A development and integration framework for optimisation-based enterprise solutions

The operations research literature includes some papers describing collaborative work between researchers and industry. However, not much literature exists that outlines methodologies to guide the development of a decision support module and its integration into an existing information management system. Here we describe a framework to aid the collaborative development of an optimisation solution by researchers and information system developers. The proposed framework also helps in the effective integration of the information management system and the decision support module. The framework is divided into three main components: a data model, a data extractor and validator, and a solution visualisation and auxiliary platform. We also describe our experience and positive results from applying the proposed development and integration framework to a project involving the development on an optimisation-based solution for workforce scheduling and optimisation problems. We hope that this contribution would be particularly useful for less experienced researchers and practitioners who embark on a collaborative development of a decision support module based on optimisation techniques

A computational study and heuristic algorithms for the home healthcare scheduling and routing problem

The workforce scheduling and routing problem (WSRP) arises in many scenarios in which skilled workers need to deliver services at different locations. Examples of these scenarios include nurses visiting patients at home, technicians carrying out repairs at customers’ locations, security guards performing rounds at different premises, etc. Hence, finding a solution to this type of problem involves tackling both the \emph{scheduling of tasks} to be carried out and the \emph{routing of workers} to visit the locations of tasks. The focus of this thesis is a challenging real-world problem in the planning and scheduling of healthcare delivery, denoted by home healthcare (HHC) problem. The first intended contribution of this PhD research is to provide to the research community six benchmark datasets for a real-world workforce scheduling and routing problem arising in healthcare delivery as well as benchmark results obtained with heuristic methods. The next contribution is an algorithm to obtain lower bound values for the HHC problem that is capable of providing results when mathematical techniques are not applicable. The lower bounds are obtained by splitting and relaxing the problem into a scheduling and a routing subproblems, and calculating individual lower bounds for each subproblem. In order to further understand the HHC problem, its multiobjective characteristics were assessed. Understanding the relationships between objectives in a multiobjective optimisation problem is important for developing tailored and efficient solving techniques. In particular, when tackling combinatorial optimisation problems with many objectives that arise in real-world scenarios, better support for the decision maker can be achieved through better understanding of the often complex fitness landscape. This thesis makes a contribution in this direction by presenting a technique that allows a visualisation and analysis of the local and global relationships between objectives in optimisation problems with many objectives. The proposed technique uses four steps: first the global pairwise relationships are analysed using the Kendall correlation method; then the ranges of the values found on the given Pareto front are estimated and assessed; next these ranges are used to plot a map using Gray code, similar to Karnaugh maps, that has the ability to highlight the trade-offs between multiple objectives; and finally local relationships are identified using scatter-plots. Results show that each dataset has different characteristics, and the relationships between objectives and their importance vary across datasets; also, instances of the same dataset share similar fitness landscapes. A Variable Neighbourhood Search (VNS) metaheuristic algorithm is proposed to tackle the HHC problem, incorporating three heuristics tailored to the problem-domain. The first heuristic restricts the search space using a priority list of candidate workers; the second heuristic seeks to reduce the violation of worker availabilities soft constraints; the third heuristic estimate the objective costs of all possible individual assignments and uses the estimated costs instead of the objective function, hence substantially increasing the speed of the search. Two greedy constructive heuristics are presented to give the VNS a good starting point. It is shown that the proposed VNS obtains substantial improvements in the quality of solutions regardless of the datasets unique features. The proposed VNS provides the current benchmark results for the set of real-world HHC scenarios considered. The last contribution of this thesis is a multiobjective solving methodology that exploits the fact that instances of the same dataset share similar fitness landscapes. The methodology consists of obtaining an approximation set of a single instance and using that approximation set, goal programming and the proposed VNS to reach target solutions in the remaining instances. Results show that the methodology is accurate enough to reach the target solution and is able to provide quick results when multiobjective algorithms take long computational times

A development and integration framework for optimisation-based enterprise solutions

The operations research literature includes some papers describing collaborative work between researchers and industry. However, not much literature exists that outlines methodologies to guide the development of a decision support module and its integration into an existing information management system. Here we describe a framework to aid the collaborative development of an optimisation solution by researchers and information system developers. The proposed framework also helps in the effective integration of the information management system and the decision support module. The framework is divided into three main components: a data model, a data extractor and validator, and a solution visualisation and auxiliary platform. We also describe our experience and positive results from applying the proposed development and integration framework to a project involving the development on an optimisation-based solution for workforce scheduling and optimisation problems. We hope that this contribution would be particularly useful for less experienced researchers and practitioners who embark on a collaborative development of a decision support module based on optimisation techniques

A Genetic Algorithm for a Workforce Scheduling and Routing Problem

The Workforce Scheduling and Routing Problem refers to the assignment of personnel to visits across various geographical locations. Solving this problem demands tackling scheduling and routing constraints while aiming to minimise the total operational cost. This paper presents a Genetic Algorithm (GA) tailored to tackle a set of real-world instances of this problem. The proposed GA uses a customised chromosome representation to maintain the feasibility of solutions. The performance of several genetic operators is investigated in relation to the tailored chromosome representation. This paper also presents a study of parameter settings for the proposed GA in relation to the various problem instances considered. Results show that the proposed GA, which incorporates tailored components, performs very well and is an effective baseline evolutionary algorithm for this difficult problem

Using goal programming on estimated Pareto fronts to solve multiobjective problems

Modern multiobjective algorithms can be computationally inefficient in producing good approximation sets for highly constrained many-objective problems. Such problems are common in real-world applications where decision-makers need to assess multiple conflicting objectives. Also, different instances of real-world problems often share similar fitness landscapes because key parts of the data are the same across these instances. We we propose a novel methodology that consists of solving one instance of a given problem scenario using computationally expensive multiobjective algorithms to obtain a good approximation set and then using Goal Programming with efficient single-objective algorithms to solve other instances of the same problem scenario. We propose three goal-based objective functions and show that on a real-world home healthcare planning problem the methodology can produce improved results in a shorter computation time

A Variable Neighbourhood Search for nurse scheduling with balanced preference satisfaction

The nurse scheduling problem (NSP) is a combinatorial optimisation problem widely tackled in the literature. Recently, a new variant of this problem was proposed, called nurse scheduling problem with balanced preference satisfaction (NSPBPS). This paper further investigates this variant of the NSP as we propose a new algorithm to solve the problem and obtain a better balance of overall preference satisfaction. Initiall, the algorithm converts the problem to a bottleneck assignment problem and solves it to generate an initial feasible solution for the NSPBPS. Posteriorly, the algorithm applies the Variable Neighbourhood Search (VNS) metaheuristic using two sets of search neighbourhoods in order to improve the initial solution. We empirically assess the performance of the algorithm using the NSPLib benchmark instances and we compare our results to other results found in the literature. The proposed VNS algorithm exhibits good performance by achieving solutions that are fairer (in terms of preference satisfaction) for the majority of the scenarios

A variable neighbourhood search for the workforce scheduling and routing problem

The workforce scheduling and routing problem (WSRP) is a combinatorial optimisation problem where a set of workers must perform visits to geographically scattered locations. We present a Variable Neighbourhood Search (VNS) metaheuristic algorithm to tackle this problem, incorporating two novel heuristics tailored to the problem-domain. The first heuristic restricts the search space using a priority list of candidate workers and the second heuristic seeks to reduce the violation of specific soft constraints. We also present two greedy constructive heuristics to give the VNS a good starting point. We show that the use of domain-knowledge in the design of the algorithm can provide substantial improvements in the quality of solutions. The proposed VNS provides the first benchmark results for the set of real-world WSRP scenarios considered

Towards an efficient API for optimisation problems data

The literature presents many application programming interfaces (APIs) and frameworks that provide state of the art algorithms and techniques for solving optimisation problems. The same cannot be said about APIs and frameworks focused on the problem data itself because with the peculiarities and details of each variant of a problem, it is virtually impossible to provide general tools that are broad enough to be useful on a large scale. However, there are benefits of employing problem-centred APIs in a R&D environment: improving the understanding of the problem, providing fairness on the results comparison, providing efficient data structures for different solving techniques, etc. Therefore, in this work we propose a novel design methodology for an API focused on an optimisation problem. Our methodology relies on a data parser to handle the problem specification files and on a set of efficient data structures to handle the information on memory, in an intuitive fashion for researchers and efficient for the solving algorithms. Also, we present the concepts of a solution dispenser that can manage solutions objects in memory better than built-in garbage collectors. Finally, we describe the positive results of employing a tailored API to a project involving the development of optimisation solutions for workforce scheduling and routing problems

A Variable Neighbourhood Search for nurse scheduling with balanced preference satisfaction

The nurse scheduling problem (NSP) is a combinatorial optimisation problem widely tackled in the literature. Recently, a new variant of this problem was proposed, called nurse scheduling problem with balanced preference satisfaction (NSPBPS). This paper further investigates this variant of the NSP as we propose a new algorithm to solve the problem and obtain a better balance of overall preference satisfaction. Initiall, the algorithm converts the problem to a bottleneck assignment problem and solves it to generate an initial feasible solution for the NSPBPS. Posteriorly, the algorithm applies the Variable Neighbourhood Search (VNS) metaheuristic using two sets of search neighbourhoods in order to improve the initial solution. We empirically assess the performance of the algorithm using the NSPLib benchmark instances and we compare our results to other results found in the literature. The proposed VNS algorithm exhibits good performance by achieving solutions that are fairer (in terms of preference satisfaction) for the majority of the scenarios

An application programming interface with increased performance for optimisation problems data

An optimisation problem can have many forms and variants. It may consider different objectives, constraints, and variables. For that reason, providing a general application programming interface (API) to handle the problem data efficiently in all scenarios is impracticable. Nonetheless, on a R&D environment involving personnel from distinct backgrounds, having such an API can help with the development process because the team can focus on the research instead of implementations of data parsing, objective function calculation, and data structures. Also, some researchers might have a stronger background in programming than others, hence having a standard efficient API to handle the problem data improves reliability and productivity. This paper presents a design methodology to enable the development of efficient APIs to handle optimisation problems data based on a data-centric development framework. The proposed methodology involves the design of a data parser to handle the problem definition and data files and on a set of efficient data structures to hold the data in memory. Additionally, we bring three design patterns aimed to improve the performance of the API and techniques to improve the memory access by the user application. Also, we present the concepts of a Solution Builder that can manage solutions objects in memory better than built-in garbage collectors and provide an integrated objective function so that researchers can easily compare solutions from different solving techniques. Finally, we describe the positive results of employing a tailored API to a project involving the development of optimisation solutions for workforce scheduling and routing problems