Cost Function Networks to Solve Large Computational Protein Design Problems

International audienc

Cost Function Networks to Solve Large Computational Protein Design Problems

International audienc

A tensor based hyper-heuristic for nurse rostering

Nurse rostering is a well-known highly constrained scheduling problem requiring assignment of shifts to nurses satisfying a variety of constraints. Exact algorithms may fail to produce high quality solutions, hence (meta)heuristics are commonly preferred as solution methods which are often designed and tuned for specific (group of) problem instances. Hyper-heuristics have emerged as general search methodologies that mix and manage a predefined set of low level heuristics while solving computationally hard problems. In this study, we describe an online learning hyper-heuristic employing a data science technique which is capable of self-improvement via tensor analysis for nurse rostering. The proposed approach is evaluated on a well-known nurse rostering benchmark consisting of a diverse collection of instances obtained from different hospitals across the world. The empirical results indicate the success of the tensor-based hyper-heuristic, improving upon the best-known solutions for four of the instances

Orienteering Problem: A survey of recent variants, solution approaches and applications

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ

Robust vehicle routing in disaster relief and ride-sharing: models and algorithms

In this dissertation, the variants of vehicle routing problems (VRPs) are specifically considered in two applications: disaster relief routing and ride-sharing. In disaster relief operations, VRPs are important, especially in the immediate response phase, as vehicles are an essential part of the supply chain for delivering critical supplies. This dissertation addresses the capacitated vehicle routing problem (CVRP) and the split delivery vehicle routing problem (SDVRP) with uncertain travel times and demands when planning vehicle routes for delivering critical supplies to the affected population in need after a disaster. A robust optimization approach is used for the CVRP and the SDVRP considering the five objective functions: minimization of the total number of vehicles deployed (minV), the total travel time/travel cost (minT), the summation of arrival times (minS), the summation of demand-weighted arrival times (minD), and the latest arrival time (minL), out of which we claim that minS, minD, and minL are critical for deliveries to be fast and fair for relief efforts, while minV and minT are common cost-based objective functions in the traditional VRP. In ride-sharing problem, the participants\u27 information is provided in a short notice, for which driver-rider matching and associated routes need to be decided quickly. The uncertain travel time is considered explicitly when matching and route decisions are made, and a robust optimization approach is proposed to handle it properly. To achieve computational tractability, a new two-stage heuristic method that combines the extended insertion algorithm and tabu search (TS) is proposed to solve the models for large-scale problems. In addition, a new hybrid algorithm named scoring tabu search with variable neighborhood (STSVN) is proposed to solve the models and compared with TS. The solutions of the CVRP and the SDVRP are compared for different examples using five different metrics in which the results show that the latter is not only capable of accommodating the demand greater than the vehicle capacity but also is quite effective to mitigate demand and travel time uncertainty, thereby outperforms CVRP in the disaster relief routing perspective. The results of ride-sharing problem show the influence of parameters and uncertain travel time on the solutions. The performance of TS and STSVN are compared in terms of solving the models for disaster relief routing and ride-sharing problems and the results show that STSVN outperforms TS in searching the near-optimal/optimal solutions within the same CPU time

A model of anytime algorithm performance for bi-objective optimization

International audienceAnytime algorithms allow a practitioner to trade-off runtime for solution quality. This is of particular interest in multi-objective combinatorial optimization since it can be infeasible to identify all efficient solutions in a reasonable amount of time. We present a theoretical model that, under some mild assumptions, characterizes the “optimal” trade-off between runtime and solution quality, measured in terms of relative hypervolume, of anytime algorithms for bi-objective optimization. In particular, we assume that efficient solutions are collected sequentially such that the collected solution at each iteration maximizes the hypervolume indicator, and that the non-dominated set can be well approximated by a quadrant of a superellipse. We validate our model against an “optimal” model that has complete knowledge of the non-dominated set. The empirical results suggest that our theoretical model approximates the behavior of this optimal model quite well. We also analyze the anytime behavior of an ε-constraint algorithm, and show that our model can be used to guide the algorithm and improve its anytime behavior

Optimization problems in the postal sector

Supply chain optimization is a widely studied field of operations research. Nevertheless, adapting the existing solutions to the specifications of each company is an interesting and stimulating challenge. With this in mind, the project described herein, developed in partnership with CTT, looks to provide the company with precious tools to more efficiently manage the labour allocated to mail delivery and increase the productivity of the workforce as a whole. To achieve these objectives, it follows up on a previous work by Pereira[26], where an extension of the Vehicle Routing Problem (VRP) was proposed to optimize the last-mile delivery step of the mail distribution procedure, but this time giving particular relevance to the adequacy of the model developed to the intricacies imposed by the company and exploring suitable adaptations. One of the requirements, for standardization purposes, is the creation of segments, composed of sets of postal codes that serve as input to the optimization model. Finally, it was necessary to merge this work with the company’s workflow by integrating the model with SISMA, a productivity assessment tool already used by CTT.A otimização de uma cadeia de abastecimento é um campo vastamente estudado no âm- bito da investigação operacional. Contudo, adaptar as soluções existentes aos critérios de cada empresa é um desafio bastante interessante e estimulante. Tendo isto em consi- deração, este projeto, desenvolvido em parceria com os CTT – Correios de Portugal, S.A. (CTT), procura fornecer à empresa ferramentas que permitam uma gestão eficiente da força de trabalho afeta à distribuição de correio. Para atingir este propósito, este trabalho teve como ponto de partida uma proposta de- senvolvida por Pereira[26], onde uma adaptação do Vehicle Routing Problem (VRP) foi desenvolvida para otimizar a etapa last-mile do processo de distribuição. No presente trabalho, dá-se uma atenção redobrada à compatibilidade do modelo desenvolvido com as complexidades impostas pela empresa e explora-se algumas melhorias consideradas apropriadas. Um dos requisitos, para manter alguma estibilidade nos resultados, é a introdução de segmentos, compostos por conjuntos contíguos de códigos postais, que ali- mentam o modelo. Finalmente, para combinar este trabalho com o fluxo de trabalho da empresa, fez-se a integração do modelo de otimização com o SISMA, uma ferramenta de avalição de produtividade já utilizada pelos CTT

Reactive scheduling to treat disruptive events in the MRCPSP

Esta tesis se centra en diseñar y desarrollar una metodología para abordar el MRCPSP con diversas funciones objetivo y diferentes tipos de interrupciones. En esta tesis se exploran el MRCPSP con dos funciones objetivo, a saber: (1) minimizar la duración del proyecto y (2) maximizar el valor presente neto del proyecto. Luego, se tiene en cuenta dos tipos diferentes de interrupciones, (a) interrupción de duración, e (b) interrupción de recurso renovable. Para resolver el MRCPSP, en esta tesis se proponen tres estrategias metaheurísticas: (1) algoritmo memético para minimizar la duración del proyecto, (2) algoritmo adaptativo de forrajeo bacteriano para maximizar el valor presente neto del proyecto y (3) algoritmo de optimización multiobjetivo de forrajeo bacteriano (MBFO) para resolver el MRCPSP con eventos de interrupción. Para juzgar el rendimiento del algoritmo memético y de forrajeo bacteriano propuestos, se ha llevado a cabo un extenso análisis basado en diseño factorial y diseño Taguchi para controlar y optimizar los parámetros del algoritmo. Además se han puesto a prueba resolviendo las instancias de los conjuntos más importantes en la literatura: PSPLIB (10,12,14,16,18,20 y 30 actividades) y MMLIB (50 y 100 actividades). También se ha demostrado la superioridad de los algoritmos metaheurísticos propuestos sobre otros enfoques heurísticos y metaheurísticos del estado del arte. A partir de los estudios experimentales se ha ajustado la MBFO, utilizando un caso de estudio.DoctoradoDoctor en Ingeniería Industria