Mohamed Wahab Mohamed Ismail

Professor

University of Toronto

Canada

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About

Professor Mohamed Wahab Mohamed Ismail is a faculty member at Toronto Metropolitan University, Canada. His research areas encompass optimization and decision-making in supply chain logistics, including freight contracts, vehicle routing, and retail forecasting. Additionally, he explores the intersection of aviation policy and manufacturing innovation, as well as energy efficiency rebate strategies in Ontario. His work utilizes advanced methodologies such as reinforcement learning and machine learning to address complex practical problems.

Articles (29)

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Collaborators (4)

Piyal Sarkar

Assistant Professor

Pennsylvania State University

UNITED STATES

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mark goh

National University of Singapore (NUS)

SINGAPORE

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Mucahit Cevik

Associate Professor

University of Toronto

CANADA

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Karim Keshavjee

Assistant Professor, Teaching Stream

University of Toronto

CANADA

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Mohamed Wahab Mohamed Ismail

Research Interests

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About

Articles (29)

Optimal periodic review order-up-to policy in a single-vendor multi-retailer cold chain with continuous multi-stage quality degradation

Multi-period binding freight contract using swing options

Learning-based column generation approach for the vehicle routing problem with release dates and incompatible loading constraints

The impact of low-altitude airspace opening policy on aviation manufacturing innovation: A double machine learning approach

A Hidden Markov Modulated Deep Learning Model for Retail Forecasting with Interpretation

Reinforcement learning-based algorithm for the dynamic multi-depot crowdsourced delivery problem

An income-tailored energy efficiency rebate policy: Multi-dimensional benefit evaluation approach for upgrading heating furnaces in Ontario, Canada

Deep reinforcement learning-based column generation for the two-dimensional vector variable-sized packing problem

Contextual bandits learning-based branch-and-price-and-cut algorithm for the two-dimensional vector packing problem with conflicts and time windows

Game‐theoretic approaches to product introduction strategies for durable products

Explaining Exchange Rate Forecasts with Macroeconomic Fundamentals Using Interpretive Machine Learning

An exact algorithm for simultaneous pickup and delivery problem with split demand and time windows

Low-Carbon supply chain optimisation with carbon emission reduction level and warranty period: nash bargaining fairness concern

A multi-dimensional evaluation of upgrading fenestration systems in single-family dwellings and implications for rebate structures

Coordinated replenishment policies for a single-supplier multi-retailer cold chain for fresh produce

Weather rebate sharing contract for enhancing supply chain performance

Multi-objective mixed integer programming modelling for closed-loop supply chain network design: an enhanced Benders decomposition algorithm

A machine learning framework for predicting weather impact on retail sales

From cart to delivery: deciphering shipping and return influence on e-tailer strategies in digital marketplace

Weather rebate contracts for different risk attitudes of supply chain members

A real options approach to value manufacturing flexibilities with regime-switching product demand

Hybrid branch-and-price-and-cut algorithm for the two-dimensional vector packing problem with time windows

Channel leadership and performance for a closed-loop supply chain considering competition

Is Baidu index really powerful to predict the Chinese stock market volatility? New evidence from the internet information

High cardiovascular disease risk-associated with the incidence of Type 2 diabetes among prediabetics

A tug of war of forecasting the US stock market volatility: Oil futures overnight versus intraday information

Does the US stock market information matter for European equity market volatility: a multivariate perspective?

Bridging the supply chain resilience research and practice gaps: pre and post COVID-19 perspectives

Forecasting crude oil market volatility using variable selection and common factor

Collaborators (4)

Piyal Sarkar

mark goh

Mucahit Cevik

Karim Keshavjee

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