A study of genetic operators for the Workforce Scheduling and Routing Problem

The Workforce Scheduling and Routing Problem (WSRP) is concerned with planning visits of qualified workers to different locations to perform a set of tasks, while satisfying each task time-window plus additional requirements such as customer/workers preferences. This type of mobile workforce scheduling problem arises in many real-world operational scenarios. We investigate a set of genetic operators including problem-specific and well-known generic operators used in related problems. The aim is to conduct an in-depth analysis on their performance on this very constrained scheduling problem. In particular, we want to identify genetic operators that could help to minimise the violation of customer/workers preferences. We also develop two cost-based genetic operators tailored to the WSRP. A Steady State Genetic Algorithm (SSGA) is used in the study and experiments are conducted on a set of problem instances from a real-world Home Health Care scenario (HHC). The experimental analysis allows us to better understand how we can more effectively employ genetic operators to tackle WSRPs

Selecting genetic operators to maximise preference satisfaction in a workforce scheduling and routing problem

The Workforce Scheduling and Routing Problem (WSRP) is a combinatorial optimisation problem that involves scheduling and routing of workforce. Tackling this type of problem often requires handling a considerable number of requirements, including customers and workers preferences while minimising both operational costs and travelling distance. This study seeks to determine effective combinations of genetic operators combined with heuristics that help to find good solutions for this constrained combinatorial optimisation problem. In particular, it aims to identify the best set of operators that help to maximise customers and workers preferences satisfaction. This paper advances the understanding of how to effectively employ different operators within two variants of genetic algorithms to tackle WSRPs. To tackle infeasibility, an initialisation heuristic is used to generate a conflict-free initial plan and a repair heuristic is used to ensure the satisfaction of constraints. Experiments are conducted using three sets of real-world Home Health Care (HHC) planning problem instances

On the Effectiveness of Genetic Search in Combinatorial Optimization

In this paper, we study the efficacy of genetic algorithms in the context of combinatorial optimization. In particular, we isolate the effects of cross-over, treated as the central component of genetic search. We show that for problems of nontrivial size and difficulty, the contribution of cross-over search is marginal, both synergistically when run in conjunction with mutation and selection, or when run with selection alone, the reference point being the search procedure consisting of just mutation and selection. The latter can be viewed as another manifestation of the Metropolis process. Considering the high computational cost of maintaining a population to facilitate cross-over search, its marginal benefit renders genetic search inferior to its singleton-population counterpart, the Metropolis process, and by extension, simulated annealing. This is further compounded by the fact that many problems arising in practice may inherently require a large number of state transitions for a near-optimal solution to be found, making genetic search infeasible given the high cost of computing a single iteration in the enlarged state-space.NSF (CCR-9204284

Métaheuristiques hybrides pour les problèmes de recouvrement et recouvrement partiel d'ensembles appliqués au problème de positionnement des trous de forage dans les mines

RÉSUMÉ La première étape du cycle minier est l’exploration minérale. Dans cette étape, des longs trous de forage sont forés dans les zones de minéralisation pour extraire des échantillons. Les échantillons sont ensuite analysés et un modèle 3D de la distribution des minéraux dans la mine est construit. Puisque le forage coûte très cher, les géologues et ingénieurs miniers tentent de positionner leurs trous d’une façon qui minimise le coût de forage. Par contre, les techniques courantes utilisées pour minimiser le coût de forage sont peu sophistiquées et ne trouvent généralement pas la solution optimale. Dans cette thèse, nous utilisons des techniques de recherche opérationnelle pour résoudre le problème de positionnement des trous de forage dans les mines. Nous modélisons le problème sous forme d’une variante du problème de recouvrement d’ensembles, qui est un problème très populaire en recherche opérationnelle, et résolvons ce problème à l’aide d’algorithmes métaheuristiques, notamment l’algorithme génétique, la recherche locale itérée et la recherche taboue. Pour évaluer l’efficacité de notre approche, nous comparons les solutions trouvées par notre approche aux solutions trouvées par les approches industrielles sur des problèmes réels. Les résultats obtenus montrent que notre approche permet une réduction des coûts de forage allant jusqu’à 35%. Un autre aspect très important de cette thèse est la résolution du problème de recouvrement d’ensembles (SCP) à l’aide de métaheuristiques. Nous proposons une nouvelle formulation du SCP et un nouvel algorithme pour le résoudre. La nouvelle formulation élimine les problèmes de faisabilité et redondances du SCP. Nos expérimentations ont montré que l’algorithme proposé trouve des meilleurs résultats que la majorit (si pas tous) les algorithmes métaheuristiques existants pour le SCP.---------- ABSTRACT The first steps in the mining cycle are exploration and feasibility. In the exploration stage, geologists start by estimating the potential locations of mineral deposits. Then, they drill many long holes inside the mine to extract samples. The samples are then analyzed and a 3D model representing the distribution of mineralization in the mine is constructed. Because drilling is expensive, geologists and mining engineers try to position their drill holes to cover most potential sites with a minimum amount of drilling. However, the current techniques used to position the drill holes are inefficient and do not generally find the optimal solution. In this thesis, we use operations research techniques to solve the drill holes placement problem. We model the drill holes placement problem as a variant of the set covering problem (which is a very popular optimization problem) and solve the modelled problem using the combination of multiple metaheuristic algorithms, namely the genetic algorithm, iterated local search and tabu search. To evaluate the effectiveness of our approach, we compare the solutions found using our approach to the solutions found by industrial approaches on real world problems. The obtained results show that our approach allow saving up to 35% of drilling cost. Another primary aspect of the thesis is the resolution of the set covering problem (SCP) using metaheuristic approaches. We propose a new formulation of the SCP and a new metaheuristic algorithm to solve it. The new formulation is specially designed for metaheuristic approaches and allows solving the SCP without having to deal with feasibility and set redundancy. Computational results show that our metaheuristic approach is more effective than most (if not all) metaheuristic approaches for the SCP