49 research outputs found
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
A new ant colony optimization model for complex graph-based problems
Tesis doctoral inédita leída en la Universidad Autónoma de Madrid. Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura: julio de 2014Nowadays, there is a huge number of problems that due to their complexity have
employed heuristic-based algorithms to search for near-to-optimal (or even optimal)
solutions. These problems are usually NP-complete, so classical algorithms are not
the best candidates to address these problems because they need a large amount of
computational resources, or they simply cannot find any solution when the problem
grows. Some classical examples of these kind of problems are the Travelling Salesman
Problem (TSP) or the N-Queens problem. It is also possible to find examples in real and
industrial domains related to the optimization of complex problems, like planning,
scheduling, Vehicle Routing Problems (VRP), WiFi network Design Problem (WiFiDP)
or behavioural pattern identification, among others.
Regarding to heuristic-based algorithms, two well-known paradigms are Swarm
Intelligence and Evolutionary Computation. Both paradigms belongs to a subfield
from Artificial Intelligence, named Computational Intelligence that also contains
Fuzzy Systems, Artificial Neural Networks and Artificial Immune Systems areas.
Swarm Intelligence (SI) algorithms are focused on the collective behaviour of selforganizing
systems. These algorithms are characterized by the generation of collective
intelligence from non-complex individual behaviour and the communication schemes
amongst them. Some examples of SI algorithms are particle swarm optimization, ant
colony optimization (ACO), bee colony optimization o bird flocking.
Ant Colony Optimization (ACO) are based on the foraging behaviour of these insects.
In these kind of algorithms, the ants take different decisions during their execution
that allows them to build their own solution to the problem. Once any ant has
finished its execution, the ant goes back through the followed path and it deposits,
in the environment, pheromones that contains information about the built solution.
These pheromones will influence the decision of future ants, so there is an indirect
communication through the environment called stigmergy.
When an ACO algorithm is applied to any of the optimization problems just described,
the problem is usually modelled into a graph. Nevertheless, the classical graph-based
representation is not the best one for the execution of ACO algorithms because it
presents some important pitfalls. The first one is related to the polynomial, or even
exponential, growth of the resulting graph. The second pitfall is related to those
problems that needs from real variables because these problems cannot be modelled
using the classical graph-based representation.
On the other hand, Evolutionary Computation (EC) are a set of population-based
algorithms based in the Darwinian evolutionary process. In this kind of algorithms
there is one (or more) population composed by different individuals that represent a
possible solution to the problem. For each iteration, the population evolves by the use
of evolutionary procedures which means that better individuals (i.e. better solutions)
are generated along the execution of the algorithm. Both kind of algorithms, EC
and SI, have been traditionally applied in previous NP-hard problems. Different
population-based strategies have been developed, compared and even combined to
design hybrid algorithms.
This thesis has been focused on the analysis of classical graph-based representations
and its application in ACO algorithms into complex problems, and the development of
a new ACO model that tries to take a step forward in this kind of algorithms. In this
new model, the problem is represented using a reduced graph that affects to the ants
behaviour, which becomes more complex. Also, this size reduction generates a fast
growth in the number of pheromones created. For this reason, a new metaheuristic
(called Oblivion Rate) has been designed to control the number of pheromones stored
in the graph.
In this thesis different metaheuristics have been designed for the proposed system
and their performance have been compared. One of these metaheuristics is the
Oblivion Rate, based on an exponential function that takes into account the number
of pheromones created in the system. Other Oblivion Rate function is based on a bioinspired
swarm algorithm that uses some concepts extracted from the evolutionary
algorithms. This bio-inspired swarm algorithm is called Coral Reef Opmization (CRO)
algorithm and it is based on the behaviour of the corals in a reef.
Finally, to test and validate the proposed model, different domains have been used
such as the N-Queens Problem, the Resource-Constraint Project Scheduling Problem,
the Path Finding problem in Video Games, or the Behavioural Pattern Identification
in users. In some of these domains, the performance of the proposed model has been
compared against a classical Genetic Algorithm to provide a comparative study and
perform an analytical comparison between both approaches.En la actualidad, existen un gran número de problemas que debido a su complejidad
necesitan algoritmos basados en heurísticas para la búsqueda de solucionas subóptimas
(o incluso óptimas). Normalmente, estos problemas presentan una complejidad
NP-completa, por lo que los algoritmos clásicos de búsqueda de soluciones no son
apropiados ya que necesitan una gran cantidad de recursos computacionales, o simplemente,
no son capaces de encontrar alguna solución cuando el problema crece. Ejemplos
clásicos de este tipo de problemas son el problema del vendedor viajero (o TSP
del inglés Travelling Salesman Problem) o el problema de las N-reinas. También se
pueden encontrar ejemplos en dominios reales o industriales que generalmente están
ligados a temas de optimización de sistemas complejos, como pueden ser problemas de
planificación, scheduling, problemas de enrutamiento de vehículos (o VRP del inglés
Vehicle Routing Problem), el diseño de redes Wifi abiertas (o WiFiDP del inglés WiFi
network Design Problem), o la identificación de patrones de comportamiento, entre
otros.
En lo referente a los algoritmos basados en heuristicas, dos paradigmas muy
conocidos son los algoritmos de enjambre (Swarm Intelligence) y la computación
evolutiva (Evolutionary Computation). Ambos paradigmas pertencen al subárea de la
Inteligencia Artificial denominada Inteligencia Computacional, que además contiene
los sistemas difusos, redes neuronales y sistemas inmunológicos artificiales.
Los algoritmos de inteligencia de enjambre, o Swarm Intelligence, se centran en
el comportamiento colectivo de sistemas auto-organizativos. Estos algoritmos se
caracterizan por la generación de inteligencia colectiva a partir del comportamiento,
no muy complejo, de los individuos y los esquemas de comunicación entre ellos.
Algunos ejemplos son particle swarm optimization, ant colony optimization (ACO),
bee colony optimization o bird flocking.
Los algoritmos de colonias de hormigas (o ACO del inglés Ant Colony Optimization)
se basan en el comportamiento de estos insectos en el proceso de recolección de
comida. En este tipo de algoritmos, las hormigas van tomando decisiones a lo largo
de la simulación que les permiten construir su propia solución al problema. Una
vez que una hormiga termina su ejecución, deshace el camino andado depositando en
el entorno feronomas que contienen información sobre la solución construida. Estas
feromonas influirán en las decisiones de futuras hormigas, por lo que produce una
comunicación indirecta utilizando el entorno. A este proceso se le llama estigmergia.
Cuando un algoritmo de hormigas se aplica a alguno de los problemas de optimización
descritos anteriormente, se suele modelar el problema como un grafo sobre el cual
se ejecutarán las hormigas. Sin embargo, la representación basada en grafos
clásica no parece ser la mejor para la ejecución de algoritmos de hormigas porque
presenta algunos problemas importantes. El primer problema está relacionado con
el crecimiento polinómico, o incluso expnomencial, del grafo resultante. El segundo
problema tiene que ver con los problemas que necesitan de variables reales, o de coma
flotante, porque estos problemas, con la representación tradicional basada en grafos,
no pueden ser modelados.
Por otro lado, los algoritmos evolutivos (o EC del inglés Evolutionary Computation)
son un tipo de algoritmos basados en población que están inspirados en el
proceso evolutivo propuesto por Darwin. En este tipo de algoritmos, hay una, o
varias, poblaciones compuestas por individuos diferentes que representan problems
solutiones al problema modelado. Por cada iteración, la población evoluciona mediante
el uso de procedimientos evolutivos, lo que significa que mejores individuos (mejores
soluciones) son creados a lo largo de la ejecución del algoritmo. Ambos tipos de
algorithmos, EC y SI, han sido tradicionalmente aplicados a los problemas NPcompletos
descritos anteriormente. Diferentes estrategias basadas en población han
sido desarrolladas, comparadas e incluso combinadas para el diseño de algoritmos
híbridos.
Esta tesis se ha centrado en el análisis de los modelos clásicos de representación
basada en grafos de problemas complejos para la posterior ejecución de algoritmos
de colonias de hormigas y el desarrollo de un nuevo modelo de hormigas que pretende
suponer un avance en este tipo de algoritmos. En este nuevo modelo, los problemas
son representados en un grafo más compacto que afecta al comportamiento de las
hormigas, el cual se vuelve más complejo. Además, esta reducción en el tamaño
del grafo genera un rápido crecimiento en el número de feronomas creadas. Por
esta razón, una nueva metaheurística (llamada Oblivion Rate) ha sido diseñada para
controlar el número de feromonas almacenadas en el grafo.
En esta tesis, varias metaheuristicas han sido diseñadas para el sistema propuesto y
sus rendimientos han sido comparados. Una de estas metaheurísticas es la Oblivion
Rate basada en una función exponencial que tiene en cuenta el número de feromonas
creadas en el sistema. Otra Oblivion Rate está basada en un algoritmo de enjambre
bio-inspirado que usa algunos conceptos extraídos de la computación evolutiva. Este
algoritmo de enjambre bio-inspirado se llama Optimización de arrecifes de corales (o
CRO del inglés Coral Reef Optimization) y está basado en el comportamiento de los
corales en el arrecife.
Finalmente, para validar y testear el modelo propuesto, se han utilizado diversos
dominios de aplicación como son el problema de las N-reinas, problemas de
planificación de proyectos con restricciones de recursos, problemas de búsqueda de
caminos en entornos de videojuegos y la identificación de patrones de comportamiento
de usuarios. En algunos de estos dominios, el rendimiento del modelo propuesto
ha sido comparado contra un algoritmo genético clásico para realizar un estudio
comparativo, y analítico, entre ambos enfoques
Hierarchical Multi-Project Planning and Supply Chain Management: an Integrated Framework
This work focuses on the need for new knowledge to allow hierarchical multi-project management to be conducted in the construction industry, which is characterised by high uncertainty, fragmentation, complex decisions, dynamic changes and long-distance communication. A dynamic integrated project management approach is required at strategic, tactical and operational levels in order to achieve adaptability.
The work sees the multi-project planning and control problem in the context of supply chain management at main contractor companies. A portfolio manager must select and prioritise the projects, bid and negotiate with a wide range of clients, while project managers are dealing with subcontractors, suppliers, etc whose relationships and collaborations are critical to the optimisation of schedules in which time, cost and safety (etc) criteria must be achieved.
Literature review and case studies were used to investigate existing approaches to hierarchical multi-project management, to identify the relationships and interactions between the parties concerned, and to investigate the possibilities for integration. A system framework was developed using a multi-agent-system architecture and utilising procedures adapted from literature to deal with short, medium and long-term planning. The framework is based on in-depth case study and integrates time-cost trade-off for project optimisation with multi-attribute utility theory to facilitate project scheduling, subcontractor selection and bid negotiation at the single project level. In addition, at the enterprise level, key performance indicator rule models are devised to align enterprise supply chain configuration (strategic decision) with bid selection and bid preparation/negotiation (tactical decision) and project supply chain selection (operational decision). Across the hierarchical framework the required quantitative and qualitative methods are integrated for project scheduling, risk assessment and subcontractor evaluation. Thus, experience sharing and knowledge management facilitate project planning across the scattered construction sites.
The mathematical aspects were verified using real data from in-depth case study and a test case. The correctness, usefulness and applicability of the framework for users was assessed by creating a prototype Multi Agent System-Decision Support System (MAS-DSS) which was evaluated empirically with four case studies in national, international, large and small companies. The positive feedback from these cases indicates strong acceptance of the framework by experienced practitioners. It provides an original contribution to the literature on planning and supply chain management by integrating a practical solution for the dynamic and uncertain complex multi-project environment of the construction industry
Theory and Practice for System Services Providers in Complex Value and Service Systems: ISSS 2013 Proceedings
In September 2013, the „International Symposium on Service Science (ISSS)“ offered various participants a unique platform for advancing research and discussions in service science for the fifth consecutive year. Being held as part of the “Leipzig Days of Applied Informatics/Leipziger Tage der Angewandten Informatik”, researchers and practitioners alike joined in their effort to better understand the emergence of system services providers in complex value chains and service systems. The proceedings book documents some of their insights and wants to serve as reference for the advancing discussion.:Research Paper Session:
Stefan Kirn, Johannes Murray, Marc Premm, Michael Schüle, Tobias Widmer
Towards a Research Framework for Multiagent Organizations
Sven Tackenberg, Sönke Duckwitz, Christopher M. Schlick
Simulation- and Optimization-based Development of Proposals for Service and Engineering Projects
Michael Sonnenberg, Boris Ansorge, Michael Becker
Potential of Service Engineering in the Field of Renewable Energies
Discussion Paper Session:
Sebastian Schneider, Susanne Mütze-Niewöhner
Process-Oriented Simulation of Complex Service Provision Based on the Design Structure Matrix
Axel Hummel, René Keßler, Arndt Döhler, Stefan Kühne
Simulation as a Decision-Making Support Tool for Full-Service E-Commerce Providers
Bernd Pfitzinger, Thomas Jestädt, Dragan Macos
Enhancing dependability through simulations: The example of the German toll system
Routis Forum:
Nicola Saccani
Towards a maturity assessment of service business development by manufacturers. A framework
Deniz Özcan, Christina Niemöller, Michael Fellmann, Michel Matijacic, Gerald Däuble,
Michael Schlicker, Oliver Thomas, Markus Nüttgens
A Use Case-driven Approach to the Design of Service Support Systems:
Making Use of Semantic Technologies
Sibylle Hermann, Walter Ganz, Philipp Westner
The path to a computer-aided design system for services
Lars-Peter Meyer, Michael Thieme, Kyrill Meyer
Round-Trip Engineering for System Service
A Unified Framework for Solving Multiagent Task Assignment Problems
Multiagent task assignment problem descriptors do not fully represent the complex interactions in a multiagent domain, and algorithmic solutions vary widely depending on how the domain is represented. This issue is compounded as related research fields contain descriptors that similarly describe multiagent task assignment problems, including complex domain interactions, but generally do not provide the mechanisms needed to solve the multiagent aspect of task assignment. This research presents a unified approach to representing and solving the multiagent task assignment problem for complex problem domains. Ideas central to multiagent task allocation, project scheduling, constraint satisfaction, and coalition formation are combined to form the basis of the constrained multiagent task scheduling (CMTS) problem. Basic analysis reveals the exponential size of the solution space for a CMTS problem, approximated by O(2n(m+n)) based on the number of agents and tasks involved in a problem. The shape of the solution space is shown to contain numerous discontinuous regions due to the complexities involved in relational constraints defined between agents and tasks. The CMTS descriptor represents a wide range of classical and modern problems, such as job shop scheduling, the traveling salesman problem, vehicle routing, and cooperative multi-object tracking. Problems using the CMTS representation are solvable by a suite of algorithms, with varying degrees of suitability. Solution generating methods range from simple random scheduling to state-of-the-art biologically inspired approaches. Techniques from classical task assignment solvers are extended to handle multiagent task problems where agents can also multitask. Additional ideas are incorporated from constraint satisfaction, project scheduling, evolutionary algorithms, dynamic coalition formation, auctioning, and behavior-based robotics to highlight how different solution generation strategies apply to the complex problem space
A dynamic scheduling model for construction enterprises
The vast majority of researches in the scheduling context focused on finding optimal or near-optimal predictive schedules under different scheduling problem characteristics. In the construction industry, predictive schedules are often produced in advance in order to direct construction operations and to support other planning activities. However, construction projects operate in dynamic environments subject to various real-time events, which usually disrupt the predictive optimal schedules, leading to schedules neither feasible nor optimal. Accordingly, the development of a dynamic scheduling model which can accommodate these real-time events would be of great importance for the successful implementation of construction scheduling systems.
This research sought to develop a dynamic scheduling based solution which can be practically used for real time analysis and scheduling of construction projects, in addition to resources optimization for construction enterprises. The literature reviews for scheduling, dynamic scheduling, and optimization showed that despite the numerous researches presented and application performed in the dynamic scheduling field within manufacturing and other industries, there was dearth in dynamic scheduling literature in relation to the construction industry. The research followed two main interacting research paths, a path related to the development of the practical solution, and another path related to the core model development.
The aim of the first path (or the proposed practical solution path) was to develop a computer-based dynamic scheduling framework which can be used in practical applications within the construction industry. Following the scheduling literature review, the construction project management community s opinions about the problem under study and the user requirements for the proposed solution were collected from 364 construction project management practitioners from 52 countries via a questionnaire survey and were used to form the basis for the functional specifications of a dynamic scheduling framework. The framework was in the form of a software tool fully integrated with current planning/scheduling practices with all core modelling which can support the integration of the dynamic scheduling processes to the current planning/scheduling process with minimal experience requirement from users about optimization.
The second research path, or the dynamic scheduling core model development path, started with the development of a mathematical model based on the scheduling models in literature, with several extensions according to the practical considerations related to the construction industry, as investigated in the questionnaire survey. Scheduling problems are complex from operational research perspective; so, for the proposed solution to be functional in optimizing construction schedules, an optimization algorithm was developed to suit the problem's characteristics and to be used as part of the dynamic scheduling model's core. The developed algorithm contained few contributions to the scheduling context (such as schedule justification heuristics, and rectification to schedule generation schemes), as well as suggested modifications to the formulation and process of the adopted optimization technique (particle swarm optimization) leading to considerable improvement to this techniques outputs with respect to schedules quality.
After the completion of the model development path, the first research path was concluded by combining the gathered solution's functional specifications and the developed dynamic scheduling model into a software tool, which was developed to verify & validate the proposed model s functionalities and the overall solution s practicality and scalability.
The verification process started with an extensive testing of the model s static functionality using several well recognized scheduling problem sets available in literature, and the results showed that the developed algorithm can be ranked as one of the best state-of-the-art algorithms for solving resource-constrained project scheduling problems. To verify the software tool and the dynamic features of the developed model (or the formulation of data transfers from one optimization stage to the next), a case study was implemented on a construction entity in the Arabian Gulf area, having a mega project under construction, with all aspects to resemble an enterprise structure. The case study results showed that the proposed solution reasonably performed under large scale practical application (where all optimization targets were met in reasonable time) for all designed schedule preparation processes (baseline, progress updates, look-ahead schedules, and what-if schedules).
Finally, to confirm and validate the effectiveness and practicality of the proposed solution, the solution's framework and the verification results were presented to field experts, and their opinions were collected through validation forms. The feedbacks received were very positive, where field experts/practitioners confirmed that the proposed solution achieved the main functionalities as designed in the solution s framework, and performed efficiently under the complexity of the applied case study
Methods to Support the Project Selection Problem With Non-Linear Portfolio Objectives, Time Sensitive Objectives, Time Sensitive Resource Constraints, and Modeling Inadequacies
The United States Air Force relies upon information production activities to gain insight regarding uncertainties affecting important system configuration and in-mission task execution decisions. Constrained resources that prevent the fulfillment of every information production request, multiple information requestors holding different temporal-sensitive objectives, non-constant marginal value preferences, and information-product aging factors that affect the value-of-information complicate the management of these activities. This dissertation reviews project selection research related to these issues and presents novel methods to address these complications. Quantitative experimentation results demonstrate these methods’ significance