Su Nguyen | La Trobe University (original) (raw)

Papers by Su Nguyen

Research paper thumbnail of A learning and optimizing system for order acceptance and scheduling

The International Journal of Advanced Manufacturing Technology, 2016

Research paper thumbnail of Enhancing genetic programming based hyper-heuristics for dynamic multi-objective job shop scheduling problems

2015 IEEE Congress on Evolutionary Computation (CEC), 2015

Research paper thumbnail of A Dispatching rule based Genetic Algorithm for Order Acceptance and Scheduling

Proceedings of the 2015 on Genetic and Evolutionary Computation Conference - GECCO '15, 2015

Research paper thumbnail of Decision Support Framework For Order Review/Release Based On Workload

Research paper thumbnail of In search of the key to delivery improvement

Abstract Many concepts and policies have been designed for job shops environments, aiming at high... more Abstract Many concepts and policies have been designed for job shops environments, aiming at high delivery reliability. Improvements have been searched in a wide variety of aspects. For design-oriented research it is of utmost importance to have effective search ...

Research paper thumbnail of ETLib Public Release 2.0

Research paper thumbnail of Evolving Stochastic Dispatching Rules for Order Acceptance and Scheduling via Genetic Programming

Lecture Notes in Computer Science, 2013

Research paper thumbnail of Automated Design of Production Scheduling Heuristics: A Review

IEEE Transactions on Evolutionary Computation, 2015

ABSTRACT Hyper-heuristics have recently emerged as a powerful approach to automate the design of ... more ABSTRACT Hyper-heuristics have recently emerged as a powerful approach to automate the design of heuristics for a number of different problems. Production scheduling is a particularly popular application area for which a number of different hyperheuristics have been developed and shown to be effective, efficient, easy to implement, and reusable in different shop conditions. In particular, they seem a promising way to tackle highly dynamic and stochastic scheduling problems, an aspect that is specifically emphasised in this survey. Despite their success and the substantial number of papers in this area, there is currently no systematic discussion of the design choices and critical issues involved in the process of developing such approaches. This review strives to fill this gap by summarising the state of the art, suggesting a taxonomy, and providing the interested researchers and practitioners with guidelines for the design of hyper-heuristics in production scheduling. This paper also identifies challenges and open questions and highlights various directions for future work.

Research paper thumbnail of Learning Reusable Initial Solutions for Multi-objective Order Acceptance and Scheduling Problems with Genetic Programming

Lecture Notes in Computer Science, 2013

Research paper thumbnail of Selection Schemes in Surrogate-Assisted Genetic Programming for Job Shop Scheduling

Lecture Notes in Computer Science, 2014

Research paper thumbnail of Dynamic Multi-objective Job Shop Scheduling: A Genetic Programming Approach

Studies in Computational Intelligence, 2013

Research paper thumbnail of Evolving Reusable Operation-Based Due-Date Assignment Models for Job Shop Scheduling with Genetic Programming

Lecture Notes in Computer Science, 2012

Research paper thumbnail of A Sequential Genetic Programming Method to Learn Forward Construction Heuristics for Order Acceptance and Scheduling

Research paper thumbnail of Enhancing Branch-and-Bound Algorithms for Order

Research paper thumbnail of A New Binary Particle Swarm Optimisation Algorithm for Feature Selection

Lecture Notes in Computer Science, 2014

Research paper thumbnail of An efficient differential evolution algorithm for multi-mode resource-constrained project scheduling problems

International Journal of Operational Research, 2012

This paper considers a general resource-constrained project scheduling problem, in which activiti... more This paper considers a general resource-constrained project scheduling problem, in which activities may be executed in more than one operating mode with both renewable and nonrenewable resources. Each mode may have different durations and requires different amounts of renewable and nonrenewable resources. To solve this NP-hard problem, an efficient differential evolution (eDE) algorithm is proposed with linear decreasing crossover factor and adaptive penalty cost. The purpose of these modifications is to enhance the ability of DE algorithm to quickly search for better solutions by encouraging solutions to escape from local solution and effectively evolve solutions in the search space. The performance of the proposed algorithm is compared with other algorithms in literature and shows that the proposed algorithm is very efficient. It outperforms several heuristics in terms of lower average deviation from the optimal makespan. Moreover, it is capable of finding quality solutions for large scale problems in reasonable computational time.

Research paper thumbnail of Genetic programming for order acceptance and scheduling

2013 IEEE Congress on Evolutionary Computation, 2013

ABSTRACT This paper focuses on order acceptance and scheduling (OAS) problem, where both acceptan... more ABSTRACT This paper focuses on order acceptance and scheduling (OAS) problem, where both acceptance and sequencing decisions have to be handled simultaneously. Because of its complexity, designing effective heuristics or meta-heuristics for OAS is challenging. This paper will investigate how genetic programming (GP) can be used to deal with OAS. The goal of this paper is to develop new GP frameworks to evolve high-performance scheduling rules/heuristics for OAS. The new frameworks are developed based on two key aspects: (1) separating acceptance and sequencing decisions, and (2) enhancing the quality of scheduling rules by embedding heuristic search mechanisms. The experimental results show that separating decisions is not trivial and can easily lead to overfitting issues. Meanwhile, embedding heuristic ideas into the scheduling rules can help search for better solutions for OAS.

Research paper thumbnail of A hybrid discrete particle swarm optimisation method for grid computation scheduling

2014 IEEE Congress on Evolutionary Computation (CEC), 2014

Research paper thumbnail of Learning iterative dispatching rules for job shop scheduling with genetic programming

The International Journal of Advanced Manufacturing Technology, 2013

ABSTRACT This study proposes a new type of dispatching rule for job shop scheduling problems. The... more ABSTRACT This study proposes a new type of dispatching rule for job shop scheduling problems. The novelty of these dispatching rules is that they can iteratively improve the schedules by utilising the information from completed schedules. While the quality of the schedule can be improved, the proposed iterative dispatching rules (IDRs) still maintain the easiness of implementation and low computational effort of the traditional dispatching rules. This feature makes them more attractive for large-scale manufacturing systems. A genetic programming (GP) method is developed in this paper to evolve IDRs for job shop scheduling problems. The results show that the proposed GP method is significantly better than the simple GP method for evolving composite dispatching rules. The evolved IDRs also show their superiority to the benchmark dispatching rules when tested on different problem instances with makespan and total weighted tardiness as the objectives. Different aspects of IDRs are also investigated and the insights from these analyses are used to enhance the performance of IDRs.

Research paper thumbnail of Automatic Design of Scheduling Policies for Dynamic Multi-objective Job Shop Scheduling via Cooperative Coevolution Genetic Programming

IEEE Transactions on Evolutionary Computation, 2000

ABSTRACT A scheduling policy strongly influences the performance of a manufacturing system. Howev... more ABSTRACT A scheduling policy strongly influences the performance of a manufacturing system. However, the design of an effective scheduling policy is complicated and time consuming due to the complexity of each scheduling decision, as well as the interactions among these decisions. This paper develops four new multi-objective genetic programming-based hyperheuristic (MO-GPHH) methods for automatic design of scheduling policies, including dispatching rules and due-date assignment rules in job shop environments. In addition to using three existing search strategies, nondominated sorting genetic algorithm II, strength Pareto evolutionary algorithm 2, and harmonic distance-based multi-objective evolutionary algorithm, to develop new MO-GPHH methods, a new approach called diversified multi-objective cooperative evolution (DMOCC) is also proposed. The novelty of these MO-GPHH methods is that they are able to handle multiple scheduling decisions simultaneously. The experimental results show that the evolved Pareto fronts represent effective scheduling policies that can dominate scheduling policies from combinations of existing dispatching rules with dynamic/regression-based due-date assignment rules. The evolved scheduling policies also show dominating performance on unseen simulation scenarios with different shop settings. In addition, the uniformity of the scheduling policies obtained from the proposed method of DMOCC is better than those evolved by other evolutionary approaches.

Research paper thumbnail of A learning and optimizing system for order acceptance and scheduling

The International Journal of Advanced Manufacturing Technology, 2016

Research paper thumbnail of Enhancing genetic programming based hyper-heuristics for dynamic multi-objective job shop scheduling problems

2015 IEEE Congress on Evolutionary Computation (CEC), 2015

Research paper thumbnail of A Dispatching rule based Genetic Algorithm for Order Acceptance and Scheduling

Proceedings of the 2015 on Genetic and Evolutionary Computation Conference - GECCO '15, 2015

Research paper thumbnail of Decision Support Framework For Order Review/Release Based On Workload

Research paper thumbnail of In search of the key to delivery improvement

Abstract Many concepts and policies have been designed for job shops environments, aiming at high... more Abstract Many concepts and policies have been designed for job shops environments, aiming at high delivery reliability. Improvements have been searched in a wide variety of aspects. For design-oriented research it is of utmost importance to have effective search ...

Research paper thumbnail of ETLib Public Release 2.0

Research paper thumbnail of Evolving Stochastic Dispatching Rules for Order Acceptance and Scheduling via Genetic Programming

Lecture Notes in Computer Science, 2013

Research paper thumbnail of Automated Design of Production Scheduling Heuristics: A Review

IEEE Transactions on Evolutionary Computation, 2015

ABSTRACT Hyper-heuristics have recently emerged as a powerful approach to automate the design of ... more ABSTRACT Hyper-heuristics have recently emerged as a powerful approach to automate the design of heuristics for a number of different problems. Production scheduling is a particularly popular application area for which a number of different hyperheuristics have been developed and shown to be effective, efficient, easy to implement, and reusable in different shop conditions. In particular, they seem a promising way to tackle highly dynamic and stochastic scheduling problems, an aspect that is specifically emphasised in this survey. Despite their success and the substantial number of papers in this area, there is currently no systematic discussion of the design choices and critical issues involved in the process of developing such approaches. This review strives to fill this gap by summarising the state of the art, suggesting a taxonomy, and providing the interested researchers and practitioners with guidelines for the design of hyper-heuristics in production scheduling. This paper also identifies challenges and open questions and highlights various directions for future work.

Research paper thumbnail of Learning Reusable Initial Solutions for Multi-objective Order Acceptance and Scheduling Problems with Genetic Programming

Lecture Notes in Computer Science, 2013

Research paper thumbnail of Selection Schemes in Surrogate-Assisted Genetic Programming for Job Shop Scheduling

Lecture Notes in Computer Science, 2014

Research paper thumbnail of Dynamic Multi-objective Job Shop Scheduling: A Genetic Programming Approach

Studies in Computational Intelligence, 2013

Research paper thumbnail of Evolving Reusable Operation-Based Due-Date Assignment Models for Job Shop Scheduling with Genetic Programming

Lecture Notes in Computer Science, 2012

Research paper thumbnail of A Sequential Genetic Programming Method to Learn Forward Construction Heuristics for Order Acceptance and Scheduling

Research paper thumbnail of Enhancing Branch-and-Bound Algorithms for Order

Research paper thumbnail of A New Binary Particle Swarm Optimisation Algorithm for Feature Selection

Lecture Notes in Computer Science, 2014

Research paper thumbnail of An efficient differential evolution algorithm for multi-mode resource-constrained project scheduling problems

International Journal of Operational Research, 2012

This paper considers a general resource-constrained project scheduling problem, in which activiti... more This paper considers a general resource-constrained project scheduling problem, in which activities may be executed in more than one operating mode with both renewable and nonrenewable resources. Each mode may have different durations and requires different amounts of renewable and nonrenewable resources. To solve this NP-hard problem, an efficient differential evolution (eDE) algorithm is proposed with linear decreasing crossover factor and adaptive penalty cost. The purpose of these modifications is to enhance the ability of DE algorithm to quickly search for better solutions by encouraging solutions to escape from local solution and effectively evolve solutions in the search space. The performance of the proposed algorithm is compared with other algorithms in literature and shows that the proposed algorithm is very efficient. It outperforms several heuristics in terms of lower average deviation from the optimal makespan. Moreover, it is capable of finding quality solutions for large scale problems in reasonable computational time.

Research paper thumbnail of Genetic programming for order acceptance and scheduling

2013 IEEE Congress on Evolutionary Computation, 2013

ABSTRACT This paper focuses on order acceptance and scheduling (OAS) problem, where both acceptan... more ABSTRACT This paper focuses on order acceptance and scheduling (OAS) problem, where both acceptance and sequencing decisions have to be handled simultaneously. Because of its complexity, designing effective heuristics or meta-heuristics for OAS is challenging. This paper will investigate how genetic programming (GP) can be used to deal with OAS. The goal of this paper is to develop new GP frameworks to evolve high-performance scheduling rules/heuristics for OAS. The new frameworks are developed based on two key aspects: (1) separating acceptance and sequencing decisions, and (2) enhancing the quality of scheduling rules by embedding heuristic search mechanisms. The experimental results show that separating decisions is not trivial and can easily lead to overfitting issues. Meanwhile, embedding heuristic ideas into the scheduling rules can help search for better solutions for OAS.

Research paper thumbnail of A hybrid discrete particle swarm optimisation method for grid computation scheduling

2014 IEEE Congress on Evolutionary Computation (CEC), 2014

Research paper thumbnail of Learning iterative dispatching rules for job shop scheduling with genetic programming

The International Journal of Advanced Manufacturing Technology, 2013

ABSTRACT This study proposes a new type of dispatching rule for job shop scheduling problems. The... more ABSTRACT This study proposes a new type of dispatching rule for job shop scheduling problems. The novelty of these dispatching rules is that they can iteratively improve the schedules by utilising the information from completed schedules. While the quality of the schedule can be improved, the proposed iterative dispatching rules (IDRs) still maintain the easiness of implementation and low computational effort of the traditional dispatching rules. This feature makes them more attractive for large-scale manufacturing systems. A genetic programming (GP) method is developed in this paper to evolve IDRs for job shop scheduling problems. The results show that the proposed GP method is significantly better than the simple GP method for evolving composite dispatching rules. The evolved IDRs also show their superiority to the benchmark dispatching rules when tested on different problem instances with makespan and total weighted tardiness as the objectives. Different aspects of IDRs are also investigated and the insights from these analyses are used to enhance the performance of IDRs.

Research paper thumbnail of Automatic Design of Scheduling Policies for Dynamic Multi-objective Job Shop Scheduling via Cooperative Coevolution Genetic Programming

IEEE Transactions on Evolutionary Computation, 2000

ABSTRACT A scheduling policy strongly influences the performance of a manufacturing system. Howev... more ABSTRACT A scheduling policy strongly influences the performance of a manufacturing system. However, the design of an effective scheduling policy is complicated and time consuming due to the complexity of each scheduling decision, as well as the interactions among these decisions. This paper develops four new multi-objective genetic programming-based hyperheuristic (MO-GPHH) methods for automatic design of scheduling policies, including dispatching rules and due-date assignment rules in job shop environments. In addition to using three existing search strategies, nondominated sorting genetic algorithm II, strength Pareto evolutionary algorithm 2, and harmonic distance-based multi-objective evolutionary algorithm, to develop new MO-GPHH methods, a new approach called diversified multi-objective cooperative evolution (DMOCC) is also proposed. The novelty of these MO-GPHH methods is that they are able to handle multiple scheduling decisions simultaneously. The experimental results show that the evolved Pareto fronts represent effective scheduling policies that can dominate scheduling policies from combinations of existing dispatching rules with dynamic/regression-based due-date assignment rules. The evolved scheduling policies also show dominating performance on unseen simulation scenarios with different shop settings. In addition, the uniformity of the scheduling policies obtained from the proposed method of DMOCC is better than those evolved by other evolutionary approaches.