Fangfang Zhang

Fangfang Zhang
Victoria University of Wellington · School of Engineering and Computer Science

Doctor of Philosophy

About

54
Publications
6,733
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
646
Citations
Citations since 2016
51 Research Items
642 Citations
2016201720182019202020212022050100150200250
2016201720182019202020212022050100150200250
2016201720182019202020212022050100150200250
2016201720182019202020212022050100150200250
Introduction
Evolutionary Computation, Genetic Programming, Hyper-heuristic, Job Shop Scheduling

Publications

Publications (54)
Article
Full-text available
Dynamic flexible job-shop scheduling (DFJSS) is a challenging combinational optimization problem that takes the dynamic environment into account. Genetic programming hyperheuristics (GPHH) have been widely used to evolve scheduling heuristics for job-shop scheduling. A proper selection of the terminal set is a critical factor for the success of GPH...
Article
Full-text available
Dynamic flexible job shop scheduling is a challenging combinatorial optimisation problem due to its complex environment. In this problem, machine assignment and operation sequencing decisions need to be made simultaneously under the dynamic environments. Genetic programming, as a hyper-heuristic approach, has been successfully used to evolve schedu...
Article
Full-text available
Dynamic flexible job shop scheduling (JSS) has received widespread attention from academia and industry due to its practical application value. It requires complex routing and sequencing decisions under unpredicted dynamic events. Genetic programming (GP), as a hyperheuristic approach, has been successfully applied to evolve scheduling heuristics f...
Article
Full-text available
Dynamic flexible job shop scheduling is an important combinatorial optimisation problem with complex routing and sequencing decisions under dynamic environments. Genetic programming, as a hyper-heuristic approach, has been successfully applied to evolve scheduling heuristics for job shop scheduling. However, its training process is time-consuming,...
Article
Full-text available
Evolutionary multitask learning has achieved great success due to its ability to handle multiple tasks simultaneously. However, it is rarely used in the hyperheuristic domain, which aims at generating a heuristic for a class of problems rather than solving one specific problem. The existing multitask hyperheuristic studies only focus on heuristic s...
Article
Full-text available
Evolutionary multitask multiobjective learning has been widely used for handling more than one multiobjective task simultaneously. However, it is rarely used in dynamic combinatorial optimization problems, which have valuable practical applications such as dynamic flexible job-shop scheduling (DFJSS) in manufacturing. Genetic programming (GP), as a...
Chapter
Full-text available
Dynamic flexible job shop scheduling (DFJSS) is a critical and challenging problem in production scheduling such as order picking in the warehouse. Given a set of machines and a number of jobs with a sequence of operations, DFJSS aims to generate schedules for completing jobs to minimise total costs while reacting effectively to dynamic changes. Ge...
Conference Paper
Full-text available
Assigning ambulances to emergencies in real-time, ensuring both that patients receive adequate care and that the fleet remains capable of responding to any potential new emergency, is a critical component of any ambulance service. Thus far, most techniques to manage this problem are as convoluted as the problem itself. As such, many real-world medi...
Chapter
Full-text available
Dynamic job shop scheduling has a wide range of applications in reality such as order picking in warehouse. Using genetic programming to design scheduling heuristics for dynamic job shop scheduling problems becomes increasingly common. In recent years, multitask genetic programming-based hyper-heuristic methods have been developed to solve similar...
Article
Full-text available
Genetic programming has achieved great success for learning scheduling heuristics in dynamic job shop scheduling. In theory, generating a large number of offspring for genetic programming, known as brood recombination, can improve its heuristic generation ability. However, it is time-consuming to evaluate extra individuals. Phenotypic characterisat...
Article
Full-text available
Multitasking learning has been successfully used in handling multiple related tasks simultaneously. In reality, there are often many tasks to be solved together, and the relatedness between them is unknown in advance. In this paper, we focus on multitask genetic programming for the dynamic flexible job shop scheduling problems, and address two chal...
Chapter
This chapter shows the details of the definition of different types of job shop scheduling problems. Then, it introduces different approaches such as exact methods, heuristics, and hyper-heuristics, with a focus on hyper-heuristics in evolutionary learning. This chapter also describes how to use scheduling heuristics to handle job shop scheduling p...
Chapter
This chapter introduces how to improve the quality of generated offspring via genetic programming for dynamic scheduling by measuring the importance of subtrees and selecting crossover points based on the subtree importance. This chapter proposes two strategies to measure the subtree importance. One is based on the frequency of features. The other...
Chapter
This chapter introduces how genetic programming is used to design scheduling construction heuristics, one of the key components of most production scheduling algorithms. Details about attributes extracted from production data and representations of scheduling construction heuristics are provided in this chapter. The advantages and disadvantages of...
Chapter
This chapter shows how genetic programming can learn a Pareto front of scheduling heuristics to cope with multiple conflicting objectives. A variety of search techniques, evaluation mechanisms, and choices of training/test simulation scenarios are discussed and examined in this chapter. The experiments show that scheduling heuristics evolved by mul...
Chapter
This chapter shows how to use surrogate techniques to improve the transfer effectiveness between tasks in multitask learning. This chapter introduces how surrogates are built and used to help share knowledge between tasks. The results show that the proposed surrogate-assisted multitask genetic programming can learn effective scheduling heuristics f...
Chapter
This chapter introduces how genetic programming can be integrated into generic optimisation solvers and boost their performance for production scheduling. A simple genetic programming algorithm is introduced to evolve variable selectors for optimisation solvers to reduce the computational efforts required to obtain high-quality or optimal solutions...
Chapter
This chapter investigates the multitask dynamic scheduling problems where different tasks have different and unknown relatedness. This chapter shows how to measure the relatedness between dynamic scheduling tasks, and how to use the relatedness information to choose assisted task to enhance positive knowledge transfer between tasks. The results sho...
Chapter
This chapter introduces how to use multi-fidelity models to improve the training efficiency of genetic programming for dynamic flexible job shop scheduling. How to design multiple surrogate models and how to share knowledge among the built surrogates are introduced. The results show that the proposed algorithm can significantly reduce the training...
Chapter
This chapter introduces multi-objective genetic programming for dynamic flexible job shop scheduling. Two multi-objective genetic programming algorithms are developed, one is incorporated with the NSGA-II strategy, and the other is incorporated with the SPEA2 strategy. The results show that multi-objective genetic programming with the NSGA-II strat...
Chapter
This chapter shows how to represent individuals to learn the routing rule and the sequencing rule simultaneously in genetic programming for dynamic flexible job shop scheduling. Two strategies are introduced, one is the genetic programming with cooperative coevolution, the other is the genetic programming with multi-tree representation. The results...
Chapter
This chapter shows how to use feature selection to reduce the search space of genetic programming to learn scheduling heuristics for dynamic scheduling. In addition, this chapter introduces an individual mimicking strategy to generate individuals with only the selected features. Thus, the learned scheduling heuristics for dynamic scheduling contain...
Chapter
This chapter summarises the whole book based on parts (i.e., genetic programming for static production scheduling, dynamic production scheduling, multi-objective production scheduling, and multitask genetic programming for production scheduling) as well as the details of the chapters in the corresponding part. In addition, this chapter provides fur...
Chapter
Full-text available
This chapter investigates how genetic programming evolves scheduling improvement heuristics and the links between the scheduling improvement heuristics presented in this book and other meta-heuristics in the literature. Extended attribute sets and several evaluation mechanisms are introduced in this chapter to allow GP to evolve scheduling improvem...
Chapter
This chapter investigates how genetic programming copes with scenarios when multiple scheduling decisions and multiple conflicting objectives are considered. In this chapter, due date assignment and sequence decisions are evolved simultaneously by genetic programming. A cooperative coevolution technique is examined to enhance the searchability of g...
Chapter
This chapter attempts to solve multiple related dynamic scheduling tasks simultaneously, and adapts traditional multitask learning into the hyper-heuristic domain with genetic programming for this purpose. This chapter verifies the effectiveness of traditional multitask learning in genetic programming for dynamic scheduling and identifies a number...
Thesis
Full-text available
Dynamic flexible job shop scheduling (DFJSS) has received widespread attention from academia and industry due to its reflection in real-world scheduling applications such as order picking in the warehouse and the manufacturing industry. It requires complex routing and sequencing decisions under unpredicted dynamic events. Genetic programming, as a...
Book
Full-text available
This book introduces readers to an evolutionary learning approach, specifically genetic programming (GP), for production scheduling. The book is divided into six parts. In Part I, it provides an introduction to production scheduling, existing solution methods, and the GP approach to production scheduling. Characteristics of production environments,...
Conference Paper
Full-text available
Genetic programming, as a hyper-heuristic approach, has been successfully used to evolve scheduling heuristics for job shop scheduling. However, the environments of job shops vary in configurations, and the scheduling heuristic for each job shop is normally trained independently, which leads to low efficiency for solving multiple job shop schedulin...
Conference Paper
Full-text available
Dynamic flexible job shop scheduling (DFJSS) has been widely studied in both academia and industry. Both machine assignment and operation sequencing decisions need to be made simultaneously as an operation can be processed by a set of machines in DFJSS. Using scheduling heuristics to solve the DFJSS problems becomes an effective way due to its effi...
Conference Paper
Full-text available
Dynamic flexible job shop scheduling (DFJSS) is a very valuable practical application problem that can be applied in many fields such as cloud computing and manufacturing. In DFJSS, machine assignment and operation sequencing decisions need to be made simultaneously in dynamic environments with unpredicted events such as new job arrivals. Schedulin...
Article
Full-text available
In recent years, many researchers have attempted to determine the mechanisms of how corporate social responsibility (CSR) brings financial benefits to a firm. However, many chief financial officers (CFOs) throughout the world are uncertain about the strategic value of CSR, and no consensus has been reached on defining how CSR creates value. Drawing...
Conference Paper
Full-text available
Dynamic flexible job shop scheduling (DFJSS) is an important and a challenging combinatorial optimisation problem. Genetic programming hyper-heuristic (GPHH) has been widely used for automatically evolving the routing and sequencing rules for DFJSS. The terminal set is the key to the success of GPHH. There are a wide range of features in DFJSS that...
Conference Paper
Full-text available
Dynamic flexible job shop scheduling (DFJSS) is a very important problem with a wide range of real-world applications such as cloud computing and manufacturing. In DFJSS, it is critical to make two kinds of real-time decisions (i.e. the routing decision that assigns machine to each job and the sequencing decision that prioritises the jobs in a mach...
Conference Paper
Full-text available
Genetic programming (GP) has been widely used for automatically evolving priority rules for solving job shop scheduling problems. However, one of the main drawbacks of GP is the intensive computation time. This paper aims at investigating appropriate surrogates for GP to reduce its computation time without sacrificing its performance in solving dyn...
Conference Paper
Full-text available
Flexible job shop scheduling (FJSS) can be regarded as an optimization problem in production scheduling that captures practical and challenging issues in real-world scheduling tasks such as order picking in manufacturing and cloud computing. Given a set of machines and jobs, FJSS aims to determine which machine to process a particular job (by routi...
Conference Paper
Full-text available
Trucks play a significant role in transporting containers between the seaside and storage yard at a container terminal. This paper exhibits a cooperative strategy for scheduling trucks, which allows trucks working or acting together toward a common purpose that can reduce truck-unload rate and cut back the make span. The objective is to minimize th...
Conference Paper
Full-text available
The yard truck scheduling (YTS) and the storage allocation problem (SAP) are two significant sub-issues in container terminal operations. This paper takes them as a whole optimization problem (YTS-SAP) and analyzes the factor of different travel speeds of trucks based on different loads. The goal is to minimize the total time cost of the summation...
Conference Paper
Full-text available
The nested loop adopted in the original bacterial foraging optimization (BFO) is quite time-consuming and is the main reason for the complex computational process. Thus, in our previous work, an improved BFO with structure redesigned mechanism (SRBFO) is used to address this problem. Since the bacterial chemotaxis with stochastic direction in the o...
Conference Paper
Full-text available
An algorithm performs better often due to its communication mechanisms. Different types of topology structures denote various information exchange mechanisms. This paper incorporates topology structure concept into brain storm optimization (BSO) algorithm. Three types of topology structures, which are full connected, ring connected and star connect...
Conference Paper
Full-text available
This article extends the bacterial foraging optimization (BFO) for addressing the multi-objective environmental/economic power dispatch (EED) problem. This new approach, abbreviated as MCLBFO, is proposed based on the comprehensive learning strategy to improve the search capability of BFO for the optimal solution. Besides, the fitness survival mech...
Conference Paper
Full-text available
Online product recommendations (OPRs) which include provider recommendations (PRs) and consumer reviews (CRs) are widely used in electrical environment to enhance customer loyalty. In this paper, the consumer shopping efficiency consists of screening efficiency and evaluation efficiency while the products are also classified as search products and...
Conference Paper
Full-text available
The purpose of this paper is to present a new method to solve the heterogeneous fixed fleet vehicle routing problem (HFFVRP) based on structureredesign- based bacterial foraging optimization (SRBFO). The HFFVRP is a special case of the heterogeneous vehicle routing problem (HVRP), in which the number of each type of vehicles is fixed. To deal with...
Conference Paper
Full-text available
Online product recommendations (OPRs), which include provider recommendations (PRs) and consumer reviews (CRs), are widely used in e-business to improve consumers' shopping efficiency, which consists of product screening efficiency and product evaluation efficiency. We construct a research model to explore the effect of perceived quality of OPRs on...
Conference Paper
Full-text available
The Artificial Bee Colony (ABC) algorithm is a new swarm optimization algorithm with good numerical functions optimization results. In order to enhance the performance ability of ABC algorithm, a hybrid ABC (HAB) algorithm is presented where swarming behavior of bacterial foraging optimization algorithm is introduced into the ABC algorithm to do lo...

Network

Cited By