Multi-project scheduling with 2-stage decomposition

A non-preemptive, zero time lag multi-project scheduling problem with multiple modes and limited renewable and nonrenewable resources is considered. A 2-stage decomposition approach is adopted to formulate the problem as a hierarchy of 0-1 mathematical programming models. At stage one, each project is reduced to a macro-activity with macro-modes resulting in a single project network where the objective is the maximization of the net present value and the cash flows are positive. For setting the time horizon three different methods are developed and tested. A genetic algorithm approach is designed for this problem, which is also employed to generate a starting solution for the exact solution procedure. Using the starting times and the resource profiles obtained in stage one each project is scheduled at stage two for minimum makespan. The result of the first stage is subjected to a post-processing procedure to distribute the remaining resource capacities. Three new test problem sets are generated with 81, 84 and 27 problems each and three different configurations of solution procedures are tested

Centralized versus market-based approaches to mobile task allocation problem: State-of-the-art

Centralized approach has been adopted for finding solutions to resource allocation problems (RAPs) in many real-life applications. On the other hand, market-based approach has been proposed as an alternative to solve the problem due to recent advancement in ICT technologies. In spite of the existence of some efforts to review the pros and cons of each approach in RAPs, the studies cannot be directly applied to specific problem domains like mobile task allocation problem which is characterised with high level of uncertainty on the availability of resources (workers). This paper aims to review existing studies on task allocation problems(TAPs) focusing on those two approaches and their comparison and identify major issues that need to be resolved for comparing the two approaches in mobile task allocation problems. Mobile Task Allocation Problem (MTAP) is defined and its problematic structures are explained in relation with task allocation to mobile workers. Solutions produced by each approach to some applications and variations of MTAP are also discussed and compared. Finally, some future research directions are identified in order to compare both approaches in function of uncertainty emerging from the mobile nature of the MTAP

A multi-agent system with application in project scheduling

The new economic and social dynamics increase project complexity and makes scheduling problems more difficult, therefore scheduling requires more versatile solutions as Multi Agent Systems (MAS). In this paper the authors analyze the implementation of a Multi-Agent System (MAS) considering two scheduling problems: TCPSP (Time-Constrained Project Scheduling), and RCPSP (Resource-Constrained Project Scheduling). The authors propose an improved BDI (Beliefs, Desires, and Intentions) model and present the first the MAS implementation results in JADE platform.multi-agent architecture, scheduling, project management, BDI architecture, JADE.

Aprendizaje multi-agente utilizando trial and error para la nivelación de recursos durante el (re)scheduling de múltiples proyectos

In a multi-project context within enterprise networks, reaching feasible solutions to the (re)scheduling problem represents a major challenge, mainly when scarce resources are shared among projects. Thus, the multi-project (re)scheduling must achieve the most efficient possible resource usage without increasing the prescribed project constraints, considering the Resource Leveling Problem (RLP), whose objective is to level the consumption of resources shared in order to minimize their idle times and to avoid overallocation conflicts. In this work, a multi-agent solution that allows solving the Resource Constrained Multi-project Scheduling Problem (RCMPSP) and the Resource Investment Problem (RIP) is extended to incorporate indicators on agents’ payoff functions to address the Resource Leveling Problem in a decentralized and autonomous way, through decoupled rules based on Trial-and-Error approach. The proposed agent-based simulation model is tested through a set of project instances that vary in their structure, parameters, number of resources shared, etc. Results obtained are assessed through different scheduling goals, such as project total duration, project total cost and leveling resource usage. Our results are far better compared to the ones obtained with alternative approaches. This proposal shows that the interacting agents that implement decoupled learning rules find a solution which can be understood as a Nash equilibrium.En un contexto de múltiples proyectos dentro de redes empresariales, alcanzar soluciones factibles al problema de (re)scheduling representa un gran desafío, principalmente al compartir recursos escasos entre proyectos. Así, el (re)scheduling de múltiples proyectos debe lograr el uso de recursos más eficiente posible sin incrementar las restricciones de proyecto planteadas, considerando el Problema de Nivelación de Recursos, cuyo objetivo es nivelar el consumo de recursos compartidos para minimizar tiempos ociosos y evitar conflictos de sobre-asignaciones. En este trabajo, una solución multi-agente para resolver el Problema de Scheduling de Múltiples Proyectos con Restricción de Recursos y el Problema de Inversión de Recursos es extendida para incorporar indicadores en las funciones de recompensa de los agentes para abordar el Problema de Nivelación de Recursos de manera autónoma y descentralizada a través de reglas desacopladas basadas en el enfoque de Aprendizaje por prueba y error. El Modelo de Simulación basado en agentes propuesto es verificado mediante un conjunto de instancias de proyecto que varían en estructura, parámetros, número de recursos compartidos, etc. Los resultados obtenidos se evalúan mediante diferentes objetivos de scheduling, como duración total del proyecto, costo total del proyecto y nivelación en el uso de recursos. Nuestros resultados presentan mejoras en comparación a los obtenidos en enfoques alternativos. Esta propuesta muestra que los agentes interactuantes que implementan reglas de aprendizaje desacopladas encuentran una solución que puede entenderse como un equilibrio de Nash.Facultad de Informátic

Multi-agent Learning by Trial and Error for Resource Leveling during Multi-Project (Re)scheduling

In a multi-project context within enterprise networks, reaching feasible solutions to the (re)scheduling problem represents a major challenge, mainly when scarce resources are shared among projects. The multi-project (re)scheduling must achieve the most efficient possible resource usage without increasing the prescribed project constraints, considering the Resource Leveling Problem (RLP), whose objective is to level the consumption of resources shared in order to minimize their idle times and to avoid overallocation conflicts. In this work, a multi-agent solution that allows solving the Resource Constrained Multi-project Scheduling Problem (RCMPSP) and the Resource Investment Problem is extended to incorporate indicators on agents? payoff functions to address the Resource Leveling Problem in a decentralized and autonomous way, through decoupled rules based on Trial-and-Error approach. The proposed agent-based simulation model is tested through a set of project instances that vary in their structure, parameters, number of resources shared, etc. Results obtained are assessed through different scheduling goals, such as project total duration, project total cost and leveling resource usage. Our results are far better compared to the ones obtained with alternative approaches. This proposal shows that the interacting agents that implement decoupled learning rules find a solution which can be understood as a Nash equilibrium.Fil: Tosselli, Laura. Universidad Tecnológica Nacional; ArgentinaFil: Bogado, Verónica Soledad. Universidad Tecnológica Nacional; ArgentinaFil: Martínez, Ernesto Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentin

Aprendizaje multi-agente utilizando trial and error para la nivelación de recursos durante el (re)scheduling de múltiples proyectos

In a multi-project context within enterprise networks, reaching feasible solutions to the (re)scheduling problem represents a major challenge, mainly when scarce resources are shared among projects. Thus, the multi-project (re)scheduling must achieve the most efficient possible resource usage without increasing the prescribed project constraints, considering the Resource Leveling Problem (RLP), whose objective is to level the consumption of resources shared in order to minimize their idle times and to avoid overallocation conflicts. In this work, a multi-agent solution that allows solving the Resource Constrained Multi-project Scheduling Problem (RCMPSP) and the Resource Investment Problem (RIP) is extended to incorporate indicators on agents’ payoff functions to address the Resource Leveling Problem in a decentralized and autonomous way, through decoupled rules based on Trial-and-Error approach. The proposed agent-based simulation model is tested through a set of project instances that vary in their structure, parameters, number of resources shared, etc. Results obtained are assessed through different scheduling goals, such as project total duration, project total cost and leveling resource usage. Our results are far better compared to the ones obtained with alternative approaches. This proposal shows that the interacting agents that implement decoupled learning rules find a solution which can be understood as a Nash equilibrium.En un contexto de múltiples proyectos dentro de redes empresariales, alcanzar soluciones factibles al problema de (re)scheduling representa un gran desafío, principalmente al compartir recursos escasos entre proyectos. Así, el (re)scheduling de múltiples proyectos debe lograr el uso de recursos más eficiente posible sin incrementar las restricciones de proyecto planteadas, considerando el Problema de Nivelación de Recursos, cuyo objetivo es nivelar el consumo de recursos compartidos para minimizar tiempos ociosos y evitar conflictos de sobre-asignaciones. En este trabajo, una solución multi-agente para resolver el Problema de Scheduling de Múltiples Proyectos con Restricción de Recursos y el Problema de Inversión de Recursos es extendida para incorporar indicadores en las funciones de recompensa de los agentes para abordar el Problema de Nivelación de Recursos de manera autónoma y descentralizada a través de reglas desacopladas basadas en el enfoque de Aprendizaje por prueba y error. El Modelo de Simulación basado en agentes propuesto es verificado mediante un conjunto de instancias de proyecto que varían en estructura, parámetros, número de recursos compartidos, etc. Los resultados obtenidos se evalúan mediante diferentes objetivos de scheduling, como duración total del proyecto, costo total del proyecto y nivelación en el uso de recursos. Nuestros resultados presentan mejoras en comparación a los obtenidos en enfoques alternativos. Esta propuesta muestra que los agentes interactuantes que implementan reglas de aprendizaje desacopladas encuentran una solución que puede entenderse como un equilibrio de Nash.Facultad de Informátic

Aprendizaje multi-agente utilizando trial and error para la nivelación de recursos durante el (re)scheduling de múltiples proyectos

In a multi-project context within enterprise networks, reaching feasible solutions to the (re)scheduling problem represents a major challenge, mainly when scarce resources are shared among projects. Thus, the multi-project (re)scheduling must achieve the most efficient possible resource usage without increasing the prescribed project constraints, considering the Resource Leveling Problem (RLP), whose objective is to level the consumption of resources shared in order to minimize their idle times and to avoid overallocation conflicts. In this work, a multi-agent solution that allows solving the Resource Constrained Multi-project Scheduling Problem (RCMPSP) and the Resource Investment Problem (RIP) is extended to incorporate indicators on agents’ payoff functions to address the Resource Leveling Problem in a decentralized and autonomous way, through decoupled rules based on Trial-and-Error approach. The proposed agent-based simulation model is tested through a set of project instances that vary in their structure, parameters, number of resources shared, etc. Results obtained are assessed through different scheduling goals, such as project total duration, project total cost and leveling resource usage. Our results are far better compared to the ones obtained with alternative approaches. This proposal shows that the interacting agents that implement decoupled learning rules find a solution which can be understood as a Nash equilibrium.En un contexto de múltiples proyectos dentro de redes empresariales, alcanzar soluciones factibles al problema de (re)scheduling representa un gran desafío, principalmente al compartir recursos escasos entre proyectos. Así, el (re)scheduling de múltiples proyectos debe lograr el uso de recursos más eficiente posible sin incrementar las restricciones de proyecto planteadas, considerando el Problema de Nivelación de Recursos, cuyo objetivo es nivelar el consumo de recursos compartidos para minimizar tiempos ociosos y evitar conflictos de sobre-asignaciones. En este trabajo, una solución multi-agente para resolver el Problema de Scheduling de Múltiples Proyectos con Restricción de Recursos y el Problema de Inversión de Recursos es extendida para incorporar indicadores en las funciones de recompensa de los agentes para abordar el Problema de Nivelación de Recursos de manera autónoma y descentralizada a través de reglas desacopladas basadas en el enfoque de Aprendizaje por prueba y error. El Modelo de Simulación basado en agentes propuesto es verificado mediante un conjunto de instancias de proyecto que varían en estructura, parámetros, número de recursos compartidos, etc. Los resultados obtenidos se evalúan mediante diferentes objetivos de scheduling, como duración total del proyecto, costo total del proyecto y nivelación en el uso de recursos. Nuestros resultados presentan mejoras en comparación a los obtenidos en enfoques alternativos. Esta propuesta muestra que los agentes interactuantes que implementan reglas de aprendizaje desacopladas encuentran una solución que puede entenderse como un equilibrio de Nash.Facultad de Informátic

Applying autonomy to distributed satellite systems: Trends, challenges, and future prospects

While monolithic satellite missions still pose significant advantages in terms of accuracy and operations, novel distributed architectures are promising improved flexibility, responsiveness, and adaptability to structural and functional changes. Large satellite swarms, opportunistic satellite networks or heterogeneous constellations hybridizing small-spacecraft nodes with highperformance satellites are becoming feasible and advantageous alternatives requiring the adoption of new operation paradigms that enhance their autonomy. While autonomy is a notion that is gaining acceptance in monolithic satellite missions, it can also be deemed an integral characteristic in Distributed Satellite Systems (DSS). In this context, this paper focuses on the motivations for system-level autonomy in DSS and justifies its need as an enabler of system qualities. Autonomy is also presented as a necessary feature to bring new distributed Earth observation functions (which require coordination and collaboration mechanisms) and to allow for novel structural functions (e.g., opportunistic coalitions, exchange of resources, or in-orbit data services). Mission Planning and Scheduling (MPS) frameworks are then presented as a key component to implement autonomous operations in satellite missions. An exhaustive knowledge classification explores the design aspects of MPS for DSS, and conceptually groups them into: components and organizational paradigms; problem modeling and representation; optimization techniques and metaheuristics; execution and runtime characteristics and the notions of tasks, resources, and constraints. This paper concludes by proposing future strands of work devoted to study the trade-offs of autonomy in large-scale, highly dynamic and heterogeneous networks through frameworks that consider some of the limitations of small spacecraft technologies.Postprint (author's final draft

An agent-based simulation model using decoupled learning rules to (re)schedule multiple projects

Competitive pressures and business globalization have led many organizations, mainly technology-based and innovation-oriented companies, to adopt project-based organizational structures. In a multi-project context within enterprise networks, reaching feasible solutions to the multi-project (re)scheduling problem represents a major challenge, where autonomy and decentralization of the environment favor agent-based simulation This work presents and validates a simulation-based multi-agent model using the fractal company concept to solve the complex multi-project (re)scheduling problem in enterprise networks. The proposed agent-based model is tested trough a set of project instances that vary in project structure, project parameters, number of resources shared, unplanned events that affect them, etc. Results obtained are assessed through different scheduling goals, such project total duration, project total cost, leveling resource usage, among others to show that decoupled learning rules allows finding a solution which can be understood as a Nash equilibrium for the interacting agents and it is far better compared to the ones obtained with existing approaches.Eje: XVIII Workshop de Agentes y Sistemas Inteligentes (WASI).Red de Universidades con Carreras en Informática (RedUNCI