Adaptive multiple crossover genetic algorithm to solve workforce scheduling and routing problem

Abstract

The Workforce Scheduling and Routing Problem refers to the assignment of personnel to visits, across various geographical locations. Solving this problem demands tackling numerous scheduling and routing constraints while aiming to minimise the operational cost. One of the main obstacles in designing a genetic algorithm for this problem is selecting the best set of operators that enable better performance in a Genetic Algorithm (GA). This paper presents an adaptive multiple crossover genetic algorithm to tackle the combined setting of scheduling and routing problems. A mix of problem-specific and traditional crossovers are evaluated by using an online learning process to measure the operator's effectiveness. Best performing operators are given high application rates and low rates are given to the worse performing ones. Application rates are dynamically adjusted according to the learning outcomes in a non-stationary

A constraint-programming based decomposition method for the Generalised Workforce Scheduling and Routing Problem (GWSRP)

“…Nevertheless, the boundary between these two categories is not always clearly defined and, for example, some studies addressing HHC problems do not consider coordination constraints (Bertels and Fahle, 2006), (Maenhout and Vanhoucke, 2009), (Chahed et al, 2009) while other articles take coordination constraints into account (Blais et al, 2003), (Eveborn et al, 2006), (Bachouch et al, 2011). Similar remarks hold for the WSRP without coordination constraints in (Castillo et al, 2009), (Goel and Meisel, 2013), (Laesanklang et al, 2015), , (Laesanklang and Landa-Silva, 2017) and (Algethami et al, 2018) and with coordination constraints in (Laesanklang et al, 2016). Let us note that Vehicle Routing Problems (VRP) or VRP with Time-Windows (VRPTW) are commonly studied without coordination constraints, but recent studies include theses considerations, (Drexl, 2012) presents a survey of VRP with theses constraints and their applications.…”

Hyperheuristic Based Migrating Birds Optimization Algorithm for a Fairness Oriented Shift Scheduling Problem

“…Many metaheuristic based solution techniques have been applied for solving the workforce scheduling problems so far. ey include artificial bee colony algorithm [21], particle swarm optimization [22], migrating birds optimization [23], genetic algorithm (GA) [24], an adaptive multiple crossover GA [25], and a modified differential evolution (DE) algorithm ( [26][27][28][29]). A tabu-search hyperheuristics solution for nurse scheduling problem is presented in Burke et al's work [30].…”

A multiperiod workforce scheduling and routing problem with dependent tasks

“…The authors propose a Branch-and-Price algorithm and test its performance on real-world data from a German groundhandling agency. Algethami et al [4] design an adaptive multiple crossovers genetic algorithm (AMCGA) with six genetic operators for the WSRP, minimizing operational and penalty costs. The rationale behind the AMCGA is to apply adaptive allocation rules regarding problem-specific and traditional crossovers, which are evaluated to measure their effectiveness.…”