Paper Info
Title
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 GA performance. This paper presents a novel 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 operators are given high application rates and low rates are given to the worse. Application rates are dynamically adjusted according to the learning outcomes in a non-stationary environment. Experimental results show that the combined performances of all the operators were better than using only one operator used in isolation. This study provided an important opportunity to advance the understanding of the best method to use crossover operators for this highly-constrained optimisation problem effectively.
Year
DOI
Venue
2019
10.1007/s10732-018-9385-x
Journal of Heuristics
Keywords
Field
DocType
Genetic algorithms, Adaptive algorithms, Genetic operators, Routing, Scheduling, Workforce planning
Online learning,Workforce scheduling,Mathematical optimization,Crossover,Scheduling (computing),Operational costs,Operator (computer programming),Workforce planning,Genetic algorithm,Mathematics
Journal
Volume
Issue
ISSN
25.0
SP4-5
1572-9397
Citations 
PageRank 
References 
0
0.34
23
Authors
3
Name
Order
Citations
PageRank
Haneen Algethami100.34
anna martinezgavara2162.77
Dario Landa Silva331628.38