network was designed to reduce the costs through a two-stage stochastic programming attitude. The combined metaheuristic method (genetic and simulated annealing algorithms) was used to solve the model. By treating the reliability of entities and routes and its effect on reducing cost as an essential criterion in the mentioned problem, it was showed that a reliable system has lower costs than an unreliable system.

The purpose of this systematic review is to identify and categorize the application of soft operations research methods in healthcare settings. A systematic review was conducted to identify published papers on the application of soft operations research methods in the healthcare setting, using Google Scholar, Scopus, PubMed, Emerald, Elsevier, Web of Science, and ProQuest databases through December 2021. A total of 69 papers met our selection criteria for the systematic review. Soft operations research methods were used in a wide range of healthcare fields, including healthcare management, health informatics, e-health, and medical education, for identifying requirements, problem-solving, system design and implementation, process improvement, policymaking, knowledge management, and managing resilience, and marketing. This study contained restrictions on access to the full text of some articles and dissertations that had little impact on the study’s quality. The present study demonstrates the use of soft operations research methods in various areas of the healthcare system to better understand problematical situations. This paper can help to use soft operations research methods further in the healthcare problems, especially in the design and implementation of e-health and emerging new technology.

In today's competitive market, reducing costs and time is one of the most important issues that has occupied the minds of managers and researchers. This issue is especially important in the field of supply chain management and transportation because by reducing time and cost, manufacturers and service providers can gain a competitive advantage over competitors. Accordingly, vehicle routing issues are one of the most important issues in this field because it is directly related to the time of service or product delivery and also by optimizing the network, reduces the cost of the entire network. Therefore, in this study, the intention was to evaluate the problem of vehicle routing (trucks) by considering the time constraints and using a multi-objective approach. Therefore, we discussed each of the factors separately based on the issue. The results of this study show. In this research, the model with two objective functions will be solved by two metaheuristic algorithms NSGA-II and MOPSO Managers are concerned with time and cost management in today's competitive markets, which is seen as a source of competitive advantage. The present study aims to find a solution to a bi-objective function model by employing two metaheuristic algorithms, NSGA-II and MOPSO. Additionally, a criterion for comparing algorithms is presented. The findings show that the MOPSO algorithm yields the optimal solution.  The contribution of the present study in comparison with other previous studies can be summarized as follows: Environmental protection based on reducing pollution and its effects as well as reducing costs. Finding the desired route taking into account the complexity and difficulty of the route. Managers are concerned with time and cost management in today's competitive markets, which is seen as a source of competitive advantage. The present study aims to find a solution to a bi-objective function model by employing two metaheuristic algorithms, NSGA-II and MOPSO. Additionally, a criterion for comparing algorithms is presented. The findings show that the MOPSO algorithm yields the optimal solution. The contribution of the present study compared to other previous studies can be environmental protection and cost reduction that the two factors are compared and the results of the two methods are analyzed.