نشریه پژوهشهای مهندسی صنایع در سیستمهای تولید
پیاپی 19 (پاییز و زمستان 1400)

Because members of a supply chain, such as manufacturers and retailers, are constantly interacting or competing with each other over time and in a repetitive process, modeling the supply chain structure in the form of dynamic games becomes more important and necessary. Increasing public access to cyberspace and, consequently, increasing online shopping and increasing the desire of manufacturers to communicate more closely with the consumer community has led to the creation of a new structure in the theoretical framework of the supply chain, called direct or online. By creating a direct sales department in a supply chain, retailers in customer acquisition and ultimately more profit consider themselves a competitor to the manufacturer. So far, in none of the models presented in the theoretical framework, competitive pricing and dynamic advertising in the two-part supply chain has been discussed; Therefore, the purpose of this study is to investigate the equilibrium behavior in the coffee”s  supply chain despite online sales, Also, the state variable in this study is the amount of credit that is obtained by global advertising of  manufacturer and this is one of the differences aspects between the present study and previous research. The results of the study of the model in the coffee industry supply chain include a higher equilibrium price of online sales of the product than the retail price. Also, the volume of producer's advertisements in the online section is more than the volume of local retail advertisements, and the volume of retail advertisements is more than the volume of nationwide manufacturer's advertisements. In this regard, the profit of the producer in the online sector is always more than the profit of the retailer in the traditional retail sector. Sensitivity analysis of equilibrium levels of optimal variables with respect to changes in game parameters also shows three incremental, decreasing and combined trends in changes in equilibrium levels with respect to increasing the value of model parameters.

Lubricant supply chain can be considered as one of the most important supply chains in various industries. This industry is responsible for supplying one of the most strategic materials in many other industry. From manufacturing to logistics and transportation industries, they somehow need the products that are produced and offered in this industry. Therefore, the performance of these its supply chains can have a significant impact on other supply chains. Agility in lubricant supply chains is affected by several factors, with domestic origin and international origin. Analysis of these factors and their interactions can be very usefull for managers to increase agility and provide them with appropriate solutions to increase competitiveness in the market. Fuzzy Cognitive Maps (FCM) are powerful tools for analyzing cause-and-effect relationships between factors and can show their interactions by drawing a network of factors and relationships to make the analysis more accurate and comprehensive. In this paper, using a fuzzy cognitive map approach, the factors affecting the lubricant supply chain agility were analyzed. Planning and scheduling, system for identifying future needs, creating direct communications with customers, monitoring customers' voices and using their opinions in future plans, creating integrated information systems along the supply chain, creating motivational systems to increase employee participation, growth creativity, etc. can increase agility. The level and measure of interactions between agility factors are ontained as an important result of our approach.

The pharmaceutical industry in Iran suffers from problems such as improper distribution and scheduling of drugs that have delayed the delivery of drugs to patients or on the other hand a huge volume of expired drugs. Also, paying attention to environmental and social issues along with economic ones is a serious approach to achieving sustainable development. Therefore, in this paper, economic, environmental, and social objective functions are considered for drug distribution in Iran according to the amount of demand. The purpose is to design a new model for the drug distribution network following the conditions of an epidemic (COVID-19) and to investigate its effect on the drug supply chain. Besides, in this paper, by adding backup suppliers, in addition to neutralizing the negative effects of Coronavirus, its effects are also examined and analyzed. To obtain the Pareto front, the improved version of augmented Ɛ-constraint (AUGMECON2) has been utilized. Using the proposed model, supply chain managers are able to make tactical decisions (product flow rate in the network) with the most profit, the most positive social impact, reduce greenhouse gasesو and reduce disease transmission. They can also greatly reduce or eliminate the serious problems of their supply chain network by creating resilience and sustainability in their network for the future, in critical situations such as the COVID-19 pandemic. The results showed that the suggested model has suitable efficiency for Coronavirus conditions, and can significantly reduce the destructive and harmful effects of this deadly epidemic

One of the most attractive and profitable industries and businesses in today's world is ceramic and tile. Applying new marketing strategies in strategic entrepreneurship on social networks can be a new way to grow and improve this industry. The purpose of the present applied- developmental research is to design a strategic entrepreneurial marketing model in the ceramic and tile industry of Yazd province. This research is exploratory in nature, and exploratory mix in terms of data collection. In the qualitative section, the participant group: Academic Experts and Managers of Ceramic and Tile Manufacturing Companies were selected by non-random method and snowball method with 35 persons. The statistical sample of the quantitative section consists of 275 companies were selected by stratified random sampling. The results of the quantitative phase showed that causal conditions affecting the ceramic tile industry include creating entertainment in social media advertising, interacting, customizing products and word of mouth advertising. Interfering conditions include communication, market-based corporate governance and new investment and contextual conditions include trends, human capital and culture. The strategies presented also include; strategic goals, company growth and Knowledge creation were determined to improve productivity and mental engagement and corporate survival were identified as strategic entrepreneurial outcomes. According to these research findings, it is suggested that policymakers and planners of the ceramic tile industry should pay attention to word-of-mouth advertising on social networks. Keywords: Strategic Entrepreneurial Marketing, Ceramic Tile Industry, Social Networking, word-of-mouth Advertising. The results of this study showed that product customization is a positive factor in shaping interaction and increasing word of mouth.

Material Requirements Planning (MRP) is a priority planning method that calculates the materials needed to meet the demand for all products and parts in one or more parts of the factory. Because choosing the right supplier for the organization brings significant savings, the most important activity of the buying operation is choosing the right supplier. The aim of this study is to design an extended inventory model to identify and Supplier Selection and Material Requirements Planning in the supply chain. For this purpose, an inventory system including the main parameters of initial inventory level, defect rate and decision when supply shortage was designed. Then the distribution model was designed for the relationship between the inventories of supply chain units and distributors. After modeling the demand for each supply chain unit, the inventory model was performed to Material Requirements Planning and Supplier Selection based on dynamic simulation for the supply chain. Material Requirements Planning was then sensitized based on changes in demand and changes in production capacity and changes in inventory and finally suppliers were selected. The results showed that with increasing demand, the cost of new raw materials also increases. Also, at 80% of the base production capacity, the Material Requirements Planning is unworkable and is not able to meet the basic demand. Reduced capacity leads to high inventory costs. With 110 and 120% of base capacity, there was a reduction in production costs. Also, with high initial inventory, a decrease in total costs was observed, and in 120% of the basic initial inventory, although inventory costs increased, the cost of new raw materials decreased significantly. Also, the sensitivity for selecting a supplier to the fixed cost parameter of ordering from the supplier is less than other cost parameters

Advances in modern computer technologies and measurement systems embedded in various fields, there is a significant growth in the volume, variety and velocity of the data produced, which is a rich source of information. Tensor data is one of the most important types of complex structured data with high dimensions that traditional methods are not able to solve this type of data. Tensor regression models have many applications in optimizing problems in which there are a number of independent numerical variables as input to the problem that can be changed to create a better output. In this study, a regression model is presented in which the independent variables are numerical and the response variable is tensor. A set of linear algebraic methods and tensor approaches are proposed in order to find patterns within a set of points in space and their relationship to process variables. Friction stir welding (FSW) is a motivational example of this study to confirm the results. The proposed model is coded in R software. The results showed that the proposed model has a good performance in predicting the output of processes by dynamically adjusting its parameters

Managing energy consumption at the same time as scheduling production operations has particular importance because optimal scheduling can also help reduce energy consumption. This paper considers an identical parallel machines scheduling problem considering common operations to minimize simultaneously total energy consumption and total tardiness. For this purpose, the bi-objectives Mixed Integer Linear Programming model is presented and for small scale instances, an augmented ε – constraint method is applied to obtain an optimal Pareto front. To tackle strongly NP-hard problem, NSGA-II Algorithm and NRGA Algorithm are developed to solve medium and large-scale instances. The efficiency and performance of the proposed algorithms are evaluated by performing computational experiments on the sample instances. The results show that the NSGA-II Algorithm can generate approximate Pareto fronts with better convergency, so that the performance of this algorithm in comparison with NRGA algorithm in terms of relative percentage deviation (RPD) in the indicators (Q) and (MID) 30% and 22% is better, respectively. On the other hand, the NRGA Algorithm is faster and can obtain more non-dominated solutions with better diversity, so that the performance of this algorithm compared to the NSGA-II algorithm in terms of relative percentage deviation (RPD) in the indicators (D) and (NPS) 12% and 8% is better, respectively

In this study, pricing and retail-servicing decisions are investigated on a dual-channel supply chain including one manufacturer and one retailer. For this reason, the game-theoretic approach is used to make the decisions under both the competitive and cooperative situations in the considered supply chain. Under the competitive situation, the players maximize their profits independently, while under the cooperative situation, the manufacturer and retailer cooperate to maximize the profit of the whole system. A numerical example is presented to illustrate the obtained results. Then, the analysis obtained from the investigated models will be presented. The results indicate that the retailer’s profit increases by increasing the price in the e-tail channel as well as by decreasing the price set by the manufacturer to the retailer. Under the competitive situation, the retail price increases as the retail services increase. The change of the retail price with respect to the retail services is greater than the change of the etail price. Under the cooperative situation, the retail price of the product increases by increasing the retail services. In addition, the change of the retail price with respect to the retail services is higher than the change of the etail price. Finally, a sensitivity analysis is performed on the considered parameters to determine the effect of the change of the parameters on the players’ profits

Recently, food and agricultural supply chains have attracted researchers’ attention as they provide more values for their stockholders. Hence a new approach to design a closed-loop supply chain network for date products is firstly developed in this work, which is one of the most well-known, rich, and desirable fruits. Date products and by-products are also considered in this network for the use in their target markets. In this regard, a new mathematical model is formulated to optimize the total costs including fixed, processing, operating, and transportation costs in both forward and reverse flows. The main contribution of this paper is to consider an innovative supply chain network model based on the unique characteristics of the date product. In this closed-loop network, the sustainability of the date product in the supply chain network is investigated for the first time. Also, product waste collection to the recycling centers is introduced in this model. To address the developed model, a set of meta-heuristic algorithms and a hybrid one along with the CPLEX solver of GAMS are utilized. Moreover, to validate the proposed model and the performance of these algorithms, several problem sizes are generated and solved. To achieve the best results, the parameters of the proposed algorithms are tuned based on the Taguchi method. Last but not least, sensitivity analyses are conducted and the results show a meaningful decrease in the overall costs of the date industry considering by-products and waste collection in the reverse flow.

As a relatively emerging topic in optimization, green routing could reduce the fixed and variable costs induced by different parts of a routing and transportation system and the costs imposed by pollution. This study proposes green transportation routing with a time window under confirmed stable conditions that consider transportation capacity restrictions, speed and time of delivery, and allocation of authorized drivers. This being the case, the goals of selecting the shortest route to goods transfer with the lowest costs caused by pollution, overdue fines, and maintenance of costs could be attained. Accordingly, the Lagrangian relaxation algorithm was employed, and the mathematical model of mixed-integer was developed to address the problem. Lagrange coefficient takes advantage of subgradient methods and closed methods. Lagrangian relaxation is applied separately to address two restrictions. The algorithm saves time by immediate problem solving than the Baron approach. These papers seek to simultaneously use complex restrictions close to actual incidents and could be more similar to natural conditions than other developed models.

In this paper, the single machine scheduling problem with linear deteriorating jobs under release times is considered where the objective is to minimize the number of tardy jobs. The problem is proved NP-hard according to the literature review. At first, a mathematical model is presented to the problem and a Branch and Bound algorithm with considering dominance rules and lower bounds is supposed to solve the problem optimally. Computational results are presented in four parts to evaluate the performance of the proposed algorithm and the effect of related parameters on the algorithm. According to the variance analysis test, it was found that the efficiency of the branch and bound algorithm is high so that it is able to solve the most problems with job size 30 within a reasonable time and the average percentage of entire fathomed nodes in all the problems is at least 85.61 percentage. It was also shown the problems with larger λ and smaller deterioration rates are difficult and the average solution time of the algorithm is high for them. On the other hand, if the due date of the jobs was big or small, the problem will be simple and the solution time is less than the problems with medium due dates.

One of the problems in the industry is the forecast of unexpected events, and in this article we consider the single machine scheduling problem with considering to machine failure, and also looking for the minimizing tardiness and earliness penalties. In this research, a mathematical model for this problem is presented in which the processing times, idle time, release time and failure time as well as the availability time of the machine after repairs and maintenance are taken into article that failure times are predicted using machine learning algorithms. The results show that the proposed model is suitable for small dimensions with the desired parameters and to solve this problem in larger dimensions, the meta-heuristic algorithm has been used in this research. This research presents this problem in two parts: the first part is related to failure prediction and the second part is the sequence of single machine scheduling operations. In the first part, after receiving the previous data, we predict machine failures using machine learning algorithms and achieve a set of rules for process modification, and in the second part, we use the number programming model. Correctly mixed and considering these breakdowns and lack of access to machines in a single machine schedule.