دکتر مهدی یزدانی

This paper aims to develop a comprehensive model for the synergy of quality, sustainability, and agility in drug production systems. This model seeks to use data collected by automated inspection systems to improve product quality, plan preventive maintenance, and optimize production planning. Reviewing the literature on quality management, sustainability, and agility in production, an integrated model of quality, maintenance, and production (IQMP) was designed and developed using Bayesian approaches. The results show that the model can effectively improve product quality, and increase production stability and system agility against environmental changes and fluctuations. Using online inspection data in this model significantly increases its accuracy and efficiency in decisions related to quality, maintenance, and production planning. In addition to helping to improve the efficiency of production systems, this model can be used as a strategic tool for production and maintenance managers. By implementing this model in real conditions, companies can take advantage of the data collected by automatic inspection systems and make more detailed plans for maintenance and quality control.

This research deals with the risk management of outsourcing projects in automotive component manufacturing companies. The study has shown that automotive companies outsource many of their product sub-categories to external suppliers due to various constraints. This issue affects the risk management in the quality, productivity and profitability of the organization. The present research examines the risk management of outsourcing projects in automotive component manufacturing companies by using failure mode analysis and its effects and multi-criteria decision-making technique. The scope of this research includes companies that operate in the field of automotive component manufacturing. To collect information, the opinions of experts, managers and experts of outsourcing projects in automotive component manufacturing companies have been used. According to the analysis, seventy sub-criteria have been identified and the level of each risk has been examined using the failure mode analysis and its effects method. Then, ten criteria and seventeen sub-criteria were identified and prioritized using the DANP weighting method. The results showed that the most important criteria include infrastructure management, commercial, management, and contractor, and the most important sub-criteria include increased repair time, lack of awareness in plans and lengthening project time, errors in rejection or acceptance, and loss of key employees.

Efficiency is a key success factor in complex supply chain networks. It is imperative to ensure proper flow of goods and services in humanitarian supply chains in response to a disaster. In this work, a mathematical model for designing a humanitarian supply chain network and vehicle routing problem considering cross-dock is proposed where a Non-dominated sorting genetic algorithm (III) has been used for implementing the proposed model in a large-scale problem. Since the model has been implemented in a large-scale case, various sensitivity analyses are performed to extract more interesting results. Accordingly, the results have shown that the costs have more effect on the first objective function (patients compared to total injuries) and the second one (shortage), respectively. Compared to the other two objective functions, the impact on the cost function is negligible. The effect of transportation cost of relief goods/ supplies from the supplier to the warehouse on the first objective function is higher than the others; however, the effect of this cost is further than that of the cost from the supplier to the distributor, accordingly, in comparison to the previous cost, the output has been more reacted to this cost. The transportation cost from the distributor to the warehouse (cross-docking) has less effect on the cost function unlike the transportation cost from the supplier to the warehouse. Nevertheless, as the result shows an increase in the cost can lead to a considerable increase in the ratio of patients to total injuries as well as shortage. In other words, the objective functions would deteriorate when this parameter tends to be increased.

The development of innovative ecosystems leads to the overall enhancement of innovation and creativity in countries, subsequently contributing to their economic development. Since the presence of uncertainty and external disruptions affects the performance of the innovation ecosystem, the existence of resilient innovation ecosystems that can withstand disruptions and maintain their performance under any circumstances is of great importance in the current complex and changing environment. Therefore, this research calculates the resilience index of key players in the power industry innovation ecosystem through a case study, using three indicators: "lost performance level," " Robustness," and "recovery speed." The results show that, the innovation ecosystem had a moderate level of lost performance index in the face of the simulated disruption and is not in a favorable state in terms of recovery speed and Robustness. Based on this, policymakers and managers of this ecosystem need to consider measures to enhance its resilience and take effective decisions and strategic planning to improve its response to potential disruptions. Additionally, by utilizing the simulated results, they can observe the impact of implemented policies on the resilience of the studied ecosystem.

The flexible job shop system is one of the most widely used scheduling systems in production environments, consistently attracting researchers' attention due to its diverse applications. Many studies in this field assume fixed and predetermined processing times. However, processing times can increase due to the deterioration effect, and after implementing rate-modifying activities (RMA), these times return to their original values. This study examines the flexible job shop scheduling system, considering job rejection policies, dual resource constraints (human and machine), and RMA maintenance activities.

The objective of flexible job shop scheduling is to assign each operation to a machine and a worker from a set of eligible machines and workers in a way that optimizes the sequence of operations on the machines. A mathematical model based on the mixed-integer linear programming approach was developed for this purpose. Literature review classifies the problem with the stated assumptions as NP-hard, making the use of meta-heuristic methods essential for finding near-optimal solutions. Thus, Variable Neighborhood Search (VNS), Simulated Annealing (SA), and a combined VNS-SA algorithm were employed to solve the problem.

Twenty sub-problems were analyzed, categorized into small, medium, and large-sized problems. The characteristics of each problem were defined by parameters such as the number of jobs, machines, workers, total operations, and buckets. Meta-heuristic methods, including VNS, SA, and their combination, were utilized to solve the problem. Seven neighborhood structures based on changes in assigned machines and workers, operation and job replacements, execution of RMA activities, and job acceptance/rejection were developed to enhance solution space exploration. The solution generation structure ensures feasibility within the flexible job shop system's requirements. The parameters of the meta-heuristic methods were tuned using the Taguchi method. Parameters related to the combined VNS-SA algorithm, such as initial temperature, number of neighborhood searches, and shake procedure counter, were reported. The results of the meta-heuristic methods were compared, and for small-sized problems, they were also compared with exact solutions.

The results of the twenty sub-problems solved using the three meta-heuristic approaches were compared statistically. The combined method of simulated annealing and variable neighborhood search showed superior performance in solving the problem.

The studies carried out in data coverage analysis are mostly in the form of efficiency calculation without considering the internal processes of the systems. In the present study, the approach of data coverage analysis is proposed to calculate multi-process systems, and the integrated model of DEA and BSC is used to calculate efficiency in line with the organization's strategies. In this simple model, the cause of the system's inefficiency can be identified under the processes and future improvement plans can be presented.

Nowadays “Innovation” is a key factor for developing the countries and their societies. Between ecosystems, “Innovation Ecosystem” is an emerging issues in universities and industries, which facilitate the possibility of collaboration to activate knowledge transfer, support the technology acquisition, and promoting the innovative thinking and supporting the knowledge-based economic. There is a little research related to innovation ecosystem modeling which can make some difficulties for policy maker in policy research and strategic decision making, also there is no study in comprehensive conceptual model about Niroo (power and water industries) innovation ecosystem. Then in this study to overcome this deficiency, by systematic review of recent researches, we use meta-synthesis to analyze and combination of effective agents in innovation ecosystem and representing a comprehensive conceptual model.  In this research by reviewing prestigious journals and conferences related to subject theoretical basics by meta-synthetics method, main innovation ecosystem agents were identified then by Niroo innovation ecosystem experts, which were selected Purposefully, their attributes and functions, performance index and relationship between them is validated and finalized; based of those, a simplified model is modified to express the way of employing aforesaid contents by systems dynamic. So in four different scenarios, the variation of key performance of whole ecosystem is studied and relatively strategic propositions are represented.  Novelty of this paper is its integrity and comprehensive which assist power and water industries’ managers analyze and monitor their policies effects based in this model before their decision making and make strategic planning to drive each agent effectively

One of the most important issues in the field of production scheduling, which has recently received much attention from researchers, is Dual Resource Constrained Flexible Job Shop Scheduling Problem (DRCFJSP). To deal with unexpected disruptions such as machine breakdowns, the job schedule must be robust so that in the event of a malfunction, the job schedule works properly and deviate less from the optimal solution. The purpose of this paper is to study the DRCFJSP problem with possible scenarios of machine failure or workshop disruption.

In solving the under-studied problem, the assignment of jobs and the sequence of operations on each machine should be done in such a way that under any possible scenario, the maximum completion time is minimized so that the weight combination of system performance in average mode, system performance in worst mode, the penalty for violating the time window constraints of the due dates and the variance of the objective function value is optimal according to different scenarios. For this purpose, a Robust Scenario-based Stochastic Programming (RSSP) model based on a mixed integer linear programming model has been presented for this problem and has been solved by Gams software for validation in small and medium-sized problems. Also, due to the Np-hard nature of this problem, a meta-heuristic method based on Genetic Algorithm (GA) is proposed for solving the large-sized problems. Also, the results of a case study in Alborz Yadak company related to the problem of the research are reported in the article.

The results of the proposed RSSP model indicate that GAMS software is able to solve these problems up to medium sizes in an acceptable time and achieve a controlled and robust solution. Numerical results also show the proper performance of the proposed GA as an alternative to solve the RSSP model in the large-sized problems.

Originality/Value: 

In this paper, DRCFJSP problem is studied with possible scenarios of machine failure or disruption in the workshop. Also, a RSSP model according to the mixed integer linear programming formulation and a meta-heuristic Algorithm have been presented for mentioned problem in this article.

Home Healthcare provides a wide range of home care services from prevention to rehabilitation and primary care to professional nursing services. This research presents a two-objective mathematical model based on the mixed integer linear programming approach for the home healthcare routing and scheduling problem (HHCRSP) with the objectives of minimization the nurses’ travel costs and the maximal working time difference among nurses. Considering the multimodal transportation, several initial health centers and one final health center and patient and nurse time windows are important features of the studied mathematical model. Small-sized problems have solved by using the Ɛ-constraint method on GAMS software. Also, due to the NP-hardness of the problem, MOPSO and NSGA-II algorithms have used to solve the medium and large-sized problems. The statistical results showed that the NSGA-II performed better than the MOPSO for medium and large sizes of problems in both MID and NOS performance metrics. Overall, Results of the performance metrics on different sizes of problems indicate the efficient and effective performance of NSGA-II in solving the understudied problem.

The purpose of this study is to design a multi-objective model of fuzzy closed-loop supply chain network in the automotive industry with an urban management approach. Automobile manufacturing is a complex and energetic process that consumes significant amounts of raw materials and water. To continue the competition, major automotive equipment manufacturers must strive for better product quality by continuously improving their production process and directing low-carbon emissions and increasing sustainability. In this regard, reverse supply chain networks and closed loop chains have special features that are very useful in the industry under study.

In the present study, in order to achieve the research objectives, a quantitative research method will be used and based on the purpose, it is defined in a practical way. In this study, we use the MOPSO method to facilitate its implementation and its ability to provide good convergence, as well as to maintain a proper balance between exploitation and exploration, as well as the NSGA II genetic algorithm.

In the study of the findings of the proposed algorithms, it found that the average error resulting from these algorithms is less than 0.04. The results also show that the proposed algorithms have the necessary efficiency in solving these problems.

The notable results of our model are as follows: (1) an efficient closed-loop network that demonstrates the economic benefits of considering the value of time over the recycling of a worn product. (2) It has the ability to show the capacity to achieve maximum benefits in terms of cost value as well as the environmental perspective of what capacity it should maintain.

Due to the competitiveness of the market, manufacturers have been forced to increase their activity effectiveness and efficiency. The shortening of the life cycle and the period of product supply to the market have forced manufacturers to increase the efficiency of their activities and production processes. As regards, the scheduling process and sequencing of efficient operations in manufacturing environments is one of the strategic issues for survival in the competitive market. Workshop environments such as job shop and flow shop are used in many industrial and service processes. One of the most challenging scheduling problems is the open shop scheduling one, but researches in this realm have not paid much attention to human resources. When there is no limit to the processing route of any job on shop machines, this model is referred to as an open shop. The open shop scheduling problem is a strategic issue. However, in most of available schedules in the literature, only workshop equipment, such as machines, is considered as limited resources, but in reality we are confronted with limited human and machine resources. In this study, a mixed-integer programming model is presented for the bi-objective open shop scheduling problem with limited human and machine dual resources. Small-sized problems are solved by using the exact epsilon-constraint method. According to the Np-hardness of this problem, two pareto-based meta-heuristics algorithms were used which are the Non-Dominated Sorting Genetic Algorithm (NSGAII) and Multi-objective Vibration Damping optimization (MOVDO). In order to analyze and compare the algorithms, we used four different indicators which include: The number of members of the first Pareto front, mean of ideal distance and diversity and spacing measures. Also, 30 problems in three scales (small, medium, large) have been generated. The computational results shows that the NSGAII is more functional and has better output in comparison to the other presented algorithm.

In the real world, businesses with hybrid flow-shop manufacturing environment have faced the constraints in machinery, human resource constraints and increasing salary and have tried to use their human resource better. Given the limitations of these resources, customer delivery requirements have made the job rejection essential in order to meet distinct customer requirements. Therefore, this paper considers the dual resource constrained hybrid flow-shop scheduling problem with job rejection in order to minimize the total net cost (the sum of the sum of costs obtained from rejected jobs and total penalty cost) which has high practical and operational relevance in many industries. The present study is developed a new mixed integer linear programming model for the problem. In addition, a new improved sooty tern optimization algorithm (STOA) is proposed to solve large-size problems as well as a new decoding method due to the NP-hardness of the problem. In order to evaluate the proposed optimization algorithm, 5 well-known algorithms in the literature including (Immunoglobulin-based artificial immune system (IAIS), Genetic algorithm (GA), Novel discrete artificial bee colony algorithm (DABC), Fruit fly optimization (IFFO), Effective modified migrating birds optimization (EMBO)) are adapted with the proposed problem and finally the performance of the proposed optimization algorithm is investigated against the adapted algorithms. All results and evaluations show the good performance of the new improved sooty tern optimization algorithm.

Today, green supply chain managers in leading companies strive to offer green logistics and improve their environmental performance throughout the supply chain as a strategic weapon to gain a sustainable competitive advantage by creating profitability and satisfaction across the supply chain. Therefore, considering the purpose of the research, which is to design a multi-objective model of closed-loop supply chain networks in the automotive industry according to energy efficiency and time efficiency plans, we try to model the closed-loop supply chain in the automotive industry.

In this study, we use the MOPSO method to facilitate its implementation and its ability to provide good convergence, as well as to maintain a proper balance between exploitation and exploration, as well as the NSGA II genetic algorithm.

In the study of the findings of the proposed algorithms, it found that the average error resulting from these algorithms is less than 0.04. The results also show that the proposed algorithms have the necessary efficiency in solving these problems.

We note the significant findings of our model as follows: (1) An efficient closed-loop network that shows the economic benefits of considering the value of time due to the recycling of worn-out products. (2) Has the ability to demonstrate the capacity to achieve maximum benefits in terms of cost value as well as environmental prospects.

This paper deals with the problem of two-stage assembly flow shop scheduling with considering sequence-independent setup times. The objective is to minimize total completion times of all orders. In this problem, there are several orders for one type of product. Each ordered product is formed of several different parts. At first, the parts are manufactured in a flow shop stage with some different machines and then they are assembled into a final product on a single machine. This paper presents three meta-heuristic algorithms, namely Parallel Variable Neighborhood Search (PVNS), Artificial Immune Algorithm (AIA) and Simulated Annealing (SA), for solving under studied problem. The Taguchi experimental design method as an optimization technique is employed to tune different parameters and operators of presented algorithms. Also, Numerical experiments are used to evaluate the performance of the proposed algorithms. The results show that the PVNS algorithm performs better than the other algorithms.

Nowadays, with regard to the competitiveness of the global trade and the need to meet the demands of customers, and given the increasing growth of technology in order to preserve and maintain the organization, we can use the various combinations of QFD as a powerful tool in quality engineering. . In this research, Delphi method was first used to identify the main components of the research including customer requirements and technical requirements, and then, due to the importance of weighing in the quality performance development tool, by developing a combined DEMATEL-ANP fusion decision method, and The severity of the relationships will affect the effectiveness and effectiveness of the components and weights associated with them to complete the matrix of the quality house. In order to complete the QFD method, we will use the new expansion of the Wasserman relationship. Also, in order to solve the traditional QFD problem of not considering the effect of constraints on technical engineering requirements, we will apply a combination of QFD, DEMATEL-Fuzzy ANP and DEA methods in the present study. Also, to demonstrate the effectiveness of this approach, we will consider it as a case study at the Goldiran Industry Corporation's executive washing machine production site.

In this study, a location-allocation problem is proposed regarding capacity factor in critical situation of an earthquake. Output is the selection of the best places for temporary shelters and optimized arrangement of the casualties in those places somehow minimizing casualties and damages. In the following, efficient allocation of the casualties to the medical centers will be discussed. Reaching these goals, a mathematics model proportionate to the problem conditions and constraints is presented. In literatures, location-allocation problem has been classified as a NP-Hard Problem. For these problems, metaheuristic algorithm were proposed. In this research, Imperialist Compeitive Algorithm (ICA) and Genetic Algorithm (GA) are used and the results comprised with each other. Based on the results of research, in such cases, ICA can be an opponent for Genetic Algorithm, because of the average of the solution obtained by this algorithm is rather better than Genetic Algorithm. However the GA convergence is faster than ICA. Case study is performed on region 3 of Tehran. Using available information of this region, the most fitted places for sheltering are extracted from GIS science and ARC GIS software. ICA is implemented to solve the problem. At the end, the number of optimized shelters and arrangement of inhabitants in these places and also arrangement of casualties to available medical centers in the region are presented.

In the scheduling problems, it is commonly assumed that processing times are fixed and known. In the literature of project scheduling emphasizes that the time of each activity/operation can be multi-mode and by assigning more resources, the activity time can be reduced. In these problems, in addition to activity scheduling, allocation of available limited resources to the activities should also be carried out. This assumption that processing time of activities is fixed is a weakness in scheduling literature. This paper develops the classic problem flow shop scheduling to multi-mode resource-cosntrainted flow shop scheduling problem. This paper discusses comprehensively about mathematical modeling. In this regard, two mixed integer linear programming models with two differnet concepts are presented. The first model is location-based model and the second is sequence-based. The performance of the models are evaluated by comparing their size and computational complexities. In the size complexity, the first model requires more variables but less constraints than second Model. In the computational complexity, the first model significantly outperforms than the second Model. Also, the first model, besides solving more problems as optimally, requires less time to solve than the second model .

In this paper, the dual-resource constrained flexible job-shop scheduling problem (DRCFJSP) with objective of minimizing the makespan is investigated. Under studied problem is NP-hard and mainly includes three sub-problems. The first one is to assign each operation to a machine out of a set of capable machines, the second one is to determine a worker among a set of skilled workers for processing each operation on the selected machine and the third one deals with sequencing the assigned operations on the machines considering workers in order to optimize the performance measure. In this paper, we provide a mathematical model for this problem and then propose a hybrid meta-heuristic algorithm for solving the problem. The proposed hybrid algorithm uses variable neighborhood search and simulated annealing algorithms to search in the solution space. Computational study with randomly generated test problems is performed to evaluate the performance of the proposed algorithm. The results show the proposed algorithms are effective approaches for solving the DRCFJSP.