Metaheuristic and Multiobjective Approaches for Space Allocation

This thesis presents an investigation on the application of metaheuristic techniques to tackle the space allocation problem in academic institutions. This is a combinatorial optimisation problem which refers to the distribution of the available room space among a set of entities (staff, research students, computer rooms, etc.) in such a way that the space is utilised as efficiently as possible and the additional constraints are satisfied as much as possible. The literature on the application of optimisation techniques to approach the problem mentioned above is scarce. This thesis provides a description and formulation of the problem. It also proposes and compares a range of heuristics for the initialisation of solutions and for neighbourhood exploration. Four well-known metaheuristics (iterative improvement, simulated annealing, tabu search and genetic algorithms) are adapted and tuned for their application to the problem investigated here. The performance of these techniques is assessed and benchmark results are obtained. Also, hybrid approaches are designed that produce sets of high quality and diverse solutions in much shorter time than those required by space administrators who construct solutions manually. The hybrid approaches are also adapted to tackle the space allocation problem from a two-objective perspective. It is also revealed that the use of aggregating functions or relaxed dominance to evaluate solutions in Pareto optimisation, can be more beneficial than the standard dominance relation to enhance the performance of some multiobjective optimisers in some problem domains. A range of single-solution metaheuristics are extended to create hybrid evolutionary approaches based on the scheme of cooperative local search. This scheme promotes the cooperation of a population of local searchers by means of mechanisms to share the information gained during the search. This thesis also reports the best results known so far for a set of test instances of the space allocation problem in academic institutions. This thesis pioneers the application of metaheuristics to solve the space allocation problem. The major contributions are: provides a formulation of the problem together with tests data sets, reports the best known results for these test instances, investigates the multiobjective nature of the problem and proposes a new form of hybridising metaheuristics

Metaheuristic and Multiobjective Approaches for Space Allocation

This thesis presents an investigation on the application of metaheuristic techniques to tackle the space allocation problem in academic institutions. This is a combinatorial optimisation problem which refers to the distribution of the available room space among a set of entities (staff, research students, computer rooms, etc.) in such a way that the space is utilised as efficiently as possible and the additional constraints are satisfied as much as possible. The literature on the application of optimisation techniques to approach the problem mentioned above is scarce. This thesis provides a description and formulation of the problem. It also proposes and compares a range of heuristics for the initialisation of solutions and for neighbourhood exploration. Four well-known metaheuristics (iterative improvement, simulated annealing, tabu search and genetic algorithms) are adapted and tuned for their application to the problem investigated here. The performance of these techniques is assessed and benchmark results are obtained. Also, hybrid approaches are designed that produce sets of high quality and diverse solutions in much shorter time than those required by space administrators who construct solutions manually. The hybrid approaches are also adapted to tackle the space allocation problem from a two-objective perspective. It is also revealed that the use of aggregating functions or relaxed dominance to evaluate solutions in Pareto optimisation, can be more beneficial than the standard dominance relation to enhance the performance of some multiobjective optimisers in some problem domains. A range of single-solution metaheuristics are extended to create hybrid evolutionary approaches based on the scheme of cooperative local search. This scheme promotes the cooperation of a population of local searchers by means of mechanisms to share the information gained during the search. This thesis also reports the best results known so far for a set of test instances of the space allocation problem in academic institutions. This thesis pioneers the application of metaheuristics to solve the space allocation problem. The major contributions are: provides a formulation of the problem together with tests data sets, reports the best known results for these test instances, investigates the multiobjective nature of the problem and proposes a new form of hybridising metaheuristics

Construction-based metaheuristics for personnel scheduling problems

This thesis investigates the idea of balancing different constraints in order to find optimal solutions to two personnel scheduling problems, within the framework of constructive metaheuristic approaches. The two problems considered are a nurse scheduling problem, for which finding feasible solutions is known to be difficult and for which the hard and soft constraints are in direct conflict, and a medical student scheduling problem for which there is little relevant literature this second problem also has conflicting hard and soft constraints, but presents further conflict between the different soft constraints. The methods used to solve these problems are focused on two constructive metaheuristics in particular: Greedy Randomised Adaptive Search Procedures (GRASP) and Ant Colony Optimisation (ACO) and for each approach several construction heuristics are introduced and compared. Using GRASP, a number of local search neighbourhoods are established for each problem, while for ACO the suitability of three trail definitions are compared. In order to further explore the balance which may obtained between the different constraints and objectives for the two problems, hybrid constructions are investigated, incorporating exact methods which take advantage of the underlying structures of each problem with regards to feasibility. For medical student scheduling, this exact method was developed into a new type of construction mechanism providing much improved results over a standard heuristic approach. Further enhancements investigated include the use of problem-specific feedback for nurse scheduling and the use of an intelligent memory procedure for the medical student scheduling problem. For the nurse scheduling problem, the final algorithm developed was able to rival the best in the literature so far and produce optimal solutions for all available datasets. For the medical student scheduling problem, optimal solutions are not known, but the results obtained are very promising and provide a good basis for further study of the problem.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Construction-based metaheuristics for personnel scheduling problems.

This thesis investigates the idea of balancing different constraints in order to find optimal solutions to two personnel scheduling problems, within the framework of constructive metaheuristic approaches. The two problems considered are a nurse scheduling problem, for which finding feasible solutions is known to be difficult and for which the hard and soft constraints are in direct conflict, and a medical student scheduling problem for which there is little relevant literature this second problem also has conflicting hard and soft constraints, but presents further conflict between the different soft constraints. The methods used to solve these problems are focused on two constructive metaheuristics in particular: Greedy Randomised Adaptive Search Procedures (GRASP) and Ant Colony Optimisation (ACO) and for each approach several construction heuristics are introduced and compared. Using GRASP, a number of local search neighbourhoods are established for each problem, while for ACO the suitability of three trail definitions are compared. In order to further explore the balance which may obtained between the different constraints and objectives for the two problems, hybrid constructions are investigated, incorporating exact methods which take advantage of the underlying structures of each problem with regards to feasibility. For medical student scheduling, this exact method was developed into a new type of construction mechanism providing much improved results over a standard heuristic approach. Further enhancements investigated include the use of problem-specific feedback for nurse scheduling and the use of an intelligent memory procedure for the medical student scheduling problem. For the nurse scheduling problem, the final algorithm developed was able to rival the best in the literature so far and produce optimal solutions for all available datasets. For the medical student scheduling problem, optimal solutions are not known, but the results obtained are very promising and provide a good basis for further study of the problem

Office space allocation by using mathematical programming and meta-heuristics

Office Space Allocation (OSA) is the task of efficient usage of spatial resources of an organisation. A common goal in a typical OSA problem is to minimise the wastage of space either by limiting the overuse or underuse of the facilities. The problem also contains a myriad of hard and soft constraints based on the preferences of respective organisations. In this thesis, the OSA variant usually encountered in academic institutions is investigated. Previous research in this area is rather sparse. This thesis provides a definition, extension, and literature review for the problem as well as a new parametrised data instance generator. In this thesis, two main algorithmic approaches for tackling the OSA are proposed: The first one is integer linear programming. Based on the definition of several constraints and some additional variables, two different mathematical models are proposed. These two models are not strictly alternatives to each other. While one of them provides more performance for the types of instances it is applicable, it lacks generality. The other approach provides less performance; however, it is easier to apply this model to different OSA problems. The second algorithmic approach is based on metaheuristics. A three step process in heuristic development is followed. In the first step, general local search techniques (descent methods, threshold acceptance, simulated annealing, great deluge) traverse within the neighbourhood via random relocation and swap moves. The second step of heuristic development aims to investigate large sections of the whole neighbourhood greedily via very fast cost calculation, cost update, and search for best move procedures within an evolutionary local search framework. The final step involves refinements and hybridisation of best performing (in terms of solution quality) mathematical programming and meta-heuristic techniques developed in prior steps. This thesis aims to be one of the pioneering works in the research area of OSA. The major contributions are: the analysis of the problem, a new parametrised data instance generator, mathematical programming models, and meta-heuristic approaches in order to extend the state-of-the art in this area

Office space allocation by using mathematical programming and meta-heuristics

Office Space Allocation (OSA) is the task of efficient usage of spatial resources of an organisation. A common goal in a typical OSA problem is to minimise the wastage of space either by limiting the overuse or underuse of the facilities. The problem also contains a myriad of hard and soft constraints based on the preferences of respective organisations. In this thesis, the OSA variant usually encountered in academic institutions is investigated. Previous research in this area is rather sparse. This thesis provides a definition, extension, and literature review for the problem as well as a new parametrised data instance generator. In this thesis, two main algorithmic approaches for tackling the OSA are proposed: The first one is integer linear programming. Based on the definition of several constraints and some additional variables, two different mathematical models are proposed. These two models are not strictly alternatives to each other. While one of them provides more performance for the types of instances it is applicable, it lacks generality. The other approach provides less performance; however, it is easier to apply this model to different OSA problems. The second algorithmic approach is based on metaheuristics. A three step process in heuristic development is followed. In the first step, general local search techniques (descent methods, threshold acceptance, simulated annealing, great deluge) traverse within the neighbourhood via random relocation and swap moves. The second step of heuristic development aims to investigate large sections of the whole neighbourhood greedily via very fast cost calculation, cost update, and search for best move procedures within an evolutionary local search framework. The final step involves refinements and hybridisation of best performing (in terms of solution quality) mathematical programming and meta-heuristic techniques developed in prior steps. This thesis aims to be one of the pioneering works in the research area of OSA. The major contributions are: the analysis of the problem, a new parametrised data instance generator, mathematical programming models, and meta-heuristic approaches in order to extend the state-of-the art in this area

Investigating heuristic and meta-heuristic algorithms for solving pickup and delivery problems

The development of effective decision support tools that can be adopted in the transportation industry is vital in the world we live in today, since it can lead to substantial cost reduction and efficient resource consumption. Solving the Vehicle Routing Problem (VRP) and its related variants is at the heart of scientific research for optimizing logistic planning. One important variant of the VRP is the Pickup and Delivery Problem (PDP). In the PDP, it is generally required to find one or more minimum cost routes to serve a number of customers, where two types of services may be performed at a customer location, a pickup or a delivery. Applications of the PDP are frequently encountered in every day transportation and logistic services, and the problem is likely to assume even greater prominence in the future, due to the increase in e-commerce and Internet shopping. In this research we considered two particular variants of the PDP, the Pickup and Delivery Problem with Time Windows (PDPTW), and the One-commodity Pickup and Delivery Problem (1-PDP). In both problems, the total transportation cost should be minimized, without violating a number of pre-specified problem constraints. In our research, we investigate heuristic and meta-heuristic approaches for solving the selected PDP variants. Unlike previous research in this area, though, we try to focus on handling the difficult problem constraints in a simple and effective way, without complicating the overall solution methodology. Two main aspects of the solution algorithm are directed to achieve this goal, the solution representation and the neighbourhood moves. Based on this perception, we tailored a number of heuristic and meta-heuristic algorithms for solving our problems. Among these algorithms are: Genetic Algorithms, Simulated Annealing, Hill Climbing and Variable Neighbourhood Search. In general, the findings of the research indicate the success of our approach in handling the difficult problem constraints and devising simple and robust solution mechanisms that can be integrated with vehicle routing optimization tools and used in a variety of real world applicationsEThOS - Electronic Theses Online ServiceGBUnited Kingdo

Investigating heuristic and meta-heuristic algorithms for solving pickup and delivery problems

The development of effective decision support tools that can be adopted in the transportation industry is vital in the world we live in today, since it can lead to substantial cost reduction and efficient resource consumption. Solving the Vehicle Routing Problem (VRP) and its related variants is at the heart of scientific research for optimizing logistic planning. One important variant of the VRP is the Pickup and Delivery Problem (PDP). In the PDP, it is generally required to find one or more minimum cost routes to serve a number of customers, where two types of services may be performed at a customer location, a pickup or a delivery. Applications of the PDP are frequently encountered in every day transportation and logistic services, and the problem is likely to assume even greater prominence in the future, due to the increase in e-commerce and Internet shopping. In this research we considered two particular variants of the PDP, the Pickup and Delivery Problem with Time Windows (PDPTW), and the One-commodity Pickup and Delivery Problem (1-PDP). In both problems, the total transportation cost should be minimized, without violating a number of pre-specified problem constraints. In our research, we investigate heuristic and meta-heuristic approaches for solving the selected PDP variants. Unlike previous research in this area, though, we try to focus on handling the difficult problem constraints in a simple and effective way, without complicating the overall solution methodology. Two main aspects of the solution algorithm are directed to achieve this goal, the solution representation and the neighbourhood moves. Based on this perception, we tailored a number of heuristic and meta-heuristic algorithms for solving our problems. Among these algorithms are: Genetic Algorithms, Simulated Annealing, Hill Climbing and Variable Neighbourhood Search. In general, the findings of the research indicate the success of our approach in handling the difficult problem constraints and devising simple and robust solution mechanisms that can be integrated with vehicle routing optimization tools and used in a variety of real world applicationsEThOS - Electronic Theses Online ServiceGBUnited Kingdo