@article { author = {abdollahi moghadam, mostafa and ebrahimi, seyed babak and Rahmani, Donya}, title = {A two-stage robust model for portfolio selection by using goal programming}, journal = {Journal of Industrial and Systems Engineering}, volume = {12}, number = {1}, pages = {1-17}, year = {2019}, publisher = {Iranian Institute of Industrial Engineering}, issn = {1735-8272}, eissn = {2717-3380}, doi = {}, abstract = {In portfolio selection models, uncertainty plays an important role. The parameter’s uncertainty leads to getting away from optimal solution so it is needed to consider that in models. In this paper we presented a two-stage robust model that in first stage determines the desired percentage of investment in each industrial group by using return and risk measures from different industries. One reason of this work is that general conditions of various industries is different and according to the concepts of fundamental analysis should be chosen good groups before selection assets for investment. Another reason is that the identification of several good industries helps to diversify between several groups and reduce the risk of investment. In the second stage of the model, considering assets return, systematic risk, non-systematic risk and also first stage’s result, amount of investment in each asset is determined. In both stages of the model there are uncertain parameters. To deal with uncertainty, a robust approach has been used. Since the model is a multi-objective problem, goal programming method used to solve it. The model was tested on actual data. The results showed that the portfolio formed by this model can be well-established in the conditions of high uncertainty and obtain higher returns.}, keywords = {Portfolio selection,Goal Programming,robust approach,parameter’s uncertainty}, url = {https://www.jise.ir/article_76604.html}, eprint = {https://www.jise.ir/article_76604_c3d8806e093e91429dcf5535db09a8c8.pdf} } @article { author = {Yahyatabar, Ali and Najafi, Amir Abbas}, title = {A multi-stage stochastic programming for condition-based maintenance with proportional hazards model}, journal = {Journal of Industrial and Systems Engineering}, volume = {12}, number = {1}, pages = {18-38}, year = {2018}, publisher = {Iranian Institute of Industrial Engineering}, issn = {1735-8272}, eissn = {2717-3380}, doi = {}, abstract = {Condition-Based Maintenance (CBM) optimization using Proportional Hazards Model (PHM) is a kind of maintenance optimization problem in which inspections of a system relevant to its failure rate depending on the age and value of covariates are performed in time intervals. The general approach for constructing a CBM based on PHM for a system is to minimize a long run average cost per unit of time as an objective function in which the model is considered for an infinite span of time.  In this paper, a CBM model is presented based on two types of maintenance actions (minimal repair and replacement) to determine control limits to cope with the class of real-life problems in which a system would be planned for a specified planning horizon. An effective multi-stage stochastic programming approach is used to come up with the minimum expected cost given the state scenarios of the system in periods over a planning horizon. An extensive computational study is presented to demonstrate the efficiency of the proposed model through numerical instances solved by a novel hybrid meta-heuristic algorithm. A sensitivity is also performed on cost parameters to designate the effects of minimal repair cost and replacement cost in the proposed model.}, keywords = {Condition Based Maintenance,proportional hazards model,multi-stage stochastic programming}, url = {https://www.jise.ir/article_76728.html}, eprint = {https://www.jise.ir/article_76728_4b86949a3c7e4137cf3ba59faf3a72bf.pdf} } @article { author = {Akbarian Saravi, Niloufar and Yazdanparast, Reza and Momeni, Omid and Heydarian, Delaram and Jolai, Fariborz}, title = {Location optimization of agricultural residues-based biomass plant using Z-number DEA}, journal = {Journal of Industrial and Systems Engineering}, volume = {12}, number = {1}, pages = {39-65}, year = {2018}, publisher = {Iranian Institute of Industrial Engineering}, issn = {1735-8272}, eissn = {2717-3380}, doi = {}, abstract = {Co-firing biomass plants are of extensive demand due to utilization of both agricultural residues (main) and natural gas (stand-by). Researchers have shown that one strategic decision in establishment of agricultural residues based plants, is location optimization problem. Moreover, mismatch between agricultural lands and biomass plants can lead to high transportation costs and related carbon dioxide emissions. Standard indicators are considered and used for the stated multi-objective mathematical problem. This article presents a novel approach based on Z-number data envelopment analysis (DEA) model to handle severe uncertainty associated with actual data. The multi-objective mathematical model considers environmental, economic and social aspects of biomass plants. Moreover, fuzzy DEA model is utilized to verify and validate the results of Z-number DEA model through 30 independent experiments. The obtained results indicate that “accessibility to water”, “population”, “cost of land”, and “unemployment rate” are the most significant factors in location optimization of co-firing power plants. The obtained results also indicate that “Ilam”, “Semnan”, “Kohgiluyeh and Boyer-ahmad”, “South Khorasan”, and “Chaharmahal and Bakhttiari” are the optimum locations. This is the first unique approach for location optimization of co-firing plants based on combined agricultural residues and natural gas under uncertainty. Second, a unique fuzzy mathematical optimization approach is presented. Third, it is a practical approach for biomass power plants.}, keywords = {Co-Firing Biomass Plants,location optimization,Z-Number Data Envelopment Analysis (DEA),Environmental,economic and social indicators,Fuzzy Data Envelopment Analysis (FDEA),perturbation analysis}, url = {https://www.jise.ir/article_76541.html}, eprint = {https://www.jise.ir/article_76541_6fdbb1e30fe3a605f70d7480751f92fc.pdf} } @article { author = {Khasha, Roghaye and Sepehri, Mohammad Mehdi and khatibi, toktam}, title = {An analytical model based on simulation aiming to improve patient flow in a hospital surgical suite}, journal = {Journal of Industrial and Systems Engineering}, volume = {12}, number = {1}, pages = {66-82}, year = {2018}, publisher = {Iranian Institute of Industrial Engineering}, issn = {1735-8272}, eissn = {2717-3380}, doi = {}, abstract = {Surgical suits allocate a large amount of expenses to hospitals; on the other hand, they constitute a huge part of hospital revenues. Patient flow optimization in a surgical suite by omitting or reducing bottlenecks which cause loss of time is one of the key solutions in minimizing the patients’ length of stay[1] (LOS) in the system, lowering the expenses, increasing efficiency, and also enhancing patients’ satisfaction. In this paper, an analytical model based on simulation aiming at patient flow optimization in the surgical suite has been proposed. To achieve such a goal, first, modeling of patients' workflow was created by using discrete-event simulation. Afterward, improvement scenarios were applied in the simulated model of surgical suites. Among defined scenarios, the combination scenario consisting of the omission of the waiting time between the patients’ entrance to the surgical suite and beginning of the admission procedure, being on time for the first operation, and adding a resource to the resources of the transportation and recovery room, was chosen as the best scenario. The results of the simulation indicate that performing this scenario can decrease patients’ LOS in such a system to 22.15%.}, keywords = {Simulation,discrete-event modeling,patient flow,Hospital,surgical suite}, url = {https://www.jise.ir/article_76542.html}, eprint = {https://www.jise.ir/article_76542_7408ce7bc05eca76409c4623134bb2a6.pdf} } @article { author = {Roghanian, Emad and Haghdoost, Mohaddese}, title = {Mathematical model for P-hub location problem under simultaneous disruption}, journal = {Journal of Industrial and Systems Engineering}, volume = {12}, number = {1}, pages = {83-94}, year = {2018}, publisher = {Iranian Institute of Industrial Engineering}, issn = {1735-8272}, eissn = {2717-3380}, doi = {}, abstract = {The optimal locating of facilities has large effects on economic benefits, providing satisfactory service and levels of customer satisfaction. One of the new topics discussed in location problems is hub location and hub facilities are subject to unpredictable disruptions. This paper proposes a nonlinear integer model for reliable single allocation hub location problem that considers backup hub, alternative routes, and also uses fortification approach to improve the network reliability. Due to the NP hard nature of the model, we use genetic algorithm in order to solve the defined problem and the numerical results illustrate the applicability of the proposed model as well as the efficiency of solution procedure.}, keywords = {P-median hub,Disruption,Fortification,backup hub,alternative route,Genetic Algorithm}, url = {https://www.jise.ir/article_76544.html}, eprint = {https://www.jise.ir/article_76544_d6918f14010e1b5bf6ebf9e67f216c17.pdf} } @article { author = {Tikani, Hamid and Setak, Mostafa}, title = {Ambulance routing in disaster response scenario considering different types of ambulances and semi soft time windows}, journal = {Journal of Industrial and Systems Engineering}, volume = {12}, number = {1}, pages = {95-128}, year = {2019}, publisher = {Iranian Institute of Industrial Engineering}, issn = {1735-8272}, eissn = {2717-3380}, doi = {}, abstract = {This paper studies the ambulance routing problem (ARP) in disaster situations when a large number of injured people from various locations require receiving treatments and medical aids. In such circumstances, many people summoning the ambulances but the capacity and number of emergency vehicles are not sufficient to visit all the patients at the same time. Therefore, a pivotal issue is to manage the fleet of ambulances to meet all the requests promptly and consequently mitigate human suffering. We considered three different categories of patients with various requirements. Moreover, the support ambulances are segmented into various classes based on their capabilities. A mathematical formulation is presented to obtain route plans with the aim of minimizing the latest service completion time among the patients. Since the patient’s condition gets worse and becomes life ­threatening over the time, semi-soft time window constraint is incorporated to reflect the penalties on late arrivals using survival function. Since the presented model belongs to the class of NP-hard problems, two efficient meta-heuristic algorithms based on genetic algorithm and tabu search are proposed to cope with real size problems. The experiments show that the proposed model could present proper routes and adopt the types of ambulances with the patients’ needs to increase the service quality. Moreover, the proposed metaheuristics are capable to find acceptable solutions for the problem in reasonable computational times.  }, keywords = {Ambulance routing problem,disaster response phase,survival function,vehicle classification,Metaheuristic Algorithms}, url = {https://www.jise.ir/article_76545.html}, eprint = {https://www.jise.ir/article_76545_9ca3010ef75d8dc3e121fa6fd59cc046.pdf} } @article { author = {Seraji, Hasti and Tavakkoli-Moghaddam, Reza and Soltani, Roya}, title = {A two-stage mathematical model for evacuation planning and relief logistics in a response phase}, journal = {Journal of Industrial and Systems Engineering}, volume = {12}, number = {1}, pages = {129-146}, year = {2019}, publisher = {Iranian Institute of Industrial Engineering}, issn = {1735-8272}, eissn = {2717-3380}, doi = {}, abstract = {Crises and natural disasters are always existed in human history, and continue to exist in the future; therefore, people are always affected by these natural disasters. Hence, evacuation planning after natural disasters (e.g., earthquakes, floods, tsunamis, fire, storms, warfare and nuclear explosions) is vital. Given that natural disasters cause irreparable financial loss and the loss of life every year for governments and communities, one of the important issue addressed in all countries is crisis management in recent years. By improving the conditions after natural disasters, this paper presents a two-stage mathematical model to improve post-earthquake conditions. The first stage investigates the locations of shelters for the primary accommodation of people, the location of first aid warehouses, and distances travelled by people from crisis areas to shelters in the event of the earthquake. Furthermore, relief and coverage of demands after accommodation of people in shelters are studied in the second stage of the proposed model. Then, the integer linear programming model is solved in GAMS software. Finally, the obtained results are analysed.}, keywords = {Humanitarian behaviour,evacuation planning,shelter and warehouse selection,Relief Logistics,crisis management}, url = {https://www.jise.ir/article_76547.html}, eprint = {https://www.jise.ir/article_76547_bd807e75811b38b778046f4cd5b19b96.pdf} } @article { author = {Nobari, Arash and Kheirkhah, Amir Saman and Esmaeili, Maryam}, title = {Considering chain to chain competition in forward and reverse logistics of a dynamic and integrated supply chain network design problem}, journal = {Journal of Industrial and Systems Engineering}, volume = {12}, number = {1}, pages = {147-166}, year = {2019}, publisher = {Iranian Institute of Industrial Engineering}, issn = {1735-8272}, eissn = {2717-3380}, doi = {}, abstract = {In this paper, a bi-objective model is presented for dynamic and integrated network design of a new entrant competitive closed-loop supply chain. To consider dynamism and integration in the network design problem, multiple long-term periods are regarded during planning horizon, so that each long-term period includes several short-term periods. Furthermore, a chain to chain competition between two rival supply chains is considered in both forward and reverse logistics. In the forward logistics, the rivals have to compete on the selling price, while in the reverse logistics, the supply chains compete on incentive buying price to achieve more market share. To solve the competitive stage of the proposed model, a game theoretic approach, which determines the selling and incentive buying prices of forward and reverse logistics, is used. Based on the competitive stage’s outputs, the resulted dynamic and integrated network design stage is solved using a Pareto-based multi-objective imperialist competitive algorithm. Finally, to evaluate efficiency of the proposed model and solution approach, a numerical study is implemented.}, keywords = {Supply chain network design,chain to chain competition,forward/reverse logistics game theoretic approach,pareto-based meta-heuristic algorithm}, url = {https://www.jise.ir/article_78693.html}, eprint = {https://www.jise.ir/article_78693_72016350fde197b6e12f61b0045f0eff.pdf} } @article { author = {Fakhrzad, Mohammad Bagher and Goodarzian, F. and Golmohammadi, A. M.}, title = {Addressing a fixed charge transportation problem with multi-route and different capacities by novel hybrid meta-heuristics}, journal = {Journal of Industrial and Systems Engineering}, volume = {12}, number = {1}, pages = {167-184}, year = {2019}, publisher = {Iranian Institute of Industrial Engineering}, issn = {1735-8272}, eissn = {2717-3380}, doi = {}, abstract = {In most real world application and problems, a homogeneous product is carried from an origin to a destination by using different transportation modes (e.g., road, air, rail and water). This paper investigates a fixed charge transportation problem (FCTP), in which there are different routes with different capacities between suppliers and customers. To solve such a NP-hard problem, four meta-heuristic algorithms include Red Deer Algorithm (RDA), Stochastic Fractal Search(SFS), Genetic Algorithm (GA), and Simulated Annealing (SA) and two new hybrid meta-heuristics include hybrid RDA & GA (HRDGA) algorithm and Hybrid SFS & SA (HSFSA) algorithm are utilized. Regarding the literature, this is the first attempt to employ such optimizers to solve a FCTP. To tune up their parameters of algorithms, various problem sizes are generated at random and then a robust calibration is applied by using the Taguchi method. The final output shows that Simulated Annealing (SA) algorithm is the better than other algorithms for small-scale, medium-scale, and large-scale problems. As such, based on the Gap value of algorithms, the results of LINGO software shows that it reveals a better outputs in comparison with meta-heuristic algorithms in small-scale and simulated annealing algorithm is better than other algorithms in large-scale and medium-scale problems. Finally, a set of computational results and conclusions are presented and analyzed.}, keywords = {Fixed-charge transportation problem,SA algorithm,GA algorithm,SFS algorithm,RDA algorithm,Taguchi method}, url = {https://www.jise.ir/article_78694.html}, eprint = {https://www.jise.ir/article_78694_c85791aa74ca95d96683d07212478e6a.pdf} } @article { author = {Ebrahimi, Bohlool}, title = {Efficiency distribution and expected efficiencies in DEA with imprecise data}, journal = {Journal of Industrial and Systems Engineering}, volume = {12}, number = {1}, pages = {185-197}, year = {2019}, publisher = {Iranian Institute of Industrial Engineering}, issn = {1735-8272}, eissn = {2717-3380}, doi = {}, abstract = {Several methods have been proposed for ranking the decision-making units (DMUs) in data envelopment analysis (DEA) with imprecise data. Some methods have only used the upper bound efficiencies to rank DMUs. However, some other methods have considered both of the lower and upper bound efficiencies to rank DMUs. The current paper shows that these methods did not consider the DEA axioms and may be unable to produce a rational ranking. We show that considering the imprecise data as stochastic and using the expected efficiencies to rank DMUs give better results. Indeed, we propose a new ranking approach, based on considering the DEA axioms for imprecise data that removes the existing drawbacks. Some numerical examples are provided to explain the content of the paper.}, keywords = {Data Envelopment Analysis (DEA),efficiency measure,expected efficiencies,imprecise data}, url = {https://www.jise.ir/article_79305.html}, eprint = {https://www.jise.ir/article_79305_33634c30a26090914e0d9793558b3dae.pdf} } @article { author = {Hassanzadeh Nodeh, Iman and Zegordi, Seyed Hessameddin}, title = {Simultaneous production planning and scheduling in a hybrid flow shop with time periods and work shifts}, journal = {Journal of Industrial and Systems Engineering}, volume = {12}, number = {1}, pages = {198-214}, year = {2019}, publisher = {Iranian Institute of Industrial Engineering}, issn = {1735-8272}, eissn = {2717-3380}, doi = {}, abstract = {Simultaneous production planning and scheduling has been identified as one of the most important factors that affect the efficient implementation of planning and scheduling operations for the production systems. In this paper, simultaneous production planning and scheduling is applied in a hybrid flow shop environment, which has numerous applications in real industrial settings. In this problem, it is assumed that each time period includes a number of discontinuous intervals called work shifts. A novel mixed integer linear programming model is formulated. Since this problem is NP-hard in the strong sense, a new heuristic algorithm is developed to construct a complete schedule from a solution matrix that is embedded in the proposed Tabu search. A number of test problems have been solved to compare the performance of the proposed method with the exact method. The results show that the proposed tabu search is an effective and efficient method for simultaneous production planning and scheduling in hybrid flow shop systems.}, keywords = {Simultaneous production planning and scheduling,hybrid flow shop,mixed integer linear programming,Tabu Search,work shifts}, url = {https://www.jise.ir/article_80106.html}, eprint = {https://www.jise.ir/article_80106_aa0c1fb1619a092ae51097c6f5bda84a.pdf} } @article { author = {Woldemicael, Wogiye}, title = {Reduction of production disturbances of a shoemaking industry through a discrete event simulation approach}, journal = {Journal of Industrial and Systems Engineering}, volume = {12}, number = {1}, pages = {215-243}, year = {2019}, publisher = {Iranian Institute of Industrial Engineering}, issn = {1735-8272}, eissn = {2717-3380}, doi = {}, abstract = {This study presents a reduction of production disturbances of a shoemaking industry through discrete event simulation approach. The study is conducted at Peacock Shoe factory found in Addis Ababa, Ethiopia.  This factory faces line balancing problem that becomes production disturbance for its assembly lines. Detail time study is carried out for the selected shoe model using stopwatch. Assembly process chart is used to understand the chronological sequence of assembly operations. Arena input analyzer is used to fit the input data, and K-S test is conducted to validate the goodness of fit. Hence, a simulation model for existing stitching, and lasting and finishing assembly lines are developed after taking basic simulation assumptions. The model is verified by checking a coding error of SIMAN language through try and error and validated by comparing its output with real system. Production disturbance (bottleneck) assembly line and operations are identified based on parameters such as average waiting time, WIP, production rate, capacity utilization and total flow time. To alleviate line balancing problem, five scenarios are proposed, and the detail what if the analysis is done using Arena simulation software. Scenario five is selected to reduce the level of production disturbances of the stitching assembly line. This scenario reduces the average waiting time and WIP from 2118.28 to 417.05 sec. and 252 to 85 respectively. Scenario one is selected to reduce the level of production disturbances of existing lasting and finishing assembly line. This scenario reduces the average waiting time and WIP from 2026.91 to 641.26 sec. and 169 to 65 respectively. }, keywords = {Production disturbance,Modeling,line balancing,DES}, url = {https://www.jise.ir/article_80688.html}, eprint = {https://www.jise.ir/article_80688_b36c4ed8b9f6df5f4cd30159c2b9a588.pdf} } @article { author = {Pishvaee, Mir Saman and Yousefi, Atiye}, title = {Effects of integrating physical and financial flows through a closed-loop supply chain network under uncertain demand and return}, journal = {Journal of Industrial and Systems Engineering}, volume = {12}, number = {1}, pages = {244-269}, year = {2019}, publisher = {Iranian Institute of Industrial Engineering}, issn = {1735-8272}, eissn = {2717-3380}, doi = {}, abstract = {The impact of financial challenges on the profit of a supply chain, have caused the researcher to model the supply chain network by considering the operational and financial dimensions. Also, the establishment of a closed loop supply chain (CLSC) network has a high effect on economic profit. So, the purpose of this study is to design a stochastic closed loop supply chain network by considering the operational and financial dimensions and tactical decision-making level. First, a deterministic mixed-integer linear programming model is developed. Then, the scenario-based of the proposed mixed integer linear programming model is presented. The main innovation of this research is to develop a mathematical model that simultaneously focuses on optimizing the financial and physical flows in an integrated manner and uses the financial ratios in the form of a closed loop supply chain. In order to illustrate the applicability of the proposed model, a test problem from the recent literature is used. The analysis of the results obtained from the developed stochastic mathematical model shows an averagely 4% increase in profit and a 27% reduction in semi-variance compared to deterministic developed models.  }, keywords = {Financial flow,Closed-loop supply chain,Supply chain management,Stochastic programming,scenario-based approach}, url = {https://www.jise.ir/article_80690.html}, eprint = {https://www.jise.ir/article_80690_2c2ee930cbe60ff05caaccf210b0b5f0.pdf} } @article { author = {Jamshidi, Rasoul}, title = {Stochastic human fatigue modeling in production systems}, journal = {Journal of Industrial and Systems Engineering}, volume = {12}, number = {1}, pages = {270-283}, year = {2019}, publisher = {Iranian Institute of Industrial Engineering}, issn = {1735-8272}, eissn = {2717-3380}, doi = {}, abstract = {The performance of human resources is affected by various factors such as mental and physical fatigue, skill, and available time in the production systems. Generally, these mentioned factors have effects on human reliability and consequently change the reliability of production systems. Fatigue is a stochastic factor that changes according to other factors such as environmental conditions, work type, and work duration. Many models have been proposed to quantify fatigue in order to control its effect on reliability, but most of them considered the fatigue as a deterministic variable, while this factor is uncertain. In this paper, we propose a stochastic model for human fatigue with the aim of increasing the reliability. Considering the fatigue uncertainty, we use Chance Constraint (CC), and some methods are used to convert the model into the deterministic one. In the proposed model we consider the reliability of machines and the fatigue of human as two important factors in the production systems' reliability. The proposed model has been applied to a real case and the provided results show that production system reliability can be calculated more effectively using the proposed model.}, keywords = {Human fatigue,Stochastic modeling,chance constraint}, url = {https://www.jise.ir/article_81505.html}, eprint = {https://www.jise.ir/article_81505_c4ec8171ed101c2fa118ab991380d665.pdf} } @article { author = {Setak, Mostafa and Karimpour, Asal}, title = {A mathematical model for the electric vehicle routing with time windows considering queuing system at charging stations and alternative paths}, journal = {Journal of Industrial and Systems Engineering}, volume = {12}, number = {1}, pages = {284-306}, year = {2019}, publisher = {Iranian Institute of Industrial Engineering}, issn = {1735-8272}, eissn = {2717-3380}, doi = {}, abstract = {Due to many damages that human activities have imposed on the environment, authorities, manufacturers, and industry owners have taken into account the development of supply chain more than ever. One of the most influential and essential human activities in the supply chain are transportation which green vehicles such as electric vehicles (EVs) are expected to generate higher economic and environmental impact. To this end, designing efficient routing scheme for the fleet of EVs is significant. A remarkable issue about EVs is their need to stations for charging their battery. Due to the existence of time limitations, more attention should be paid to time spent at the charging station, so considering the queuing system at charging stations makes more precise time calculations.  Furthermore, multi-graphs are more consistent with the characteristics of the transportation network. Hence, we consider alternative paths including two criterion cost and energy consumption in the network. First, we develop a mixed integer linear programming for the electric vehicle routing problem on a multi-graph with the queue in charging stations to minimize the traveling and charging costs. Since the proposed problem is NP-hard in a strong sense, we provide a simulated annealing algorithm to search the solution space efficiently and explore a large neighborhood within short computational time.  The efficiency of the model is investigated with numerical and illustrative examples. Then the sensitivity analysis is performed on the proposed model to indicate the importance of the queuing system and the impact of battery capacity on the penetration of EVs.}, keywords = {Electric vehicle routing,charging station,Queuing system,multigraph,alternative paths,Simulated Annealing Algorithm}, url = {https://www.jise.ir/article_81756.html}, eprint = {https://www.jise.ir/article_81756_e057504bab81952e53a385a79ed6502b.pdf} }