Novel Hyper-heuristics Applied to the Domain of Bin Packing

Principal to the ideology behind hyper-heuristic research is the desire to increase the level of generality of heuristic procedures so that they can be easily applied to a wide variety of problems to produce solutions of adequate quality within practical timescales.This thesis examines hyper-heuristics within a single problem domain, that of Bin Packing where the benefits to be gained from selecting or generating heuristics for large problem sets with widely differing characteristics is considered. Novel implementations of both selective and generative hyper-heuristics are proposed. The former approach attempts to map the characteristics of a problem to the heuristic that best solves it while the latter uses Genetic Programming techniques to automate the heuristic design process. Results obtained using the selective approach show that solution quality was improved significantly when contrasted to the performance of the best single heuristic when applied to large sets of diverse problem instances. Although enforcing the benefits to be gained by selecting from a range of heuristics the study also highlighted the lack of diversity in human designed algorithms. Using Genetic Programming techniques to automate the heuristic design process allowed both single heuristics and collectives of heuristics to be generated that were shown to perform significantly better than their human designed counterparts. The thesis concludes by combining both selective and generative hyper-heuristic approaches into a novel immune inspired system where heuristics that cover distinct areas of the problem space are generated. The system is shown to have a number of advantages over similar cooperative approaches in terms of its plasticity, efficiency and long term memory. Extensive testing of all of the hyper-heuristics developed on large sets of both benchmark and newly generated problem instances enforces the utility of hyper-heuristics in their goal of producing fast understandable procedures that give good quality solutions for a range of problems with widely varying characteristics

Optimization methods for scheduling in industrial applications

Scheduling optimization has always been a challenging topic in different industries especially over a long planning horizon. Decisions with a consideration of various operational factors subject to limited resources often need to be made to reduce overall costs, maximize utilization and balance resources. To this end, many researchers have developed various models and solution methodologies for deterministic scheduling problems with a consideration of a set of limited resources. As the scheduling system in different industries becomes more complex and sophisticated, additional resources should be incorporated and contradictory goals need to be more carefully evaluated to create a more practical and flexible plan. A robust plan that deals with uncertainties in scheduling has also received a lot of attention in recent years in case of unanticipated events. What’s more, extra information or requests can arise and should also be taken into account during the planning horizon, therefore a more dynamic method is preferred to update the scheduling plan, especially in multi-period problems. This work focuses on scheduling optimization problems in three different industries with a consideration of extra resources, more operational goals or uncertainties in a more dynamic environment. We also proposed multi-step methods or preprocessing procedures to solve the industrial-sized problems efficiently and obtain exact or near-optimal solutions. Chapter 1 presents an overall introduction to this dissertation and its chapters, along with the organization of it. Chapter 2 introduces a two-stage approach for minimizing the impact of daily disruptions on an airline’s published flight schedule. The problem is characterized by uncertainty in the duration of the disruption and the point in time when its length becomes known. Both a single-commodity network model and multi-commodity network model with side constraints are developed to first determine the flights that are most likely to be affected, and then to adjust their schedules to achieve system-wide optimality. The overall objective is to minimize the weighted sum of total passenger delay costs, cancellation costs, curfew violation costs, and variation from the original schedule. The two types of uncertainty are addressed by examining a range of scenarios that reflect the most likely outcomes. The results provide guidance and a measure of robustness for the flight director as the disruption unfolds. A rolling horizon approach that closely mimics current procedures used by several airlines is also presented to provide a benchmark for comparisons with the two-stage solutions. In Chapter 3, a discrete-time mixed-integer linear programming (MILP) model for a generalized flexible job-shop scheduling problem as represented by a state-task network in batch processing facilities in presented. The problem is characterized by reentrant flow, sequence-dependent changeover time, machine downtime, and skilled labor requirements. Two preprocessing procedures are proposed to reduce the size of the MILP model, and represent a major contribution of the research. The procedures reduce the number of assignment variables by exploiting job precedence and workforce qualifications. Machine availability for each task is determined as a function of possible start and end times, given duration, and maintenance schedule. The overall objective is to maximize the number of scheduled tasks while minimizing their total finish time. Computational experiments are conducted with real and randomly generated instances. The results show that optimal solutions can be obtained for medium-size problems within a reasonable amount of time, primarily due to the use of the preprocessing procedures. Chapter 4 presents a two-step approach for efficiently solving a weekly home health-care scheduling and routing problem. Two new mixed-integer programming (MIP) models are proposed, where the is first used for making patient-therapist assignments over the week, and the second for deriving daily routes. In both MIPs, the objective function contains a hierarchically weighted set of goals. The major components of the full problem are continuity of care, downgrading, workload balance, time windows, overtime, and mileage costs. A new preprocessing procedure is developed to limit the service area of each therapist to a single group of overlapping patients. Once the groups are formed, weekly schedules are constructed with the MIPs. The overall objective is to minimize the number of unscheduled visits and total travel and service costs subject to the operational and physical constraints mentioned above. Computational experiments are conducted with real data sets provided by a national home health agency. The results show that optimal solutions can be obtained quickly for large-size instances, and that they compare favorably with results obtained with a proposed integrated model as well as the actual schedules. Lastly, Chapter 5 concludes the dissertation by summarizing research contributions, key research findings, and indicating future research directions.Mechanical Engineerin

Airline reserve crew scheduling under uncertainty

This thesis addresses the problem of airline reserve crew scheduling under crew absence and journey time uncertainty. This work is primarily concerned with the allocation of reserve crew to standby duty periods. The times at which reserve crew are on duty, determine which possible crew absence or delay disruptions they can be used to absorb. When scheduling reserve crew, the goal is to minimise the expected levels of delay and cancellation disruptions that occur on the day of operation. This work introduces detailed probabilistic models of the occurrence of crew absence and delay disruptions and how reserve crew are used to absorb such disruptions. Firstly, separate probabilistic models are developed for crew absence and delay disruptions. Then, an integrated probabilistic model of absence and delay disruptions is introduced, which accounts for: delays from all causes; delay propagation; cancellations resulting from excessive delays and crew absence; the use of reserve crew to cover such disruptions given a reserve policy; and the possibility of swap recovery actions as an alternative delay recovery action. The model yields delay and cancellation predictions that match those derived from simulation to a high level of accuracy and does so in a fraction of the time required by simulation. The various probabilistic models are used in various search methodologies to find disruption minimising reserve crew schedules. The results show that high quality reserve crew schedules can be derived using a probabilistic model. A scenario-based mixed integer programming approach to modelling operational uncertainty and reserve crew use is also developed in this thesis and applied to the problem of reserve crew scheduling. A scenario selection heuristic is introduced which improves reserve crew schedule quality using fewer input scenarios. The secondary objective of this thesis is to investigate the effect of the reserve policy used on the day of operation, that is, determining when and which reserve crew should be utilised. The questions of how reserve policies can be improved and how they should be taken into account when scheduling reserve crew are addressed. It was found that the approaches developed for reserve crew scheduling lend themselves well to an online application, that is, using them to evaluate alternative reserve decisions to ensure reserve crew are used as effectively as possible. In general it is shown that `day of operation' disruptions can be significantly reduced through both improved reserve crew schedules and/or reserve policies. This thesis also points the way towards future research based on the proposed approaches

Airline reserve crew scheduling under uncertainty

This thesis addresses the problem of airline reserve crew scheduling under crew absence and journey time uncertainty. This work is primarily concerned with the allocation of reserve crew to standby duty periods. The times at which reserve crew are on duty, determine which possible crew absence or delay disruptions they can be used to absorb. When scheduling reserve crew, the goal is to minimise the expected levels of delay and cancellation disruptions that occur on the day of operation. This work introduces detailed probabilistic models of the occurrence of crew absence and delay disruptions and how reserve crew are used to absorb such disruptions. Firstly, separate probabilistic models are developed for crew absence and delay disruptions. Then, an integrated probabilistic model of absence and delay disruptions is introduced, which accounts for: delays from all causes; delay propagation; cancellations resulting from excessive delays and crew absence; the use of reserve crew to cover such disruptions given a reserve policy; and the possibility of swap recovery actions as an alternative delay recovery action. The model yields delay and cancellation predictions that match those derived from simulation to a high level of accuracy and does so in a fraction of the time required by simulation. The various probabilistic models are used in various search methodologies to find disruption minimising reserve crew schedules. The results show that high quality reserve crew schedules can be derived using a probabilistic model. A scenario-based mixed integer programming approach to modelling operational uncertainty and reserve crew use is also developed in this thesis and applied to the problem of reserve crew scheduling. A scenario selection heuristic is introduced which improves reserve crew schedule quality using fewer input scenarios. The secondary objective of this thesis is to investigate the effect of the reserve policy used on the day of operation, that is, determining when and which reserve crew should be utilised. The questions of how reserve policies can be improved and how they should be taken into account when scheduling reserve crew are addressed. It was found that the approaches developed for reserve crew scheduling lend themselves well to an online application, that is, using them to evaluate alternative reserve decisions to ensure reserve crew are used as effectively as possible. In general it is shown that `day of operation' disruptions can be significantly reduced through both improved reserve crew schedules and/or reserve policies. This thesis also points the way towards future research based on the proposed approaches

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts