Mahmoud ALrefaei | Jordan University of Science and Technology (original) (raw)

Papers by Mahmoud ALrefaei

In this paper, we present a simulated annealing algorithm for solving multi-objective simulation ... more In this paper, we present a simulated annealing algorithm for solving multi-objective simulation optimization problems. The algorithm is based on the idea of simulated annealing with constant temperature, and uses a rule for accepting a candidate solution that depends on the individual estimated objective function values. The algorithm is shown to converge almost surely to an optimal solution. It is applied to a multi-objective inventory problem; the numerical results show that the algorithm converges rapidly.

In this paper, we dealt with one problem in designing a production line, which is the problem of ... more In this paper, we dealt with one problem in designing a production line, which is the problem of buffer allocation. A selection approach was developed and tested for selecting the best design for a huge number of alternatives set. The proposed selection approach is a combination between cardinal and ordinal optimization. The algorithm involves four procedures; ordinal optimization, optimal computing budget allocation, subset selection and indifference-zone. The purpose of this paper is to use the proposed selection approach to find the optimal allocation of buffers that maximizes the mean production rate (throughput) in short, unbalanced and reliable production lines. Numerical results are presented to demonstrate the efficiency of the selection algorithm in finding the best buffer profile where its mean production rate is at its maximum.

The purpose of this paper is to develop a framework for supplier selection considering the sustai... more The purpose of this paper is to develop a framework for supplier selection considering the sustainability factors. It utilizes an integrated Triple-Bottom Line (3BL) and Analytic Network Process (ANP) approach. ANP is a flexible analytical program that enables to find the best possible solution to complex problems by breaking down a problem into a systematic network of inter-relationships of the various levels and attributes. This method therefore may not only aid in selecting the best alternative but also helps to understand why an supplier should be preferred over the other suppliers’.

This paper summarizes the simulation technique and technology used for assessing the Key Performa... more This paper summarizes the simulation technique and technology used for assessing the Key Performance Indicators (KPIs) for the supply chain of a large-scale steel producer. To this end, a Discrete Event Simulation (DES) model of the underlying supply chain is built, verified and validated, and set to produce a selected set of KPIs that characterize the supply chain performance in terms of responsiveness, efficiency, and productivity. A base model of the steel producer supply chain is first developed based on collected information and the process structure. The model is then verified based on the intended flow and logic of the company supply chain. Special logic is developed into the model to track customer orders and produce the selected set of the supply chain KPIs. The model behavior is validated with company officials and tested using the collected data. Preliminary results were encouraging and were reported in terms of supply chain KPIs. At this stage, the developed simulation m...

PLOS ONE, 2015

This paper puts forward a new automatic clustering algorithm based on Multi-Objective Particle Sw... more This paper puts forward a new automatic clustering algorithm based on Multi-Objective Particle Swarm Optimization and Simulated Annealing, "MOPSOSA". The proposed algorithm is capable of automatic clustering which is appropriate for partitioning datasets to a suitable number of clusters. MOPSOSA combines the features of the multi-objective based particle swarm optimization (PSO) and the Multi-Objective Simulated Annealing (MOSA). Three cluster validity indices were optimized simultaneously to establish the suitable number of clusters and the appropriate clustering for a dataset. The first cluster validity index is centred on Euclidean distance, the second on the point symmetry distance, and the last cluster validity index is based on short distance. A number of algorithms have been compared with the MOPSOSA algorithm in resolving clustering problems by determining the actual number of clusters and optimal clustering. Computational experiments were carried out to study fourteen artificial and five real life datasets.

Qatar Foundation Annual Research Forum Proceedings, 2013

Linear programming is one of the frequently applied tools in supply chain management. However, ma... more Linear programming is one of the frequently applied tools in supply chain management. However, managers and decision makers may lack information about exact values of most of the parameters used in the optimization models. Fortunately, fuzzy linear programming comes up with a powerful tool to deal with this kind of incomplete data. In this paper, the flexible approach of fuzzy linear programming is proposed and used to solve supply chain management of steel manufacturing company. This approach reformulated some constraints from conventional linear programming to fuzzy linear programming and provides alternative solutions to decision makers. The results obtained indicate that the fuzzy linear programming gives more flexibility to the decision maker to achieve some aspiration level in order to choose what he considers as the best optimal solution. Subject Classification: 65K05, 90Cxx

In this paper, we propose a stopping criterion for the stochastic simulated annealing with consta... more In this paper, we propose a stopping criterion for the stochastic simulated annealing with constant temperature. In this procedure, we perform the annealing algorithm several replications to get estimate of the error gap between the estimated optimal value and the actual optimal value. The proposed procedure is applied for solving a particular discrete stochastic optimization problem. In the computational part, we consider a queueing simulation model. The numerical results indicate that the proposed technique indeed converges to the optimal solution.

We consider the problem of selecting one of the best simulated systems when the number of alterna... more We consider the problem of selecting one of the best simulated systems when the number of alternatives is large. We propose two sequential algorithms for selecting a good enough simulated system based on the idea of ordinal optimization that focuses on ordinal rather the cardinal of the competent systems. In the first algorithm, we use the idea of ordinal optimization together with the Ranking and Selection procedure. In the second algorithm, we use the ordinal optimization with the optimal computing budget allocation algorithm. Numerical experiments for comparing these algorithms are presented.

In this research, we have developed a linear programming formulation to describe Qatar steel manu... more In this research, we have developed a linear programming formulation to describe Qatar steel manufacturing supply chain from suppliers to consumers. The purpose of the model is to provide answers regarding the optimal amount of raw materials to be requested from suppliers, the optimal amount of finished products to be delivered to each customer and the optimal inventory level of raw materials. The model is validated and solved using GAMS software. Sensitivity analysis on the proposed model is conducted in order to draw useful conclusions regarding the factors that play the most important role in the efficiency of the supply chain.

ABSTRACT Supply Chain Management (SCM) is focused on developing, optimizing, and operating effici... more ABSTRACT Supply Chain Management (SCM) is focused on developing, optimizing, and operating efficient supply chains. Efficient supply chains are characterized by cost effective decisions, lean flow and structure, high degree of integration, and well-chosen Key Performance Indicators (KPIs). Although there exists a large body of literature on optimizing individual supply chain elements (transportation, distribution, inventory, location, etc.), the literature does not provide an effective methodology that can address the complexity of the supply chain of a large scale industry such as steel producers. This paper, therefore, builds on existing research methods of supply chain modeling and optimization to propose a framework for optimizing supply chain performance of a steel producer. The framework combines deterministic modeling using Linear Programming (LP) with stochastic simulation modeling and optimization. A holistic LP deterministic optimization model is first used to characterize and optimize the supply chain variables. The model minimizes the annual operating cost of the steel company’s supply chain. Simulation-based optimization with Simulated Annealing is then used to determine the operational levels of the supply chain drivers that meet a desired level of customer satisfaction. The proposed approach is applied to the supply chain of a major steel producer in the Arabian Gulf.

In this paper, we present a simulated annealing algorithm for solving multi-objective simulation ... more In this paper, we present a simulated annealing algorithm for solving multi-objective simulation optimization problems. The algorithm is based on the idea of simulated annealing with constant temperature, and uses a rule for accepting a candidate solution that depends on the individual estimated objective function values. The algorithm is shown to converge almost surely to an optimal solution. It is applied to a multi-objective inventory problem; the numerical results show that the algorithm converges rapidly.

RAIRO - Operations Research, 2015

ABSTRACT We consider the problem of appointment scheduling for outpatient departments in health c... more ABSTRACT We consider the problem of appointment scheduling for outpatient departments in health care systems. The objective is to design an appointment system that minimizes the average waiting time per patient, while at the same time ensuring the effective use of resources, by maximizing doctor utilization and minimizing the average number of patients in the clinic. We model the appointment system problem as a multi-objective optimization problem with three objectives. Several new alternative appointment systems are considered, and the new systems are modelled and simulated using the software Arena. Subsequently, a new version of ranking and selection approaches is used to compare the alternative systems, by constructing a set of Pareto optimal solutions that consists of non-dominated systems with a predetermined level of confidence. Finally, we present the numerical results obtained by implementing the proposed procedure on an outpatient clinic, taking into account the no-show patients as well as the walk-in patients.

We consider the problem of stochastic optimization, where the objective function values are not a... more We consider the problem of stochastic optimization, where the objective function values are not available and need to be simulated to get their estimates. When the function values are available one can use the simulated annealing algorithm. In this paper, we modify an algorithm that uses the hill climbing feature of simulated annealing with fixed temperature to search the feasible solution set. The proposed algorithm uses indifference zone approach of ranking and selection method to compare the current optimal solution and the potential solution that guarantee the optimal solution with a pre specified level of confidence. The algorithm is tested on a (s, S) inventory problem and compared to other competing algorithm. The numerical results show that the proposed method outperforms the competing method and indeed locate the optimal solution quickly.

ABSTRACT In this paper, the cooling schedule set up for Multi-Objective Simulated Annealing algor... more ABSTRACT In this paper, the cooling schedule set up for Multi-Objective Simulated Annealing algorithm (MOSA) is studied. The MOSA algorithm is used to solve multi objective optimization problem by finding the Pareto set of solutions. To apply the MOSA algorithm, a cooling schedule must be determined, which has two main components; the initial temperature, and the rate at which the temperature is decrement. These two components affect the performance of the algorithm. We study the effect of the initial temperature on the MOSA algorithm, and for each value of initial temperature, several temperature decrements are performed. During the algorithm's process, the number of iterations is kept fixed. The 0\1 multi objective knapsack problem is used to illustrate the impact of the initial and decrement temperatures on finding the Pareto set.

Multi-objective optimization problems have been solved widely by determination of a Pareto optima... more Multi-objective optimization problems have been solved widely by determination of a Pareto optimal set. Practically, the decision-makers need to choose only one solution to implement on their system, which is a challenge for them especially when the number of solutions in the Pareto set is large. In this paper, new method has been proposed to get a good solution for multi-objective optimization problem. The method consists of two stages; the first stage used the Multi Objective Simulated Annealing algorithm to find the Pareto set that contains the non-dominated solutions, whereas the second stage used the optimal computing allocation technique to reduce the number of solutions in the Pareto set to one solution that depends on ranking the preferences of the objective functions. To validate this method, multi-objective 0\1 knapsack problem was analyzed.

In this paper, we present a simulated annealing algorithm for solving multi-objective simulation ... more In this paper, we present a simulated annealing algorithm for solving multi-objective simulation optimization problems. The algorithm is based on the idea of simulated annealing with constant temperature, and uses a rule for accepting a candidate solution that depends on the individual estimated objective function values. The algorithm is shown to converge almost surely to an optimal solution. It is applied to a multi-objective inventory problem; the numerical results show that the algorithm converges rapidly.

In this paper, we dealt with one problem in designing a production line, which is the problem of ... more In this paper, we dealt with one problem in designing a production line, which is the problem of buffer allocation. A selection approach was developed and tested for selecting the best design for a huge number of alternatives set. The proposed selection approach is a combination between cardinal and ordinal optimization. The algorithm involves four procedures; ordinal optimization, optimal computing budget allocation, subset selection and indifference-zone. The purpose of this paper is to use the proposed selection approach to find the optimal allocation of buffers that maximizes the mean production rate (throughput) in short, unbalanced and reliable production lines. Numerical results are presented to demonstrate the efficiency of the selection algorithm in finding the best buffer profile where its mean production rate is at its maximum.

The purpose of this paper is to develop a framework for supplier selection considering the sustai... more The purpose of this paper is to develop a framework for supplier selection considering the sustainability factors. It utilizes an integrated Triple-Bottom Line (3BL) and Analytic Network Process (ANP) approach. ANP is a flexible analytical program that enables to find the best possible solution to complex problems by breaking down a problem into a systematic network of inter-relationships of the various levels and attributes. This method therefore may not only aid in selecting the best alternative but also helps to understand why an supplier should be preferred over the other suppliers’.

This paper summarizes the simulation technique and technology used for assessing the Key Performa... more This paper summarizes the simulation technique and technology used for assessing the Key Performance Indicators (KPIs) for the supply chain of a large-scale steel producer. To this end, a Discrete Event Simulation (DES) model of the underlying supply chain is built, verified and validated, and set to produce a selected set of KPIs that characterize the supply chain performance in terms of responsiveness, efficiency, and productivity. A base model of the steel producer supply chain is first developed based on collected information and the process structure. The model is then verified based on the intended flow and logic of the company supply chain. Special logic is developed into the model to track customer orders and produce the selected set of the supply chain KPIs. The model behavior is validated with company officials and tested using the collected data. Preliminary results were encouraging and were reported in terms of supply chain KPIs. At this stage, the developed simulation m...

PLOS ONE, 2015

This paper puts forward a new automatic clustering algorithm based on Multi-Objective Particle Sw... more This paper puts forward a new automatic clustering algorithm based on Multi-Objective Particle Swarm Optimization and Simulated Annealing, "MOPSOSA". The proposed algorithm is capable of automatic clustering which is appropriate for partitioning datasets to a suitable number of clusters. MOPSOSA combines the features of the multi-objective based particle swarm optimization (PSO) and the Multi-Objective Simulated Annealing (MOSA). Three cluster validity indices were optimized simultaneously to establish the suitable number of clusters and the appropriate clustering for a dataset. The first cluster validity index is centred on Euclidean distance, the second on the point symmetry distance, and the last cluster validity index is based on short distance. A number of algorithms have been compared with the MOPSOSA algorithm in resolving clustering problems by determining the actual number of clusters and optimal clustering. Computational experiments were carried out to study fourteen artificial and five real life datasets.

Qatar Foundation Annual Research Forum Proceedings, 2013

Linear programming is one of the frequently applied tools in supply chain management. However, ma... more Linear programming is one of the frequently applied tools in supply chain management. However, managers and decision makers may lack information about exact values of most of the parameters used in the optimization models. Fortunately, fuzzy linear programming comes up with a powerful tool to deal with this kind of incomplete data. In this paper, the flexible approach of fuzzy linear programming is proposed and used to solve supply chain management of steel manufacturing company. This approach reformulated some constraints from conventional linear programming to fuzzy linear programming and provides alternative solutions to decision makers. The results obtained indicate that the fuzzy linear programming gives more flexibility to the decision maker to achieve some aspiration level in order to choose what he considers as the best optimal solution. Subject Classification: 65K05, 90Cxx

In this paper, we propose a stopping criterion for the stochastic simulated annealing with consta... more In this paper, we propose a stopping criterion for the stochastic simulated annealing with constant temperature. In this procedure, we perform the annealing algorithm several replications to get estimate of the error gap between the estimated optimal value and the actual optimal value. The proposed procedure is applied for solving a particular discrete stochastic optimization problem. In the computational part, we consider a queueing simulation model. The numerical results indicate that the proposed technique indeed converges to the optimal solution.

We consider the problem of selecting one of the best simulated systems when the number of alterna... more We consider the problem of selecting one of the best simulated systems when the number of alternatives is large. We propose two sequential algorithms for selecting a good enough simulated system based on the idea of ordinal optimization that focuses on ordinal rather the cardinal of the competent systems. In the first algorithm, we use the idea of ordinal optimization together with the Ranking and Selection procedure. In the second algorithm, we use the ordinal optimization with the optimal computing budget allocation algorithm. Numerical experiments for comparing these algorithms are presented.

In this research, we have developed a linear programming formulation to describe Qatar steel manu... more In this research, we have developed a linear programming formulation to describe Qatar steel manufacturing supply chain from suppliers to consumers. The purpose of the model is to provide answers regarding the optimal amount of raw materials to be requested from suppliers, the optimal amount of finished products to be delivered to each customer and the optimal inventory level of raw materials. The model is validated and solved using GAMS software. Sensitivity analysis on the proposed model is conducted in order to draw useful conclusions regarding the factors that play the most important role in the efficiency of the supply chain.

ABSTRACT Supply Chain Management (SCM) is focused on developing, optimizing, and operating effici... more ABSTRACT Supply Chain Management (SCM) is focused on developing, optimizing, and operating efficient supply chains. Efficient supply chains are characterized by cost effective decisions, lean flow and structure, high degree of integration, and well-chosen Key Performance Indicators (KPIs). Although there exists a large body of literature on optimizing individual supply chain elements (transportation, distribution, inventory, location, etc.), the literature does not provide an effective methodology that can address the complexity of the supply chain of a large scale industry such as steel producers. This paper, therefore, builds on existing research methods of supply chain modeling and optimization to propose a framework for optimizing supply chain performance of a steel producer. The framework combines deterministic modeling using Linear Programming (LP) with stochastic simulation modeling and optimization. A holistic LP deterministic optimization model is first used to characterize and optimize the supply chain variables. The model minimizes the annual operating cost of the steel company’s supply chain. Simulation-based optimization with Simulated Annealing is then used to determine the operational levels of the supply chain drivers that meet a desired level of customer satisfaction. The proposed approach is applied to the supply chain of a major steel producer in the Arabian Gulf.

In this paper, we present a simulated annealing algorithm for solving multi-objective simulation ... more In this paper, we present a simulated annealing algorithm for solving multi-objective simulation optimization problems. The algorithm is based on the idea of simulated annealing with constant temperature, and uses a rule for accepting a candidate solution that depends on the individual estimated objective function values. The algorithm is shown to converge almost surely to an optimal solution. It is applied to a multi-objective inventory problem; the numerical results show that the algorithm converges rapidly.

RAIRO - Operations Research, 2015

ABSTRACT We consider the problem of appointment scheduling for outpatient departments in health c... more ABSTRACT We consider the problem of appointment scheduling for outpatient departments in health care systems. The objective is to design an appointment system that minimizes the average waiting time per patient, while at the same time ensuring the effective use of resources, by maximizing doctor utilization and minimizing the average number of patients in the clinic. We model the appointment system problem as a multi-objective optimization problem with three objectives. Several new alternative appointment systems are considered, and the new systems are modelled and simulated using the software Arena. Subsequently, a new version of ranking and selection approaches is used to compare the alternative systems, by constructing a set of Pareto optimal solutions that consists of non-dominated systems with a predetermined level of confidence. Finally, we present the numerical results obtained by implementing the proposed procedure on an outpatient clinic, taking into account the no-show patients as well as the walk-in patients.

We consider the problem of stochastic optimization, where the objective function values are not a... more We consider the problem of stochastic optimization, where the objective function values are not available and need to be simulated to get their estimates. When the function values are available one can use the simulated annealing algorithm. In this paper, we modify an algorithm that uses the hill climbing feature of simulated annealing with fixed temperature to search the feasible solution set. The proposed algorithm uses indifference zone approach of ranking and selection method to compare the current optimal solution and the potential solution that guarantee the optimal solution with a pre specified level of confidence. The algorithm is tested on a (s, S) inventory problem and compared to other competing algorithm. The numerical results show that the proposed method outperforms the competing method and indeed locate the optimal solution quickly.

ABSTRACT In this paper, the cooling schedule set up for Multi-Objective Simulated Annealing algor... more ABSTRACT In this paper, the cooling schedule set up for Multi-Objective Simulated Annealing algorithm (MOSA) is studied. The MOSA algorithm is used to solve multi objective optimization problem by finding the Pareto set of solutions. To apply the MOSA algorithm, a cooling schedule must be determined, which has two main components; the initial temperature, and the rate at which the temperature is decrement. These two components affect the performance of the algorithm. We study the effect of the initial temperature on the MOSA algorithm, and for each value of initial temperature, several temperature decrements are performed. During the algorithm's process, the number of iterations is kept fixed. The 0\1 multi objective knapsack problem is used to illustrate the impact of the initial and decrement temperatures on finding the Pareto set.

Multi-objective optimization problems have been solved widely by determination of a Pareto optima... more Multi-objective optimization problems have been solved widely by determination of a Pareto optimal set. Practically, the decision-makers need to choose only one solution to implement on their system, which is a challenge for them especially when the number of solutions in the Pareto set is large. In this paper, new method has been proposed to get a good solution for multi-objective optimization problem. The method consists of two stages; the first stage used the Multi Objective Simulated Annealing algorithm to find the Pareto set that contains the non-dominated solutions, whereas the second stage used the optimal computing allocation technique to reduce the number of solutions in the Pareto set to one solution that depends on ranking the preferences of the objective functions. To validate this method, multi-objective 0\1 knapsack problem was analyzed.