Genetic algorithms for workforce scheduling and routing problem - Algethami, Haneen (2017)
The Workforce Scheduling and Routing Problem (WSRP) is described as 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. With current computational capabilities, small WSRPs are solvable using exact methods. However, it is difficult to solve when they are larger. The difficulty of WSRP is further increased when processing conflicting assignments or dealing with workers unavailability at customer's areas. Genetic Algorithms (GAs) have proved their effectiveness in these regards, because of their search capability to acquire good solutions in a reasonable computational time. A GA consists of many components, which can be chosen and combined in numerous procedures. In the case of solving scheduling and routing problems separately, different GAs have been proposed. When solving WSRP problem instances, it has been quite common to use the design components, intended for scheduling or routing problems. In this thesis, 42 real-world Home Health Care (HHC) planning problem datasets are used as instances of the WSRP. Different GA components are presented in this study, tailored for the combined settings. This has made major contributions to understanding how GAs works in a challenging real-world problem. Research interests in this work are categorised into two parts. The first part aims to understand how to employ different genetic operators effectively when solving WSRPs. The work intends to design and select the best combination of components that provide good solutions. Accordingly, seven well-known crossovers, three mutation operators and eight cost-based operators are implemented. In addition, two repair heuristics to tackle infeasibility. Nevertheless, a direct chromosome representation has resulted in poor solutions. Thus, there is a need for more tailored components for this problem. Therefore, an indirect chromosome representation, designed specifically to tackle WSRPs, is presented. The aim is to ensure initial solutions feasibility. Due to the quality of solutions, the GA introduced is considered an effective baseline evolutionary algorithm for WSRP. This work also suggested that each problem set requires different parameter settings. The second research interest intends to increase the GA efficiency. One approach is to investigate the effect of using adaptive components on the quality of WSRPs solutions. The aim is to adaptively alter parameter values instead of tuning an algorithm to a specific instance. Three aspects are adjusted during the run according to different rules: operator rates, population size, and crossover operator function. Thus, six variations of a diversity-based adaptive GA is presented. Not only the adaptive GA has improved the results, especially for large WSRP scenarios, but also it reduces the computational time. Another aspect investigated is the effect of using a group of crossover operators rather than using one operator throughout the search. Six crossover operators, well known and problem-specific are used as part of a multiple crossover GA framework. To evaluate an operator effectiveness, a reinforcement-learning model is developed with three performance measurements. The most successful variant of this algorithm finds the best-known results for the larger problem instances and matching the best-known results for some of the smaller ones. When combining this method with the adaptive GA, it provided some of the best results, as compared to established algorithms. The presented methods have contributed in reducing the operational costs for this constrained combinatorial optimisation problem.