M
M. I. M. Wahab
Researcher at Ryerson University
Publications - 68
Citations - 1731
M. I. M. Wahab is an academic researcher from Ryerson University. The author has contributed to research in topics: Supply chain & Volatility (finance). The author has an hindex of 21, co-authored 59 publications receiving 1251 citations.
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EOQ models for a coordinated two-level international supply chain considering imperfect items and environmental impact
TL;DR: In this article, the optimal production-shipment policy for items with imperfect quality in three different scenarios is presented to minimize the total expected cost per unit time in a two-level supply chain.
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Forecasting the realized volatility of the oil futures market: A regime switching approach
TL;DR: In this paper, the authors introduce Markov regime switching models to the Heterogeneous Autoregressive model of the Realized Volatility (HAR-RV) models to forecast the realized volatility of the crude oil futures market.
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Economic order quantity model for items with imperfect quality with learning in inspection
TL;DR: In this article, the authors extended Salameh and Jaber's work for the case where there is learning in inspection, and developed mathematical models with numerical examples provided and results discussed for the cases of (i) partial transfer of learning, (ii) total transfer of LTL, and (iii) no transfer of Learning.
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Short Communication: Economic order quantity model for items with imperfect quality, different holding costs, and learning effects: A note
M. I. M. Wahab,Mohamad Y. Jaber +1 more
TL;DR: The results show that the lot size with different holding costs for good and defective items increases when the percentage of defective increases, as one would expect in the real manufacturing environment.
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A generic approach to measuring the machine flexibility of manufacturing systems
TL;DR: A generic model to measure machine flexibility with consideration of uncertainties in the system is proposed, which includes part characteristics such as processing time and processing cost, the number of operations that a machine can perform, and uncertainties in demand and machine-part assignment.