- Professor of Operations Research and Director of Postgraduate Research at UNSW Csnberraedit
Research Interests:
Research Interests:
Research Interests:
Performance issues when dealing with a large number of features are well-known for classification algorithms. Feature selection aims at mitigating these issues by reducing the number of features in the data. Hence, in this paper, a... more
Performance issues when dealing with a large number of features are well-known for classification algorithms. Feature selection aims at mitigating these issues by reducing the number of features in the data. Hence, in this paper, a feature selection approach based on a multi-operator differential evolution algorithm is proposed. The algorithm partitions the initial population into a number of sub-populations evolving using a pool of distinct mutation strategies. Periodically, the sub-populations exchange information to enhance their diversity. This multi-operator approach reduces the sensitivity of the standard differential evolution to the selection of an appropriate mutation strategy. Two classifiers, namely decision trees and k-nearest neighborhood, are used to evaluate the generated subsets of features. Experimental analysis has been conducted on several real data sets using a 10-fold cross validation. The analysis shows that the proposed algorithm successfully determines efficient feature subsets, which can improve the classification accuracy of the classifiers under consideration. The usefulness of the proposed method on large scale data set has been demonstrated using the KDD Cup 1999 intrusion data set, where the proposed method can effectively remove irrelevant features from the data.
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Research Interests:
This paper presents the study of a real-time procurement and production mechanism for a three-stage supply chain system with multiple suppliers, subject to unexpected disruptions. In the first part of the paper, a mathematical model was... more
This paper presents the study of a real-time procurement and production mechanism for a three-stage supply chain system with multiple suppliers, subject to unexpected disruptions. In the first part of the paper, a mathematical model was developed for the optimization of replenishment and production decisions for each node after the occurrence of a transportation disruption. In addition, an experiment was conducted to study the effects of disruptions to the system using predefined scenarios, where the supplier’s prioritization of disruption mitigation strategies was explored. Various disruption scenarios were predefined by combining different disruption types and locations as well as different combinations of suppliers. It will be shown that the solution to the transportation disruption was more sensitive to the lot size when the lost sales cost was large. However, when the lost sales cost was low, the sensitivity to the lot size decreased, and the setup cost and inventory holding co...
Research Interests:
Research Interests:
Research Interests:
Supply chains face risks from various unexpected events that make disruptions almost inevitable. This paper presents a disruption recovery model for a single stage production and inventory system, where finished product supply is randomly... more
Supply chains face risks from various unexpected events that make disruptions almost inevitable. This paper presents a disruption recovery model for a single stage production and inventory system, where finished product supply is randomly disrupted for periods of random duration. A production facility that manufactures a single product following the Economic Production Quantity policy is considered. The model is solved using a search algorithm combined with a penalty function method to find the best recovery plan. It is shown that the optimal recovery schedule is dependent on the extent of the disruption, as well as the back order cost and lost sales cost parameters. The proposed model is seen to be a very useful tool for manufacturers to make quick decisions on the optimal recovery plan after the occurrence of a disruption.
Research Interests:
Research Interests:
The job scheduling problem (JSP) is considered as one of the most complex combinatorial optimization problems. JSP is not an independent task, but is rather a part of a company business case. In this paper, we have studied JSPs under... more
The job scheduling problem (JSP) is considered as one of the most complex combinatorial optimization problems. JSP is not an independent task, but is rather a part of a company business case. In this paper, we have studied JSPs under sudden machine breakdown scenarios that introduce a risk of not completing the jobs on time. We have first solved JSPs using an improved memetic algorithm and extended the algorithm to deal with the disruption situations, and then developed a simulation model to analyze the risk of using a job order and delivery scenario. This paper deals with job scheduling under ideal conditions and rescheduling under machine breakdown, and provides a risk analysis for a production business case. The extended algorithm provides better understanding and results than existing algorithms, the rescheduling shows a good way of recovering from disruptions, and the risk analysis shows an effective way of maximizing return under such situations.
Research Interests:
ABSTRACT The Permutation Flow Shop Scheduling Problem (PFSP) is a complex combinatorial optimization problem. PFSP has been widely studied as a static problem using heuristics and metaheuristics. In reality, PFSPs are not usually static,... more
ABSTRACT The Permutation Flow Shop Scheduling Problem (PFSP) is a complex combinatorial optimization problem. PFSP has been widely studied as a static problem using heuristics and metaheuristics. In reality, PFSPs are not usually static, but are rather dynamic, as customer orders are placed at random time intervals. In the dynamic problem, two tasks must be considered: (i) should a new order be accepted? and (ii) if accepted, how can this schedule be ordered, when some orders may be already under process and or be in the queue for processing? For the first task, we propose a simple heuristic based decision process, and for the second task, we developed a Genetic Algorithm (GA) based approach that is applied repeatedly for re-optimization as each new order arrives. The usefulness of the proposed approach has been demonstrated by solving a set of test problems. In addition the proposed approach, along with a simulation model, has been tested for maximizing the revenue of a flow shop production business under different order arrival scenarios. Finally, a case study is presented to show the applicability of the proposed approach in practice.
Research Interests:
ABSTRACT Recently, the success history based parameter adaptation for differential evolution algorithm with linear population size reduction has been claimed to be a great algorithm for solving optimization problems. Neuro-dynamic is... more
ABSTRACT Recently, the success history based parameter adaptation for differential evolution algorithm with linear population size reduction has been claimed to be a great algorithm for solving optimization problems. Neuro-dynamic is another recent approach that has shown remarkable convergence for certain problems, even for high dimensional cases. In this paper, we proposed a new algorithm by embedding the concept of neuro-dynamic into a modified success history based parameter adaptation for differential evolution with linear population size reduction. We have also proposed an adaptive mechanism for the appropriate use of the success history based parameter adaptation for differential evolution with linear population size reduction and neuro-dynamic during the search process. The new algorithm has been tested on the CEC’2015 single objective real-parameter competition problems. The experimental results show that the proposed algorithm is capable of producing good solutions that are clearly better than those obtained from the success history based parameter adaptation for differential evolution with linear population size reduction and a few of the other state-of-the-art algorithms considered in this paper
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Research Interests:
ABSTRACT In a recent article published in Applied Mathematical Modelling 27(2013) 1275-1281, [1] proposed two policies to deal with the integrated system comprises raw material supplier, manufacturer and buyer. In the first policy they... more
ABSTRACT In a recent article published in Applied Mathematical Modelling 27(2013) 1275-1281, [1] proposed two policies to deal with the integrated system comprises raw material supplier, manufacturer and buyer. In the first policy they considered equal shipment interval while for the second policy they assumed equal shipment lot size. In this paper, we propose an optimal policy where the shipment intervals as well as the lot sizes are varied.
Research Interests:
Research Interests:
Research Interests:
Research Interests:
In this chapter, we will review some of the most representative research in the field of evolutionary multiobjective optimization. We will discuss the historical roots of multiobjective optimization, the motivation to use evolutionary... more
In this chapter, we will review some of the most representative research in the field of evolutionary multiobjective optimization. We will discuss the historical roots of multiobjective optimization, the motivation to use evolutionary algorithms, and the most popular techniques currently in use. Then, we will discuss some of the research currently under way, including our own. At the end, we
Research Interests:
Research Interests:
... L. Rogers/1-930708-28-9 • Heuristic and Optimization for Knowledge Discovery Ruhul AminSarker, Hussein Aly ... have been possible without the ongoing professional support from Senior Editor Dr. Mehdi Khosrowpour ... Ruhul Sarker,... more
... L. Rogers/1-930708-28-9 • Heuristic and Optimization for Knowledge Discovery Ruhul AminSarker, Hussein Aly ... have been possible without the ongoing professional support from Senior Editor Dr. Mehdi Khosrowpour ... Ruhul Sarker, Hussein Abbass and Charles Newton Editors ...
Research Interests:
Research Interests:
Research Interests:
... CHOICES OF HEURISTICS Peter Ross and Emma Hart ... Market information arrives in a continual stream of data-points, called bars. One rule might say: “return true if the closing price bars ago exceeds the closing price bars ago by at... more
... CHOICES OF HEURISTICS Peter Ross and Emma Hart ... Market information arrives in a continual stream of data-points, called bars. One rule might say: “return true if the closing price bars ago exceeds the closing price bars ago by at least an amount Another ...
Research Interests:
ABSTRACT The Permutation Flow Shop Scheduling Problem (PFSP) is a well-known combinatorial optimization problem. In this paper, a Genetic Algorithm (GA) based approach has been developed to solve PFSP, with the objective of minimizing the... more
ABSTRACT The Permutation Flow Shop Scheduling Problem (PFSP) is a well-known combinatorial optimization problem. In this paper, a Genetic Algorithm (GA) based approach has been developed to solve PFSP, with the objective of minimizing the makespan for a set of jobs. Two new priority rules; such as Gap Filling (GF) technique and Job Shifting (JS), have been introduced to enhance the performance of the GA. The algorithm has been used to solve a set of standard benchmark problems and the results have been compared with state-of-the-art algorithms. The comparison demonstrates that the overall performance of the algorithm is quite satisfactory.
Research Interests:
Research Interests: Evolutionary Computation, Business Economics, Clustering Algorithms, Multiobjective Optimization, Multi Criteria Decision Making, and 15 moreComparative Study, Genetic Algorithm, Food Processing, Optimization Problem, Multiobjective Evolutionary Optimization, Numerical Simulation, Business and Management, Mathematical Sciences, Artificial Immune System, Multi objective optimization, Evolutionary Algorithm, Pareto front, Pareto Optimality, Particle Swarm Optimizer, and Clonal Selection
Research Interests:
ABSTRACT A considerable number of differential evolution variants have been proposed in the last few decades. However, no variant was able to consistently perform over a wide range of test problems. In this paper, propose two novel... more
ABSTRACT A considerable number of differential evolution variants have been proposed in the last few decades. However, no variant was able to consistently perform over a wide range of test problems. In this paper, propose two novel differential evolution based algorithms are proposed for solving constrained optimization problems. Both algorithms utilize the strengths of multiple mutation and crossover operators. The appropriate mix of the mutation and crossover operators, for any given problem, is determined through an adaptive learning process. In addition, to further accelerate the convergence of the algorithm, a local search technique is applied to a few selected individuals in each generation. The resulting algorithms are named as Self-Adaptive Differential Evolution Incorporating a Heuristic Mixing of Operators. The algorithms have been tested by solving 60 constrained optimization test instances. The results showed that the proposed algorithms have a competitive, if not better, performance in comparison to the-state-of-the-art algorithms.
Research Interests:
ABSTRACT
Research Interests:
In this paper, we discuss a practical oil production planning optimization problem. For oil wells with insufficient reservoir pressure, gas is usually injected to artificially lift oil, a practice commonly referred to as enhanced oil... more
In this paper, we discuss a practical oil production planning optimization problem. For oil wells with insufficient reservoir pressure, gas is usually injected to artificially lift oil, a practice commonly referred to as enhanced oil recovery (EOR). The total gas that can be used for oil extraction is constrained by daily availability limits. The oil extracted from each well is known to be a nonlinear function of the gas injected into the well and varies between wells. The problem is to identify the optimal amount of gas that needs to be injected into each well to maximize the amount of oil extracted subject to the constraint on the total daily gas availability. The problem has long been of practical interest to all major oil exploration companies as it has the potential to derive large financial benefit. In this paper, an infeasibility driven evolutionary algorithm is used to solve a 56 well reservoir problem which demonstrates its efficiency in solving constrained optimization problems. Furthermore, a multi-objective formulation of the problem is posed and solved using a number of algorithms, which eliminates the need for solving the (single objective) problem on a regular basis. Lastly, a modified single objective formulation of the problem is also proposed, which aims to maximize the profit instead of the quantity of oil. It is shown that even with a lesser amount of oil extracted, more economic benefits can be achieved through the modified formulation.
Research Interests: Algorithms, Evolutionary algorithms, Evolutionary Computation, Petroleum, Production Planning, and 11 moreEnhanced Oil Recovery, Optimization Problem, Mathematical Sciences, EC, Profitability, Evolutionary Algorithm, Theoretical Models, Integer Linear Programming, Ec, Economic Benefit, and Integer linear program
Genetic algorithms (GAs) have been shown to be quite effective at solving a wide range of difficult problems. They are very efficient at exploring the entire search space; however, they are relatively poor at finding the precise local... more
Genetic algorithms (GAs) have been shown to be quite effective at solving a wide range of difficult problems. They are very efficient at exploring the entire search space; however, they are relatively poor at finding the precise local optimal solution in the region in which the ...
Research Interests:
Research Interests:
Research Interests: Evolutionary Computation, Business Economics, Clustering Algorithms, Multiobjective Optimization, Multi Criteria Decision Making, and 15 moreComparative Study, Genetic Algorithm, Food Processing, Optimization Problem, Multiobjective Evolutionary Optimization, Numerical Simulation, Business and Management, Mathematical Sciences, Artificial Immune System, Multi objective optimization, Evolutionary Algorithm, Pareto front, Pareto Optimality, Particle Swarm Optimizer, and Clonal Selection
Fuzzy optimization models provide a powerful decision support tool for optimization models in fuzzy environment. In this paper fuzzy goal programming (FGP) is integrated with the fuzzy analytic hierarchy process (FAHP) to determine... more
Fuzzy optimization models provide a powerful decision support tool for optimization models in fuzzy environment. In this paper fuzzy goal programming (FGP) is integrated with the fuzzy analytic hierarchy process (FAHP) to determine optimal plant and distribution centre locations in a supply chain with special focus on the operational efficiencies of the distribution centres. The integrated FGP-FAHP model incorporates multiple
Research Interests: Supply Chain Management, Pragmatics, Manufacturing, Fuzzy Logic, Fuzzy set theory, and 15 moreMathematical Programming, Risk Management, Supply Chain, Facility Location, Optimization, Retailing, Decision support tool, Fuzzy optimization, Matrix Converter, Fuzzy Goal Programming, Decision Maker, Multiple Objectives, Distribution Center, Decision Process, and Fuzzy analytic hierarchy process
ABSTRACT