JOURNAL OF INDUSTRIAL ENGINEERING RESEARCH IN PRODUCTION SYSTEMS (IERPS)
Year:2023 | Volume:11 | Issue:22|Papers Count:12

The process of health monitoring and correct prognosis of time to failure occurrence is still considered by many researchers of systems reliability to seek more effective use of available facilities. In the present study, a one-component system with four general failure mechanisms has been considered, one of which is excessive degradation under normal operating conditions and the other is accelerating the degradation process. In this paper, continuous degradation of the Gamma process is considered with two discrete and continuous noise factors or stresses, which create three different failure mechanisms in the accelerated lifetime condition. The discrete noise factor follows the Poisson distribution function and the continuous noise factor follows the normal distribution function. These four situations are investigated in the present paper and in each of them, the estimation of the reliability function and the remaining useful lifetime (RUL) or health prognosis of the equipment is obtained in order to reduce the probability of failure in this single-component system. A hybrid approach using statistical process control (SPC) with data transformation method is used to monitor the noise factors. It was also shown that with the significance of the noise factor or factors the scale parameter of the gamma distribution increases and, the reliability and remaining useful lifetime decrease. An example is solved at the end to illustrate the proposed method.

In today's world, due to the increase in competition between organizations and the increase in environmental and social concerns, sustainable supply chain management is a competitive advantage for organizations. Sustainable supply chain management is an approach in supply chain management with the aim to consider economic, environmental and social issues together. In this study, we consider two-echelon supply chain that consists both manufacturer and retailer in which the manufacturer decides on green quality of the product and level of health products donation as a social responsibility, and retailer decides on the selling price of the product to the end customers. We assumed that the customer has a social and environmental awareness and the demand is a function of the selling price, level of donation and green quality of the product. Generally, with an increase in the level of donation and the greenness of the product, the demand increases, and with an increase in the selling price of the product, the demand decreases. The results show that the coordination model in this sustainable supply chain is an appropriate alternative in comparison with the other models.

In this paper, the problem of real-time scheduling of multi-factury production network in the smart manufacturing system with job shop environment is studied. In this smart manufacturing system, a number of independently owned factories are joined together to form a multi-agent production network, which is also called a virtual production network. In such a network, each factory focuses on its interests and communicates with each other by sharing information such as machine breakdowns and job transfers. Therefore, it can be stated that studying the problem of distributed scheduling in the environment of smart factories is important and will have a significant effect on obtaining desirable and ideal results. At first, a bi-objective mixed integer linear programming model is presented; then an approach to solve the dynamic real-time scheduling problem is proposed. Considering the successful applications of the Lagrangian relaxation algorithm in solving scheduling problems, in this research, the improved Lagrangian relaxation algorithm is used to solve the problem. To examine the performance of the proposed algorithm, its results were compared with solving the original model that was solved by the augmented epsilon constraint method. The obtained results showed that the proposed Lagrangian relaxation algorithm has a better performance than the augmented epsilon constraint method

This research has presented a three-objective nonlinear mathematical programming model for optimizing the vaccine distribution chain in the pharmaceutical industry. With regard to stability issues, this involves considering location allocation, inventory under uncertain demand, and a crowded queue system with regard to stability and minimizing vaccination waiting times, ordering costs, vaccine holding costs, establishing new vaccination centers, and reducing harmful environmental waste caused by vaccinations and establishing new vaccination centers. As this problem is NP-hard, the NSGA_II algorithm was used. In a numerical example, sensitivity analysis showed that increasing the average waiting time decreased the total costs of the problem, while increasing the amount of generated garbage increased the total costs of the network. Moreover, a sensitivity analysis of the model at various levels of uncertainty revealed that with an increase in uncertainty, the network will have more people who need vaccines, which increases the network's costs. Due to the limited capacity of the centers and the fixed parameters of the network, costs increase. In contrast, the increased number of vaccines ordered and ready for distribution results in an increase in the average waiting time in distribution centers and waste. As a result of reducing the capacity of inoculation centers due to the fixed amount of vaccine orders, more centers were built, resulting in an increase in construction costs and increase in greenhouse gases due to the construction of a new center and transportation. Thus, the rate of entry to each center will decrease, as well as the length of the queue, ultimately leading to a decrease in average waiting time. In addition, 15 numerical examples have been examined to demonstrate the efficiency and design of the proposed algorithm.

Uncertainty is a common occurrence in real-world issues that often make decision-making difficult. Specifically, when it comes to managing a supply chain, the decision of which supplier to choose is complicated by a lack of information and knowledge, which leads to different levels of uncertainty in the attributes and alternatives, making it hard to make the right decision. In this article, a multi attribute decision-making method within a qualitative reasoning framework, where the assessment of problem attributes is carried out using different backgrounds and levels of experts' knowledge, is presented, and the score of the alternatives by evaluation a K-dimensional vector of qualitative labels is determined. To achieve it, the qualitative reasoning framework is first described, and then the proposed method in the same framework is described. The validity of the proposed method was verified by two practical examples. An example demonstrated the effectiveness of the proposed model and the results were compared with other methods. Finally, a real example was employed to select the best supplier. The conditions for the practical example are chosen in such a way, that it cannot be solved by using the previous methods. The results of this study explain the advantage of using multiscale linguistic space over other linguistic methods and also by analyzing two practical examples, the validity of the proposed method and its advantage over other methods in the qualitative reasoning space is confirmed.

In the current study, the competition between two substitutable products is investigated on a supply chain containing two manufacturers and two sellers. In this structure, two substitutable products are made by two competing manufacturers and are then delivered to customers through two competing sellers. One of the manufacturers sells his products with no investment on advertising, whereas another one aims to attract more customers by investing on the advertisement level. In this setting, a cooperative advertising contract is implemented between the manufacturer and seller of the second product in which the seller undertakes a portion of the related investment. Then, the game theory is applied to make the pricing and advertising decisions under the considered problem. Note that in the previous studies, the issue of the cooperative advertising has been considered for a specific product and then the game theory has been used in order to set the decisions among its supply chain members, while in this research, two substitutable products are considered and the effect of implementing a cooperative advertising contract is investigated on their demands and the profit values of their supply chain members. Regarding the results, increasing the elasticity of the demand for a product with respect to its advertisement level leads to enhancing the profits for its members and reducing the profits for the others. Moreover, if the required budget does not exceed a specific value, then investing on the advertisement level of a product is beneficial for its members.

Industrial ecology is the identification and application of solutions aimed at reducing the environmental effects of products and processes of industrial systems. Industrial symbiosis is a practical application of industrial ecology where manufacturing companies cooperate by creating a network in the field of local exchange of water, energy, or industrial waste recycling. Industrial symbiosis plays an important role in reducing total costs in green supply chain management and its sustainability. Previous researches in the field of green supply chain management have somehow improved its performance by using different tools, but less on the impact of the new and effective industrial symbiosis concept has been discussed. In the current research, for the first time, a mathematical model is presented to apply the concept of industrial symbiosis in green supply chain management to minimize total costs with minimal carbon emissions. The proposed model is a multi-period single product and the lack of demand is allowed. To validate, the proposed model was used in the steel industry with 30 factories during 22 planning periods and with 15 types of raw materials. GAMS software is used to solve the model. The results of the numerical solution of the model have the greatest reduction of carbon emissions and adverse environmental effects with minimum total costs in factories. The sensitivity analysis showed that the value of the objective function of the model is high in relation to the changes in the parameters of the amount of the reproduced product, the rate of the collected return product, the amount of the returned product, the rate of the final disposed product, and the penalty for exceeding the permitted carbon emission limit. The results showed the alignment of green supply chain management policies and industrial coexistence.

This study focuses on the development of a stochastic bi-objective hub location-routing model for a railway rapid transit network design problem. Due to the use of railway rapid transit systems in the hub-level sub-network (i.e., the network among the hub nodes) and the spoke-level sub-network (i.e., the network that connect spoke nodes to each other and to hub nodes), the decisions to make concern the location of hub nodes, spoke nodes, hub edges and spoke edges, and the determination of hub and spoke lines, the percentage of satisfied demands, and the way of routing the demands, simultaneously. Uncertainty is assumed for demands represented by a finite set of scenarios. The problem is formulated through a two-stage stochastic modeling framework. The aim is to maximize the total expected profit and to minimize the total expected service time. The performance of the model is evaluated through computational tests using the well-known AP dataset. The computational results confirm the importance of considering the stochastic model and the conflicting profit and time objectives for the given problem. Some managerial insights are also provided through the analysis of the resulting networks under various parameter settings and the investigation of the effect of these settings on the characteristics of the obtained solutions and the interactions among the different aspects of the studied complex decision problem

Both in the academic environment and in the realm of operation, it has been shown that there is a close relationship between product quality and maintenance of equipment. Thus, coordination of the decisions associated with maintenance and quality can lead to decrease in operational costs of a production system and improve the overall productivity. To coordinate the decisions related to maintenance and statistical process control, this thesis develops integrated mathematical models. The system performance is compared in two states: (1) while there is a coordination between the decisions of maintenance and SPC, (2) while the decisions of maintenance and SPC are conducted separately, and indeed, there is no coordination between SPC and maintenance. To determine the system performance in state 1, an integrated model of SPC and maintenance is developed. Optimization of the integrated model determines the value of ECT in state 1. To determine the system performance in the uncoordinated state,i.e., state 2, the following steps are performed: (1) a stand-alone model of maintenance is proposed, (2) a stand-alone model of SPC is developed, (3) according to the results of optimization of step 1 and 2, the value of ECT in the uncoordinated state is computed. Finally, comparison of the values of ECT in the coordinated state and uncoordinated state clarifies that, due to the performance of the integrated model, how much saving in operational cost is obtained. For the systems investigated in the thesis, using factorial designs, the effect of the system parameters on the decision variables and the objective function is analyzed. Also, it is investigated that, with respect to the operational costs, what circumstances are more suitable for implementing the integrated models of SPC and maintenance. According to the results of the study, integration of the decisions associated with maintenance and SPC leads to a better performance. The amount of saving obtained from the coordination is significant for the different production systems.

The ability to monitor supplier performance is a critical capability to maintain a strong organization-supplier relationship, especially for organizations where the supplier plays a significant role in their end product and market retention. In this article, a multi-variable multi-profile monitoring model is presented, taking into account the risk conditions in two stages for monitoring the quality of parts and supplier products. In the first step, the criteria and indicators for evaluating the quality process as well as the risk function are extracted according to the library resources and the opinion of experts and elites of the relevant industry. In the next phase, in the first phase of monitoring, using the Hotelling’s  Statistic Based on the Successive Differences, the parameters of the model are calculated and monitored, and in the second phase, the likelihood ratio method is used to monitor the supplier's quality process over time, so that if the presence of a warning will show the control chart in the shortest possible time. This model has been implemented in the automotive industry of Iran Khodro Company and the supplier of gearbox parts by Niro Travezah Company.

In this paper, we consider a Cournot Market for Chain-To-Chain Competition, which includes two distinct competing supply chains. Each supply chain has one Manufacturer, one Visitor, and one Retailer. The Retailer is in common between the two chains. This article will investigate the impact of using visitors to attract retailers as much as possible by the manufacturer. Each visitor should try to convince retailers to order more by paying a cost named Visit Cost in this article. After selling each unit of considered products, the manufacturer would pay a percentage of the wholesale price. It is assumed that there is a Stackelberg game between the manufacturer and visitor in each supply chain, in which the manufacturer is the leader. Furthermore, there is a Nash competition between manufacturers and the retailer.

Milk and its products have a high nutritional value due to the variety and abundance of available compounds. There have always been many risks in the process of preparing and producing these valuable products. In order to produce this product in a healthy way and to prevent additional costs that may be imposed on the production complex at different stages of production, it is necessary to identify the existing risks. For this purpose, a new hybrid approach has been presented and used in a case study. In this research, in order to collect data, three methods of interview, questionnaire, documents, and library documents were used. First, risk items were identified through interviews with experts. Then, the Best-Worst Method and Analytic Hierarchy Process fuzzy questionnaires were distributed. Next, using the collected data, intuitive fuzzy weights of factors and items were calculated. Ultimately, the decision matrix was formed and the risk items were prioritized using the Simple Additive Weighting method. Items “not performing proper ventilation and vacuum”, “increasing the microbial load of milk” and “the pressure of the homogenizer not reaching 120 bar” were assigned the first to third priority. In addition, in order to control and reduce the identified risk items, corrective actions have been presented