نشریه پژوهشهای مهندسی صنایع در سیستمهای تولید
پیاپی 20 (بهار و تابستان 1401)

The wood and paper industry is one of the oldest industries with a high level of income generation and employment. The supply chain of this industry includes the exploitation of natural resources and wood cultivation to industrial factories for the production of wooden boards and workshops for the production of wood and paper products. This chain in Iran is involved in challenges that have reduced the productivity of this industry. This research introduces an integrated approachbased on the Quality Function Deployment (QFD) and Multi Criteria Decision Making (MCDM) techniques to identify the most important issues in supply chain of Iran's wood and paper industries and to provide proper improvement solutions. In this way, 45 problems were identified in the field of wood and paper industry by reviewing the literature. The issues identified by the TOPSIS method were ranked according to 4 criteria of the level of influence on production, price, sales and quality. In this stage, 23 problems were selected to enter the House of Quality (HoQ) matrix, and 12 solutions were presented to solve and improve them. The final weight of problems and solutions and the correlation between solutions were identified according to the HoQ matrix. The results show that the most important issues facing the wood industry are the lack of proper and accurate production statistics, many problems and cumbersome rules in the process of receiving bank facilities, the lack of supervision by government departments on the wood market, and the low awareness of producers about the wood market. Also, the most important solutions for improvement are planting early trees, providing raw materials for wood structure, creating settlements and industrial clusters related to wood products, allocating government resources to the market, monitoring more government agencies on wood products, and creating the necessary standards on the market. It is noteworthy that for the implementation of the solutions, attention must be paid to the correlation between the solutions so that the effect of synergy is created in the implementation of the solutions.

Today, project-oriented organizations have several parallel projects that can start planning in parallel, and most of these companies are facing funding crises in order for projects to be successful. On the other hand, by completing executive projects. And assigning it to a sponsor or employer brings a good profit to the organization. In case of improper selection of projects to start and lack of integration between financial resources, in addition to causing loss of resources and energy of the organization, it will cause failure of the organizational structure and stop current projects and create much higher losses in the organization. Therefore, in this research, a multi-objective MINLP mathematical model was presented. The mathematical model of this research includes multiple objectives of maximizing the profit from the selection of the production project, minimizing the cost and implementation risk of the projects. Also, information about the parameters of the mathematical model based on the receipt from the Kayson construction and production company has been used. Finally, the proposed model was evaluated and validated by two algorithms, MOSA and MOKA. In the validation performed in the study and based on the performance of the optimal solutions, it was shown that the MOSA algorithm is more efficient.

The hub location problem is one of the most fundamental and crucial issues in transportation systems and decision-making. The primary purpose of a transportation network is to transfer traffic between demand points via a hub, and hubs are essential to this process. The failure of hubs has garnered considerable attention of researchers in recent years. This research examines the problem of hierarchical hub location using bi-level programming. This study presents a model for optimal hub coverage. In non-central hubs, disruptions are intentional. The objective function of the second level is to minimize the problem's coverage by disabling the hubs, whereas the objective function of the first level is to minimize the problem's damage while expanding its coverage. The studied problem was solved using the simulated annealing and the full enumeration method. The proposed model has been solved for a variety of different scenarios, including fluctuating demand and hub count, fluctuating discount factors between hubs, and fluctuating coverage radii. According to the numerical results, as the covering radius increases, the number of located nodes and the problem's coverage also increase. In conclusion, an analysis of the employed solution methods concludes that the proposed meta-heuristic method is both effective and applicable to larger data sets.

Background & Objectives Obesity can create physical and mental disabilities in social interactions and communication skills. One of the factors affecting obesity is body image, i.e., mental representation of body size, shape and form. Body image comprises cognitive, emotional and behavioral components. Also, obesity may cause anxiety. Overeating is another psychological factor that can affect obese women. Cognitive–behavioral therapy is one of the treatment techniques that may positively affect the psychological status of obese women. Group cognitive–behavioral therapy is a type of psychotherapy that combines cognitive and learning theories with therapeutic techniques derived from cognitive therapy and group behavioral therapy. Another helpful therapy is mindfulness–based reduction stress therapy. It is one of the psychological approaches that may be effective in reducing psychological disorders in obese women. This study aimed to compare the effectiveness of these two stress reductive approaches on improvinNowadays, due to environmental issues, government laws and economic interests, attention to product collection and reproduction has increased. In this thesis, a two-level closed loop supply chain is considered, which includes a manufacturer and a retailer, and the demand is met through manufactured and remanufactured products. The quality of second-hand products that are remanufactured is not as same as the quality of new products, it is random and follows the distribution. Also, the rate of return of goods is considered random and follows a uniform distribution. The manufacturer determines the wholesale price of the remanufactured and manufactured products in order to maximize their profit, and the retailer determines the retail price of the manufactured and remanufactured products and the length of the cycle in order to maximize the profit. The interaction between the retailer and the manufacturer has been modeled by using the game theory approach in the static and Stackelberg (either the leader manufacturer or the leader retailer) games. According to studies, the profit of the supply chain (the manufacturer and the retailer) in Stackelberg game is more than static game.

Current societies are obliged to make the necessary plans for effective response and reducing the destructive effects of disasters. In this research, a mathematical planning model under uncertainty has been developed for earthquake relief and response. In the presented model to increase the reliability, the possibility of facility failure is considered according to the intensity of the earthquake. Distribution centers are considered to be of two types, the first type is local distribution centers, which use public centers and are close to the accident points, these types of centers are prone to failure because they use public centers. Another type is the reliable distribution centers that are built outside the accident area and have a very low probability of failure due to spending more money to build them. In the new model presented, in addition to considering the reliability capabilities, it has been tried to provide a more complete model for planning in the transportation issue by considering the multi-trips mode in the vehicles. Uncertainty is presented using the probability approach based on the modeling scenario and a case study from the city of Tehran to show the performance of the proposed model. The results obtained from the proposed model show that effective and efficient aid delivery is done in terms of time and cost, therefore it can help crisis managers in response to the crisis in order to provide the required budget and appropriate logistics planning.

Recently, supply chains and supply networks are developing towards intertwined supply networks. These new, dynamic structures are different from linear supply chains with static structures and require a revision of some traditional concepts. The aim of this research is mathematical modeling of the three-level supply network (including suppliers, firms, customers) based on resilience. Its approach is quantitative and mathematical modeling. In this research, first the problem is modeled and solved using real data in GAMS software, and after determining the optimal values of the decision variables, the results are analyzed. Also, a sensitivity analysis has been performed on the effective parameter (production capacity). The analysis of the results showed that increase of the capacity parameter did not affect the total cost of the network due to the constant demand. The proposed model, while optimizing the intertwined supply networks, is able to strengthen the resilience of the network in the face of disruption. On the other hand, the effectiveness of the proposed approach has been shown by using it in a case study of the intertwined supply network of tiles and ceramics.

Reliability is an important indicator that should be taken into account when making strategic decisions in the design of the supply chain so that the system continues to function with the least loss when a member malfunctions or fails. In this article, an attempt is made to reduce the long-term costs of the chain and increase the service level by increasing the reliability. One of the innovations of this article is to present a new method for calculating the supply chain reliability index according to the definition and nature of reliability. Calculating the reliability of each of the levels with the calculation method of parallel systems is not correct, according to the proof in the text of the article, and the reliability of each member should be based on the amount of product or raw materials that each member supplies from that level. Produce or maintain, be different that these cases were not considered in the previous research. Value is the final indicator of the success of a system. In this article, the value of the supply chain is defined using three indicators of the SCOR model, including cost, responsiveness (delivery time) and reliability. The proposed model belongs to the category of non-linear integer programming problems (MINLP), which is designed with the aim of maximizing the value of the supply chain. Due to the complexity of the problem in large dimensions and proving that it is NP-hard, the meta-heuristic algorithm developed by genetics has been used to solve it.

Air pollution and CO2 emissions are important global issues, and reducing carbon emissions is one of the most important goals. In the supply chain of perishable products, delivery and distribution are significant issues, which many researchers have always paid attention to. In this research, we have used mixed integer linear programming to model the green supply chain, which includes two objective functions profit maximization and CO2 emission minimization. Due to the occurrence of uncertainties, cost and price are uncertain, we have used the robust optimization approach, and presented a new robust optimization model. This problem is included in NP-hard problems and the exact method is not efficient in large dimensions. The exact solution method of the constraint epsilon, and for the large-scale problems, meta-heuristic algorithms of NSGA, MOSA and MOPSO were used. In order to obtain the best and most accurate solutions in meta-heuristic algorithms, the parameters were adjusted with the help of Taguchi's design. According to the results of validity tests, the average difference with the optimal solution is 0.2 to 0.8% and in robust models, it is 0.0.7 to 0.9%, which is acceptable. The lowest execution time is related to the MOSA algorithm, NSGA and MOPSO respectively, and the efficiency of these algorithms is not significantly different according to the standard multi-objective criteria.

Supplier selection and determining appropriate replenishment policies is one of the major concerns in production systems and supply chains. This paper describes the supply chain of integrated buyers and suppliers of perishable product. The retailer supplies the product it needs from different manufacturers. Products purchased by manufacturers are perishable products, and their Perishable rate is expressed as a fixed percentage of retailers' and manufacturers' inventories. In this situation, the selection of suppliers and the determination of the quantity to purchase from each supplier have a great impact on the optimization of the supply chain. Based on the fixed demand of the end customers, the retailer tries to determine its optimal replenishment policy, and based on this optimal policy, the process of purchasing raw materials from manufacturers, as well as the time and amount of purchase from each manufacturer will be determined. Among these, the supplier that can deliver the product within the specified time and at the lowest cost will be selected. The main purpose of this paper is to select suppliers and determine replenishment policies in a way that minimizes the total cost of this chain of supply, including retailer and supplier costs. This model uses a mixed-integer linear programming approach. Finally, this model was checked with a numerical example.

The orders received from customers in retail companies must be picked in the warehouse before delivery to customers. Order picking is the most costly and time-consuming process in a warehouse and customer orders are usually batched and picked in common tours to reduce travel distance and picking time. On the other hand, the widespread use of the internet and e-commerce has significantly increased the number and decreased the size of orders issued by customers and as a result, the order batching and picking operations in the warehouse has become more complicated. Besides, adopting appropriate policies to distribute orders among customers by taking into account issues such as the time and cost of distributing orders and due dates, is of great importance. Moreover, considering the operations of order picking and distribution in an integrated manner can significantly reduce costs and increase the level of service provided to customers. In this research, the order batching and distribution operations are considered integratedly and a mathematical model is proposed to address the integrated problem by considering minimization of total tardiness as objective function. respectively. For model validation purposes, an iterated local search metaheuristic approach is proposed to solve the proposed model. By using the generated data, it has been shown that the metaheuristic algorithm is able to obtain quality solutions for the problem. Due to the fact that an optimization solver is unable to optimize a small size problem in a realistic time, an upper bound was defined for the purpose of comparing the algorithm results with that. Our comparison of results indicates that for relatively larger sized sample problem the quality of obtained solutions by meta-heurietsic algorithm, in comparion with upper bound, having suitable quality as expected.

Nowadays, time series prediction is one of the fundamental research purposes, owing to the importance of prediction in various real-world applications. Transportation management is one of the main issues of each municipality that needs prediction. Accurate and reliable forecasting is one of the fundamental goals of an intelligent transportation system. In the literature, it is defined several variables of traffic management such as speed, time, and flow. In the proposed paper, a case of an Airline is considered. The associated managers are planning to propose a new service for passengers. There is a need to predict the number of passengers on arriving flights to a metropolis in Iran. This variable is inherently similar to the flow in traffic management. In the literature on traffic management, most of the studies implemented a linear or nonlinear modeling method to predict the future and ignore the advantage of hybrid methods. Several hybrid ARIMA-ANN methods have been proposed to specify the underlying relationships among the data. This paper utilizes a hybrid ARIMA-ANN model which decomposes the data into lowvolatile, and high-volatile components to predict accurately. Also, the current paper develops a new hybrid method, ARIMA-Local method, to specify the efficiency of other provided nonlinear methods in a hybrid structure. The obtained results for the discussed case are reported. This study signifies the accuracy of ARIMA-ANN model in predicting, while also the ARIMA-Local method is efficient in forecasting in comparison to the individual models of ANN and Exponential smoothing.

When a process is statistically in-control, one may be interested in assessing the pWhen a process is statistically in-control, one may be interested in assessing the process performance based on the process capability analysis. Although an extensive literature exists on process capability analysis when quality characteristic of interest is continuous or discrete but the existing methods are not applicable to the case when a process or product is represented by a linear profile. A profile is a relationship between a response variable and one or more independent variables. Manufacturing operations are often involved with multistage processes, in which the output of a stage is the input of its subsequent stage. This property is known as the cascade property. Using common indices to assess the capability may lead to incorrect results as the cascade effect is ignored. This study presents a method to conduct process capability analysis in a two-stage process when quality of a product or process can be characterized by a simple linear profile. A numerical simulation evaluates the performance of the proposed method for a two-stage process. The results indicate effective performance of the proposed methodrocess performance based on the process capability analysis. Although an extensive literature exists on process capability analysis when quality characteristic of interest is continuous or discrete but the existing methods are not applicable to the case when a process or product is represented by a linear profile. A profile is a relationship between a response variable and one or more independent variables. Manufacturing operations are often involved with multistage processes, in which the output of a stage is the input of its subsequent stage. This property is known as the cascade property. Using common indices to assess the capability may lead to incorrect results as the cascade effect is ignored. This study presents a method to conduct process capability analysis in a two-stage process when quality of a product or process can be characterized by a simple linear profile. A numerical simulation evaluates the performance of the proposed method for a two-stage process. The results indicate effective performance of the proposed method