University of HormozganInternational Journal of Industrial Engineering and Management Science2409-18718220211201An Experimental Study of Auctions Behavior With Risk Preferences1715803310.22034/ijiems.2022.360921.1057ENAhmadMakuiSchool of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran0000-0001-6249-530XKhadijehNabourehSchool of Industrial Engineering, Iran University of Science and Technology, Tehran, IranSeyed JafarSadjadiSchool of Industrial Engineering, Iran University of Science and Technology, Tehran, IranJournal Article20220906There is an inherent risk in purchasing and selling an object at auction. Recent experimental studies demonstrate that risk preferences influence the bidding behavior of buyers, but they report varying results for the effects of risk attitudes on the expected revenues of different auctions. For example, literature reports that when bidders are risk-preferring, revenues in premium auctions are much greater than in a risk-averse setting, while reporting the opposite of that claim for the FPA. We conducted an experiment to investigate that claim about the hybrid Dutch auction (HDA), taking into account two distinct risk preference groups. Our findings indicate that, similar to the FPA, revenues in the HDA grow with risk aversion. Bidders with lower values bid more aggressively than bidders with higher values in both risk-averse and risk-loving treatments. The results also revealed that the amount and variance of shading increase significantly with value. Moreover, greater competition has a greater impact on the stability of the HDA with risk-seeking and risk-averse bidders than on the expected revenues. Finally, the study indicates that a small number of participants may be the reason why some experiments found that auctions generate less revenue than they would in a symmetric equilibrium and that participants rarely follow the equilibrium strategy.https://www.ijiems.com/article_158033_4e71f8d71023c25e89bc810c81b1a47b.pdfUniversity of HormozganInternational Journal of Industrial Engineering and Management Science2409-18718220211201Estimating the Remaining Useful Life of Equipment Based on an Optimal Deep Learning Model and Cross-Correlation Based Similarity Analysis81716091510.22034/ijiems.2022.347463.1055ENAmirMoballeghtohidCMO at REIS Future Canada Inc, Winnipeg, CanadaFazilatAhmadzadehCEO at REIS Future Canada Inc, Winnipeg, CanadaOmidAhmadzadehCTO at REIS Future Canada Inc, Winnipeg, CanadaJournal Article20220616Determining the remaining useful life (RUL) of key assets in a manufacturing company is one of the most important maintenance engineering activities to improve system reliability and reduce maintenance costs. Knowing the RUL of the equipment can help the decision-making process regarding the proper maintenance of the equipment (for example, repair or replacement). In this regard, one of the challenges is to determine the appropriate forecasting model. This includes designing a mathematical model as well as finding a model that is trained with the most similar data to the data obtained from the current state of the equipment. In this research, to design an appropriate forecasting model, a DE algorithm is proposed to optimize the LSTM deep learning model architecture. Also, to find a suitable reference forecasting model, the cross-correlation criterion has been used as a similarity index. This index takes into account time lags and can determine the most similar learning data set to the current state of the equipment data. To evaluate the proposed model, the FEMTO-ST Institute bearings data were used, which included run-to-failure vibration data of 6 learning bearings and 11 test bearings. To evaluate the proposed optimized forecasting model, competing forecasting models including optimized MLP, optimized SVR, and optimized GPR has been used. Also, the proposed similarity index (cross-correlation) has been compared with the Pearson correlation coefficient and inverse Euclidean distance. The evaluation results show that the proposed model of this research has a better performance than competing models.https://www.ijiems.com/article_160915_a2b11158f8c272e13367b7dfa6bcd9a8.pdf