Rong Qu

University of Nottingham | Notts · School of Computer Science

This paper presents an investigation of a simple generic hyper-heuristic approach upon a set of widely used constructive heuristics (graph coloring heuristics) in timetabling. Within the hyperheuristic framework, a Tabu Search approach is employed to search for permutations of graph heuristics which are used for constructing timetables in exam and...

We present a computational study on 112 instances of the Workforce Scheduling and Routing Problem (WSRP). This problem has applications in many service provider industries where employees visit customers to perform activities. Given their similarity, we adapt a mathematical programming model from the literature on vehicle routing problem with time...

Exam timetabling is one of the most important administra- tive activities that takes place in academic institutions. In this paper we present a critical discussion of the research on exam timetabling in the last decade or so. This last ten years has seen an increased level of attention on this important topic. There has been a range of significant...

Portfolio optimization involves the optimal assignment of limited capital to different available financial assets to achieve a reasonable trade-off between profit and risk. We consider an alternative Markowitz’s mean-variance model, in which the variance is replaced with an industry standard risk measure, Value-at-Risk (VaR), in order to better ass...

In the context of workforce scheduling, there are many scenarios in which personnel must carry out tasks at different locations hence requiring some form of transportation. Examples of these type of scenarios include nurses visiting patients at home, technicians carrying out repairs at customers’ locations and security guards performing rounds at d...

This paper proposes a dual-network-based feature extractor, perceptive capsule network (PCapN), for multivariate time series classification (MTSC), including a local feature network (LFN) and a global relation network (GRN). The LFN has two heads (i.e., Head_A and Head_B), each containing two squash CNN blocks and one dynamic routing block to extra...

This paper proposes an efficient federated distillation learning system (EFDLS) for multi-task time series classification (TSC). EFDLS consists of a central server and multiple mobile users, where different users may run different TSC tasks. EFDLS has two novel components: a feature-based student-teacher (FBST) framework and a distance-based weight...

Bin packing is a typical optimization problem with many real-world application scenarios. In the online bin packing problem, a sequence of items is revealed one at a time, and each item must be packed into a bin immediately after its arrival. Inspired by duality in optimization, we proposed pattern-based adaptive heuristics for the online bin packi...

This paper proposes an efficient federated distillation learning system (EFDLS) for multi-task time series classification (TSC). EFDLS consists of a central server and multiple mobile users, where different users may run different TSC tasks. EFDLS has two novel components, namely a feature-based student-teacher (FBST) framework and a distance-based...

The increasing applications of Polarimetric SAR (PolSAR) image classification demand for effective superpixels algorithms. Fuzzy superpixels algorithms reduce the misclassification rate by dividing pixels into superpixels, which are groups of pixels of homogenous appearance, and undetermined pixels. However, two key issues remain to be addressed in...

This paper formulates a virtual machine placement (VMP) problem, where the total power consumption of physical machines (PMs) and switches and the total network bandwidth resource consumption among VMs are jointly minimized. To address the problem, we present an energy- and traffic-aware ant colony optimization (ETA-ACO) algorithm. Three novel sche...

In the past decade, considerable advances have been made in the field of computational intelligence and operations research. However, the majority of these optimisation approaches have been developed for determinis-tically formulated problems, the parameters of which are often assumed perfectly predictable prior to problem-solving. In practice, thi...

Full truckload transportation (FTL) in the form of freight containers represents one of the most important transportation modes in international trade. Due to large volume and scale, in FTL, delivery time is often less critical but cost and service quality are crucial. Therefore, efficiently solving large scale multiple shift FTL problems is becomi...

Full truckload transportation (FTL) in the form of freight containers represents one of the most important transportation modes in international trade. Due to large volume and scale, in FTL, delivery time is often less critical but cost and service quality are crucial. Therefore, efficiently solving large scale multiple shift FTL problems is becomi...

CAV (connected and autonomous vehicle) is a crucial part of intelligent transportation systems. CAVs utilize both sensors and communication components to make driving decisions. A large number of companies, research organizations, and governments have researched extensively on the development of CAVs. The increasing number of autonomous and connect...

Connected and Autonomous Vehicle (CAV)-related initiatives have become some of the fastest expanding in recent years, and have started to affect the daily lives of people. More and more companies and research organizations have announced their initiatives, and some have started CAV road trials. Governments around the world have also introduced poli...

Limited attention has been paid to assessing the generality performance of hyper-heuristics. The performance of hyper-heuristics has been predominately assessed in terms of optimality which is not ideal as the aim of hyper-heuristics is not to be competitive with state of the art approaches but rather to raise the level of generality, i.e. the abil...

Recently, deep learning has been highly successful in image classification. Labeling the PolSAR data, however, is time-consuming and laborious and in response semi-supervised deep learning has been increasingly investigated in PolSAR image classification. Semi-supervised deep learning methods for PolSAR image classification can be broadly divided i...

In mobile edge computing (MEC), smart mobile devices (SMDs) with limited computation resources and battery lifetime can offload their computing-intensive tasks to MEC servers, thus to enhance the computing capability and reduce the energy consumption of SMDs. Nevertheless, offloading tasks to the edge incurs additional transmission time and thus hi...

In recent research, hyper-heuristics have attracted increasing attention in various fields. The most appealing feature of hyper-heuristics is that they aim to provide more generalized solutions to optimization problems by searching in a high-level space of heuristics instead of direct problem domains. Despite the promising findings in hyper-heurist...

This paper defines a new combinatorial optimization problem, namely General Combinatorial Optimization Problem (GCOP), whose decision variables are a set of parametric algorithmic components, i.e. algorithm design decisions. The solutions of GCOP, i.e. compositions of algorithmic components, thus represent different generic search algorithms. Th...

Portfolio optimization is one of the most important problems in the finance field. The traditional Markowitz mean-variance model is often unrealistic since it relies on the perfect market information. In this work, we propose a two-stage stochastic portfolio optimization model with a comprehensive set of real-world trading constraints to address th...

Community detection aims to identify topological structures and discover patterns in complex networks, which presents an important problem of great significance. The problem can be modeled as an NP hard combinatorial optimization problem, to which multi-objective optimization has been applied, addressing the common resolution limitation problem in...

Policy making involves an extensive research phase during which existing policies which are similar to the one under development need to be retrieved and analysed. This phase is time-consuming for the following reasons: (i) there is no unified format for policy documents; (ii) there is no unified repository of policies; and (iii) there is no retrie...

Object detection is one of the most important and challenging branches of computer vision, which has been widely applied in people’s life, such as monitoring security, autonomous driving and so on, with the purpose of locating instances of semantic objects of a certain class. With the rapid development of deep learning algorithms for detection task...

This paper studies a real-life container transportation problem with a wide planning horizon divided into multiple shifts. The trucks in this problem do not return to depot after every single shift but at the end of every two shifts. The mathematical model of the problem is first established, but it is unrealistic to solve this large scale problem...

Detecting change areas among two or more remote sensing images is a key technique in remote sensing. It usually consists of generating and analyzing a difference image thus to produce a change map. Analyzing the difference image to obtain the change map is essentially a binary classification problem, and can be solved by optimization algorithms. Th...

Object detection is one of the most important and challenging branches of computer vision, which has been widely applied in peoples life, such as monitoring security, autonomous driving and so on, with the purpose of locating instances of semantic objects of a certain class. With the rapid development of deep learning networks for detection tasks,...

Nursing workforce management is a challenging decision-making task in hospitals. The decisions are made across different timescales and levels from strategic long-term staffing budget to mid-term scheduling. These decisions are interconnected and impact each other, therefore are best taken by considering staffing and scheduling together. Moreover,...

In this paper, a Mixed-Shift Vehicle Routing Problem is proposed based on a real-life container transportation problem. In a long planning horizon of multiple shifts, transport tasks are completed satisfying the time constraints. Due to the different travel distance and time of tasks, there are two types of shifts (long shift and short shift) in th...

The three articles in this special section focus on the development of automated design of machine learning and search algorithms. There is a demand, especially from industry and business, to automate the design of machine learning and search algorithms, thereby removing the heavy reliance on human experts. Machine learning and search techniques pl...

In Wireless Sensor Networks (WSN), maintaining a high coverage and extending the network lifetime are two conflicting crucial issues considered by real world service providers. In this paper, we consider the coverage optimization problem in WSN with three objectives to strike the balance between network lifetime and coverage. These include minimizi...

The previous chapters have introduced the four types of hyper-heuristics, presented the theoretical foundations and examined various applications of hyper-heuristics. This chapter provides an overview of some advanced topics and recent trends in hyper-heuristics, namely, hybrid hyper-heuristics, hyper-heuristics for automated design, automated desi...

Research into solving combinatorial optimization problems such as timetabling, vehicle routing and rostering problems has involved deriving techniques that improve the results obtained by existing techniques for known benchmark sets. These benchmark sets are made publicly available for performance comparisons of different techniques in solving thes...

Personnel scheduling problems arise from various real-world scenarios, including supermarket staff scheduling, call centre staff allocation, police force scheduling, and, the most studied, nurse rostering in hospitals. Due to the demands of quality healthcare, limited resources, and the tight constraints of specific legislation worldwide, the nurse...

In solving combinatorial optimization problems, a low-level constructive heuristic is used to create an initial solution, which forms a starting point for optimization techniques to solve the problem. These heuristics are problem dependent and are rules of thumb, manually derived based on human intuition. Deriving constructive heuristics is a time-...

Examination timetabling represents one of the earliest and most studied problem domains in hyper-heuristics (HH). Different interesting research issues have been addressed in the literature, from high-level heuristic selection mechanisms and acceptance criteria and designing intelligent low-level heuristics, to fundamental studies on the formal def...

Along with the continuous developments in hyper-heuristic (HH), various descriptive definitions for HH have emerged, leading to classifications of HH. Initially, hyper-heuristics have been defined as a search technique “to decide (select) at a higher abstraction level which low-level heuristics to apply” [51], “to combine simple heuristics” [162],...

Hyper-heuristics aim to provide heuristic algorithms of a higher level of generality that produce good results for all problems in a domain rather than just for one or two problem instances but poor results for the others. Cross-domain hyper-heuristics extend this scope of generality across domains. These hyper-heuristics aim at producing good resu...

Vehicle routing problems (VRP) [72, 50, 186] represent one of the most investigated combinatorial optimization problems [134], due to the problem complexity and their potential impact on real-world applications especially in logistics and supply chains. The basic VRP involves constructing a set of closed routes from and to a depot, each serviced by...

Selection constructive hyper-heuristics select a low-level heuristic at each point in the construction of a solution to a combinatorial optimization problem. As discussed in Chapter 1, the purpose of low-level construction heuristics is to construct complete solutions, or initial solutions for optimization. Solving a problem begins at an initial st...

Selection perturbative hyper-heuristics select which low-level perturbative heuristic to apply at each point of improvement to a given initial complete solution to a problem. The initial solution is usually created either randomly or using a constructive low-level heuristic. It is usually iteratively refined by applying a perturbative low-level heu...

The packing of items into a container or bin so as to minimize cost is a common problem experienced in industry. This chapter examines the use of hyperheuristics for solving bin packing problems presented in Appendix B.1. Selection constructive hyper-heuristics and generation constructive hyper-heuristics have been successfully employed to solve bi...

Recent research advances have been made in different types of hyper-heuristics (HH), namely selection HH and generation HH, employing both constructive and perturbative low-level heuristics (llh). Among the four types of HH, selection HH (Chapters 2, 3) received more research attention than generation HH (Chapters 4, 5). This may be due to the rese...

Low-level perturbative heuristics are used to improve a solution created either randomly or using a constructive heuristic for a combinatorial optimization problem. The low-level perturbative heuristics are problem dependent, and often move operators defined for the problem domain when solving the problem using local search techniques, e.g. the 2-o...

Empty container repositioning has become one of the important issues in ocean shipping industry. Researchers often solve these problems using linear programming or simulation. For large-scale problems, heuristic algorithms showed to be preferable due to their flexibility and scalability. In this paper we consider large-scale the liner routing plann...

Quay crane scheduling is critical in reducing operation costs at container terminals. Designing a schedule to handling containers in an efficient order can be difficult. For this problem which is proved NP-hard, heuristic algorithms are effective to obtain preferable solutions within limited computational time. When solving discrete optimization pr...

Amongst the wide-ranging areas of the timetabling problems, educational timetabling was reported as one of the most studied and researched areas in the timetabling literature. In this paper, our focus is the university examination timetabling. Despite many approaches proposed in the timetabling literature, it has been observed that there is no sing...

The Open Periodic Vehicle Routing Problem with Time Windows (OPVRPTW) is a practical transportation routing and scheduling problem arising from real-world scenarios. It shares some common features with some classic VRP variants. The problem has a tightly constrained large scale solution space, and requires well-balanced diversification and intensif...

Load balancing is one of the most important issues in the practical deployment of multicast with network coding. However, this issue has received little research attention. This paper studies how traffic load of network coding based multicast (NCM) is disseminated in a communications network, with load balancing considered as an important factor. T...

This paper proposes a change detection algorithm in synthetic aperture radar (SAR) images based on the salient image guidance and an accelerated genetic algorithm (S-aGA). The difference image is first generated by logarithm ratio operator based on the bi-temporal SAR images acquired in the same region. Then a saliency detection model is applied in...

Recommender system (RS) plays an important role in helping users find the information they are interested in and providing accurate personality recommendation. It has been found that among all the users, there are some user groups called “core users” or “information core” whose historical behavior data are more reliable, objective and positive for...

With the ever fast developments of technologies in science and engineering, it is believed that CAV (connected and autonomous vehicles) will come into our daily life soon. CAV could be used in many different aspects in our lives such as public transportation and agriculture, and so on. Although CAV will bring huge benefits to our lives and society,...

Network coding enables higher network throughput, more balanced traffic, and securer data transmission. However, complicated mathematical operations incur when packets are combined at intermediate nodes, which, if not operated properly, lead to very high network resource consumption and unacceptable delay. Therefore, it is of vital importance to mi...

The Vehicle Routing Problem with Time Windows (VRPTW) consists of constructing least cost routes from a depot to a set of geographically scattered service points and back to the depot, satisfying service time intervals and capacity constraints. A Variable Neighbourhood Search algorithm which can simultaneously optimize both objectives of VRPTW (to...

In this paper, a dynamic truck dispatching problem of a marine container terminal is described and discussed. In this problem, a few containers, encoded as work instructions, need to be transferred between yard blocks and vessels by a fleet of trucks. Both the yard blocks and the quay are equipped with cranes to support loading/unloading operations...

Amongst the wide-ranging areas of the timetabling problems, educational timetabling was reported as one of the most studied and researched areas in the timetabling literature. In this paper, our focus is the university examination timetabling. Despite many approaches proposed in the timetabling literature, it has been observed that there is no sing...

An optimisation problem can have many forms and variants. It may consider different objectives, constraints, and variables. For that reason, providing a general application programming interface (API) to handle the problem data efficiently in all scenarios is impracticable. Nonetheless, on an R&D environment involving personnel from distinct backgr...

Literature presents many APIs and frameworks focusing on providing state of the art algorithms and solving techniques for optimisation problems. The same can not be said about APIs and frameworks focused on problem data itself and the reason is simple: due to the peculiarities and details of each variant of a problem, it is virtually impossible to...

This paper studies and models the multicast routing problem with network coding in dynamic network environment, where computational and bandwidth resources are to be jointly optimized. A quantum inspired evolutionary algorithm (QEA) is developed to address the problem above, where a restart scheme is devised for well adapting QEA for tracing the ev...

One of the most important issues in multicast is how to achieve a balanced traffic load within a communications network. This paper formulates a load balancing optimization problem in the context of multicast with network coding and proposes a modified population based incremental learning (PBIL) algorithm for tackling it. A novel probability vecto...

In this paper, we investigate a constrained portfolio selection problem with cardinality constraint, minimum size and position constraints, and non-convex transaction cost. A hybrid method named Local Search Branch-and-Bound (LS-B&B) which integrates local search with B&B is proposed based on the property of the problem, i.e. cardinality constraint...

The literature presents many application programming interfaces (APIs) and frameworks that provide state of the art algorithms and techniques for solving optimisation problems. The same cannot be said about APIs and frameworks focused on the problem data itself because with the peculiarities and details of each variant of a problem, it is virtually...

This paper studies the state-of-art constrained portfolio optimisation models, using exact solver to identify the optimal solutions or lower bound for the benchmark instances at the OR-library with extended constraints. The effects of pre-assignment, round-lot, and class constraints based on the quantity and cardinality constrained Markowitz model...