F.J. Hwang is the Senior Lecturer in Operational Research in the Faculty of Science at UTS. He received his B.Sc. with 1st-class honours (Total Credit Points: 148 [50 subjects]; GPA: 86.10/100; Rank: 1st/182) in Transportation Science from Feng Chia University (Ranked 89th in the Times Higher Education World University Under 50 Rankings 2012), Taiwan. He earned his M.Sc. with 1st-class honours (Total Credit Points: 40 [16 subjects]; Thesis Mark: 90.00; GPA: 89.37/100; Rank: 1st/25) in Transportation Science with Applied Mathematics minor, and his Ph.D. (2007~2011, Total Credit Points: 40 [17 subjects]; Thesis Mark: 94.00; GPA: 92.04/100) in Information Management, both from National Chiao Tung University (Ranked 51-100th for statistics & operational research in the QS World University Rankings by Subject 2016), Taiwan. He was elected to the membership of the Phi-Tau-Phi Scholastic Honour Society twice.
F.J. served as the Logistics Officer (Ensign) at the General Headquarters of Taiwan Navy from 2002 to 2004. From 2004 to 2006, he worked as a research assistant at the Institute of Statistical Science, Academia Sinica, Taiwan. He served as an assistant researcher and adjunct lecturer at the Department of Information Management, CCIT from 2006 to 2007. From 2011 to 2012, he worked as a postdoctoral fellow at National Tsing Hua University and National Chiao Tung University. Having worked as a visiting postdoctoral research fellow with the NSC Fellowship at Warwick Business School, UK for the entire year 2012, he joined UTS as the Lecturer in Operational Research in February 2013.
- Guest Editor, "Machine Learning Methods for Distributed Sensor Network Applications", International Journal of Distributed Sensor Network (ISSN 1550-1477) (SCI, IF=1.614, SJR Q2)
- Guest Editor, "Deep Learning Applications in Electronics Industries", Electronics (ISSN 2079-9292) (SCI, IF=1.764, JCR Q3)
- Guest Editor, "Machine Learning and Deep Learning Methods for Wireless Network Applications", EURASIP Journal on Wireless Communications and Networking (ISSN 1687-1499) (SCI, IF=1.592, SJR Q2)
- Guest Editor, "Machine Learning, Deep Learning, and Optimization Techniques for Transportation", Mathematical Problems in Engineering (ISSN 1563-5147) (SCI, IF=1.179, SJR Q2)
- Former Guest Editor, "Deep Learning Techniques for Agronomy Applications", Agronomy (ISSN 2073-4395) (SCI, IF=2.259, JCR Q1)
- Former Guest Editor, "Applications of Internet of Things", Symmetry (ISSN 2073-8994) (SCI, IF=2.143, JCR Q2)
- Former Guest Editor, "Optimization Techniques for Intelligent Transportation System", Systems (ISSN 2079-8954) (Emerging Sources Citation Index)
- Former Guest Editor, Journal of Electrical and Computer Engineering (ISSN 2090-0147) (Emerging Sources Citation Index; EI)
- General Co-Chair, International Conference on Engineering, Science, and Industrial Applications (ICESI) 2019
- Committee Member, International Conference on Business, Big-Data, and Decision Sciences (ICBBD) 2019
- Session Chair, International Conference on Electrical Engineering and Industrial Engineering (ICEEIE) 2017
- General Co-Chair, International Conference on Applied Mathematics, Modeling and Simulation (AMMS) 2017
- Scientific Committee Member, International Conference on Artificial Intelligence and Industrial Engineering (AIIE) 2017
- Committee Member, International Conference on Business, Big-Data, and Decision Sciences (ICBBD) 2017
- Honorary Member, Phi-Tau-Phi Scholastic Honour Society
- Listee, Marquis Who's Who in the World 2016
- Member, UTS Science Faculty Board
- Member, School Teaching and Learning Advisory Committee
- Member, Optimisation Group, UTS Transport Research Centre
Ad-hoc Journal Referee:
Applied Sciences (SCI), Asia-Pacific Journal of Operational Research (SCI), Computers & Industrial Engineering (SCI), Discrete Applied Mathematics (SCI), Discrete Optimization (SCI), EURASIP Journal on Wireless Communications and Networking (SCI), Expert Systems with Applications (SCI), IEEE Transactions on Reliability (SCI), International Journal of Industrial Engineering (SCI), Information & Management (SCI; SSCI), Information Sciences (SCI), ISPRS International Journal of Geo-Information (SCI), Journal of Combinatorial Optimization (SCI), Journal of Hydrology (SCI), Journal of Industrial and Management Optimization (SCI), Journal of Industrial and Production Engineering (EI), Journal of the Operational Research Society (SCI; SSCI), Journal of Scheduling (SCI), Mathematical Problems in Engineering (SCI), Neural Computing and Applications (SCI), Physica A: Statistical Mechanics and its Applications (SCI), Symmetry (SCI), The Scientific World Journal (SCI),
- 1998 Cho-Chang Tsung Ford Motor Co. Scholarship (NTD 50,000≈AUD 2,222)
- 2000 Leading First Class Honours with University Medal (Feng Chia University, FCU)
- 2000 Honorary Membership of the Phi-Tau-Phi Scholastic Society (Division of FCU)
- 2002 Leading First Class Honours with Distinction (National Chiao Tung University, NCTU)
- 2002 Honorary Membership of the Phi-Tau-Phi Scholastic Society (Division of NCTU)
- 2004 Distinguished Compulsory Military Service Award (Taiwan Navy)
- 2011 NSC Postdoctoral Research Fellowship – NCTU (NTD 827,994≈AUD 36,800)
- 2012 The Best Doctoral Dissertation Award (Second Prize) of the Operations Research Society of Taiwan
- 2012 NSC International Postdoctoral Research Fellowship – Top Universities between the UK and Taiwan (NTD 1,300,000≈AUD 57,778)
- 2016 Australian Society for Operations Research Rising Star Nomination
- 2017 Albert Nelson Marquis Achievement Award
- 2017 ICEEIE Keynote Speech
- 2018 Xiamen University Nanqiang Scholar International Forum Invited Speech
- 2018 Tongji University International Talents Forum Invited Speech
- 2018 Sun Yat-sen University International Scholar Zhuhai Forum Invited Speech
- 2018 Science Industry Connect Forum Invited Speech
2019 Wuhan University International Forum for Interdisciplinary Sciences and
Engineering Invited Speech
Scheduling Problems Subject to Fixed Job Sequences: Complexity Analysis and Algorithm Design [2011-2013]
- Researchers: Prof. B.M.T. Lin; Dr. F.J. Hwang
- Funding Amount: NTD 2,925,000 ≈ AUD 130,000
- Funding Scheme: NSC Project 100-2410-H-009-015-MY2
Project Scheduling with Resource Availability Cost under Uncertainty [2012-2013]
- Researchers: Prof. B.M.T. Lin; Dr. F.J. Hwang
- Funding Amount: NTD 1,630,000 ≈ AUD 72,444
- Funding Scheme: NSC Project 100-2911-I-009-016
Global Optimisation Techniques for the Container Loading Problems [2015-2016]
- Researchers: A/Prof. Javen Huang; Dr. F.J. Hwang
- Funding Amount: RMB 50,000 ≈ AUD 9,765
- Funding Scheme: China FRFCU 268SWJTU15WCX01
An Optimal Logarithmic-Prime Method and Its Applications [2017-2018]
- Researchers: A/Prof. Javen Huang; Dr. F.J. Hwang
- Funding Amount: NTD 749,000 ≈ AUD 33,289
- Funding Scheme: MOST Project 106-2410-H-030-086-
- Optimisation on Transportation Services Procurement Based on Combinatorial Auction [2018-2019]
- Big Data Supply Chain Optimisation for Online Food Delivery [2019-2022]
Can supervise: YES
Operations Research and Industrial Optimisation
- Robust optimisation, Production modelling and scheduling, Algorithm design
Management Information Science and Data Science
- Deep learning, Big data, Precision medicine and smart healthcare
Intelligent Transportation and Logistics
- Data-driven logistics, Supply chain modelling, Intelligent transport systems
- Evolutionary computation, Neural networks, Parallel computing
Current Ph.D. Students
- A. Zhou (UTS Research Training Program (RTP) Scholarship), Spring 2018
- B. Hu (UTS International Research Scholarship (IRS) and UTS President's (UTSP) Scholarship), Spring 2019
- M. Khatami, Autumn 2018 [Co-supervision]
- M. Ahmadian, Spring 2018 [Co-supervision]
- A. Almohammadi, Autumn 2020 [Co-supervision]
Current Master's/Honours Students
- D. Leong, Autumn 2019 [Co-supervision]
- J. Ilacqua, Honours (1st-Class) degree, 2015 [Co-supervision]
- O. Czibula, Ph.D. degree, UTS, 2017 [Co-supervision]
- A. Zhou, Honours (1st-Class) degree, 2018
- Z. Xu, M.Sc. student (Sichuan University), Autumn 2019 [Visiting/Exchange Program]
- S. Yan, M.Sc. student (Sichuan University), Autumn 2019 [Visiting/Exchange Program]
- F. Yang, Ph.D. student (Southwest Jiaotong University, Chinese Government Scholarship), Spring 2019 [Visiting/Exchange Program]
- O. Mashkani (UTS International Research Training Program (IRTP) Scholarship), 2019
- Recruitment: Our team is seeking excellent students intending to pursue the Ph.D. or M.Sc./Honours degree in operational research and data science with the applications in industry, transportation, logistics, or healthcare. All the highly-motivated students with solid related backgrounds are welcome to contact us by sending the full CV, academic transcript(s), and expression of interest to firstname.lastname@example.org. The qualified students will be supported to apply for the scholarships from UTS and other sources. Students who are interested in the dual/joint doctoral degree or exchange programs via UTS’s Key Technology Partnership strategy are also perfectly welcome.
B.Sc. in Mathematics/Statistics
Lecturing and Co-ordination:
37242 Optimisation in Quantitative Management
35344 Network and Combinatorial Optimisation
37131 Introduction to Linear Dynamical Systems
00000 Scheduling Optimisation: Algorithm and Applications (Honours seminar subject)
00000 Computational Complexity Analysis (Honours seminar subject)
Chen, C-H, Hwang, FJ & Kung, H-Y 2019, 'Travel time prediction system based on data clustering for waste collection vehicles', IEICE Transactions on Information and Systems, vol. E102-D, no. 7, pp. 1374-1383.View/Download from: UTS OPUS or Publisher's site
In recent years, intelligent transportation system (ITS) techniques have been widely exploited to enhance the quality of public services. As one of the worldwide leaders in recycling, Taiwan adopts the waste collection and disposal policy named “trash doesn't touch the ground”, which requires the public to deliver garbage directly to the collection points for awaiting garbage collection. This study develops a travel time prediction system based on data clustering for providing real-time information on the arrival time of waste collection vehicle (WCV). The developed system consists of mobile devices (MDs), on-board units (OBUs), a fleet management server (FMS), and a data analysis server (DAS). A travel time prediction model utilizing the adaptive-based clustering technique coupled with a data feature selection procedure is devised and embedded in the DAS. While receiving inquiries from users’ MDs and relevant data from WCVs’ OBUs through the FMS, the DAS performs the devised model to yield the predicted arrival time of WCV. Our experiment result demonstrates that the proposed prediction model achieves an accuracy rate of 75.0% and outperforms the reference linear regression method and neural network technique, the accuracy rates of which are 14.7% and 27.6%, respectively. The developed system is effective as well as efficient and has gone online.
© 2019 by the authors. This editorial introduces the Special Issue, entitled “Deep Learning (DL) Techniques for Agronomy Applications”, of Agronomy. Topics covered in this issue include three main parts: (I) DL-based image recognition techniques for agronomy applications, (II) DL-based time series data analysis techniques for agronomy applications, and (III) behavior and strategy analysis for agronomy applications. Three papers on DL-based image recognition techniques for agronomy applications are as follows: (1) “Automatic segmentation and counting of aphid nymphs on leaves using convolutional neural networks,” by Chen et al.; (2) “Estimating body condition score in dairy cows from depth images using convolutional neural networks, transfer learning, and model ensembling techniques,” by Alvarez et al.; and (3) “Development of a mushroom growth measurement system applying deep learning for image recognition,” by Lu et al. One paper on DL-based time series data analysis techniques for agronomy applications is as follows: “LSTM neural network based forecasting model for wheat production in Pakistan,” by Haider et al. One paper on behavior and strategy analysis for agronomy applications is as follows: “Research into the E-learning model of agriculture technology companies: analysis by deep learning,” by Lin et al.
Chen, C-H, Song, F, Hwang, FJ & Wu, L 2019, 'A probability density function generator based on neural networks', Physica A: Statistical Mechanics and its Applications.View/Download from: UTS OPUS or Publisher's site
In order to generate a probability density function (PDF) for fitting the probability distributions of practical data, this study proposes a deep learning method which consists of two stages: (1) a training stage for estimating the cumulative distribution function (CDF) and (2) a performing stage for predicting the corresponding PDF. The CDFs of common probability distributions can be utilised as activation functions in the hidden layers of the proposed deep learning model for learning actual cumulative probabilities, and the differential equation of the trained deep learning model can be used to estimate the PDF. Numerical experiments with single and mixed distributions are conducted to evaluate the performance of the proposed method. The experimental results show that the values of both CDF and PDF can be precisely estimated by the proposed method.
Khatami, M, Salehipour, A & Hwang, FJ 2019, 'Makespan minimization for the m-machine ordered flow shop scheduling problem', Computers & Operations Research, vol. 111, pp. 400-414.View/Download from: UTS OPUS or Publisher's site
Chen, C-H, Al-Masri, E, Hwang, FJ, Ktoridou, D & Lo, K-R 2018, 'Introduction to the special issue: Applications of internet of things', Symmetry, vol. 10, no. 9, pp. 1-1.View/Download from: UTS OPUS or Publisher's site
© 2018 by the authors. This editorial introduces the special issue, entitled "Applications of Internet of Things", of Symmetry. The topics covered in this issue fall under four main parts: (I) communication techniques and applications, (II) data science techniques and applications, (III) smart transportation, and (IV) smart homes. Four papers on sensing techniques and applications are included as follows: (1) "Reliability of improved cooperative communication over wireless sensor networks", by Chen et al.; (2) "User classification in crowdsourcing-based cooperative spectrum sensing", by Zhai andWang; (3) "IoT's tiny steps towards 5G: Telco's perspective", by Cero et al.; and (4) "An Internet of things area coverage analyzer (ITHACA) for complex topographical scenarios", by Parada et al. One paper on data science techniques and applications is as follows: "Internet of things: a scientometric review", by Ruiz-Rosero et al. Two papers on smart transportation are as follows: (1) "An Internet of things approach for extracting featured data using an AIS database: an application based on the viewpoint of connected ships", by He et al.; and (2) "The development of key technologies in applications of vessels connected to the Internet", by Tian et al. Two papers on smart home are as follows: (1) "A novel approach based on time cluster for activity recognition of daily living in smart homes", by Liu et al.; and (2) "IoT-based image recognition system for smart home-delivered meal services", by Tseng et al.
Huang, Y-H & Hwang, FJ 2018, 'Global optimization for the three-dimensional open-dimension rectangular packing problem', Engineering Optimization, vol. 50, no. 10, pp. 1789-7809.View/Download from: UTS OPUS or Publisher's site
Hwang, FJ & Lin, BMT 2018, 'Survey and extensions of manufacturing models in two-stage flexible flow shops with dedicated machines', Computers & Operations Research, vol. 98, pp. 103-112.View/Download from: UTS OPUS or Publisher's site
This study considers the manufacturing environments in which $m+1$ machines are configured as two-stage flexible flow shops with dedicated machines (F2DM). The F2DM scheduling problems arise naturally from practical production and fabrication systems, and they are classified into two categories, whose machine settings are antithetical to each other. In model 1, a single common bottleneck machine is installed at stage 1 and m parallel dedicated machines comprise stage 2. The second model has the m dedicated machines at stage 1 and the bottleneck machine at stage 2. Categorizing the literature according to the performance metrics, we survey the existing research results of the two models and propose several new solution procedures with improved computational complexity. The complexity results are summarized, and suggestions are made for future research.
Chen, C-H, Wu, C-L, Lo, C-C & Hwang, FJ 2017, 'An augmented reality question answering system based on ensemble neural networks', IEEE Access, vol. 5, pp. 17425-17435.View/Download from: UTS OPUS or Publisher's site
Lin, BMT, Hwang, FJ & Gupta, JND 2017, 'Two-machine flowshop scheduling with three-operation jobs subject to a fixed job sequence', Journal of Scheduling, vol. 20, no. 3, pp. 293-302.View/Download from: UTS OPUS or Publisher's site
Czibula, OG, Gu, H, Hwang, FJ, Kovalyov, MY & Zinder, Y 2016, 'Bi-criteria sequencing of courses and formation of classes for a bottleneck classroom', Computers and Operations Research, vol. 65, pp. 53-63.View/Download from: UTS OPUS or Publisher's site
In this paper, the problem of class formation and sequencing for multiple courses subject to a bottleneck
classroom with an ordered bi-criteria objective is studied. The problem can be modelled as a singlemachine
batch scheduling problem with incompatible job families and parallel job processing in batches,
where the batch size is family-dependent. For the minimisation of the number of tardy jobs, the strong
NP-hardness is proven. For the performance measure of the maximum cost, we consider single criterion
and bi-criteria cases. We present an Oðn2log nÞ algorithm, n is the number of jobs, for both cases. An
Integer Programming model as well as Simulated Annealing and Genetic Algorithm matheuristics to
solve a fairly general case of the bi-criteria problem is presented and computationally tested.
Huang, Y-H, Hwang, FJ & Lu, H-C 2016, 'An effective placement method for the single container loading problem', Computers and Industrial Engineering, vol. 97, pp. 212-221.View/Download from: UTS OPUS or Publisher's site
Hwang, FJ & Lin, BMT 2016, 'Two-stage flexible flow shop scheduling subject to fixed job sequences', Journal of the Operational Research Society, vol. 67, no. 3, pp. 506-515.View/Download from: UTS OPUS or Publisher's site
© 2016 Operational Research Society Ltd. This paper investigates the scheduling problem in a two-stage flexible flow shop, which consists of m stage-1 parallel dedicated machines and a stage-2 bottleneck machine, subject to the condition that n l jobs per type l∈1,..., m are processed in a fixed sequence. Four regular performance metrics, including the total completion time, the maximum lateness, the total tardiness, and the number of tardy jobs, are considered. For each considered objective function, we aim to determine an optimal interleaving processing sequence of all jobs coupled with their starting times on the stage-2 bottleneck machine. The problem under study is proved to be strongly NP-hard. An O(m 2 Π l=1 m n l 2) dynamic programming algorithm coupled with numerical experiments is presented.
Lin, BMT, Hwang, FJ & Kononov, AV 2016, 'Relocation scheduling subject to fixed processing sequences', Journal of Scheduling, vol. 19, no. 2, pp. 153-163.View/Download from: UTS OPUS or Publisher's site
© 2015 Springer Science+Business Media New York This study addresses a relocation scheduling problem that corresponds to resource-constrained scheduling on two parallel dedicated machines where the processing sequences of jobs assigned to the machines are given and fixed. Subject to the resource constraints, the problem is to determine the starting times of all jobs for each of the six considered regular performance measures, namely, the makespan, total weighted completion time, maximum lateness, total weighted tardiness, weighted number of tardy jobs, and number of tardy jobs. By virtue of the proposed dynamic programming framework, the studied problem for the minimization of makespan, total weighted completion time, or maximum lateness can be solved in (Formula presented.) time, where (Formula presented.) and (Formula presented.) are the numbers of jobs on the two machines. The simplified case with a common job processing time can be solved in (Formula presented.) time. For the objective function of total weighted tardiness or weighted number of tardy jobs, this problem is proved to be NP-hard in the ordinary sense, and the case with a common job processing length is solvable in (Formula presented.) time. The studied problem for the minimization of number of tardy jobs is solvable in (Formula presented.) time. The solvability of the common-processing-time problems can be generalized to the m-machine cases, where (Formula presented.).
Yang, C-N, Lin, BMT, Hwang, FJ & Wang, M-C 2016, 'Acquisition planning and scheduling of computing resources', Computers and Operations Research, vol. 76, pp. 167-182.View/Download from: UTS OPUS or Publisher's site
Hwang, FJ, Kovalyov, MY & Lin, BM 2014, 'Scheduling for fabrication and assembly in a two-machine flowshop with a fixed job sequence', Annals Of Operations Research, vol. 217, no. 1, pp. 263-279.View/Download from: UTS OPUS or Publisher's site
This paper studies a problem of scheduling fabrication and assembly operations in a two-machine flowshop, subject to the same predetermined job sequence on each machine. In the manufacturing setting, there are n products, each of which consists of two components: a common component and a unique component which are fabricated on machine 1 and then assembled on machine 2. Common components of all products are processed in batches preceded by a constant setup time. The manufacturing process related to each single product is called a job. We address four regular performance measures: the total job completion time, the maximum job lateness, the total job tardiness, and the number of tardy jobs. Several optimality properties are presented. Based upon the concept of critical path and block schedule, a generic dynamic programming algorithm is developed to find an optimal schedule in O(n7) time.
Hwang, FJ & Lin, B 2012, 'Two-stage assembly-type flowshop batch scheduling problem subject to a fixed job sequence', Journal of the Operational Research Society, vol. 63, no. 6, pp. 839-845.View/Download from: UTS OPUS or Publisher's site
This paper discusses a two-stage assembly-type flowshop scheduling problem with batching considerations subject to a fixed job sequence. The two-stage assembly flowshop consists of m stage-1 parallel dedicated machines and a stage-2 assembly machine which processes the jobs in batches. Four regular performance metrics, namely, the total completion time, maximum lateness, total tardiness, and number of tardy jobs, are considered. The goal is to obtain an optimal batching decision for the predetermined job sequence at stage 2. This study presents a two-phase algorithm, which is developed by coupling a problem-transformation procedure with a dynamic program. The running time of the proposed algorithm is O(mn + n(5)), where n is the number of jobs.
Hwang, FJ, Kovalyov, MY & Lin, BM 2012, 'Total completion time minimization in two-machine flow shop scheduling problems with a fixed job sequence', Discrete Optimization, vol. 9, no. 1, pp. 29-39.View/Download from: UTS OPUS or Publisher's site
This paper addresses scheduling n jobs in a two-machine flow shop to minimize the total completion time, subject to the condition that the jobs are processed in the same given sequence on both machines. A new concept of optimal schedule block is introduced, and polynomial time dynamic programming algorithms employing this concept are derived for two specific problems. In the first problem, the machine-2 processing time of a job is a step increasing function of its waiting time between the machines, and a decision about machine-1 idle time insertion has to be made. This problem is solved in O(n2) time. In the second problem, the jobs are processed in batches and each batch is preceded by a machine-dependent setup time. An O(n5) algorithm is developed to find an optimal batching decision.
Hwang, FJ & Lin, B 2011, 'Coupled-task scheduling on a single machine subject to a fixed job sequence', Computers and Industrial Engineering, vol. 60, no. 4, pp. 690-698.View/Download from: UTS OPUS or Publisher's site
This paper investigates single-machine coupled-task scheduling where each job has two tasks separated by an exact delay. The objective of this study is to schedule the tasks to minimize the makespan subject to a given job sequence. We introduce several intriguing properties of the fixed-job-sequence problem under study. While the complexity status of the studied problem remains open, an O(n2) algorithm is proposed to construct a feasible schedule attaining the minimum makespan for a given permutation of 2n tasks abiding by the fixed-job-sequence constraint. We investigate several polynomially solvable cases of the fixed-job-sequence problem and present a complexity graph of the problem.
Lin, B & Hwang, FJ 2011, 'Total completion time minimization in a 2-stage differentiation flowshop with fixed sequences per job type', Information Processing Letters, vol. 111, no. 5, pp. 208-212.View/Download from: UTS OPUS or Publisher's site
This paper addresses the total completion time minimization in a two-stage differentiation flowshop where the sequences of jobs per type are predetermined. The two-stage differentiation flowshop consists of a stage-1 common machine and m stage-2 parallel dedicated machines. The goal is to determine an optimal interleaved processing sequence of all jobs at the first stage. We propose an View the MathML source dynamic programming algorithm, where nk is the number of type-k jobs. The running time is polynomial when m is constant.
Liu, G, Guo, C, Chen, C-H, Guo, W & Hwang, FJ 2019, 'A mobile positioning method based on integrated heterogeneous networks for commercial vehicle operation systems', Proceedings of the IEEE International Conference on Industrial Internet, IEEE International Conference on Industrial Internet, Orlando, USA.
Zhou, A, Tseng, P-H & Hwang, FJ 2019, 'Analyzing liner shipping routes from Asia to Europe for when the Northern Sea Route becomes a viable commercial alternative', The 30th European Conference on Operational Research, Dublin.
Mashkani, O, Hwang, FJ & Salehipour, A 2018, 'The operating room scheduling problem based on patient priority', The 26th ASOR National Conference for the Australian Society of Operations Research and Defence Operations Research Symposium, Melbourne.
Zhou, A, Salehipour, A & Hwang, FJ 2018, 'Robust single machine scheduling with uncertain interval processing times for minimising weighted completion time', The 29th European Conference on Operational Research, Valencia.
Single-machine scheduling is a classical combinatorial optimisation problem that has been widely addressed in literature. Considering the minimisation of the total weighted completion time with deterministic job processing times, the WSPT dispatching priority rule can simply yield the optimal solution. This study copes with the case of uncertain job processing times bound by the preassigned intervals, of which the processing time of each job could take any value between its corresponding lower and upper bounds. The objective is to minimise the maximum regret for any scenario generated within an instance. Given a set of jobs, an instance refers to the generation of the intervals of processing times and weights for jobs, whilst a scenario stands for the realisation of the job processing times generated within the intervals. Four heuristic algorithms utilising the lower and upper bounds are proposed in this paper. We develop the first heuristic with a pessimistic sense by considering only the upper bound, whilst the second one takes the optimistic approach using only the lower bound. The interval length is taken into account in the third heuristic, and the fourth one considers the position of the midpoint in relation to the mean of the midpoint of other jobs. We generated the instances by utilising an instance generation method available in the literature. The performance comparisons of the proposed algorithms in terms of the effectiveness and efficiency are then provided.
Lin, BMT & Hwang, FJ 2016, 'Scheduling in two-stage flexible flow shops with dedicated machines', INFORMS International Meeting Website, INFORMS International Meeting, Hawaii.
This study discusses two manufacturing environments, where m+1 machines are configured as two-stage flexible flow shops. In model 1, the bottleneck machine M0 is installed at stage 1, and the m dedicated machines constitute the stage 2. The second configuration has the m dedicated machines at stage 1 and the bottleneck machine M0 at stage 2. We present a concise survey of the existing results of the two models and report new algorithms and complexity status in the context of processing sequences fixed on the dedicated machines.
Kovalyov, MY, Sevastyanov, SV, Lin, BMT & Hwang, FJ 2014, 'Optimal interval partitioning at given points', New Challenges in Scheduling Theory, Aussois, France.
Lin, BM, Hwang, FJ & Kononov, A 2011, 'Resource-constrained scheduling with two parallel dedicated machines subject to fixed processing sequences', The 24th Conference of the European Chapter on Combinatorial Optimization (ECCO 2011), ECCO, Amsterdam.
Hwang, FJ, Kovalyov, MY & Lin, BM 2010, 'Minimization of total completion time in flowshop scheduling subject to fixed job sequences', The 12th International Workshop on Project Management and Scheduling (PMS-10), PMS, Tours.
- Department of Management, Macquarie University, Australia
- United Institute of Informatics Problems, National Academy of Sciences of Belarus, Belarus
- Faculty of Economics & Business, KU Leuven, Belgium
- School of Economics & Management, Beijing Jiaotong University, China
- Transport BIM Research Centre, Chang'an University, China
- College of Mathematics and Computer Science, Fuzhou University, China
- Business School, Sichuan University, China
- School of Economics & Management, Southwest Jiaotong University, China
- Sobolev Institute of Mathematics, Russian Academy of Sciences, Russia
- Internet of Things Laboratory, Chunghwa Telecommunication Laboratories, Taiwan
- Department of Transportation Technology & Management, Feng Chia University, Taiwan
- Institute of Information Management, National Chiao Tung University, Taiwan
- Department of Business Management, National Taipei University of Technology, Taiwan
- Department of Medical Research, Taipei Medical University - Shuang Ho Hospital, Taiwan
- Engineering Department, SUNY Polytechnic Institute, USA
- College of Business Administration, the University of Alabama in Huntsville, USA