@article { author = {Sobhanifard, Farideh and Shahraki, Mohammad Reza}, title = {An Integrated Neural Networks and MCMC Model to Predicting Bank’s Efficiency}, journal = {Advances in Industrial Engineering}, volume = {54}, number = {1}, pages = {1-14}, year = {2020}, publisher = {University of Tehran}, issn = {2783-1744}, eissn = {2783-1744}, doi = {10.22059/jieng.2021.312818.1743}, abstract = {In the banking industry, there is intense competition between banks to attract resources and facilities. With the development of new services, bank managers try to improve their services and attract more customer deposits by differentiating between competitors' services. This research uses a two-stage TOPSIS method with the combination of neural network model and Monte Carlo simulation trading method to analyze and compare bank productivity forecasts with the 4 efficiency criteria of the banking industry. TOPSIS was first used in two steps to rate the efficiency of banks and then a model was created for banking performance with clear forecasting ability. Secondly, an MCMC sampling method and ANN training was presented. Integrated neural networks and MCMCs were used which are consistent with TOPSIS results. The simulation effect of the selected variables was predicted and their effect on performance was observed. The proposed method was used successfully for predicting performance and ranking banks based on the relative importance of performance criteria expressed by considering the performance levels in the TOPSIS method. Then, the artificial neural network was modeled using the results obtained from the TOPSIS method, an effective model for appropriate prediction of bank performance. Based on the results of the proposed model and the level of importance of performance measures, cost and revenue structure were considered to be the main causes of inefficiency}, keywords = {Forecast,TOPSIS,neural networks,Monte Carlo,Efficiency}, url = {https://aie.ut.ac.ir/article_81137.html}, eprint = {https://aie.ut.ac.ir/article_81137_ee6fdff70c9f4076650f404fcbb70821.pdf} } @article { author = {Nasiri, Mohammad Mahdi and Zenoozadeh, Mohammad}, title = {Timetabling of Metro Trains in a Dynamic Demand Situation Considering the Capacity of Trains and Stations on Peak and Off-Peak Times}, journal = {Advances in Industrial Engineering}, volume = {54}, number = {1}, pages = {15-23}, year = {2020}, publisher = {University of Tehran}, issn = {2783-1744}, eissn = {2783-1744}, doi = {10.22059/jieng.2021.317846.1749}, abstract = {This paper aims to propose a mathematical model in order to minimize total waiting time of passengers in metro systems. The main contribution of this paper is considering the capacity of trains and stations, as well as the assumption of a constant interval for travelling between two successive stations. To reach this aim, the sum of dwell time and travel time are assumed constant. The dwell time is considered a function of number of passengers who can board the train. To show the effectiveness of the proposed model, a numerical example is studied. The parameters of the metro system are considered according to Tehran Urban and Suburban Railway Operation Co. The results show that an increase in the capacity of trains and the number of trains separately leads to the reduction of total waiting time. Furthermore, the best amount of Headway in order to minimize the waiting time is six minutes.}, keywords = {Dynamic Demand,Mathematical model,Metro Timetabling,Scheduling,waiting time}, url = {https://aie.ut.ac.ir/article_81138.html}, eprint = {https://aie.ut.ac.ir/article_81138_8a5d4ec4d6193ba986e9c89ff2cc85a0.pdf} } @article { author = {Sheikhalishahi, Mohammad and Gharoun, Hasan and Goldansaz, Seyed Mohammad Reza}, title = {Multi-Objective Optimization of Nurse Scheduling Problem by Modeling Teamwork and Decision Making Style}, journal = {Advances in Industrial Engineering}, volume = {54}, number = {1}, pages = {25-40}, year = {2020}, publisher = {University of Tehran}, issn = {2783-1744}, eissn = {2783-1744}, doi = {10.22059/jieng.2021.317869.1750}, abstract = {This study presents a multi-objective nurse scheduling model by considering and integrating teamwork and decision making styles in order to maximize job satisfaction. To achieve high job satisfaction, teamwork which minimizes incompatibility among team members is considered. Teamwork has sustainable impact on job satisfaction in healthcare. In this study, a new mathematical model is proposed for scheduling nurses based on teamwork. First, nursing teams are generated by considering decision making styles. Then, each team is assigned to work shifts in the planning horizon. The unique multi-objective mathematical model considers the inconsistency of nurses’ decision making styles, reliability of teams, allocation costs and penalty of violating soft constraints as the objective functions. A real case study is considered to show the applicability of the proposed model. Finally, the proposed multi objective model is solved using goal programming method. Sensitivity analysis shows the robustness of the proposed mathematical programming model and solution methodology.}, keywords = {Nurse Scheduling,Team working,Decision Making Style,reliability}, url = {https://aie.ut.ac.ir/article_81139.html}, eprint = {https://aie.ut.ac.ir/article_81139_34e552856c9663d7a4602050e38c7920.pdf} } @article { author = {Jamili, Amin}, title = {A Two Stage Recourse Stochastic Mathematical Model for the Tramp Ship Routing with Time Windows Problem}, journal = {Advances in Industrial Engineering}, volume = {54}, number = {1}, pages = {41-52}, year = {2020}, publisher = {University of Tehran}, issn = {2783-1744}, eissn = {2783-1744}, doi = {10.22059/jieng.2021.318000.1751}, abstract = {Nowadays, the majority of international trade in goods is carried by sea, and especially by ships deployed in the industrial and tramp segments. This paper addresses routing the tramp ships and determining the schedules including the arrival times to the ports, berthing times at the ports, and the departure times in an operational planning level. In the operational planning level, the weather can be almost exactly forecasted, however in some routes some uncertainties may remain. In this paper, the voyaging times between some of the ports are considered to be uncertain. To that end, a two stage stochastic mathematical model is proposed. In order to find near to optimum solutions in a limited amount of time, a new hybrid heuristic algorithm is proposed to solve large-size examples. Moreover, a case study is defined and tested with the presented model. The computational results show that this mathematical model is promising and can represent acceptable solutions. Specifically, the value of the stochastic solution, VSS, is computed, and the results show that using two stage stochastic with recourse improve 1.1% of the objective value.}, keywords = {Scheduling,Uncertainty,Hybrid Heuristic Algorithm}, url = {https://aie.ut.ac.ir/article_81140.html}, eprint = {https://aie.ut.ac.ir/article_81140_87dd89b660e077f8bc253689b225a3e9.pdf} } @article { author = {Changizi, Mehdi and Rahmani, Donya and Rmezanian, Reza}, title = {A Multi-Visit Heterogeneous Drone Routing Model Considering Recharging Decision in Disaster}, journal = {Advances in Industrial Engineering}, volume = {54}, number = {1}, pages = {53-73}, year = {2020}, publisher = {University of Tehran}, issn = {2783-1744}, eissn = {2783-1744}, doi = {10.22059/jieng.2021.320964.1756}, abstract = {The complex nature of disasters has required communities and governments to implement plans to reduce the disturbing effects of these disasters. With the breakdown and destruction of road infrastructure in times of disaster, the need to use an Unmanned Aerial Vehicle (UAV) fleet under the concept of humanitarian logistics has become increasingly essential. Therefore, we present a Multi-Visit Drone Routing Problem in this paper. The relief goods are delivered to the disaster-affected areas by using heterogeneous drones. We use a linear approximation function to calculate energy consumption. We formulated the proposed bi-objective Mixed Integer Linear Programming (MILP) model by a compromise programming method. To validate the proposed model and to show the model’s efficiency, we generate several test problems with the data extracted by experts. The computational results show the satisfactory performance of the model for the delivery of relief items to the damaged nodes by humanitarian drones in the shortest possible time.}, keywords = {natural disaster,Humanitarian Logistics,Heterogeneous drones,Linear approximation function,Energy consumption}, url = {https://aie.ut.ac.ir/article_81141.html}, eprint = {https://aie.ut.ac.ir/article_81141_0fe1d002a8890e8db2fa11ebca14ae3a.pdf} } @article { author = {Abdolazimi, Omid and Salehi Esfandarani, Mitra and Salehi, Maryam and Shishebori, Davood}, title = {A Comparison of Solution Methods for the Multi-Objective Closed Loop Supply Chains}, journal = {Advances in Industrial Engineering}, volume = {54}, number = {1}, pages = {75-98}, year = {2020}, publisher = {University of Tehran}, issn = {2783-1744}, eissn = {2783-1744}, doi = {10.22059/jieng.2021.321634.1758}, abstract = {Increased pressure on natural resources, rising production costs, and multiple disposal challenges resulted in a growing global demand for integrated closed sustainable supply chain networks. In this paper, a bi-objective mixed integer linear programming model is developed to minimize the overall cost and maximize the use of eco-friendly materials and clean technology. The paper evaluates the exact, heuristic, and metaheuristic methods in solving the proposed model in both small and large sizes. The sensitivity analysis was conducted on LP-metric method as it outperformed the other two exact methods in solving the small size problems. The evaluation of LP-metric, modified ε-constraint, and TH as the exact methods, and Lagrange relaxation algorithm as the heuristic method in terms of solution value and CPU time revealed the inability of exact methods in solving the large size problems. The best combination of effective parameters for meta-heuristic algorithms were determined using the Taguchi method. The evaluation of MOPSO, NSGA-II, SPEA-II, and MOEA/D as the metaheuristic methods by means of Number of Pareto Solutions (NPS), Mean Ideal Distance (MID), The Spread of Non-dominance Solutions (SNS), and CPU Time revealed the performance of these methods in solving the proposed model in a large size. The implementation of VIKOR technique identified the SPEA-II as the best method among the meta-heuristic methods. This study provides a holistic view regarding the importance of selecting an appropriate solution methodology based on the problem dimension to ensure obtaining the optimum and accurate solution within the reasonable processing time.}, keywords = {Closed-Loop Supply Chain (CLSC),Exact methods,Lagrange Relaxation Algorithm,Heuristic and Meta-Heuristic Aalgorithms,VIKOR Technique}, url = {https://aie.ut.ac.ir/article_81143.html}, eprint = {https://aie.ut.ac.ir/article_81143_4f5ad99bbbe3b40ccb51d3183bc0f8aa.pdf} }