فهرست مطالب ali bozorgi-amiri

This study develops an integrated classic-interval-valued fuzzy TOPSIS approach to assess performance of different departments of a hospital. Although the concept of performance measurement has been discussed in previous studies, its concurrent integration with lean, agile, and green strategies is less investigated. Therefore, this research has been completed in three linked phases as follows. Firstly, the critical metrics of lean, agile, and green strategies are extracted from previous literature. Secondly, a fuzzy TOPSIS approach is developed to compare three departments of a hospital using the investigated metrics of previous phase. According to the obtained results of this phase, the eye, and emergency departments are the best and worst departments, respectively. Next, the obtained results are checked by an interval-valued fuzzy TOPSIS approach for further investigation in the third phase. Similarly, approving the previous results, eye department provides the best performance according to the second approach.  As a contribution, this research showed that different MCDM techniques might provide a same result. Furthermore, the developed methodology can be applied to assess and compare the performance of different departments.

This study addresses the Hub Location-Routing Problem (HLRP) in transportation networks, considering the inherent uncertainty in travel times between nodes. We employed a method centered on data-driven robust optimization, utilizing Support Vector Clustering (SVC) to form an uncertainty set grounded in empirical data. The proposed methodology is compared against traditional uncertainty sets, showcasing its superior performance in providing robust solutions. A comprehensive case study on a retail store's transportation network in Tehran is presented, demonstrating significant differences in hub locations, allocations, and vehicle routes between deterministic and robust models. The SVC-based model proves to be particularly effective, yielding substantially improved objective function values compared to polyhedral and box uncertainty sets. The study concludes by highlighting the practical significance of this research and suggesting future directions for advancing transportation network optimization under uncertainty.

Feature selection is the process of picking the most effective feature among a considerable number of features in the dataset. However, choosing the best subset that gives a higher performance in classification is challenging. This study constructed and validated multiple metaheuristic algorithms to optimize Machine Learning (ML) models in diagnosing Alzheimer’s. This study aims to classify Cognitively Normal (CN), Mild Cognitive Impairment (MCI), and Alzheimer’s by selecting the best features. The features include Freesurfer features extracted from Magnetic Resonance Imaging (MRI) images and clinical data. We have used well-known ML algorithms for classifying, and after that, we used multiple metaheuristic methods for feature selection and optimizing the objective function of the classification. We considered the objective function a macro-average F1 score because of the imbalanced data. Our procedure not only reduces the irreverent features but also increases the classification performance. Results showed that metaheuristic algorithms could improve the performance of ML methods in diagnosing Alzheimer’s by 20%. We found that classification performance can be significantly enhanced by using appropriate metaheuristic algorithms. Metaheuristic algorithms can help find the best features for medical classification problems, especially Alzheimer’s.

The aim of this paper was to develop a binary bi-level optimization model for the emergency warehouse location-allocation problem in terms of national and regional levels. This type of modeling is suitable for countries where the design of the disaster emergency network is decentralized. The upper-level decision-maker makes a decision regarding the location and allocation of national warehouses through considering the location of regional warehouses and allocating them to the demand cities. Each regional warehouse can provide a service for the demand cities within a specified distance threshold, ultimately affecting the efficiency of the solution algorithms. The optimization model parameters were calculated in terms of the real data in Iran. To solve the small size problem, an exact method was proposed from the explicit complete enumeration. Due to the complexity of the model with the large size, two innovative hybrid genetic algorithms, namely HG-ES-1 and HG-ES-2, were suggested. The results obtained from solving the problems showed that the HG-ES-1 algorithm outperformed HG-ES-2. The findings further indicated the proper functioning of the solution approaches.

This paper presents a two-phase approach to design an optimal personnel transportation network for the Bistoon Power Plant. In the first phase, a mathematical model of location-allocation for locating bus stops and allocating staff to bus stops has been formulated with the objective of minimizing the total walking distance for staff. For the second phase, a mathematical model of location-allocation-routing with time window for selecting vehicles with the appropriate capacity for each route, locating the parking places (start nodes), vehicle routing, and trip scheduling is discussed with the two objectives of minimizing transportation costs and minimizing the maximum travel time of staff to ensure fairness among staff. One of the features of this study is the consideration of the distance between vehicle parking areas and the first demand nodes on each route in order to locate parking areas. Other expected side benefits of the implementation of this research are decreasing the total travel distances, traffic congestion, and air pollution. Despite the large number of nodes and data of the case study, the proposed mathematical models are solved by the exact solution method. In order to solve the two-objective model, the augmented epsilon constraint method has been used to find the Pareto solution set.

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.

COVID-19 has infected more than 543 million people and killed more than 6 million people since it was first diagnosed in Wuhan, China, on December 1, 2019. Vaccines were needed to combat the epidemic from the start of the pandemic due to the high incidence of morbidity and mortality. After the development of vaccines, due to the need for extensive vaccination to stop the spread of the disease, the supply chain of COVID-19 vaccines and the need to develop mathematical optimization models became vital. Lack of admission capacity at vaccination centers is one of the main problems facing vaccination, which slows down the process and increases infection risk. For this purpose, this paper proposes a mathematical optimization model for the COVID-19 vaccine supply chain network design, considering two objectives maximizing the minimum demand coverage and minimizing the total time. With its equitable approach, the first objective function increases demand coverage. A second objective function accelerates vaccination by optimizing activities like allocating vaccines from storage centers to distribution centers and reducing the risk of spreading diseases by reducing transportation times to vaccination centers. According to this model, temporary vaccination centers can enhance or maintain vaccination rates by supplementing existing vaccination centers' admission capacity. Two numerical examples were used to validate the proposed mathematical model. The model's performance was then assessed using sensitivity analysis on its key parameters, demonstrating the effectiveness of temporary vaccination centers.

This study attempts to identify the technical requirements that have more effect on meeting customer satisfaction by examining the customer requirements and Tourism Mobile Applications (TMAs) technical requirements and in fact using the Quality Function Deployment (QFD) approach. In this regard, users’ technical requirements are identified by using literature review and experts’ interviews, as well as preparing different kinds of questionnaires. Then, the weights of these needs (as one of the inputs of QFD) are determined using fuzzy cognitive mapping (FCM) and, by drawing the house of quality, the final goal of this research can be achieved, which is to obtain the weight of the importance of the technical requirements according to the extracted needs. . These technical requirements include network environment, user interface, user experience, security, database, page layout, voice input, camera input, models design, controller design, search tool, API, ranking, ranking, commenting system, SEO, user roles, and content.. The results show that, “Content” has the greatest impact on meeting users’ satisfaction, by 8.1% weight percentage. In fact, the output of this survey is a guide to service providers to develop a TMA at the lowest possible cost which can increase customer satisfaction.

In this paper, a set of sustainability criteria is introduced and a hybrid Multi Criteria Decision Making (MCDM) method is performed in order to identify and classify a set of criteria for selecting a project portfolio. Proposed criteria based on fuzzy DEMATEL technique in an uncertain environment  are assessed to determine the relation among all of the criteria. Moreover, ISM method is used to level the proposed criteria which are effective in the process of selecting the project or not. The results obtained from the proposed method demonstrates that profit, cost, soil, atmosphere, energy, waste and risk are the most effective criteria in selecting project portfolio, especially in construction project selection. Furthermore, environmental issues play an important role in the selection of project portfolio while social issues are not as much significant as others. Technical requirement, water, security, and public utility are less effective criteria in selecting project portfolio. Besides, biodiversity, social integration, and responsibility criteria are the most effected criteria in selecting project portfolio.

Over the past decade, establishing sustainability has become the center stage in manufacturing products as most elements of sustainability meaning economic, social and environmental pillars are improving. However, developing processes leading to product sustainability throughout its life cycle, especially in the new product development process groups, is still in its infancy. The present study focuses on ranking the product development process groups and identifying the most effective one in product sustainability through Delphi-GAHP and COCOSO methods. To carry out this task, product life cycle, the main new product design and development process groups, gateway planning, deliverable items, and product sustainability pillars have been introduced and the necessary data has been collected with the help of automotive industry experts. The most effective process group in product sustainability was selected and the deliverables in the selected process group were ranked to isolate the most effective deliverable item in product life cycle sustainability. Evidently in future research, the findings of this study can be employed in establishing sustainability in product development processes with developing the attributes and components of this new deliverable item.

The purpose of this research is to develop specialized competencies for dean of faculties of University of Tehran. Also, their counterpart training courses and that of details are provided by which the university is able to empower dean effectively. The methodology is qualitative by which content analysis and systematic review are adapted. Job descriptions of the deans are coded qualitatively through which competencies are extracted. Afterwards, extracted competencies are modified by systematic review and Meta-synthesis methods. Finally, the results are validated by expert opinions. The proposed model categorized as 9 competencies including 17 sub categories. Competencies are presented in the form of communication and support, education and research, and human resource management. The communication and support competency includes factors such as ability to effectively represent and defend the faculty and ability to interact effectively. Educational and research competency consists of leadership and scientific credibility, ability to provide services to the society, ability to improve teaching-learning processes in the faculty, and ability to enhance faculty research processes and mindfulness of faculty policies, rules and programs. Moreover, human resource management competency includes ability to promote programs related to staff and students, and ability to recruit and develop faculty members effectively. Finally, to improve deans performances based on the competency model, training courses are provided. Notably, results are compatible with other universities and they may utilizes the results well enough.

One of the key processes in the banking system, is cash distribution from Treasury Branches and ATMs in the city. Treasury location, fleet, routes, geographical distribution of branches and ATMs, the distribution of money demand, customer satisfaction, are some of the influential factors in optimizing the money transfer process. Furthermore, today’s environmental issues, especially the issue of greenhouse gas (GHG) emissions, have been concentrated. In this paper, a new model of the location-routing vehicles for a bank cash distribution network is developed, in which three objectives of reducing GHG emissions, reducing costs of location-routing, and increasing customer satisfaction are considered simultaneously. Regarding to the difficulty of finding optimal solutions in such integrated models of location-routing problems, a multi-objective genetic algorithm-based on Prometheus methods are proposed. The obtained results are compared with the non-dominated sorting genetic algorithm (NSGA-II) and multi-objective imperialist algorithm (MOICA). The comparison of these results shows the performance of the proposed algorithm for the given problems.

Macroeconomic investments in recent years has grown dramatically. Since the number of sources are usually less than the number of proposing projects to the organization, project selection and decision-making in this regard is considered as an inevitable issue. Wrong selection, will have negative consequences, such as wasting resources and also eliminate resources which can be properly used in a more appropriate project results in benefits for the organization. Therefore, a method for selecting project portfolio using a mathematical model and focusing on sustainability factors is proposed. In this paper, we present a multi-objective mathematical programming model that is a comprehensive and also a practical model for portfolio selection of construction projects because it uses sustainability criteria to evaluate projects as one of the objective functions. Multi-objective models can also be used to contrast the objectives with each other in project portfolio selection. Other innovations of the proposed model in this paper are multi-period modeling that specifies the precise timing of the selection of selected projects over 10 defined periods. A robust model is then proposed in order to considering the uncertainty, in this paper contains the uncertainty is the duration of the project. The results show that the robust model in terms of mean objective function under different realizations performs better than the deterministic model and may be because the robust model unlike the deterministic model considers the uncertainties caused by the disturbances.

Quick response to the relief needs right after disasters through efficient emergency logistics distribution is vital to the alleviation of disaster impact in the affected areas. In this paper, by focusing on the distribution of relief commodities after disaster, the best possible allocation for the affected areas is specified and shortest path to vehicle transporting is determined. The objective of the proposed model is the minimization of the maximum distance traveled by each vehicle in order to achieve fairness in response to the wounded. In our proposed model, the location of demand is uncertain and determined by the simulation approach. The proposed approach solves the proposed model and determines appropriate allocation and best route for vehicles according to the allocation, simultaneously. Consequently, using genetic algorithm with two-part chromosome structure in routing and allocation problems. Computational results show the efficiency and effectiveness of the proposed model and algorithm for solving real decision-making problems.

Banks, in general, have a direct impact on the macro-economy of all countries. Recognizing the criteria which have momentous influence on bank branches’ efficiency is the main purpose of this research. An artificial neural network approach, one of the most applicable data mining techniques, is adopted to identify the criteria that influence the branches' efficiency the most (according to the result of efficiency evaluation base on MCDM). Then, the optimal group of input criteria is determined in order to achieve the most efficient performance. Branches that enjoy more appropriate inputs would have better conditions to increase their efficiency, possess more acceptable position and gain more adequate results. In this paper, utilizing data mining science, we have endeavored to suggest a suitable method in recognizing the most significant inputs with positive impact on enhancing efficiency of branches by the incorporation of relatively neglected indicators which fit the particular conditions of Iranian banks. The strength of this article compared to other related researches is that it provides a mechanism according to which senior managers in the banking sector will be able to identify the most important indicators and implement the best conditions to achieve the highest level of efficiency in the collection.

Constant development and change in the supply chain lead the system to meet various risks. Thus, a proper procedure should be adopted to cope with such issues. This study addresses a bi-objective model to design a resilient and robust forward supply chain under uncertainty and multiple disruptions. The investigated objective functions include minimizing the total cost and the total non-resiliency of the network, which is tackled using the ε-constraint method. Notably, resilience strategies and two-stage stochastic programming are respectively considered to cope with disruption and operational risks. Ultimately, some random numerical benchmarked examples are applied to the model to confirm the proposed formulation’s performance. The results indicate that considering risks in the system leads to increased costs, but it would be profitable in the long term. Notably, a resilient chain can prevent system failure and enhance capabilities to reduce risk exposure costs and damages.

Every year, various natural disasters affect many people around the world. After a disaster occurs, it is critical to dispatch relief resources through efficient emergency logistics distribution so as to alleviate death and suffering of victims. The velocity and efficiency of relief operations depend on logistics capabilities in procurement, receipt, transmission and distribution goods to affected areas. Decision-making at times of a disaster is usually done based on experience. Hence, it is essential that an effective tool is provided in order to manage humanitarian logistics operations. In this paper, two of the main relief logistics decisions are considered in the distribution of resources to the affected areas to include scheduling and routing decisions. For this purpose, a dynamic, multi-commodity, multi-depot, multi-trip linear mathematical programming model is developed under conditions of uncertainty that simultaneously captures many important aspects relevant to the real world. Multi-period planning provides possibilities for planners to adjust plans respect to new events and past actions as more information becomes available and improve the effectiveness of plans. Since the proposed model has its own complexities, a grouping metaheuristic algorithm is presented and this is the first time that the problem is viewed from a grouping perspective. To evaluate the validity and efficiency of the proposed model, a case study for the megacity of Tehran is presented. The findings demonstrate the applicability of the presented model to solve real problems.

 Reviewing related studies to supply chain networks shows that various factors influence the supply chain network's optimal design. Demand changes in market zones, changes in customers' locations, various competitive conditions in different regions, network costs fluctuations, and different disruption pose risks to the supply chain network in terms of optimal condition. In such conditions, to help the network return to its optimality, the network needs to be redesigned. In this respect, this paper has formulated a multi-period multi-product mathematical model for redesigning the warehouses considering backorder shortage. In this regard, decisions such as the construction of new facilities, closing non-optimal facilities, increasing the active facilities' capacity, the optimal amount of order/shortage in each active warehouse in each period have been investigated. Due to the inherent uncertainty in real-world business, parameters related to cost and demand are considered uncertain. Then, to deal with uncertain parameters in the proposed model, Jimenez's possibilistic programming has been used. The obtained results show that redesigning the supply chain network by setting up new facilities/closing non-optimal facilities can significantly reduce inventory and transportation costs and shortages.

Given the importance of health tourism in Iran and the body of problems surrounding the implementation of health tourism along with the challenges of attracting tourism in Iran, after an analysis of the external environment based on PESTEL and PORTER methods and internal environment based on performance indicators and EFQM method, along with an analysis of high-level documents and stakeholders, we determined to formulate a strategy of health tourism supply chain in Razavi hospital of Mashhad in through an experimental study. Strategies were developed using SWOT matrix are based on comments and views of health tourism experts. They were then prioritized by QSPM matrix and following the implementation of strategies, strategic objectives and indicators were determined as a balanced scorecard. The results suggest that the use of above techniques can contribute to health tourism industry. 

Evaluating the sustainability in the crop supply chain plays an important role in creating an efficient supply chain and increasing food safety in developing countries. Therefore, in the present study, an efficient network of greenhouses, agricultural lands, processing plants, and agricultural distribution centers (farmer’s markets) have been designed with regards to the sustainability dimension and with the purpose of minimizing the costs, minimizing the greenhouse gas emissions, and maximizing employment. The uncertainty has been identified as an inevitable part of studies in some parameters such as Brix, deterioration rate, employment rate, ideal yield, and water consumption rate. Also, the use of agricultural methods and techniques such as crop rotation policy, the use of nurtured seedling, and the consideration of Brix are among the issues that have been considered to increase the efficiency of the products and consequently to increase the economic efficiency. The model proposed with augmented ε-constraint method has been solved, and its efficiency has been investigated by presenting a case study in Iran. The results of the model solution indicate that the use of different planting policies, the consideration of Brix index, and effective attention to crop rotation policy has led to higher productivity of agricultural lands, production of higher quality products by expending lower prices and preserving environmental resources such as soil and water; so that the water consumption is reduced by 65.258%. Furthermore, the proposed model increases employment in the region and reduces unemployment by 11.2%

Free trade zone has attracted significant attention, especially in developing countries. It facilitates attracting foreign capital and skilled workforce and experts to achieve economic development, which is its ultimate goal. An efficient free trade zone has different features, most of which are related to its location. Therefore, location selection has an important role in its success. Facility location planning is a strategic decision that is very expensive, but can decrease future costs. This paper aims to find the optimal location for establishing a free trade zone. The current paper applies multi-criteria decision-making (MCDM) to capture all the features and essentials of a thriving free trade zone. To this end, a novel hybrid MCDM method is developed to obtain the optimal solution with fewer paired comparisons and less reliance on estimations. Then, to assess the applicability of the developed method, a real case study was conducted in Mazandaran Province, Iran. Finally, the results of the proposed method were evaluated by comparison with the results of the AHP method.

Today presentation an appropriate scientific system for disaster management logistic is essential to reduce physical injuries and financial damages. Therefore, in this paper, a multi-objective mathematical model has been designed for a multi depot multi-mode heterogeneous vehicle routing problem in order to distribute relief commodities. To accelerate the relief operation, affected points were be clustered by ANFIS structure and then prioritizing the points of clusters by affecting factors on the route reliability using of a permanent matrix. As well as, we consider the possibility of damage to the vehicle (disruption) and increasing unmet demand and fuzzy demand. Finally, the proposed mathematical model solved using the augmented ε-constraint and the results presented

Crisis is an inevitable fact of the human’s life. Fortunately, science and technology development has highlycontributed to the reduction of losses and casualties, but it has not reduced the happenings or damages to zero.Crisis management is mentioned as one of the most important scientific-practical issues nowadays that everycountry stray toward it. This paper targeted the response phase of crisis management that is considered as the mostimportant crisis management phase. The basic operations such as relief and rescue, evacuation of the injured andvictims, and relief commodities distribution are carried out in this phase. In this study, the locating of temporarydepots and routing of vehicles were taken into account by considering the reliability of the roads and allocatingthe distribution centers. The model is multi-objective and aimed at achieving the following goals: 1) Minimizingthe maximum shortage of the disaster points. 2) Minimizing the maximum time of the vehicles by considering thevelocity and normal speed of vehicles. The proposed method augmented Epsilon Constraint generalized modelfor Case study in Sichuan, China. The results showed the effectiveness and applicability of the proposed modelwas reliable for product distribution centers and making decisions about allocation and assignment of temporaryaccommodation centers in different parts of logistics network in conditions of crisis .

Nowadays, due to population increase and expansion of the transportation networks, the monitoring of this network, control, and prevention of accidents and crimes are very important. The main tasks of traffic patrols, like other emergency-security facilities, are the monitoring and prevention of crime as well as handling and fining in the case of committing crimes. Traffic patrols should be present in the scene and also in high traffic congestion, to reduce accidents and crimes. This paper presents a bi-objective and stochastic optimization model to design an emergency-security system. This network includes traffic patrol vehicles and manpower in patrol vehicles. The objective of the proposed model is to maximize the number of vehicles passing through patrols and minimize the costs according to different scenarios. To solve the model, the epsilon-constraint method is used which simultaneously determines the location of the patrols, allocation of demand points to patrols, and determining the number of existing manpower in patrols. To evaluate and analyze the proposed model, a numerical example is used.

One of the main critical steps that should be taken during natural disasters is the assignment and distribution of resources among affected people. In such situations, this can save many lives. Determining the demands for critical items (i.e., the number of injured people) is very important. Accordingly, a number of casualties and injured people have to be known during a disaster. Obtaining an acceptable estimation of the number of casualties adds to the complexity of the problem. In this paper, a location-routing problem is discussed for urgent therapeutic services during disasters. The problem is formulated as a bi-objective Mixed-Integer Linear Programming (MILP) model. The objectives are to concurrently minimize the time of offering relief items to the affected people and minimize the total costs. The costs include those related to locations and transportation means (e.g., ambulances and helicopters) that are used to carry medical personnel and patients. To address the bi-objectiveness and verify the efficiency and applicability of the proposed model, the ε-constraint method is employed to solve several randomly-generated problems with CLEPX solver in GAMS. The obtained results include the objective functions, the number of the required facility, and the trade-offs between objectives. Then, the parameter of demands (i.e., number of casualties), which has the most important role, is examined using a sensitivity analysis and the managerial insights are discussed.