دکتر رضا توکلی مقدم

A few studies have addressed the failure or reliability of machines regarding cell formation problems. The general assumption in cell formation is that most machines are 100% reliable; however, they are not in a practical situation. Machine failure can severely affect the system performance and cause a delay in the scheduled date. A cellular manufacturing system is a philosophy among group technology ones, which is controlled by dividing a large system to multiple smaller sub-systems and facilitate manufacturing system management. This study presents a mathematical programming model for production planning problems in industrial units with reliability that prepares the conditions to utilize alternative routes for parts, which minimizes the lost costs along with maintenance costs. Since the considered problem is an NP-hard one, a genetic algorithm is used to solve the model. The presented mathematical model minimizes system costs, and the costs related to intra- and inter-cellular movements and maintenance by minimizing the costs of machine failures.

In this research, based on the conditions and limitations of Iran Khodro's automatic warehouse, a non-linear three-objective mathematical programming model is proposed. Since the demand for warehouse pallets has high uncertainty due to the fluctuation of customer demand, the mathematical model has been based on the P-Robustness method to deal with the effect of changing demand on the optimal solution, and then the problem is converted to a single-objective mathematical model. It turns out that two meta-heuristic algorithms e.g., genetic algorithm and simulated annealing algorithm have been used to solve it in large scales. To check the performance of the two algorithms, the T-test in Minitab software was used to compare the average values of the objective function from 15 times solving numerical problems in different dimensions. Introducing a new index for better allocation of pallets to storage locations in Iran Khodro's automatic warehouse has reduced the distance, time, energy, and costs of storage retrieval and handling, which due to the high turnover of parts in the warehouse, can be concluded that significant savings have been achieved

The purpose of this research is to present the model of training strategic managers of the country based on the data-based theory approach in the Ministry of Economy and Finance. According to its purpose, the research method is applicable, and qualitative in terms of implementation; content analysis type, and, exploratory in terms of nature. The statistical population of the research includes 12 university professors in the field of management and senior managers of the Ministry of Economy and Finance, and the sampling was done in a purposeful and snowball manner, and the interviews continued until the theoretical saturation. Grounded theory was used for data analysis, and MAXQDA 2020 software was used for coding the interviews. The results indicated that a total of 8 categories and 50 indicators were identified and extracted, and in the results of the leveling of the final model of the country's strategic managers training model, the consequences are the most effective factors in the country's strategic managers training model using the data-based method and, phenomenon-based and strategies are in the next priority. Contextual conditions and intervening conditions are placed in the third level of influence on the final model of training strategic managers of the country according to the data-based method, and causal conditions have the least impact on the final model of training strategic managers of the country.

The aim of this paper is to present a method to optimize maintenance planning for a exible manufacturing system. Such a system can be considered as a multicomponent system. Two types of methods may be used in the maintenance optimization of multi-component systems, i.e., static or dynamic methods. Static methods provide a xed maintenance planning, whereas dynamic methods redene the groups of maintenance operations at each decision time. Dynamic or opportunistic maintenance can incorporate up to date information such as 1) machines condition, 2) the number of maintenance teams, and 3) production-related constraints in redening the groups of maintenance operations. As the literature review shows, the existing dynamic or opportunistic maintenance models are mainly developed to specify classes of multi-component systems that are expected to operate continuously without considering the production-related constraints and performance indicators. The objective of this paper is to develop the existing dynamic opportunistic maintenance approaches for exible production systems that operate intermittently. To this end, a mixed-integer nonlinear mathematical model is developed to simultaneously decide on the maintenance grouping as well as lot sizing and production schedule. Moreover, the proposed model considers further underlying assumptions such as 1) the limited number of maintenance teams, 2) initial inventory, 3) assembly operations, 4) lot sizing, 5) sequence-dependent setup times, 6) safety stock levels, and 7) lots with unequal and variable sizes. The objective includes the costs of preventive and corrective repair activities as well as various production costs consisting of production and setup costs, tardiness penalty costs, and safety stock penalty costs. Due to the nonlinear nature of the failure rate of the production machines, techniques for solving linear mathematical models cannot be used. From this, a linear approximation of the model is presented. The validity and eciency of the proposed model were analyzed by implementation in a numerical experiment.

The idea that any deviation should be recognized as soon as possible will often be impractical. Despite the existence of numerous assignable causes in the process, their eects may be so small and minor against the permissible tolerance. Identifying them seems uneconomical from practical sights. If the process reaches a high level of capability, the production may be acceptable even though assignable causes befall. Since customer expectation will not be aected in this case, it is not economical to stop the process. By considering the level of speci- cations, some changes in the average can be allowed. Dividing the conditions of the monitored process into just black and white can be simplistic. In such cases, traditional control charts with two zones are not applicable. By dening the zone of indierence, permissible deviations can be tolerated. For such a situation, Acceptance Control Chart (ACC) is developed based on three zones. Suppose that a statistically assignable cause is detected using the traditional control charts; however, no signal is observed by the ACC. Thus, this change does not result in a nonconforming output, and there is no need to stop production since no operational loss occurs. The most important assumptions of the ACC are the normality and independence of the monitored data. In some industrial/non-industrial processes (e.g., continuous production processes, nancial processes, network monitoring, and environmental phenomena), serial correlation can be extracted among samples which violates the assumption of independence. Autocorrelation reduces the performance of traditional control charts by producing frequent false signals in the in-control state or makes them respond slowly to the detection of the outof- control state. The main purpose of this study is to develop an ACC for monitoring the data of the most widely used autocorrelated process, namely the rst-order autoregressive process AR(1). In this regard, two types of ACC are extended for the residuals of AR(1) processes. Upon evaluating the performance of monitoring methods using the average run length (ARL), it is found that the proposed EWMA chart has better results. Moreover, the economic-statistical design of the proposed chart is carried out at a lower cost.

In order to optimally manage the pressure and reduce the non-revenue water as much as possible, taking into account the costs of each activity, it is necessary to examine the priority of each solution by scientific methods and considering the costs, efficiency and duration of implementation. Therefore, in order to avoid cost wastage and efficient use of financial resources, it is necessary to use a comprehensive and systematic approach to identify different strategies that can be used to manage urban water pressure, the benefits and Compare their disadvantages. In the statistical population of this research, the knowledge and experience of 35 experts in the water and wastewater industry were used. Also in order to prioritize the proposed operations with the aim of reducing non-revenue water based on three criteria: the impact of the project, the duration of the project and the cost of the project, the integrated TOPSIS-VIKOR method has been used. Findings indicate that in the first priority, the installation of online data loggers pressure gauges, the second priority is the continuous updating of water maps and GIS network mapping, and in the third priority the completion of its information layers including pipe life, material and diameter, were located.

The hotel industry has become a competitive industry at the international level in recent decades, and countries have tended to use developed models and new techniques, and provide innovations to maximize income from it. As a result, it is critical to pay attention to how we can manage hotel income while noticing travel and passenger transportation costs and use modeling compatible with this field to optimize goal achievement.

The problems of optimizing hotel revenue management, passenger cost management, and analyzing how to expand the transportation used by them have been studied in this research. One of the key issues studied is to predict how to transport a passenger and choose its type based on different modes of travel such as air, rail, water, and road based on the amount of the passenger’s budget.

Many effective factors and criteria have been considered in the modeling done, and the amount of hotel reception capacity in the selected cities of travelers and the provision of various types of rooms with different pricing, and the examination of elements related to the services provided to travelers by the hotel and different accesses of the hotel, which is based on the hotel’s revenue model, affect on. It is useful to estimate the state of competitive factors of hotels.  Noteworthy, the transfer and mode of transportation have been determined to predict the level of demand for hotel reservations for all types of travelers during different periods in different tourism seasons. This subject is based on the traveler’s budget allocated for paying expenses during the travel pattern and the related results extracted from the estimated income model, as well as the influencing factors in choosing the hotel and transportation.

Originality/Value: 

In the current study, the design of NP-Hard problems led to the use of exact methods in small-sized problems and two multi-objective meta-heuristic algorithms, namely NSGA-II and MOPSO, in medium- and large-sized problems. The computation results show that the proposed algorithms are efficient and suitable methods for problem-solving.

This paper optimizes a tour route for tourists in groups considering time windows and tourist fatigue. It is based on points of interests, which are
grouped in clusters and is a branch of an orienteering problem, known as a Tourist Trip Design Problem with clustered points of interests. There are a variety of transportation modes for trips, in which all the tourists in a group can choose one respecting some constraints, such as time, distance, and possibility of using a specific vehicle. Each point of view has starting and finishing service times for tourists. Therefore, each point of interest can be visited only in a special time window. In such problems, human health and energy should be noticed so that tourists can enjoy most of the tour and the total utility is increased. One of important factors in human health and energy is fatigue. To apply the factor into the problem and express it implicitly, points of interests are grouped into three clusters based on activities that tourists do in each kind of point of views: tourist attractions, shopping malls, and resting places. In each route, tourists must visit at least one place of each cluster so that fatigue can be relieved. A mixed-integer linear programming model with two objective functions is proposed. The model is verified and assessed through five numerical examples that is designed for a hypothetical tourist area. The example is solved by GAMS software using the CPLEX solver. Also, the sensitivity analysis based on each objective function separately is performed on some of the parameters, such as visiting costs and time windows. Therefore, both tourists and managers with certain points of interests can plan and change them to decrease the cost and increase the utility and visits.