@article { author = {Adabavazeh, Nazila and Nikbakht, Mehrdad and Amirteimoori, Alireza}, title = {Envelopment analysis for global response to novel 2019 Coronavirus-SARS-COV-2 (COVID-19)}, journal = {Journal of Industrial Engineering and Management Studies}, volume = {7}, number = {2}, pages = {1-35}, year = {2020}, publisher = {Iran Center for Management Studies}, issn = {2476-308X}, eissn = {2476-3098}, doi = {10.22116/jiems.2020.226564.1354}, abstract = {Communities are constantly seeking to manage the damages which are caused by crises. In this regard, health centers have become the most expensive unit of the health system as they provide quick and timely health care services to reduce the effects of unexpected accidents. So, their planning and preparation should be considered as an important part of strategic health policies. The purpose of this study is to investigate performance evaluation techniques for health units, which is helpful for WHO to identify the capabilities of crisis management and the limitations of world health units. This study evaluates the performance of the world health systems dealing with Corona-virus based on parametric and nonparametric statistical techniques according to "Population, GPD Per Capita, Total Recovered, Total Cases, and Total Deaths". This descriptive cross-sectional study is performed on the World Population Review, Worldometer, WHO data of Covid-19 from 1 March -11 April 2020. Based on the results, the efficient and inefficient health system units are identified. The results of this study show that 52 medical centers have not performed efficiently. The average efficiency of inefficient units is 0.30. On this basis, most of the studied countries do not operate efficiently due to the lack of optimal use of resources. Ineffective health system units call for greater attention of WHO in promoting health culture during the crisis management of common viruses. Therefore, there is a capacity to improve efficiency by 70%. By conducting this research, in addition to the introduction of functional patterns to the top health managers, it is possible to plan more accurately to develop the capacity of health care services and save resources.}, keywords = {health system unit,Coronavirus,DEA,COVID-19,WHO}, url = {https://jiems.icms.ac.ir/article_111664.html}, eprint = {https://jiems.icms.ac.ir/article_111664_2a49ac2196a72e85584227fac9c2d84d.pdf} } @article { author = {Zaree, Mostafa and Kamranrad, Reza and Zaree, Mojtaba and Emami, Iman}, title = {Project scheduling optimization for contractor’s Net present value maximization using meta-heuristic algorithms: A case study}, journal = {Journal of Industrial Engineering and Management Studies}, volume = {7}, number = {2}, pages = {36-55}, year = {2020}, publisher = {Iran Center for Management Studies}, issn = {2476-308X}, eissn = {2476-3098}, doi = {10.22116/jiems.2020.221672.1342}, abstract = {Today's competitive conditions have caused the projects to be carried out in the least possible time with limited resources. Therefore, managing and scheduling a project is a necessity for the project. The timing of a project is to specify a sequence of times for a series of related activities. According to their priority and their latency, so that between the time the project is completed and the total cost is balanced. Given the balance between time and cost, and to achieve these goals, there are several options that should be considered among existing options and ultimately the best option to perform activities to complete the project. In this research, a mathematical model of project scheduling with multiple goals based on cost patterns and consideration of resource constraints is presented, and this problem is considered as a problem for NP-hard issues in family hybrid optimization. GA، PSO and SA Meta-heuristic algorithms are used to solve the proposed model in project scheduling and the results are compared with each other.}, keywords = {Project scheduling,NPV maximizing,payment patterns,Resource constraints,meta-heuristic algorithms}, url = {https://jiems.icms.ac.ir/article_111666.html}, eprint = {https://jiems.icms.ac.ir/article_111666_9e10ac5e21007bdff816a1d78932d3bb.pdf} } @article { author = {Iravani, Mansooreh and Bashirzadeh, Reza and Tarokh, M. J.}, title = {Developing an urban congestion pricing model by considering sustainability improvement and using a multi-objective optimization approach}, journal = {Journal of Industrial Engineering and Management Studies}, volume = {7}, number = {2}, pages = {56-76}, year = {2020}, publisher = {Iran Center for Management Studies}, issn = {2476-308X}, eissn = {2476-3098}, doi = {10.22116/jiems.2019.178753.1262}, abstract = {This paper introduces a Travel Demand Management (TDM) model in order to decrease the transportation externalities by affecting on passengers’travel choices. Thus, a bi-objective bi-modal optimization model for road pricing is developed aiming to enhance environmental and social sustainability by considering to minimize the air pollution and maximize the social welfare as its objectives. This model determines optimal prices (bus fare and car toll) and optimal bus frequency simultaneously in an integrated model. The model is based on discrete choice theory and consideres the modes’ utility functions in its formulation. The proposed model is solved by two meta-heuristic methods (Non-dominated Sorting Genetic Algorithm-II (NSGA-II), Multi-Objectives Harmony Search (MOHS)) and the numerical results of a case study in Tehran are presented. The main managerial insights resulted from this case study is that its results support the idea of “free public transportation” or subsidizing the public transport as an effective way to decrease the transport related air pollution}, keywords = {Bi-objective Optimization,public transportation pricing,Air pollution,NSGA-II,MOHS}, url = {https://jiems.icms.ac.ir/article_112792.html}, eprint = {https://jiems.icms.ac.ir/article_112792_12760d84e9e3533f3e11a1117b21b70a.pdf} } @article { author = {Rabbani, Masoud and Abazari, Amin and Farrokhi-Asl, Hamed}, title = {An economic analysis for integrated bi-objective biofuel supply chain design using support vector machine}, journal = {Journal of Industrial Engineering and Management Studies}, volume = {7}, number = {2}, pages = {77-97}, year = {2020}, publisher = {Iran Center for Management Studies}, issn = {2476-308X}, eissn = {2476-3098}, doi = {10.22116/jiems.2020.200780.1300}, abstract = {Using second-generation biomass and biofuel deal with environmental pollution and CO2 emissions. Therefore, this paper design an integrated multi-period bi-objective biofuel supply chain network using support vector machine (SVM) and economic analysis to reduce the cost of generating biofuels and CO2 emissions. The economic analysis consists of three scenarios for supplying biomass. The SVM method specifies the potential place to build the bio-refinery. The next step solves the model with the augmented ε-constraint method. Finally, results show that biomass production and imports simultaneously reduce costs by 24.5% compared to the production scenario and 4.3% compared to the import scenario. According to the results obtained, despite the increase in cost, it reduces the amount of CO2 emissions. So, the Pareto solution resulted from the augmented ε-constraint method for the problem is determined as one of the most effective techniques to help the decision-makers.}, keywords = {bio-refinery,Biomass,SVM,economical analysis,CO2 emission}, url = {https://jiems.icms.ac.ir/article_112843.html}, eprint = {https://jiems.icms.ac.ir/article_112843_9625de8100f1bb4bef2413a863ec1ece.pdf} } @article { author = {Hematian, Milad and Seyyedesfahani, Mirmehdi and Mahdavi, Iraj and Mahdavi Amiri, Nezam and Rezaeian, Javad}, title = {A multi-objective optimization model for multiple project scheduling and multi-skill human resource assignment problem based on learning and forgetting effect and activities' quality level}, journal = {Journal of Industrial Engineering and Management Studies}, volume = {7}, number = {2}, pages = {98-118}, year = {2020}, publisher = {Iran Center for Management Studies}, issn = {2476-308X}, eissn = {2476-3098}, doi = {10.22116/jiems.2020.210566.1319}, abstract = {One of the most important aspects of human resource management is the allocation of the workforce to activities. Human resource assignment to project activities for its scheduling is one of the most real and common issues in project management and scheduling. This becomes even more significant when human resource assignment to multiple projects simultaneously is considered. On the one hand, workforces can have multi skills due to technological and scientific development so that they can be assigned to project activities based on their skill level. On the other hand, the learning effect is also taken into account to make the model more realistic. These factors can affect completion time, total cost and execution quality of projects. In this study, a multi-objective optimization model for multi-project scheduling and multi-skilled human resource assignment problem based on the learning effect and activities' quality is presented. A mixed-integer linear programming model (MILP) is developed for the proposed problem and solved by the ε-constraint method in GAMS software. Managers can select a solution based on their priority. Finally, a sensitivity analysis is done on the learning and forgetting effect to investigate their impacts on each objective function.}, keywords = {Multi-Objective Optimization,Multi-project scheduling,multi-skilled human resources,learning and forgetting effect,activity's quality level}, url = {https://jiems.icms.ac.ir/article_113139.html}, eprint = {https://jiems.icms.ac.ir/article_113139_77c215fda1f9d7865a1c0539215d2b57.pdf} } @article { author = {Rastbin, Sajed and Gholami Shahbandi, Mehrdad and Soudmand, Pouya}, title = {A fuzzy based genetic algorithm for optimizing the pedestrian walking network; case study historic district of Tehran}, journal = {Journal of Industrial Engineering and Management Studies}, volume = {7}, number = {2}, pages = {119-138}, year = {2020}, publisher = {Iran Center for Management Studies}, issn = {2476-308X}, eissn = {2476-3098}, doi = {10.22116/jiems.2020.176389.1256}, abstract = {Fast growth of motorized transportation infrastructures in the cities is a consequence of the urbanization process. Despite the undeniable benefits of the developments, some unwelcome social-environmental damages have been occurred. On top of the list, the movements of the pedestrians and their participation in social activities have dramatically reduced as a result of the vehicles dominancy. Pedestrianization and walking-friendly schemes are the key answer to preserve the valuable element of the urban lifestyle. This need motivated the researchers to study and propose mathematical methods to model the dynamics and behavior of the pedestrians in response to their surroundings. However, most of the models in the literature are suitable for limited small-size area and cannot be applied for a large scale urban zone. In this paper, a fuzzy macroscopic pedestrian assignment model is proposed which is applicable for a large scale network and useful for urban master plans as a decision making framework. In addition, a bi-level mixed integer programming model is presented to optimize the pedestrian walking network via selecting some projects on the network, considering the behavior of the pedestrians. Finally, the problem is solved for a large scale pedestrian network in the city of Tehran. The results show the efficiency of the algorithm where spending half of the maximum possible cost has led to a welfare gain of 82.6 percent. The problem was efficiently solved within 12.5 days which is fairly acceptable for the strategic planning of such a large scale network. The numerical results verify the necessity of the model for urban master plan horizon.}, keywords = {pedestrian modeling,Bi-level programming,Decision Making,Fuzzy logic,NSGA-II}, url = {https://jiems.icms.ac.ir/article_114673.html}, eprint = {https://jiems.icms.ac.ir/article_114673_b7d5e886adf0668f266dc1fa67b3184f.pdf} } @article { author = {Salehi, Mojtaba and Tikani, Hamid}, title = {Using revenue management technique to allocate the capacity in reliable hub network design under uncertain air passenger traffic}, journal = {Journal of Industrial Engineering and Management Studies}, volume = {7}, number = {2}, pages = {139-164}, year = {2020}, publisher = {Iran Center for Management Studies}, issn = {2476-308X}, eissn = {2476-3098}, doi = {10.22116/jiems.2020.195853.1293}, abstract = {This paper introduces a two stage stochastic programming to address strategic hub location decisions and tactical flight routes decisions for various customer classes considering uncertainty in demands. We considered the airline network with the arc capacitated single hub location problem based on complete–star p-hub network. In fact, the flight routes are allowed to stop at most two different hubs. The first stage of the model (strategic level) determines the network configuration, which does not change in a short space of time. The second stage is dedicated to specify a service network consists of determining the flight routes and providing booking limits for all itineraries and fare classes after realization of uncertain scenarios. To deal with the demands uncertainty, a stochastic variations caused by seasonally passengers’ demands through a number of scenarios is considered. Since airline transportation networks may face different disruptions in both airport hubs and communication links (for example due to the severe weather), proposed model controls the minimum reliability for the network structure. Due to the computational complexity of the resulted model, a hybrid algorithm improved by a caching technique based on genetic operators is provided to find a near optimal solution for the problem. Numerical experiments are carried out on the Turkish network data set. The performance of the solutions obtained by the proposed algorithm is compared with the pure GA and Particle Swarm Optimization (PSO) in terms of the computational time requirements and solution quality.}, keywords = {Customer segmentation,scenario generation method,Network Reliability,Stochastic programming,meta-heuristic algorithms}, url = {https://jiems.icms.ac.ir/article_117707.html}, eprint = {https://jiems.icms.ac.ir/article_117707_baee115896406b81fa361e3a34ac11f7.pdf} } @article { author = {Safaie, Nasser and Nazeri, Ali and Mottakiani, Anita}, title = {Supply chain management in hospitality and its impact on competitive advantage, hotel, and supply chain performance}, journal = {Journal of Industrial Engineering and Management Studies}, volume = {7}, number = {2}, pages = {166-185}, year = {2020}, publisher = {Iran Center for Management Studies}, issn = {2476-308X}, eissn = {2476-3098}, doi = {10.22116/jiems.2020.199227.1299}, abstract = {In this study, the relationship between supply chain management functions, supply chain performance, competitive advantage, and organizational performance in the hospitality were investigated. The objective of this study was to assess the performance of supply chains and management in the hospitality. For this purpose, a model consisting of 4 variables and 5 hypotheses was created. The statistical population of this study included all administrative staff of the 4 and 5-star hotels in Tehran. Sampling among senior and middle managers was done randomly. A total of 199 samples were collected and the designed model was tested using a structural equation approach. All study hypotheses were approved. The results indicated that using the dimensions of the supply chain management function, we can observe the positive and comprehensive impact of these factors on organizational performance, supply chain performance, and competitive advantage. In addition, supply chain performance and competitive advantage were found to have a positive and significant effect on organizational performance.}, keywords = {Supply chain management,hospitality,Competitive advantage,performance}, url = {https://jiems.icms.ac.ir/article_119483.html}, eprint = {https://jiems.icms.ac.ir/article_119483_9e3088a6f3e775585fc30380ec849548.pdf} } @article { author = {Hajirahimi, Zahra and Khashei, Mehdi}, title = {Weighted MLP-ARIMA series hybrid model for time series forecasting}, journal = {Journal of Industrial Engineering and Management Studies}, volume = {7}, number = {2}, pages = {187-201}, year = {2020}, publisher = {Iran Center for Management Studies}, issn = {2476-308X}, eissn = {2476-3098}, doi = {10.22116/jiems.2020.205148.1307}, abstract = {With the increasing importance of forecasting with the utmost degree of accuracy, utilizing hybrid frameworks become a must for obtaining more accurate and more reliable forecasting results. Series hybrid methodology is one of the most widely-used hybrid approaches that has encountered a great amount of popularity in the literature of time series forecasting and has been applied successfully in a wide variety of domains. In such hybrid methods is assumed that there is an additive relationship among different components of time series. Thus, based on this assumption, various individual models can apply separately on decomposed components, and the final forecast can be obtained. However, developed series hybrid models in the literature are constructed based on the decomposing time series into linear and nonlinear parts and generating linear-nonlinear modeling order for decomposed parts. Another assumption considered in the traditional series model is assigning equal weights to each model used for modeling linear and nonlinear components. Thus, contrary to traditional series hybrid models, to improve the performance of series hybrid models, these two basic assumptions have been violated in this paper. This study aims to propose a novel weighted MLP-ARIMA model filling the gap of series hybrid models by changing the order of sequence modeling and assigning weight for each component. Firstly, the modeling order is changed to nonlinear-linear, and then Multi-Layer Perceptron Neural Network (MLPNN) -Auto-Regressive Integrated Moving Average(ARIMA) models are employed to model and process nonlinear and linear components respectively. Secondly, each model's weights are computed by the Ordinary Least Square (OLS) weighting algorithm. Thus, in this paper, a novel improved weighted MLP-ARIMA series hybrid model is proposed for time series forecasting. The real-world benchmark data sets, including Wolf's sunspot data, the Canadian lynx data, and the British pound/US dollar exchange rate data, are elected to verify the effectiveness of the proposed weighted MLP-ARIMA series hybrid model. The simulation results revealed that the weighted MLP-ARIMA model could obtain superior performance compared to ARIMA-MLP, MLP-ARIMA, as well as the ARIMA and MLPNN individual models. The proposed hybrid model can be an effective alternative to improve forecasting accuracy obtained by traditional series hybrid methods.}, keywords = {series hybrid model,weighted MLP-ARIMA model,Auto-Regressive Integrated Moving Average (ARIMA),multi-layer perceptron neural network (MLPNN),Time series forecasting}, url = {https://jiems.icms.ac.ir/article_119888.html}, eprint = {https://jiems.icms.ac.ir/article_119888_f61d0d00c09e692413549cca94bdc6bc.pdf} } @article { author = {Fakhrzad, Mohammad Bagher and Firozpour, Mohammad Reza and Hosseini Nasab, Hasan and Sadegheih, Ahmad}, title = {Comparing supply chain risk ranking methods based on fuzzy three-dimensional integration approach}, journal = {Journal of Industrial Engineering and Management Studies}, volume = {7}, number = {2}, pages = {202-222}, year = {2020}, publisher = {Iran Center for Management Studies}, issn = {2476-308X}, eissn = {2476-3098}, doi = {10.22116/jiems.2020.213687.1326}, abstract = {For reducing risk effects in a supply chain, the appropriate risk assessment and ranking by the use of multi-criteria decision-making methods (MCDM) is important. Failure to properly assess and rank the risks makes the supply chain less efficient and competitive. Given the existence of both qualitative and quantitative criteria in a supply chain, the use of verbal preferences, given by authorities for determining the priority of qualitative factors, has higher reliability than that of the Crisp numbers. Fuzzy concept plays an important role in solving the problem of complexity of assigning quantitative fixed numbers to the values of verbal preferences. In the proposed method of this study, a comparison was made among the decision-making methods in the fuzzy environment for selecting a suitable method. To validate the proposed method, we compared it to some case studies from the literature. The results show that the proposed method has high validity and reliability in assessing the risks of a supply chain.}, keywords = {risk ranking,Supply chain,decision making methods,fuzzy three-dimensional integration mean}, url = {https://jiems.icms.ac.ir/article_120327.html}, eprint = {https://jiems.icms.ac.ir/article_120327_c481595e4e36e6555eee0b8e340d5ca7.pdf} } @article { author = {Nakhaeinejad, Mahdi}, title = {Ant colony optimization, genetic algorithm and hybrid metaheuristics: A new solution for parallel machines scheduling with sequence-dependent set-up times}, journal = {Journal of Industrial Engineering and Management Studies}, volume = {7}, number = {2}, pages = {223-239}, year = {2020}, publisher = {Iran Center for Management Studies}, issn = {2476-308X}, eissn = {2476-3098}, doi = {10.22116/jiems.2020.206474.1310}, abstract = {The parallel machine scheduling problem (PMSP) is one of the most difficult classes of problem. Due to the complexity of the problem, obtaining optimal solution for the problems with large size is very time consuming and sometimes, computationally infeasible. So, heuristic algorithms that provide near-optimal solutions are more practical and useful. The present study aims to propose a hybrid metaheuristic approach for solving the problem of unrelated parallel machine scheduling, in which, the machine and the job sequence dependent setup times are considered. A Mixed-Integer Programming (MIP) model is formulated for the unrelated PMSP with sequence dependent setup times. The solution approach is robust, fast, and simply structured. The hybridization of Genetic Algorithm (GA) with Ant Colony Optimization (ACO) algorithm is the key innovative aspect of the approach. This hybridization is made in order to accelerate the search process to near-optimal solution. After computational and statistical analysis, the two proposed algorithms are used to compare with the proposed hybrid algorithm to highlight its advantages in terms of generality and quality for short and large instances. The results show that the proposed hybrid algorithm has a very good performance as regards the instance size and provides the acceptable results.}, keywords = {scheduling,parallel machines,ant colony optimization,Genetic Algorithm,machine scheduling}, url = {https://jiems.icms.ac.ir/article_120385.html}, eprint = {https://jiems.icms.ac.ir/article_120385_e565a9cdefbc67f2688a2f2944069e16.pdf} } @article { author = {Shetaban, Samar and Seyyed Esfahani, Mir Mehdi and Saghaei, Abbas and Ahmadi, Abbas}, title = {Operations research and health systems: A literature review}, journal = {Journal of Industrial Engineering and Management Studies}, volume = {7}, number = {2}, pages = {240-260}, year = {2020}, publisher = {Iran Center for Management Studies}, issn = {2476-308X}, eissn = {2476-3098}, doi = {10.22116/jiems.2020.231406.1360}, abstract = {Todays, human health is in the best situation ever. The progress of vaccines, development of hospitals, new medicines, advanced medical equipment, and new treatments preventing death will place the health system indicators at its best state in all ages and centuries. In addition, healthcare is one of the biggest industries in developed and developing countries and is a service-oriented industry that is significant, high-quality, and safe in medical services. Healthcare has become one of the biggest sectors in terms of income and employment. Health care involves hospitals, medical devices, clinical trials, outsourcing, telemedicine, medical tourism, health insurance, and medical equipment. Nowadays, the application of operations research in various fields, including health, is on increase. Although many issues face operations research in healthcare, such issues are not analytically different from the issues in other industries. Operations research, as a quantitative systemic method, can considerably solve the problems related to the health system. The present study aimed to evaluate the application of operations research models based on the research process in health systems including Markov decision-making processes (MDPs) and partially observable Markov decision process (POMDP), etc., and compare these methods with each other. The basis of this study was evaluating the publication of scientific studies on operations research models in the health systems.}, keywords = {operations research,health systems,research process,Markov model}, url = {https://jiems.icms.ac.ir/article_120709.html}, eprint = {https://jiems.icms.ac.ir/article_120709_a61185d60a6f00289bc43071707f73c8.pdf} }