Exact/heuristic hybrids using rVNS and hyperheuristics for workforce scheduling

In this paper we study a complex real-world workforce scheduling problem. We propose a method of splitting the problem into smaller parts and solving each part using exhaustive search. These smaller parts comprise a combination of choosing a method to select a task to be scheduled and a method to allocate resources, including time, to the selected task. We use reduced Variable Neighbourhood Search (rVNS) and hyperheuristic approaches to decide which sub problems to tackle. The resulting methods are compared to local search and Genetic Algorithm approaches. Parallelisation is used to perform nearly one CPU-year of experiments. The results show that the new methods can produce results fitter than the Genetic Algorithm in less time and that they are far superior to any of their component techniques. The method used to split up the problem is generalisable and could be applied to a wide range of optimisation problems

Enhancing the Performance of Search Heuristics. Variable Fitness Functions and other Methods to Enhance Heuristics for Dynamic Workforce Scheduling.

Scheduling large real world problems is a complex process and finding high quality solutions is not a trivial task. In cooperation with Trimble MRM Ltd., who provide scheduling solutions for many large companies, a problem is identified and modelled. It is a general model which encapsulates several important scheduling, routing and resource allocation problems in literature. Many of the state-of-the-art heuristics for solve scheduling problems and indeed other problems require specialised heuristics tailored for the problem they are to solve. While these provide good solutions a lot of expert time is needed to study the problem, and implement solutions. This research investigates methods to enhance existing search based methods. We study hyperheuristic techniques as a general search based heuristic. Hyperheuristics raise the generality of the solution method by using a set of tools (low level heuristics) to work on the solution. These tools are problem specific and usually make small changes to the problem. It is the task of the hyperheuristic to determine which tool to use and when. Low level heuristics using exact/heuristic hybrid method are used in this thesis along with a new Tabu based hyperheuristic which decreases the amount of CPU time required to produce good quality solutions. We also develop and investigate the Variable Fitness Function approach, which provides a new way of enhancing most search-based heuristics in terms of solution quality. If a fitness function is pushing hard in a certain direction, a heuristic may ultimately fail because it cannot escape local minima. The Variable Fitness Function allows the fitness function to change over the search and use objective measures not used in the fitness calculation. The Variable Fitness Function and its ability to generalise are extensively tested in this thesis. The two aims of the thesis are achieved and the methods are analysed in depth. General conclusions and areas of future work are also identified

A case study of controlling crossover in a selection hyper-heuristic framework using the multidimensional knapsack problem

Hyper-heuristics are high-level methodologies for solving complex problems that operate on a search space of heuristics. In a selection hyper-heuristic framework, a heuristic is chosen from an existing set of low-level heuristics and applied to the current solution to produce a new solution at each point in the search. The use of crossover low-level heuristics is possible in an increasing number of general-purpose hyper-heuristic tools such as HyFlex and Hyperion. However, little work has been undertaken to assess how best to utilise it. Since a single-point search hyper-heuristic operates on a single candidate solution, and two candidate solutions are required for crossover, a mechanism is required to control the choice of the other solution. The frameworks we propose maintain a list of potential solutions for use in crossover. We investigate the use of such lists at two conceptual levels. First, crossover is controlled at the hyper-heuristic level where no problem-specific information is required. Second, it is controlled at the problem domain level where problem-specific information is used to produce good-quality solutions to use in crossover. A number of selection hyper-heuristics are compared using these frameworks over three benchmark libraries with varying properties for an NP-hard optimisation problem: the multidimensional 0-1 knapsack problem. It is shown that allowing crossover to be managed at the domain level outperforms managing crossover at the hyper-heuristic level in this problem domain. © 2016 Massachusetts Institute of Technolog

A dynamic multiarmed bandit-gene expression programming hyper-heuristic for combinatorial optimization problems

Hyper-heuristics are search methodologies that aim to provide high-quality solutions across a wide variety of problem domains, rather than developing tailor-made methodologies for each problem instance/domain. A traditional hyper-heuristic framework has two levels, namely, the high level strategy (heuristic selection mechanism and the acceptance criterion) and low level heuristics (a set of problem specific heuristics). Due to the different landscape structures of different problem instances, the high level strategy plays an important role in the design of a hyper-heuristic framework. In this paper, we propose a new high level strategy for a hyper-heuristic framework. The proposed high-level strategy utilizes a dynamic multiarmed bandit-extreme value-based reward as an online heuristic selection mechanism to select the appropriate heuristic to be applied at each iteration. In addition, we propose a gene expression programming framework to automatically generate the acceptance criterion for each problem instance, instead of using human-designed criteria. Two well-known, and very different, combinatorial optimization problems, one static (exam timetabling) and one dynamic (dynamic vehicle routing) are used to demonstrate the generality of the proposed framework. Compared with state-of-the-art hyper-heuristics and other bespoke methods, empirical results demonstrate that the proposed framework is able to generalize well across both domains. We obtain competitive, if not better results, when compared to the best known results obtained from other methods that have been presented in the scientific literature. We also compare our approach against the recently released hyper-heuristic competition test suite. We again demonstrate the generality of our approach when we compare against other methods that have utilized the same six benchmark datasets from this test suite

New local search in the space of infeasible solutions framework for the routing of vehicles

Combinatorial optimisation problems (COPs) have been at the origin of the design of many optimal and heuristic solution frameworks such as branch-and-bound algorithms, branch-and-cut algorithms, classical local search methods, metaheuristics, and hyperheuristics. This thesis proposes a refined generic and parametrised infeasible local search (GPILS) algorithm for solving COPs and customises it to solve the traveling salesman problem (TSP), for illustration purposes. In addition, a rule-based heuristic is proposed to initialise infeasible local search, referred to as the parameterised infeasible heuristic (PIH), which allows the analyst to have some control over the features of the infeasible solution he/she might want to start the infeasible search with. A recursive infeasible neighbourhood search (RINS) as well as a generic patching procedure to search the infeasible space are also proposed. These procedures are designed in a generic manner, so they can be adapted to any choice of parameters of the GPILS, where the set of parameters, in fact for simplicity, refers to set of parameters, components, criteria and rules. Furthermore, a hyperheuristic framework is proposed for optimizing the parameters of GPILS referred to as HH-GPILS. Experiments have been run for both sequential (i.e. simulated annealing, variable neighbourhood search, and tabu search) and parallel hyperheuristics (i.e., genetic algorithms / GAs) to empirically assess the performance of the proposed HH-GPILS in solving TSP using instances from the TSPLIB. Empirical results suggest that HH-GPILS delivers an outstanding performance. Finally, an offline learning mechanism is proposed as a seeding technique to improve the performance and speed of the proposed parallel HH-GPILS. The proposed offline learning mechanism makes use of a knowledge-base to keep track of the best performing chromosomes and their scores. Empirical results suggest that this learning mechanism is a promising technique to initialise the GA’s population

A stochastic local search algorithm with adaptive acceptance for high-school timetabling

Automating high school timetabling is a challenging task. This problem is a well known hard computational problem which has been of interest to practitioners as well as researchers. High schools need to timetable their regular activities once per year, or even more frequently. The exact solvers might fail to find a solution for a given instance of the problem. A selection hyper-heuristic can be defined as an easy-to-implement, easy-to-maintain and effective 'heuristic to choose heuristics' to solve such computationally hard problems. This paper describes the approach of the team hyper-heuristic search strategies and timetabling (HySST) to high school timetabling which competed in all three rounds of the third international timetabling competition. HySST generated the best new solutions for three given instances in Round 1 and gained the second place in Rounds 2 and 3. It achieved this by using a fairly standard stochastic search method but significantly enhanced by a selection hyper-heuristic with an adaptive acceptance mechanism. © 2014 Springer Science+Business Media New York

An iterated multi-stage selection hyper-heuristic

There is a growing interest towards the design of reusable general purpose search methods that are applicable to different problems instead of tailored solutions to a single particular problem. Hyper-heuristics have emerged as such high level methods that explore the space formed by a set of heuristics (move operators) or heuristic components for solving computationally hard problems. A selection hyper-heuristic mixes and controls a predefined set of low level heuristics with the goal of improving an initially generated solution by choosing and applying an appropriate heuristic to a solution in hand and deciding whether to accept or reject the new solution at each step under an iterative framework. Designing an adaptive control mechanism for the heuristic selection and combining it with a suitable acceptance method is a major challenge, because both components can influence the overall performance of a selection hyper-heuristic. In this study, we describe a novel iterated multi-stage hyper-heuristic approach which cycles through two interacting hyper-heuristics and operates based on the principle that not all low level heuristics for a problem domain would be useful at any point of the search process. The empirical results on a hyper-heuristic benchmark indicate the success of the proposed selection hyper-heuristic across six problem domains beating the state-of-the-art approach

Ant algorithm hyperheuristic approaches for scheduling problems

For decades, optimisation research has investigated methods to find optimal solutions to many problems in the fields of scheduling, timetabling and rostering. A family of abstract methods known as metaheuristics have been developed and applied to many of these problems, but their application to specific problems requires problem-specific coding and parameter adjusting to produce the best results for that problem. Such specialisation makes code difficult to adapt to new problem instances or new problems. One methodology that intended to increase the generality of state of the art algorithms is known as hyperheuristics. Hyperheuristics are algorithms which construct algorithms: using "building block" heuristics, the higher-level algorithm chooses between heuristics to move around the solution space, learning how to use the heuristics to find better solutions. We introduce a new hyperheuristic based upon the well-known ant algorithm metaheuristic, and apply it towards several real-world problems without parameter tuning, producing results that are competitive with other hyperheuristic methods and established bespoke metaheuristic techniques

Ant algorithm hyperheuristic approaches for scheduling problems

For decades, optimisation research has investigated methods to find optimal solutions to many problems in the fields of scheduling, timetabling and rostering. A family of abstract methods known as metaheuristics have been developed and applied to many of these problems, but their application to specific problems requires problem-specific coding and parameter adjusting to produce the best results for that problem. Such specialisation makes code difficult to adapt to new problem instances or new problems. One methodology that intended to increase the generality of state of the art algorithms is known as hyperheuristics. Hyperheuristics are algorithms which construct algorithms: using "building block" heuristics, the higher-level algorithm chooses between heuristics to move around the solution space, learning how to use the heuristics to find better solutions. We introduce a new hyperheuristic based upon the well-known ant algorithm metaheuristic, and apply it towards several real-world problems without parameter tuning, producing results that are competitive with other hyperheuristic methods and established bespoke metaheuristic techniques

Mitigating Metaphors: A Comprehensible Guide to Recent Nature-Inspired Algorithms

In recent years, a plethora of new metaheuristic algorithms have explored different sources of inspiration within the biological and natural worlds. This nature-inspired approach to algorithm design has been widely criticised. A notable issue is the tendency for authors to use terminology that is derived from the domain of inspiration, rather than the broader domains of metaheuristics and optimisation. This makes it difficult to both comprehend how these algorithms work and understand their relationships to other metaheuristics. This paper attempts to address this issue, at least to some extent, by providing accessible descriptions of the most cited nature-inspired algorithms published in the last twenty years. It also discusses commonalities between these algorithms and more classical nature-inspired metaheuristics such as evolutionary algorithms and particle swarm optimisation, and finishes with a discussion of future directions for the field