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Rong Qu

University of Nottingham, Computer Science, Faculty Member

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Papers by Rong Qu

Modelling The Home Health Care Nurse Scheduling Problem For Patients With Long-Term Conditions In The UK

ECMS 2019 Proceedings edited by Mauro Iacono, Francesco Palmieri, Marco Gribaudo, Massimo Ficco, 2019

In this work, using a Behavioural Operational Research (BOR) perspective, we develop a model for ... more In this work, using a Behavioural Operational Research (BOR) perspective, we develop a model for the Home Health Care Nurse Scheduling Problem (HHCNSP) with application to renal patients taking Peritoneal Dialysis (PD) at their own homes as treatment for their Chronic Kidney Disease (CKD) in the UK. The modelling framework presented in this paper can be extended to much wider spectra of scheduling problems concerning patients with different long-term conditions in future work.

A survey of workforce scheduling and routing

In the context of workforce scheduling, there are many scenarios in which personnel must carry ou... more 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, security guards performing rounds at different premises, etc. We refer to these scenarios as Workforce Scheduling and Routing Problems (WSRP) as they usually involve the scheduling of personnel combined with some form of routing in order to ensure that employees arrive on time to the locations where tasks need to be performed. This kind of problems have been tackled in the literature for a number of years. This paper presents a survey which attempts to identify the common attributes of WSRP scenarios and the solution methods applied when tackling these problems. Our longer term aim is to achieve an in-depth understanding of how to model and solve workforce scheduling and rout...

A hybrid pricing and cutting approach for the multi-shift full truckload vehicle routing problem

European Journal of Operational Research, 2021

Full truckload transportation (FTL) in the form of freight containers represents one of the most ... more 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 becoming more and more important and requires further research. In one of our earlier studies, a set covering model and a three-stage solution method were developed for a multi-shift FTL problem. This paper extends the previous work and presents a significantly more efficient approach by hybridising pricing and cutting strategies with metaheuristics (a variable neighbourhood search and a genetic algorithm). The metaheuristics were adopted to find promising columns (vehicle routes) guided by pricing and cuts are dynamically generated to eliminate infeasible flow assignments caused by incompatible commodities. Computational experiments on real-life and artificial benchmark FTL problems showed superior performance both in terms of computational time and solution quality, when compared with previous MIP based threestage methods and two existing metaheuristics. The proposed cutting and heuristic pricing approach can efficiently solve large scale real-life FTL problems.

The General Combinatorial Optimization Problem: Towards Automated Algorithm Design

IEEE Computational Intelligence Magazine, 2020

This paper defines a new combinatorial optimisation problem, namely General Combinatorial Optimis... more This paper defines a new combinatorial optimisation problem, namely General Combinatorial Optimisation 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. The objective of GCOP is to find the optimal algorithmic compositions for solving the given optimisation problems. Solving the GCOP is thus equivalent to automatically designing the best algorithms for optimisation problems. Despite recent advances, the evolutionary computation and optimisation research communities are yet to embrace formal standards that underpin automated algorithm design. In this position paper, we establish GCOP as a new standard to define different search algorithms within one unified model. We demonstrate the new GCOP model to standardise various search algorithms as well as selection hyper-heuristics. A taxonomy is defined to distinguish several widely used terminologies in automated algorithm design, namely automated algorithm composition, configuration and selection. We would like to encourage a new line of exciting research directions addressing several challenging research issues including algorithm generality, algorithm reusability, and automated algorithm design.

Hill Climbing versus Genetic Algorithm Optimization in Solving the Examination Timetabling Problem

Proceedings of the 2nd International Conference on Operations Research and Enterprise Systems, 2013

In this paper, we compare the incorporation of Hill Climbing (HC) and Genetic Algorithm (GA) opti... more In this paper, we compare the incorporation of Hill Climbing (HC) and Genetic Algorithm (GA) optimization in our proposed methodology in solving the examination scheduling problem. It is shown that our greedy HC optimization outperforms the GA in all cases when tested on the benchmark datasets. In our implementation, HC consumes more time to execute compared to GA which manages to improve the quality of the initial schedules in a very fast and efficient time. Despite this, since the amount of time taken by HC in producing improved schedules is considered reasonable and it never fails to produce better results, it is suggested that we incorporate the Hill Climbing optimization rather than GA in our work.

A hyper-heuristic with two guidance indicators for bi-objective mixed-shift vehicle routing problem with time windows

Applied Intelligence, 2018

In this paper, a Mixed-Shift Vehicle Routing Problem is proposed based on a real-life container t... more 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 distances and time of tasks, there are two types of shifts (long shift and short shift) in this problem. The unit driver cost for long shifts is higher than that of short shifts. A mathematical model of this Mixed-Shift Vehicle Routing Problem with Time Windows (MS-VRPTW) is established in this paper, with two objectives of minimizing the total driver payment and the total travel distance. Due to the large scale and nonlinear constraints, the exact search showed is not suitable to MS-VRPTW. An initial solution construction heuristic (EBIH) and a selective perturbation Hyper-Heuristic (GIHH) are thus developed. In GIHH, five heuristics with different extents of perturbation at the low level are adaptively selected by a high level selection scheme with the Hill Climbing acceptance criterion. Two guidance indicators are devised at the high level to adaptively adjust the selection of the low level heuristics for this bi-objective problem. The two indicators estimate the objective value improvement and the improvement direction over the Pareto Front, respectively. To evaluate the generality of the proposed algorithms, a set of benchmark instances with various features is extracted from real-life historical datasets. The experiment results show that GIHH significantly improves the quality of the final Pareto Solution Set, outperforming the state-of-the-art algorithms for similar problems. Its application on VRPTW also obtains promising results.

A Constraint-directed Local Search Approach to Nurse Rostering Problems

Electronic Proceedings in Theoretical Computer Science, 2009

In this paper, we investigate the hybridization of constraint programming and local search techni... more In this paper, we investigate the hybridization of constraint programming and local search techniques within a large neighbourhood search scheme for solving highly constrained nurse rostering problems. As identified by the research, a crucial part of the large neighbourhood search is the selection of the fragment (neighbourhood, i.e. the set of variables), to be relaxed and re-optimized iteratively. The success of the large neighbourhood search depends on the adequacy of this identified neighbourhood with regard to the problematic part of the solution assignment and the choice of the neighbourhood size. We investigate three strategies to choose the fragment of different sizes within the large neighbourhood search scheme. The first two strategies are tailored concerning the problem properties. The third strategy is more general, using the information of the cost from the soft constraint violations and their propagation as the indicator to choose the variables added into the fragment. The three strategies are analyzed and compared upon a benchmark nurse rostering problem. Promising results demonstrate the possibility of future work in the hybrid approach.

A Study Of Cost Effective Scheduling Of Nurses Based On The Domain Transformation Method

ECMS 2013 Proceedings edited by: Webjorn Rekdalsbakken, Robin T. Bye, Houxiang Zhang, 2013

This paper discusses and analyses the tradeoff between the flexibility afforded with greater numb... more This paper discusses and analyses the tradeoff between the flexibility afforded with greater number of staff and the implied cost of employing extra staff in the context of the nurse-scheduling problem. If the number of staff is constant, our study allows quantification of the degree of pressure put on the staff resulting from the schedules that do not satisfy their preferences for shift allocation. We present a practical approach, based on our domain transformation methodology that achieves good quality schedules without high computational requirements.

Hierarchical Method For Nurse Rostering Based On Granular Pre-Processing Of Constraints

ECMS 2009 Proceedings edited by J. Otamendi, A. Bargiela, J. L. Montes, L. M. Doncel Pedrera, 2009

Nurse Rostering problems represent a subclass of scheduling problems that are hard to solve. Thei... more Nurse Rostering problems represent a subclass of scheduling problems that are hard to solve. Their complexity is due to the large solution spaces and the many objectives and constraints that need to be fulfilled. In this study, we propose a hierarchical method of granulation of problem domain through preprocessing of constraints. A set of zero-cost patterns in the granulated search space provides a basis for the generation of work schedules. Feasible schedules calculated for week 1 are used to define zero-cost shift patterns that can be deployed in week 2. These in turn are used for the generation of feasible schedules for week 2. The process can be applied over the extended time frame. We show that the granulation of the problem description in terms of constraints and scheduling time frames leads to a more manageable computing task.

Case-Based Reasoning for Course Timetabling Problems

The research in this thesis investigates Case-Based Reasoning (CBR), a Knowledge-Based Reasoning ... more The research in this thesis investigates Case-Based Reasoning (CBR), a Knowledge-Based Reasoning technique that proved to be capable of providing good solutions in educational course timetabling problems. Following the basic idea behind CBR, experiences in solving previous similar timetabling problems are employed to find the solutions for new problems. A basic CBR system that is hierarchically organized with structured knowledge representations by attribute graphs is proposed in Chapter Four. The system is then further improved to solve a wider range of problems, which is described in Chapter Five. Evaluations on a large number of experiments indicate that this approach could provide a significant step forward in timetabling and scheduling research. This basic system works well on relatively small problems. To deal with this drawback a multiple-retrieval approach that partitions large timetabling problems into small solvable sub-problems is presented in Chapter Six. Good results are obtained from a wide range of experiments. Research (OR) techniques in timetabling, can be employed to solve the problem effectively. In schools and universities, altering "last year's timetable" to create a solution for the problem in hand is an approach favoured by many timetabling officers. This is because the requirements in the new problem usually do not change significantly from previous instances. Thus parts of the previous timetable could be reused and a significant amount of effort and time could be saved. Chapter Six describes a multiple-retrieval approach based on the basic CBR system already developed, aiming at solving large course timetabling problems. Partitioning and composition techniques are used to decompose the problem into solvable sub-problems and to combine the sub-solutions into a final solution.

A Survey on Workforce Scheduling and Routing Problems

In the context of workforce scheduling, there are many scenarios in which personnel must carry ou... more 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, security guards performing rounds at different premises, etc. We refer to these scenarios as Workforce Scheduling and Routing Problems (WSRP) as they usually involve the scheduling of personnel combined with some form of routing in order to ensure that employees arrive on time to the locations where tasks need to be performed. This kind of problems have been tackled in the literature for a number of years. This paper presents a survey which attempts to identify the common attributes of WSRP scenarios and the solution methods applied when tackling these problems. Our longer term aim is to achieve an in-depth understanding of how to model and solve workforce scheduling and routing problems and this survey represents the first step in this quest.

Domain Transformation using Greedy Algorithm in Nurse Scheduling

International Conference on Artificial Intelligence, Energy and Manufacturing Engineering (ICAEME'2015) Jan. 7-8, 2015 Dubai (UAE), 2015

The nurse scheduling problem (NSP) is a complex optimisation problem of allocating nurses to duty... more The nurse scheduling problem (NSP) is a complex optimisation problem of allocating nurses to duty rosters in hospitals. The objective is usually to ensure that there are always sufficient nurses on duty, while taking into account individual preferences with respect to work patterns, requests for leave and financial restrictions, in such a way that all employees are treated considerately and fairly. In this paper, we extend our domain transformation-based approach to nurse scheduling. Domain transformation is an approach to solving complex problems that involves a judicious simplification of the original problem, a solution to the simplified problem and finally a transformation of this solution to compensate for the introduced simplifications. The approach we use involves information granulation of shift types that transforms the problem into a smaller solution domain. Next, schedules derived from smaller problem domains are then converted into the original problem domain. This includes a consideration of the constraints that were not represented in the smaller domain. The problem is solved using a greedy algorithm. The approach is general and can be applied within a wide range of benchmark instances. These benchmark instances have been derived from published datasets as well as from our industry collaborators. The greedy algorithm outperforms in most cases when tested with the different demands and number of nurses. The results facilitated the development of a cost-benefit analysis across different levels of staffing.

A Greedy Heuristic for Workforce Scheduling and Routing With Time-dependent Activities Constraints

Proceedings of the International Conference on Operations Research and Enterprise Systems, 2015

We present a greedy heuristic (GHI) designed to tackle five time-dependent activities constraints... more We present a greedy heuristic (GHI) designed to tackle five time-dependent activities constraints (synchronisation, overlap, minimum difference, maximum difference and minimum-maximum difference) on workforce scheduling and routing problems. These types of constraints are important because they allow the modelling of situations in which activities relate to each other time-wise, e.g. synchronising two technicians to complete a job. These constraints often make the scheduling and routing of employees more difficult. GHI is tested on set of benchmark instances from different workforce scheduling and routing problems (WSRPs). We compare the results obtained by GHI against the results from a mathematical programming solver. The comparison seeks to determine which solution method achieves more best solutions across all instances. Two parameters of GHI are discussed, the sorting of employees and the sorting of visits. We conclude that using the solver is adequate for instances with less than 100 visits but for larger instances GHI obtains better results in less time.

A Hybrid Constraint Programming Approach for Nurse Rostering Problems

Applications and Innovations in Intelligent Systems XVI, 2009

Due to the complexity of nurse rostering problems (NRPs), Constraint Programming (CP) approaches ... more Due to the complexity of nurse rostering problems (NRPs), Constraint Programming (CP) approaches on their own have shown to be ineffective in solving these highly constrained problems. We investigate a two-stage hybrid CP approach on real world benchmark NRPs. In the first stage, a constraint satisfaction model is used to generate weekly rosters consist of high quality shift sequences satisfying a subset of constraints. An iterative forward search is then adapted to extend them to build complete feasible solutions. Variable and value selection heuristics are employed to improve the efficiency. In the second stage, a simple Variable Neighborhood Search is used to quickly improve the solution obtained. The basic idea of the hybrid approach is based on the observations that high quality nurse rosters consist of high quality shift sequences. By decomposing the problems into solvable sub-problems for CP, the search space of the original problems are significantly reduced. The results on benchmark problems demonstrate the efficiency of this hybrid CP approach when compared to the state-of-the-art approaches in the literature.

A variable neighborhood descent search algorithm for delay-constrained least-cost multicast routing

The rapid evolution of real-time multimedia applications requires Quality of Service (QoS) based ... more The rapid evolution of real-time multimedia applications requires Quality of Service (QoS) based multicast routing in underlying computer networks. The constrained Steiner Tree, as the underpinning mathematical structure, is a well-known NP-complete problem. In this paper we investigate a variable neighborhood descent (VND) search, a variant of variable neighborhood search, for the delay-constrained leastcost (DCLC) multicast routing problem. The neighborhood structures designed in the VND approaches are based on the idea of path replacement in trees. They are simple, yet effective operators, enabling a flexible search over the solution space of this complex problem with multiple constraints. A large number of simulations demonstrate that our algorithm is highly efficient in solving the DCLC multicast routing problem in terms of the tree cost and execution time. To our knowledge, this is the first study of VND algorithm on the DCLC multicast routing problem. It outperforms other existing algorithms over a range of problem instances.

Granular Modelling Of Exam To Slot Allocation

ECMS 2009 Proceedings edited by J. Otamendi, A. Bargiela, J. L. Montes, L. M. Doncel Pedrera, 2009

In this paper, we are introducing a new method of granular exam-to-slot allocation based on the p... more In this paper, we are introducing a new method of granular exam-to-slot allocation based on the preprocessing of the basic student-exam information into a more abstract (granulated) entity of conflict chains. Since the conflict chains are designed to capture the mutual dependencies between exams, they enable us to reason about the exam-to-slot allocation for all exams in a chain rather than just one exam at-a-time. The initial exam-to-slot allocation, generated through the processing of conflict chains, is then refined by considering the spread of the exams in the examination session so as to minimize the appropriately defined cost function. The granular pre-processing of problem data has been shown to enhance the efficiency of the exam scheduling task and has led to the identification of very competitive exam schedules.

Solving a Practical Examination Timetabling Problem: A Case Study

Lecture Notes in Computer Science

This paper presents a Greedy-Least Saturation Degree (G-LSD) heuristic (which is an adaptation of... more This paper presents a Greedy-Least Saturation Degree (G-LSD) heuristic (which is an adaptation of the least saturation degree heuristic) to solve a real-world examination timetabling problem at the University Kebangsaan, Malaysia. We utilise a new objective function that was proposed in our previous work to evaluate the quality of the timetable. The objective function considers both timeslots and days in assigning exams to timeslots, where higher priority is given to minimise students having consecutive exams on the same day. The objective also tries to spread exams throughout the examination period. This heuristic has the potential to be used for the benchmark examination datasets (e.g. the Carter datasets) as well as other real world problems. Computational results are presented.

Hybridising heuristics within an estimation distribution algorithm for examination timetabling

Applied Intelligence, 2014

This paper presents a hybrid hyper-heuristic approach based on estimation distribution algorithms... more This paper presents a hybrid hyper-heuristic approach based on estimation distribution algorithms. The main motivation is to raise the level of generality for search methodologies. The objective of the hyper-heuristic is to produce solutions of acceptable quality for a number of optimisation problems. In this work, we demonstrate the generality through experimental results for different variants of exam timetabling problems. The hyperheuristic represents an automated constructive method that searches for heuristic choices from a given set of low-level heuristics based only on non-domain-specific knowledge. The high-level search methodology is based on a simple estimation distribution algorithm. It is capable of guiding the search to select appropriate heuristics in different problem solving situations. The probability distribution of low-level heuristics at different stages of solution construction can be used to measure their effectiveness and possibly help facilitate more intelligent hyper-heuristic search methods.

Analysis Of Backtracking In University Examination Scheduling

ECMS 2013 Proceedings edited by: Webjorn Rekdalsbakken, Robin T. Bye, Houxiang Zhang, 2013

Simulation modelling of the initial assignments of exams to time-slots provides an alternative ap... more Simulation modelling of the initial assignments of exams to time-slots provides an alternative approach to the establishment of a set of feasible solutions that are subsequently optimized. In this research, we analyze two backtracking strategies for reassigning exams after the initial allocation of exams to time-slots. We propose two approaches for backtracking, BT1 and BT2. The study indicates that backtracking is an effective approach for improving the quality of the examination schedule where BT2 has outperformed BT1 in a number of cases.

Domain transformation approach to deterministic optimization of examination timetables

Artificial Intelligence Research, 2012

To the extent reasonable and practicable the material made available in Nottingham ePrints has be... more

Modelling The Home Health Care Nurse Scheduling Problem For Patients With Long-Term Conditions In The UK

ECMS 2019 Proceedings edited by Mauro Iacono, Francesco Palmieri, Marco Gribaudo, Massimo Ficco, 2019

In this work, using a Behavioural Operational Research (BOR) perspective, we develop a model for ... more In this work, using a Behavioural Operational Research (BOR) perspective, we develop a model for the Home Health Care Nurse Scheduling Problem (HHCNSP) with application to renal patients taking Peritoneal Dialysis (PD) at their own homes as treatment for their Chronic Kidney Disease (CKD) in the UK. The modelling framework presented in this paper can be extended to much wider spectra of scheduling problems concerning patients with different long-term conditions in future work.

A survey of workforce scheduling and routing

In the context of workforce scheduling, there are many scenarios in which personnel must carry ou... more 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, security guards performing rounds at different premises, etc. We refer to these scenarios as Workforce Scheduling and Routing Problems (WSRP) as they usually involve the scheduling of personnel combined with some form of routing in order to ensure that employees arrive on time to the locations where tasks need to be performed. This kind of problems have been tackled in the literature for a number of years. This paper presents a survey which attempts to identify the common attributes of WSRP scenarios and the solution methods applied when tackling these problems. Our longer term aim is to achieve an in-depth understanding of how to model and solve workforce scheduling and rout...

A hybrid pricing and cutting approach for the multi-shift full truckload vehicle routing problem

European Journal of Operational Research, 2021

Full truckload transportation (FTL) in the form of freight containers represents one of the most ... more 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 becoming more and more important and requires further research. In one of our earlier studies, a set covering model and a three-stage solution method were developed for a multi-shift FTL problem. This paper extends the previous work and presents a significantly more efficient approach by hybridising pricing and cutting strategies with metaheuristics (a variable neighbourhood search and a genetic algorithm). The metaheuristics were adopted to find promising columns (vehicle routes) guided by pricing and cuts are dynamically generated to eliminate infeasible flow assignments caused by incompatible commodities. Computational experiments on real-life and artificial benchmark FTL problems showed superior performance both in terms of computational time and solution quality, when compared with previous MIP based threestage methods and two existing metaheuristics. The proposed cutting and heuristic pricing approach can efficiently solve large scale real-life FTL problems.

The General Combinatorial Optimization Problem: Towards Automated Algorithm Design

IEEE Computational Intelligence Magazine, 2020

This paper defines a new combinatorial optimisation problem, namely General Combinatorial Optimis... more This paper defines a new combinatorial optimisation problem, namely General Combinatorial Optimisation 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. The objective of GCOP is to find the optimal algorithmic compositions for solving the given optimisation problems. Solving the GCOP is thus equivalent to automatically designing the best algorithms for optimisation problems. Despite recent advances, the evolutionary computation and optimisation research communities are yet to embrace formal standards that underpin automated algorithm design. In this position paper, we establish GCOP as a new standard to define different search algorithms within one unified model. We demonstrate the new GCOP model to standardise various search algorithms as well as selection hyper-heuristics. A taxonomy is defined to distinguish several widely used terminologies in automated algorithm design, namely automated algorithm composition, configuration and selection. We would like to encourage a new line of exciting research directions addressing several challenging research issues including algorithm generality, algorithm reusability, and automated algorithm design.

Hill Climbing versus Genetic Algorithm Optimization in Solving the Examination Timetabling Problem

Proceedings of the 2nd International Conference on Operations Research and Enterprise Systems, 2013

In this paper, we compare the incorporation of Hill Climbing (HC) and Genetic Algorithm (GA) opti... more In this paper, we compare the incorporation of Hill Climbing (HC) and Genetic Algorithm (GA) optimization in our proposed methodology in solving the examination scheduling problem. It is shown that our greedy HC optimization outperforms the GA in all cases when tested on the benchmark datasets. In our implementation, HC consumes more time to execute compared to GA which manages to improve the quality of the initial schedules in a very fast and efficient time. Despite this, since the amount of time taken by HC in producing improved schedules is considered reasonable and it never fails to produce better results, it is suggested that we incorporate the Hill Climbing optimization rather than GA in our work.

A hyper-heuristic with two guidance indicators for bi-objective mixed-shift vehicle routing problem with time windows

Applied Intelligence, 2018

In this paper, a Mixed-Shift Vehicle Routing Problem is proposed based on a real-life container t... more 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 distances and time of tasks, there are two types of shifts (long shift and short shift) in this problem. The unit driver cost for long shifts is higher than that of short shifts. A mathematical model of this Mixed-Shift Vehicle Routing Problem with Time Windows (MS-VRPTW) is established in this paper, with two objectives of minimizing the total driver payment and the total travel distance. Due to the large scale and nonlinear constraints, the exact search showed is not suitable to MS-VRPTW. An initial solution construction heuristic (EBIH) and a selective perturbation Hyper-Heuristic (GIHH) are thus developed. In GIHH, five heuristics with different extents of perturbation at the low level are adaptively selected by a high level selection scheme with the Hill Climbing acceptance criterion. Two guidance indicators are devised at the high level to adaptively adjust the selection of the low level heuristics for this bi-objective problem. The two indicators estimate the objective value improvement and the improvement direction over the Pareto Front, respectively. To evaluate the generality of the proposed algorithms, a set of benchmark instances with various features is extracted from real-life historical datasets. The experiment results show that GIHH significantly improves the quality of the final Pareto Solution Set, outperforming the state-of-the-art algorithms for similar problems. Its application on VRPTW also obtains promising results.

A Constraint-directed Local Search Approach to Nurse Rostering Problems

Electronic Proceedings in Theoretical Computer Science, 2009

In this paper, we investigate the hybridization of constraint programming and local search techni... more In this paper, we investigate the hybridization of constraint programming and local search techniques within a large neighbourhood search scheme for solving highly constrained nurse rostering problems. As identified by the research, a crucial part of the large neighbourhood search is the selection of the fragment (neighbourhood, i.e. the set of variables), to be relaxed and re-optimized iteratively. The success of the large neighbourhood search depends on the adequacy of this identified neighbourhood with regard to the problematic part of the solution assignment and the choice of the neighbourhood size. We investigate three strategies to choose the fragment of different sizes within the large neighbourhood search scheme. The first two strategies are tailored concerning the problem properties. The third strategy is more general, using the information of the cost from the soft constraint violations and their propagation as the indicator to choose the variables added into the fragment. The three strategies are analyzed and compared upon a benchmark nurse rostering problem. Promising results demonstrate the possibility of future work in the hybrid approach.

A Study Of Cost Effective Scheduling Of Nurses Based On The Domain Transformation Method

ECMS 2013 Proceedings edited by: Webjorn Rekdalsbakken, Robin T. Bye, Houxiang Zhang, 2013

This paper discusses and analyses the tradeoff between the flexibility afforded with greater numb... more This paper discusses and analyses the tradeoff between the flexibility afforded with greater number of staff and the implied cost of employing extra staff in the context of the nurse-scheduling problem. If the number of staff is constant, our study allows quantification of the degree of pressure put on the staff resulting from the schedules that do not satisfy their preferences for shift allocation. We present a practical approach, based on our domain transformation methodology that achieves good quality schedules without high computational requirements.

Hierarchical Method For Nurse Rostering Based On Granular Pre-Processing Of Constraints

ECMS 2009 Proceedings edited by J. Otamendi, A. Bargiela, J. L. Montes, L. M. Doncel Pedrera, 2009

Nurse Rostering problems represent a subclass of scheduling problems that are hard to solve. Thei... more Nurse Rostering problems represent a subclass of scheduling problems that are hard to solve. Their complexity is due to the large solution spaces and the many objectives and constraints that need to be fulfilled. In this study, we propose a hierarchical method of granulation of problem domain through preprocessing of constraints. A set of zero-cost patterns in the granulated search space provides a basis for the generation of work schedules. Feasible schedules calculated for week 1 are used to define zero-cost shift patterns that can be deployed in week 2. These in turn are used for the generation of feasible schedules for week 2. The process can be applied over the extended time frame. We show that the granulation of the problem description in terms of constraints and scheduling time frames leads to a more manageable computing task.

Case-Based Reasoning for Course Timetabling Problems

The research in this thesis investigates Case-Based Reasoning (CBR), a Knowledge-Based Reasoning ... more The research in this thesis investigates Case-Based Reasoning (CBR), a Knowledge-Based Reasoning technique that proved to be capable of providing good solutions in educational course timetabling problems. Following the basic idea behind CBR, experiences in solving previous similar timetabling problems are employed to find the solutions for new problems. A basic CBR system that is hierarchically organized with structured knowledge representations by attribute graphs is proposed in Chapter Four. The system is then further improved to solve a wider range of problems, which is described in Chapter Five. Evaluations on a large number of experiments indicate that this approach could provide a significant step forward in timetabling and scheduling research. This basic system works well on relatively small problems. To deal with this drawback a multiple-retrieval approach that partitions large timetabling problems into small solvable sub-problems is presented in Chapter Six. Good results are obtained from a wide range of experiments. Research (OR) techniques in timetabling, can be employed to solve the problem effectively. In schools and universities, altering "last year's timetable" to create a solution for the problem in hand is an approach favoured by many timetabling officers. This is because the requirements in the new problem usually do not change significantly from previous instances. Thus parts of the previous timetable could be reused and a significant amount of effort and time could be saved. Chapter Six describes a multiple-retrieval approach based on the basic CBR system already developed, aiming at solving large course timetabling problems. Partitioning and composition techniques are used to decompose the problem into solvable sub-problems and to combine the sub-solutions into a final solution.

A Survey on Workforce Scheduling and Routing Problems

In the context of workforce scheduling, there are many scenarios in which personnel must carry ou... more 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, security guards performing rounds at different premises, etc. We refer to these scenarios as Workforce Scheduling and Routing Problems (WSRP) as they usually involve the scheduling of personnel combined with some form of routing in order to ensure that employees arrive on time to the locations where tasks need to be performed. This kind of problems have been tackled in the literature for a number of years. This paper presents a survey which attempts to identify the common attributes of WSRP scenarios and the solution methods applied when tackling these problems. Our longer term aim is to achieve an in-depth understanding of how to model and solve workforce scheduling and routing problems and this survey represents the first step in this quest.

Domain Transformation using Greedy Algorithm in Nurse Scheduling

International Conference on Artificial Intelligence, Energy and Manufacturing Engineering (ICAEME'2015) Jan. 7-8, 2015 Dubai (UAE), 2015

The nurse scheduling problem (NSP) is a complex optimisation problem of allocating nurses to duty... more The nurse scheduling problem (NSP) is a complex optimisation problem of allocating nurses to duty rosters in hospitals. The objective is usually to ensure that there are always sufficient nurses on duty, while taking into account individual preferences with respect to work patterns, requests for leave and financial restrictions, in such a way that all employees are treated considerately and fairly. In this paper, we extend our domain transformation-based approach to nurse scheduling. Domain transformation is an approach to solving complex problems that involves a judicious simplification of the original problem, a solution to the simplified problem and finally a transformation of this solution to compensate for the introduced simplifications. The approach we use involves information granulation of shift types that transforms the problem into a smaller solution domain. Next, schedules derived from smaller problem domains are then converted into the original problem domain. This includes a consideration of the constraints that were not represented in the smaller domain. The problem is solved using a greedy algorithm. The approach is general and can be applied within a wide range of benchmark instances. These benchmark instances have been derived from published datasets as well as from our industry collaborators. The greedy algorithm outperforms in most cases when tested with the different demands and number of nurses. The results facilitated the development of a cost-benefit analysis across different levels of staffing.

A Greedy Heuristic for Workforce Scheduling and Routing With Time-dependent Activities Constraints

Proceedings of the International Conference on Operations Research and Enterprise Systems, 2015

We present a greedy heuristic (GHI) designed to tackle five time-dependent activities constraints... more We present a greedy heuristic (GHI) designed to tackle five time-dependent activities constraints (synchronisation, overlap, minimum difference, maximum difference and minimum-maximum difference) on workforce scheduling and routing problems. These types of constraints are important because they allow the modelling of situations in which activities relate to each other time-wise, e.g. synchronising two technicians to complete a job. These constraints often make the scheduling and routing of employees more difficult. GHI is tested on set of benchmark instances from different workforce scheduling and routing problems (WSRPs). We compare the results obtained by GHI against the results from a mathematical programming solver. The comparison seeks to determine which solution method achieves more best solutions across all instances. Two parameters of GHI are discussed, the sorting of employees and the sorting of visits. We conclude that using the solver is adequate for instances with less than 100 visits but for larger instances GHI obtains better results in less time.

A Hybrid Constraint Programming Approach for Nurse Rostering Problems

Applications and Innovations in Intelligent Systems XVI, 2009

Due to the complexity of nurse rostering problems (NRPs), Constraint Programming (CP) approaches ... more Due to the complexity of nurse rostering problems (NRPs), Constraint Programming (CP) approaches on their own have shown to be ineffective in solving these highly constrained problems. We investigate a two-stage hybrid CP approach on real world benchmark NRPs. In the first stage, a constraint satisfaction model is used to generate weekly rosters consist of high quality shift sequences satisfying a subset of constraints. An iterative forward search is then adapted to extend them to build complete feasible solutions. Variable and value selection heuristics are employed to improve the efficiency. In the second stage, a simple Variable Neighborhood Search is used to quickly improve the solution obtained. The basic idea of the hybrid approach is based on the observations that high quality nurse rosters consist of high quality shift sequences. By decomposing the problems into solvable sub-problems for CP, the search space of the original problems are significantly reduced. The results on benchmark problems demonstrate the efficiency of this hybrid CP approach when compared to the state-of-the-art approaches in the literature.

A variable neighborhood descent search algorithm for delay-constrained least-cost multicast routing

The rapid evolution of real-time multimedia applications requires Quality of Service (QoS) based ... more The rapid evolution of real-time multimedia applications requires Quality of Service (QoS) based multicast routing in underlying computer networks. The constrained Steiner Tree, as the underpinning mathematical structure, is a well-known NP-complete problem. In this paper we investigate a variable neighborhood descent (VND) search, a variant of variable neighborhood search, for the delay-constrained leastcost (DCLC) multicast routing problem. The neighborhood structures designed in the VND approaches are based on the idea of path replacement in trees. They are simple, yet effective operators, enabling a flexible search over the solution space of this complex problem with multiple constraints. A large number of simulations demonstrate that our algorithm is highly efficient in solving the DCLC multicast routing problem in terms of the tree cost and execution time. To our knowledge, this is the first study of VND algorithm on the DCLC multicast routing problem. It outperforms other existing algorithms over a range of problem instances.

Granular Modelling Of Exam To Slot Allocation

ECMS 2009 Proceedings edited by J. Otamendi, A. Bargiela, J. L. Montes, L. M. Doncel Pedrera, 2009

In this paper, we are introducing a new method of granular exam-to-slot allocation based on the p... more In this paper, we are introducing a new method of granular exam-to-slot allocation based on the preprocessing of the basic student-exam information into a more abstract (granulated) entity of conflict chains. Since the conflict chains are designed to capture the mutual dependencies between exams, they enable us to reason about the exam-to-slot allocation for all exams in a chain rather than just one exam at-a-time. The initial exam-to-slot allocation, generated through the processing of conflict chains, is then refined by considering the spread of the exams in the examination session so as to minimize the appropriately defined cost function. The granular pre-processing of problem data has been shown to enhance the efficiency of the exam scheduling task and has led to the identification of very competitive exam schedules.

Solving a Practical Examination Timetabling Problem: A Case Study

Lecture Notes in Computer Science

This paper presents a Greedy-Least Saturation Degree (G-LSD) heuristic (which is an adaptation of... more This paper presents a Greedy-Least Saturation Degree (G-LSD) heuristic (which is an adaptation of the least saturation degree heuristic) to solve a real-world examination timetabling problem at the University Kebangsaan, Malaysia. We utilise a new objective function that was proposed in our previous work to evaluate the quality of the timetable. The objective function considers both timeslots and days in assigning exams to timeslots, where higher priority is given to minimise students having consecutive exams on the same day. The objective also tries to spread exams throughout the examination period. This heuristic has the potential to be used for the benchmark examination datasets (e.g. the Carter datasets) as well as other real world problems. Computational results are presented.

Hybridising heuristics within an estimation distribution algorithm for examination timetabling

Applied Intelligence, 2014

This paper presents a hybrid hyper-heuristic approach based on estimation distribution algorithms... more This paper presents a hybrid hyper-heuristic approach based on estimation distribution algorithms. The main motivation is to raise the level of generality for search methodologies. The objective of the hyper-heuristic is to produce solutions of acceptable quality for a number of optimisation problems. In this work, we demonstrate the generality through experimental results for different variants of exam timetabling problems. The hyperheuristic represents an automated constructive method that searches for heuristic choices from a given set of low-level heuristics based only on non-domain-specific knowledge. The high-level search methodology is based on a simple estimation distribution algorithm. It is capable of guiding the search to select appropriate heuristics in different problem solving situations. The probability distribution of low-level heuristics at different stages of solution construction can be used to measure their effectiveness and possibly help facilitate more intelligent hyper-heuristic search methods.

Analysis Of Backtracking In University Examination Scheduling

ECMS 2013 Proceedings edited by: Webjorn Rekdalsbakken, Robin T. Bye, Houxiang Zhang, 2013

Simulation modelling of the initial assignments of exams to time-slots provides an alternative ap... more Simulation modelling of the initial assignments of exams to time-slots provides an alternative approach to the establishment of a set of feasible solutions that are subsequently optimized. In this research, we analyze two backtracking strategies for reassigning exams after the initial allocation of exams to time-slots. We propose two approaches for backtracking, BT1 and BT2. The study indicates that backtracking is an effective approach for improving the quality of the examination schedule where BT2 has outperformed BT1 in a number of cases.

Domain transformation approach to deterministic optimization of examination timetables

Artificial Intelligence Research, 2012

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