Improving resiliency using graph based evolutionary algorithms

Resiliency is an important characteristic of any system. It signifies the ability of a system to survive and recover from unprecedented disruptions. Various characteristics exist that indicate the level of resiliency in a system. One of these attributes is the adaptability of the system. This adaptability can be enhanced by redundancy present within the system. In the context of system design, redundancy can be achieved by having a diverse set of good designs for that particular system. Evolutionary algorithms are widely used in creating designs for engineering systems, as they perform well on discontinuous and/or high dimensional problems. One method to control the diversity of solutions within an evolutionary algorithm is the use of combinatorial graphs, or graph based evolutionary algorithms. This diversity of solutions is key factor to enhance the redundancy of a system design. In this work, the way how graph based evolutionary algorithms generate diverse solutions is investigated by examining the influence of representation and mutation. This allows for greater understanding of the exploratory nature of each representation and how they can control the number of solution generated within a trial. The results of this research are then applied to the Travelling [sic] Salesman Problem, a known NP hard problem often used as a surrogate for logistic or network design problems. When the redundancy in system design is improved, adaptability can be achieved by placing an agent to initiate a transfer to other good solutions in the event of a disruption in network connectivity, making it possible to improve the resiliency of the system --Abstract, page iii

The Traveling Salesman Problem with Stochastic and Correlated Customers

It is well-known that the cost of parcel delivery can be reduced by designingroutes that take into account the uncertainty surrounding customers’ presences. Thus far, routing problems with stochastic customer presences have relied on the assumption that all customer presences are independent from each other. However, the notion that demographic factors retain predictive power for parcel-delivery efficiency suggests that shared characteristics can be exploited to map dependencies between customer presences. This paper introduces the correlated probabilistic traveling salesman problem (CPTSP). The CPTSP generalizes the traveling salesman problem with stochastic customer presences, also known as the probabilistic traveling salesman problem (PTSP), to account for potentialcorrelations between customer presences. I propose a generic and flexible model formulation for the CPTSP using copulas that maintains computational and mathematical tractability in high-dimensional settings. I also present several adaptations of existing exact and heuristic frameworks to solve the CPTSP effectively. Computational experiments on real-world parcel-delivery data reveal that correlations between stochastic customer presences do not always affect route decisions, but could have a considerable impact on route costestimates