Performance Analyses of Graph Heuristics and Selected Trajectory Metaheuristics on Examination Timetable Problem

Examination timetabling problem is hard to solve due to its NP-hard nature, with a large number of constraints having to be accommodated. To deal with the problem effectually, frequently heuristics are used for constructing feasible examination timetable while meta-heuristics are applied for improving the solution quality. This paper presents the performances of graph heuristics and major trajectory metaheuristics or S-metaheuristics for addressing both capacitated and un-capacitated examination timetabling problem. For constructing the feasible solution, six graph heuristics are used. They are largest degree (LD), largest weighted degree (LWD), largest enrolment degree (LE), and three hybrid heuristic with saturation degree (SD) such as SD-LD, SD-LE, and SD-LWD. Five trajectory algorithms comprising of tabu search (TS), simulated annealing (SA), late acceptance hill climbing (LAHC), great deluge algorithm (GDA), and variable neighborhood search (VNS) are employed for improving the solution quality. Experiments have been tested on several instances of un-capacitated and capacitated benchmark datasets, which are Toronto and ITC2007 dataset respectively. Experimental results indicate that, in terms of construction of solution of datasets, hybridizing of SD produces the best initial solutions. The study also reveals that, during improvement, GDA, SA, and LAHC can produce better quality solutions compared to TS and VNS for solving both benchmark examination timetabling datasets

New Swarm-Based Metaheuristics for Resource Allocation and Schwduling Problems

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura : 10-07-2017Esta tesis tiene embargado el acceso al texto completo hasta el 10-01-201

Solving urban transit route design problem using selection hyper-heuristics

The urban transit routing problem (UTRP) focuses on finding efficient travelling routes for vehicles in a public transportation system. It is one of the most significant problems faced by transit planners and city authorities throughout the world. This problem belongs to the class of difficult combinatorial problems, whose optimal solution is hard to find with the complexity that arises from the large search space, and the number of constraints imposed in constructing the solution. Hyper-heuristics have emerged as general-purpose search techniques that explore the space of low level heuristics to improve a given solution under an iterative framework. In this work, we evaluate the performance of a set of selection hyper-heuristics on the route design problem of bus networks, with the goal of minimising the passengers’ travel time, and the operator’s costs. Each selection hyper-heuristic is empirically tested on a set of benchmark instances and statistically compared to the other selection hyper-heuristics to determine the best approach. A sequence-based selection method combined with the great deluge acceptance method achieved the best performance, succeeding in finding improved results in much faster run times over the current best known solutions

Evolutionary multi-objective optimization in scheduling problems

Ph.DDOCTOR OF PHILOSOPH

Multi-objective tools for the vehicle routing problem with time windows

Most real-life problems involve the simultaneous optimisation of two or more, usually conflicting, objectives. Researchers have put a continuous effort into solving these problems in many different areas, such as engineering, finance and computer science. Over time, thanks to the increase in processing power, researchers have created methods which have become increasingly sophisticated. Most of these methods have been based on the notion of Pareto dominance, which assumes, sometimes erroneously, that the objectives have no known ranking of importance. The Vehicle Routing Problem with Time Windows (VRPTW) is a logistics problem which in real-life applications appears to be multi-objective. This problem consists of designing the optimal set of routes to serve a number of customers within certain time slots. Despite this problem’s high applicability to real-life domains (e.g. waste collection, fast-food delivery), most research in this area has been conducted with hand-made datasets. These datasets sometimes have a number of unrealistic features (e.g. the assumption that one unit of travel time corresponds to one unit of travel distance) and are therefore not adequate for the assessment of optimisers. Furthermore, very few studies have focused on the multi-objective nature of the VRPTW. That is, very few have studied how the optimisation of one objective affects the others. This thesis proposes a number of novel tools (methods + dataset) to address the above- mentioned challenges: 1) an agent-based framework for cooperative search, 2) a novel multi-objective ranking approach, 3) a new dataset for the VRPTW, 4) a study of the pair-wise relationships between five common objectives in VRPTW, and 5) a simplified Multi-objective Discrete Particle Swarm Optimisation for the VRPTW

Multi-objective tools for the vehicle routing problem with time windows

Most real-life problems involve the simultaneous optimisation of two or more, usually conflicting, objectives. Researchers have put a continuous effort into solving these problems in many different areas, such as engineering, finance and computer science. Over time, thanks to the increase in processing power, researchers have created methods which have become increasingly sophisticated. Most of these methods have been based on the notion of Pareto dominance, which assumes, sometimes erroneously, that the objectives have no known ranking of importance. The Vehicle Routing Problem with Time Windows (VRPTW) is a logistics problem which in real-life applications appears to be multi-objective. This problem consists of designing the optimal set of routes to serve a number of customers within certain time slots. Despite this problem’s high applicability to real-life domains (e.g. waste collection, fast-food delivery), most research in this area has been conducted with hand-made datasets. These datasets sometimes have a number of unrealistic features (e.g. the assumption that one unit of travel time corresponds to one unit of travel distance) and are therefore not adequate for the assessment of optimisers. Furthermore, very few studies have focused on the multi-objective nature of the VRPTW. That is, very few have studied how the optimisation of one objective affects the others. This thesis proposes a number of novel tools (methods + dataset) to address the above- mentioned challenges: 1) an agent-based framework for cooperative search, 2) a novel multi-objective ranking approach, 3) a new dataset for the VRPTW, 4) a study of the pair-wise relationships between five common objectives in VRPTW, and 5) a simplified Multi-objective Discrete Particle Swarm Optimisation for the VRPTW

A Polyhedral Study of Mixed 0-1 Set

We consider a variant of the well-known single node fixed charge network flow set with constant capacities. This set arises from the relaxation of more general mixed integer sets such as lot-sizing problems with multiple suppliers. We provide a complete polyhedral characterization of the convex hull of the given set

Module reallocation problem in the context of multi-campus university course timetabling

Ph.DDOCTOR OF PHILOSOPH

Move Acceptance in Local Search Metaheuristics for Cross-domain Heuristic Search

Many real-world combinatorial optimisation problems (COPs) are computationally hard problems and search methods are frequently preferred as solution techniques. Traditionally, an expert with domain knowledge designs, and tailors the search method for solving a particular COP. This process is usually expensive, requiring a lot of effort and time and often results in problem specific algorithms that can not be applied to another COP. Then, the domain expert either needs to design a new search method, or reconfigure an existing search method to solve that COP. This prompted interest into developing more general, problem-domain-independent high-level search methods that can be re-used, capable of solving not just a single problem but multiple COPs. The cross-domain search problem is a relatively new concept and represents a high-level issue that involves designing a single solution method for solving a multitude of COPs preferably with the least or no expert intervention. Cross-domain search methods are algorithms designed to tackle the cross-domain search problem. Such methods are of interest to researchers and practitioners worldwide as they offer a single off-the-shelf go-to approach to problem solving. Furthermore, if a cross-domain search method has a good performance, then it can be expected to solve `any' given COP well and in a reasonable time frame. When a practitioner is tasked with solving a new or unknown COP, they are tasked with a decision-making dilemma. This entails the decision of what algorithm they should use, what parameters should be used for that algorithm, and whether any other algorithm can outperform it. A well designed cross-domain search method that performs well and does not require re-tuning can fulfil this dilemma allowing practitioners to find good-enough solutions to such problems. Researchers on the other hand strive to find high-quality solutions to these problems; however, such a cross-domain search method provides them with a good benchmark to which they can compare their solution methods to, and should ultimately aim to outperform. In this work, move acceptance methods, which are a component of traditional search methods, such as metaheuristics and hyper-heuristics, are explored under a cross-domain search framework. A survey of the existing move acceptance methods as a part of local search metaheuristics is conducted based on the hyper-heuristic literature as solution methods to the cross-domain search problem. Furthermore, a taxonomy is provided for classifying them based on their design characteristics. The cross-domain performance of existing move acceptance methods, covering the taxonomy, is compared across a total of 45 problem instances spanning 9 problem domains, and the effects of parameter tuning versus choice of the move acceptance method are explored. A novel move acceptance method (HAMSTA) is proposed to overcome the shortcomings of the existing methods to improve the cross-domain performance of a local search metaheuristic. HAMSTA is capable of outperforming the cross-domain performances of existing methods that are re-tuned for each domain, despite itself using only a single cross-domain parameter configuration derived from tuning experiments that considers 2 instances each from 4 domains; hence, HAMSTA requires no expert intervention to re-configure it to perform well for solving multiple COPs with 37 problem instances unseen by HAMSTA, 25 of which are from unseen domains. HAMSTA is therefore shown to have the potential to fulfil the aforementioned decision-making dilemma

Move Acceptance in Local Search Metaheuristics for Cross-domain Heuristic Search

Many real-world combinatorial optimisation problems (COPs) are computationally hard problems and search methods are frequently preferred as solution techniques. Traditionally, an expert with domain knowledge designs, and tailors the search method for solving a particular COP. This process is usually expensive, requiring a lot of effort and time and often results in problem specific algorithms that can not be applied to another COP. Then, the domain expert either needs to design a new search method, or reconfigure an existing search method to solve that COP. This prompted interest into developing more general, problem-domain-independent high-level search methods that can be re-used, capable of solving not just a single problem but multiple COPs. The cross-domain search problem is a relatively new concept and represents a high-level issue that involves designing a single solution method for solving a multitude of COPs preferably with the least or no expert intervention. Cross-domain search methods are algorithms designed to tackle the cross-domain search problem. Such methods are of interest to researchers and practitioners worldwide as they offer a single off-the-shelf go-to approach to problem solving. Furthermore, if a cross-domain search method has a good performance, then it can be expected to solve `any' given COP well and in a reasonable time frame. When a practitioner is tasked with solving a new or unknown COP, they are tasked with a decision-making dilemma. This entails the decision of what algorithm they should use, what parameters should be used for that algorithm, and whether any other algorithm can outperform it. A well designed cross-domain search method that performs well and does not require re-tuning can fulfil this dilemma allowing practitioners to find good-enough solutions to such problems. Researchers on the other hand strive to find high-quality solutions to these problems; however, such a cross-domain search method provides them with a good benchmark to which they can compare their solution methods to, and should ultimately aim to outperform. In this work, move acceptance methods, which are a component of traditional search methods, such as metaheuristics and hyper-heuristics, are explored under a cross-domain search framework. A survey of the existing move acceptance methods as a part of local search metaheuristics is conducted based on the hyper-heuristic literature as solution methods to the cross-domain search problem. Furthermore, a taxonomy is provided for classifying them based on their design characteristics. The cross-domain performance of existing move acceptance methods, covering the taxonomy, is compared across a total of 45 problem instances spanning 9 problem domains, and the effects of parameter tuning versus choice of the move acceptance method are explored. A novel move acceptance method (HAMSTA) is proposed to overcome the shortcomings of the existing methods to improve the cross-domain performance of a local search metaheuristic. HAMSTA is capable of outperforming the cross-domain performances of existing methods that are re-tuned for each domain, despite itself using only a single cross-domain parameter configuration derived from tuning experiments that considers 2 instances each from 4 domains; hence, HAMSTA requires no expert intervention to re-configure it to perform well for solving multiple COPs with 37 problem instances unseen by HAMSTA, 25 of which are from unseen domains. HAMSTA is therefore shown to have the potential to fulfil the aforementioned decision-making dilemma