Texas A&M University-Commerce

Assistant Professor

Mohammad worked at Texas A&M University-Commerce as a Assistant Professor

Grey Wolf Optimizer Algorithm for the Two-stage Assembly Flowshop Scheduling Problem with Release Time

Elsevier

Grey Wolf Optimizer Algorithm for the Two-stage Assembly Flowshop Scheduling Problem with Release Time

Elsevier

Integrated Risk and Multi-Objective Optimization of Energy Systems

Computers & Industrial Engineering

This paper presents a risk aware energy planning model with storages as decision makers. The amount of energy bought from generators, stored in storages, and sol to users is set by storages. The paper also provides the optimal risk strategy for each storage considering energy cost, environmental impact, and failure rate as social welfare functions. A robust, simple, and verifiable framework for understanding and measuring risk scenarios for energy systems is introduced. Multiple-objective approach for integrating risk with social welfare functions is also discussed. Application of presented framework in energy industry is discussed by examples.

Grey Wolf Optimizer Algorithm for the Two-stage Assembly Flowshop Scheduling Problem with Release Time

Elsevier

Integrated Risk and Multi-Objective Optimization of Energy Systems

Computers & Industrial Engineering

This paper presents a risk aware energy planning model with storages as decision makers. The amount of energy bought from generators, stored in storages, and sol to users is set by storages. The paper also provides the optimal risk strategy for each storage considering energy cost, environmental impact, and failure rate as social welfare functions. A robust, simple, and verifiable framework for understanding and measuring risk scenarios for energy systems is introduced. Multiple-objective approach for integrating risk with social welfare functions is also discussed. Application of presented framework in energy industry is discussed by examples.

AN ENHANCED INTELLIGENT WATER DROPS ALGORITHM FOR SCHEDULING OF AN AGILE MANUFACTURING SYSTEM Read More: http://www.worldscientific.com/doi/abs/10.1142/S0219622016500024

World Scientific

International Journal of Information Technology & Decision Making

Grey Wolf Optimizer Algorithm for the Two-stage Assembly Flowshop Scheduling Problem with Release Time

Elsevier

Integrated Risk and Multi-Objective Optimization of Energy Systems

Computers & Industrial Engineering

This paper presents a risk aware energy planning model with storages as decision makers. The amount of energy bought from generators, stored in storages, and sol to users is set by storages. The paper also provides the optimal risk strategy for each storage considering energy cost, environmental impact, and failure rate as social welfare functions. A robust, simple, and verifiable framework for understanding and measuring risk scenarios for energy systems is introduced. Multiple-objective approach for integrating risk with social welfare functions is also discussed. Application of presented framework in energy industry is discussed by examples.

AN ENHANCED INTELLIGENT WATER DROPS ALGORITHM FOR SCHEDULING OF AN AGILE MANUFACTURING SYSTEM Read More: http://www.worldscientific.com/doi/abs/10.1142/S0219622016500024

World Scientific

International Journal of Information Technology & Decision Making

An Improved Optimization Method based on the Intelligent Water Drops Algorithm for the Vehicle Routing Problem

IEEE,Computational Intelligence in Production and Logistics Systems (CIPLS), 2014

Grey Wolf Optimizer Algorithm for the Two-stage Assembly Flowshop Scheduling Problem with Release Time

Elsevier

Integrated Risk and Multi-Objective Optimization of Energy Systems

Computers & Industrial Engineering

This paper presents a risk aware energy planning model with storages as decision makers. The amount of energy bought from generators, stored in storages, and sol to users is set by storages. The paper also provides the optimal risk strategy for each storage considering energy cost, environmental impact, and failure rate as social welfare functions. A robust, simple, and verifiable framework for understanding and measuring risk scenarios for energy systems is introduced. Multiple-objective approach for integrating risk with social welfare functions is also discussed. Application of presented framework in energy industry is discussed by examples.

AN ENHANCED INTELLIGENT WATER DROPS ALGORITHM FOR SCHEDULING OF AN AGILE MANUFACTURING SYSTEM Read More: http://www.worldscientific.com/doi/abs/10.1142/S0219622016500024

World Scientific

International Journal of Information Technology & Decision Making

An Improved Optimization Method based on the Intelligent Water Drops Algorithm for the Vehicle Routing Problem

IEEE,Computational Intelligence in Production and Logistics Systems (CIPLS), 2014

Grey Wolf Optimizer Algorithm for the Two-stage Assembly Flowshop Scheduling Problem with Release Time

Elsevier

Integrated Risk and Multi-Objective Optimization of Energy Systems

Computers & Industrial Engineering

This paper presents a risk aware energy planning model with storages as decision makers. The amount of energy bought from generators, stored in storages, and sol to users is set by storages. The paper also provides the optimal risk strategy for each storage considering energy cost, environmental impact, and failure rate as social welfare functions. A robust, simple, and verifiable framework for understanding and measuring risk scenarios for energy systems is introduced. Multiple-objective approach for integrating risk with social welfare functions is also discussed. Application of presented framework in energy industry is discussed by examples.

AN ENHANCED INTELLIGENT WATER DROPS ALGORITHM FOR SCHEDULING OF AN AGILE MANUFACTURING SYSTEM Read More: http://www.worldscientific.com/doi/abs/10.1142/S0219622016500024

World Scientific

International Journal of Information Technology & Decision Making

An Improved Optimization Method based on the Intelligent Water Drops Algorithm for the Vehicle Routing Problem

IEEE,Computational Intelligence in Production and Logistics Systems (CIPLS), 2014

Enhanced Intelligent Water Drops and Cuckoo Search Algorithms for Solving the Capacitated Vehicle Routing Problem

Information Sciences

The capacitated vehicle routing problem (CVRP) is investigated in this research. To tackle this problem, four state-of-the-art algorithms are employed: an improved intelligent water drops (IIWD) algorithm as a new swarm-based nature inspired optimization one; an advanced cuckoo search (ACS) algorithm; and two effective proposed hybrid meta-heuristics incorporating these methods, called local search hybrid algorithm (LSHA) and post-optimization hybrid algorithm (POHA). Both IIWD and ACS algorithms introduce new adjustments and features which improve the effectiveness of the proposed algorithms so as to optimize the CVRP. The hybrid methods, LSHA and POHA, take advantage of the merits of ACS and IIWD in exploring the solution space. These algorithms are enhanced to control the balance between diversification and intensification of the search process. Two well-known benchmark instances in the literature are solved so as to evaluate the proposed techniques. Experimental results are compared to the best obtained consequences previously reported in the literature. To present a comprehensive comparison between our proposed meta-heuristics and other state-of-the-art algorithms, some critical statistical test is employed; where the quality of our algorithms’ performance in terms of average results is also determined. It is shown that the LSHA and POHA algorithms can effectively cope with such problems, where in most of instances LSHA can yield the best gained solutions in the literature. Specifically, in some cases the best solutions are improved and the number of vehicles is reduced as well.

Grey Wolf Optimizer Algorithm for the Two-stage Assembly Flowshop Scheduling Problem with Release Time

Elsevier

Integrated Risk and Multi-Objective Optimization of Energy Systems

Computers & Industrial Engineering

This paper presents a risk aware energy planning model with storages as decision makers. The amount of energy bought from generators, stored in storages, and sol to users is set by storages. The paper also provides the optimal risk strategy for each storage considering energy cost, environmental impact, and failure rate as social welfare functions. A robust, simple, and verifiable framework for understanding and measuring risk scenarios for energy systems is introduced. Multiple-objective approach for integrating risk with social welfare functions is also discussed. Application of presented framework in energy industry is discussed by examples.

AN ENHANCED INTELLIGENT WATER DROPS ALGORITHM FOR SCHEDULING OF AN AGILE MANUFACTURING SYSTEM Read More: http://www.worldscientific.com/doi/abs/10.1142/S0219622016500024

World Scientific

International Journal of Information Technology & Decision Making

An Improved Optimization Method based on the Intelligent Water Drops Algorithm for the Vehicle Routing Problem

IEEE,Computational Intelligence in Production and Logistics Systems (CIPLS), 2014

Enhanced Intelligent Water Drops and Cuckoo Search Algorithms for Solving the Capacitated Vehicle Routing Problem

Information Sciences

The capacitated vehicle routing problem (CVRP) is investigated in this research. To tackle this problem, four state-of-the-art algorithms are employed: an improved intelligent water drops (IIWD) algorithm as a new swarm-based nature inspired optimization one; an advanced cuckoo search (ACS) algorithm; and two effective proposed hybrid meta-heuristics incorporating these methods, called local search hybrid algorithm (LSHA) and post-optimization hybrid algorithm (POHA). Both IIWD and ACS algorithms introduce new adjustments and features which improve the effectiveness of the proposed algorithms so as to optimize the CVRP. The hybrid methods, LSHA and POHA, take advantage of the merits of ACS and IIWD in exploring the solution space. These algorithms are enhanced to control the balance between diversification and intensification of the search process. Two well-known benchmark instances in the literature are solved so as to evaluate the proposed techniques. Experimental results are compared to the best obtained consequences previously reported in the literature. To present a comprehensive comparison between our proposed meta-heuristics and other state-of-the-art algorithms, some critical statistical test is employed; where the quality of our algorithms’ performance in terms of average results is also determined. It is shown that the LSHA and POHA algorithms can effectively cope with such problems, where in most of instances LSHA can yield the best gained solutions in the literature. Specifically, in some cases the best solutions are improved and the number of vehicles is reduced as well.

Minimising makespan in the two-stage assembly hybrid flow shop scheduling problem using artificial immune systems

International Journal of Production Research / Taylor&Francis

Grey Wolf Optimizer Algorithm for the Two-stage Assembly Flowshop Scheduling Problem with Release Time

Elsevier

Integrated Risk and Multi-Objective Optimization of Energy Systems

Computers & Industrial Engineering

This paper presents a risk aware energy planning model with storages as decision makers. The amount of energy bought from generators, stored in storages, and sol to users is set by storages. The paper also provides the optimal risk strategy for each storage considering energy cost, environmental impact, and failure rate as social welfare functions. A robust, simple, and verifiable framework for understanding and measuring risk scenarios for energy systems is introduced. Multiple-objective approach for integrating risk with social welfare functions is also discussed. Application of presented framework in energy industry is discussed by examples.

AN ENHANCED INTELLIGENT WATER DROPS ALGORITHM FOR SCHEDULING OF AN AGILE MANUFACTURING SYSTEM Read More: http://www.worldscientific.com/doi/abs/10.1142/S0219622016500024

World Scientific

International Journal of Information Technology & Decision Making

An Improved Optimization Method based on the Intelligent Water Drops Algorithm for the Vehicle Routing Problem

IEEE,Computational Intelligence in Production and Logistics Systems (CIPLS), 2014

Enhanced Intelligent Water Drops and Cuckoo Search Algorithms for Solving the Capacitated Vehicle Routing Problem

Information Sciences

The capacitated vehicle routing problem (CVRP) is investigated in this research. To tackle this problem, four state-of-the-art algorithms are employed: an improved intelligent water drops (IIWD) algorithm as a new swarm-based nature inspired optimization one; an advanced cuckoo search (ACS) algorithm; and two effective proposed hybrid meta-heuristics incorporating these methods, called local search hybrid algorithm (LSHA) and post-optimization hybrid algorithm (POHA). Both IIWD and ACS algorithms introduce new adjustments and features which improve the effectiveness of the proposed algorithms so as to optimize the CVRP. The hybrid methods, LSHA and POHA, take advantage of the merits of ACS and IIWD in exploring the solution space. These algorithms are enhanced to control the balance between diversification and intensification of the search process. Two well-known benchmark instances in the literature are solved so as to evaluate the proposed techniques. Experimental results are compared to the best obtained consequences previously reported in the literature. To present a comprehensive comparison between our proposed meta-heuristics and other state-of-the-art algorithms, some critical statistical test is employed; where the quality of our algorithms’ performance in terms of average results is also determined. It is shown that the LSHA and POHA algorithms can effectively cope with such problems, where in most of instances LSHA can yield the best gained solutions in the literature. Specifically, in some cases the best solutions are improved and the number of vehicles is reduced as well.

Minimising makespan in the two-stage assembly hybrid flow shop scheduling problem using artificial immune systems

International Journal of Production Research / Taylor&Francis

Multiple objective energy operation problem using Z utility theory

International Journal of Advanced Manufacturing Technology (IJAMT)

In this paper, we introduce an energy operation model using three objectives of cost, environmental impact, and failure rate. Developed model increases the flexibility of energy systems in coping with changes in demands and energy prices while satisfying operational constraints. We use the Midwest Independent Systems Operator as a case study to show the efficient frontier for these objectives. We apply Z utility theory to choose the best alternative in our case study. Computational results and complexity analysis show that problems with extensive number of entities can be solved efficiently in reasonable time.

Grey Wolf Optimizer Algorithm for the Two-stage Assembly Flowshop Scheduling Problem with Release Time

Elsevier

Integrated Risk and Multi-Objective Optimization of Energy Systems

Computers & Industrial Engineering

This paper presents a risk aware energy planning model with storages as decision makers. The amount of energy bought from generators, stored in storages, and sol to users is set by storages. The paper also provides the optimal risk strategy for each storage considering energy cost, environmental impact, and failure rate as social welfare functions. A robust, simple, and verifiable framework for understanding and measuring risk scenarios for energy systems is introduced. Multiple-objective approach for integrating risk with social welfare functions is also discussed. Application of presented framework in energy industry is discussed by examples.

AN ENHANCED INTELLIGENT WATER DROPS ALGORITHM FOR SCHEDULING OF AN AGILE MANUFACTURING SYSTEM Read More: http://www.worldscientific.com/doi/abs/10.1142/S0219622016500024

World Scientific

International Journal of Information Technology & Decision Making

An Improved Optimization Method based on the Intelligent Water Drops Algorithm for the Vehicle Routing Problem

IEEE,Computational Intelligence in Production and Logistics Systems (CIPLS), 2014

Enhanced Intelligent Water Drops and Cuckoo Search Algorithms for Solving the Capacitated Vehicle Routing Problem

Information Sciences

The capacitated vehicle routing problem (CVRP) is investigated in this research. To tackle this problem, four state-of-the-art algorithms are employed: an improved intelligent water drops (IIWD) algorithm as a new swarm-based nature inspired optimization one; an advanced cuckoo search (ACS) algorithm; and two effective proposed hybrid meta-heuristics incorporating these methods, called local search hybrid algorithm (LSHA) and post-optimization hybrid algorithm (POHA). Both IIWD and ACS algorithms introduce new adjustments and features which improve the effectiveness of the proposed algorithms so as to optimize the CVRP. The hybrid methods, LSHA and POHA, take advantage of the merits of ACS and IIWD in exploring the solution space. These algorithms are enhanced to control the balance between diversification and intensification of the search process. Two well-known benchmark instances in the literature are solved so as to evaluate the proposed techniques. Experimental results are compared to the best obtained consequences previously reported in the literature. To present a comprehensive comparison between our proposed meta-heuristics and other state-of-the-art algorithms, some critical statistical test is employed; where the quality of our algorithms’ performance in terms of average results is also determined. It is shown that the LSHA and POHA algorithms can effectively cope with such problems, where in most of instances LSHA can yield the best gained solutions in the literature. Specifically, in some cases the best solutions are improved and the number of vehicles is reduced as well.

Minimising makespan in the two-stage assembly hybrid flow shop scheduling problem using artificial immune systems

International Journal of Production Research / Taylor&Francis

Multiple objective energy operation problem using Z utility theory

International Journal of Advanced Manufacturing Technology (IJAMT)

In this paper, we introduce an energy operation model using three objectives of cost, environmental impact, and failure rate. Developed model increases the flexibility of energy systems in coping with changes in demands and energy prices while satisfying operational constraints. We use the Midwest Independent Systems Operator as a case study to show the efficient frontier for these objectives. We apply Z utility theory to choose the best alternative in our case study. Computational results and complexity analysis show that problems with extensive number of entities can be solved efficiently in reasonable time.

Multi-objective control chart design optimization using NSGA-III and MOPSO enhanced with DEA and TOPSIS

Expert Systems with Applications

X-bar control charts are widely used to monitor and control business and manufacturing processes. This study considers an X-bar control chart design problem with multiple and often conflicting objectives, including the expected time the process remains in statistical control status, the type-I error, and the detection power. An integrated multi-objective algorithm is proposed for optimizing economical control chart design. We applied multi-objective optimization methods founded on the reference-points-based non-dominated sorting genetic algorithm-II (NSGA-III) and a multi-objective particle swarm optimization (MOPSO) algorithm to efficiently solve the optimization problem. Then, two different multiple criteria decision making (MCDM) methods, including data envelopment analysis (DEA) and the technique for order of preference by similarity to ideal solution (TOPSIS), are used to reduce the number of Pareto optimal solutions to a manageable size. Four DEA methods compare the optimal solutions based on relative efficiency, and then the TOPSIS method ranks the efficient optimal solutions. Several metrics are used to compare the performance of the NSGA-III and MOPSO algorithms. In addition, the DEA and TOPSIS methods are used to compare the performance of NSGA-III and MOPSO. A well-known case study is formulated and solved to demonstrate the applicability and exhibit the efficacy of the proposed optimization algorithm. In addition, several numerical examples are developed to compare the NSGA-III and MOPSO algorithms. Results show that NSGA-III performs better in generating efficient optimal solutions.

Grey Wolf Optimizer Algorithm for the Two-stage Assembly Flowshop Scheduling Problem with Release Time

Elsevier

Integrated Risk and Multi-Objective Optimization of Energy Systems

Computers & Industrial Engineering

This paper presents a risk aware energy planning model with storages as decision makers. The amount of energy bought from generators, stored in storages, and sol to users is set by storages. The paper also provides the optimal risk strategy for each storage considering energy cost, environmental impact, and failure rate as social welfare functions. A robust, simple, and verifiable framework for understanding and measuring risk scenarios for energy systems is introduced. Multiple-objective approach for integrating risk with social welfare functions is also discussed. Application of presented framework in energy industry is discussed by examples.

AN ENHANCED INTELLIGENT WATER DROPS ALGORITHM FOR SCHEDULING OF AN AGILE MANUFACTURING SYSTEM Read More: http://www.worldscientific.com/doi/abs/10.1142/S0219622016500024

World Scientific

International Journal of Information Technology & Decision Making

An Improved Optimization Method based on the Intelligent Water Drops Algorithm for the Vehicle Routing Problem

IEEE,Computational Intelligence in Production and Logistics Systems (CIPLS), 2014

Enhanced Intelligent Water Drops and Cuckoo Search Algorithms for Solving the Capacitated Vehicle Routing Problem

Information Sciences

The capacitated vehicle routing problem (CVRP) is investigated in this research. To tackle this problem, four state-of-the-art algorithms are employed: an improved intelligent water drops (IIWD) algorithm as a new swarm-based nature inspired optimization one; an advanced cuckoo search (ACS) algorithm; and two effective proposed hybrid meta-heuristics incorporating these methods, called local search hybrid algorithm (LSHA) and post-optimization hybrid algorithm (POHA). Both IIWD and ACS algorithms introduce new adjustments and features which improve the effectiveness of the proposed algorithms so as to optimize the CVRP. The hybrid methods, LSHA and POHA, take advantage of the merits of ACS and IIWD in exploring the solution space. These algorithms are enhanced to control the balance between diversification and intensification of the search process. Two well-known benchmark instances in the literature are solved so as to evaluate the proposed techniques. Experimental results are compared to the best obtained consequences previously reported in the literature. To present a comprehensive comparison between our proposed meta-heuristics and other state-of-the-art algorithms, some critical statistical test is employed; where the quality of our algorithms’ performance in terms of average results is also determined. It is shown that the LSHA and POHA algorithms can effectively cope with such problems, where in most of instances LSHA can yield the best gained solutions in the literature. Specifically, in some cases the best solutions are improved and the number of vehicles is reduced as well.

Minimising makespan in the two-stage assembly hybrid flow shop scheduling problem using artificial immune systems

International Journal of Production Research / Taylor&Francis

Multiple objective energy operation problem using Z utility theory

International Journal of Advanced Manufacturing Technology (IJAMT)

In this paper, we introduce an energy operation model using three objectives of cost, environmental impact, and failure rate. Developed model increases the flexibility of energy systems in coping with changes in demands and energy prices while satisfying operational constraints. We use the Midwest Independent Systems Operator as a case study to show the efficient frontier for these objectives. We apply Z utility theory to choose the best alternative in our case study. Computational results and complexity analysis show that problems with extensive number of entities can be solved efficiently in reasonable time.

Multi-objective control chart design optimization using NSGA-III and MOPSO enhanced with DEA and TOPSIS

Expert Systems with Applications

X-bar control charts are widely used to monitor and control business and manufacturing processes. This study considers an X-bar control chart design problem with multiple and often conflicting objectives, including the expected time the process remains in statistical control status, the type-I error, and the detection power. An integrated multi-objective algorithm is proposed for optimizing economical control chart design. We applied multi-objective optimization methods founded on the reference-points-based non-dominated sorting genetic algorithm-II (NSGA-III) and a multi-objective particle swarm optimization (MOPSO) algorithm to efficiently solve the optimization problem. Then, two different multiple criteria decision making (MCDM) methods, including data envelopment analysis (DEA) and the technique for order of preference by similarity to ideal solution (TOPSIS), are used to reduce the number of Pareto optimal solutions to a manageable size. Four DEA methods compare the optimal solutions based on relative efficiency, and then the TOPSIS method ranks the efficient optimal solutions. Several metrics are used to compare the performance of the NSGA-III and MOPSO algorithms. In addition, the DEA and TOPSIS methods are used to compare the performance of NSGA-III and MOPSO. A well-known case study is formulated and solved to demonstrate the applicability and exhibit the efficacy of the proposed optimization algorithm. In addition, several numerical examples are developed to compare the NSGA-III and MOPSO algorithms. Results show that NSGA-III performs better in generating efficient optimal solutions.

A Hybrid Harmony Search algorithm to minimize total weighted tardiness in the permutation flow shop

2014 IEEE Symposium on Computational Intelligence in Production and Logistics Systems (CIPLS), IEEE