The Experts below are selected from a list of 222 Experts worldwide ranked by ideXlab platform

Mario Vanhoucke - One of the best experts on this subject based on the ideXlab platform.

  • Reconstructing nurse schedules: Computational insights in the Problem size parameters
    Omega, 2013
    Co-Authors: Broos Maenhout, Mario Vanhoucke
    Abstract:

    Abstract Managing nursing personnel properly is of critical importance as these resources comprise a vast share of the hospital's operational costs. The organisational support and the managerial policy decisions on the scheduling level are important determinants for the nurses' working conditions and the related quality of care. In this paper, we gain insights and understanding in the consequences and outcomes of various personnel re-rostering characteristics and strategies. We explore the boundaries of the time horizon and the nurse staffing size to consider for the nurse re-rostering Problem based on computational experiments in a real-life Problem Environment.

  • reactive personnel scheduling insights and policy decisions
    25th Annual conference of the Belgian Operations Research Society (ORBEL 25), 2011
    Co-Authors: Broos Maenhout, Mario Vanhoucke
    Abstract:

    In case of schedule disruptions caused by unplanned personnel absences, the personnel scheduler must restore feasibility by reconstructing the personnel roster. This rescheduling is a complex task as the personnel scheduler should take multiple objectives and many constraints into account. In order to reduce the complexity, we explore the boundaries of the time horizon and the personnel stang size that will be considered by the re-rostering process based on computational experiments in a real-life Problem Environment.

  • Managerial insight in reactive personnel re-rostering
    Journal of the European Mathematical Society, 2010
    Co-Authors: Broos Maenhout, Mario Vanhoucke
    Abstract:

    In case of schedule disruptions caused by unplanned personnel absences, the personnel scheduler must restore feasibility by reconstructing the personnel roster. This rescheduling is a complex task as the personnel scheduler should take multiple objectives and many constraints into account. In order to reduce the complexity, we explore the boundaries of the time horizon and the personnel staffing size that will be considered by the re-rostering process based on computational experiments in a real-life Problem Environment.

  • Reconstructing Nurse Schedules: Managerial Insights and Policy Decisions
    2009
    Co-Authors: Mario Vanhoucke, Broos Maenhout
    Abstract:

    Managing nursing personnel properly is of critical importance as these resources comprise a vast share of the hospital's operational costs. The organizational support and the managerial policy decisions on the scheduling level are important determinants for the nurses' working conditions and the related quality of care. In this paper, we use a decision support system to gain insights and understanding in the consequences and outcomes of various personnel re-rostering policy decisions and strategies. We explore the boundaries of the time horizon and the nurse staffing size to consider for the nurse re-rostering Problem based on computational experiments in a real-life Problem Environment.

Broos Maenhout - One of the best experts on this subject based on the ideXlab platform.

  • Reconstructing nurse schedules: Computational insights in the Problem size parameters
    Omega, 2013
    Co-Authors: Broos Maenhout, Mario Vanhoucke
    Abstract:

    Abstract Managing nursing personnel properly is of critical importance as these resources comprise a vast share of the hospital's operational costs. The organisational support and the managerial policy decisions on the scheduling level are important determinants for the nurses' working conditions and the related quality of care. In this paper, we gain insights and understanding in the consequences and outcomes of various personnel re-rostering characteristics and strategies. We explore the boundaries of the time horizon and the nurse staffing size to consider for the nurse re-rostering Problem based on computational experiments in a real-life Problem Environment.

  • reactive personnel scheduling insights and policy decisions
    25th Annual conference of the Belgian Operations Research Society (ORBEL 25), 2011
    Co-Authors: Broos Maenhout, Mario Vanhoucke
    Abstract:

    In case of schedule disruptions caused by unplanned personnel absences, the personnel scheduler must restore feasibility by reconstructing the personnel roster. This rescheduling is a complex task as the personnel scheduler should take multiple objectives and many constraints into account. In order to reduce the complexity, we explore the boundaries of the time horizon and the personnel stang size that will be considered by the re-rostering process based on computational experiments in a real-life Problem Environment.

  • Managerial insight in reactive personnel re-rostering
    Journal of the European Mathematical Society, 2010
    Co-Authors: Broos Maenhout, Mario Vanhoucke
    Abstract:

    In case of schedule disruptions caused by unplanned personnel absences, the personnel scheduler must restore feasibility by reconstructing the personnel roster. This rescheduling is a complex task as the personnel scheduler should take multiple objectives and many constraints into account. In order to reduce the complexity, we explore the boundaries of the time horizon and the personnel staffing size that will be considered by the re-rostering process based on computational experiments in a real-life Problem Environment.

  • Reconstructing Nurse Schedules: Managerial Insights and Policy Decisions
    2009
    Co-Authors: Mario Vanhoucke, Broos Maenhout
    Abstract:

    Managing nursing personnel properly is of critical importance as these resources comprise a vast share of the hospital's operational costs. The organizational support and the managerial policy decisions on the scheduling level are important determinants for the nurses' working conditions and the related quality of care. In this paper, we use a decision support system to gain insights and understanding in the consequences and outcomes of various personnel re-rostering policy decisions and strategies. We explore the boundaries of the time horizon and the nurse staffing size to consider for the nurse re-rostering Problem based on computational experiments in a real-life Problem Environment.

Amit Konar - One of the best experts on this subject based on the ideXlab platform.

  • Balancing Exploration and Exploitation by an Elitist Ant System with Exponential Pheromone Deposition Rule
    arXiv: Artificial Intelligence, 2008
    Co-Authors: Ayan Acharya, Deepyaman Maiti, Aritra Banerjee, Amit Konar
    Abstract:

    The paper presents an exponential pheromone deposition rule to modify the basic ant system algorithm which employs constant deposition rule. A stability analysis using differential equation is carried out to find out the values of parameters that make the ant system dynamics stable for both kinds of deposition rule. A roadmap of connected cities is chosen as the Problem Environment where the shortest route between two given cities is required to be discovered. Simulations performed with both forms of deposition approach using Elitist Ant System model reveal that the exponential deposition approach outperforms the classical one by a large extent. Exhaustive experiments are also carried out to find out the optimum setting of different controlling parameters for exponential deposition approach and an empirical relationship between the major controlling parameters of the algorithm and some features of Problem Environment.

  • A Deterministic Model for Analyzing the Dynamics of Ant System Algorithm and Performance Amelioration through a New Pheromone Deposition Approach
    2008 4th International Conference on Information and Automation for Sustainability, 2008
    Co-Authors: Ayan Acharya, Deepyaman Maiti, Amit Konar, Ramadoss Janarthanan
    Abstract:

    Ant Colony Optimization (ACO) is a metaheuristic for solving difficult discrete optimization Problems. This paper presents a deterministic model based on differential equation to analyze the dynamics of basic Ant System algorithm. Traditionally, the deposition of pheromone on different parts of the tour of a particular ant is always kept unvarying. Thus the pheromone concentration remains uniform throughout the entire path of an ant. This article introduces an exponentially increasing pheromone deposition approach by artificial ants to improve the performance of basic Ant System algorithm. The idea here is to introduce an additional attracting force to guide the ants towards destination more easily by constructing an artificial potential field identified by increasing pheromone concentration towards the goal. Apart from carrying out analysis of Ant System dynamics with both traditional and the newly proposed deposition rules, the paper presents an exhaustive set of experiments performed to find out suitable parameter ranges for best performance of Ant System with the proposed deposition approach. Simulations reveal that the proposed deposition rule outperforms the traditional one by a large extent both in terms of solution quality and algorithm convergence. Thus, the contributions of the article can be presented as follows: i) it introduces differential equation and explores a novel method of analyzing the dynamics of ant system algorithms, ii) it initiates an exponentially increasing pheromone deposition approach by artificial ants to improve the performance of algorithm in terms of solution quality and convergence time, iii) exhaustive experimentation performed facilitates the discovery of an algebraic relationship between the parameter set of the algorithm and feature of the Problem Environment.

  • Extension of Ant System algorithms with exponential pheromone deposition rule for improved performance
    International Journal of Intelligent Defence Support Systems, 2008
    Co-Authors: Ayan Acharya, Amit Konar, Aritra Banerjee, Lakhmi C. Jain
    Abstract:

    The paper extends the classical Ant System (AS) algorithms by proposing a novel approach of exponential pheromone deposition by artificial ants ensuring a concentration gradient along solution paths. The stability analysis with a deterministic mathematical model based on differential equation yields the proper range of the parameters. A roadmap of connected cities, where the shortest path between a source-destination pair is to be determined, is taken as a Problem Environment. Exhaustive simulations confirm that the proposed deposition rule, with properly chosen parameter values, outperforms the traditional one with large margin both in terms of solution quality and algorithm convergence.

Ayan Acharya - One of the best experts on this subject based on the ideXlab platform.

  • Balancing Exploration and Exploitation by an Elitist Ant System with Exponential Pheromone Deposition Rule
    arXiv: Artificial Intelligence, 2008
    Co-Authors: Ayan Acharya, Deepyaman Maiti, Aritra Banerjee, Amit Konar
    Abstract:

    The paper presents an exponential pheromone deposition rule to modify the basic ant system algorithm which employs constant deposition rule. A stability analysis using differential equation is carried out to find out the values of parameters that make the ant system dynamics stable for both kinds of deposition rule. A roadmap of connected cities is chosen as the Problem Environment where the shortest route between two given cities is required to be discovered. Simulations performed with both forms of deposition approach using Elitist Ant System model reveal that the exponential deposition approach outperforms the classical one by a large extent. Exhaustive experiments are also carried out to find out the optimum setting of different controlling parameters for exponential deposition approach and an empirical relationship between the major controlling parameters of the algorithm and some features of Problem Environment.

  • A Deterministic Model for Analyzing the Dynamics of Ant System Algorithm and Performance Amelioration through a New Pheromone Deposition Approach
    2008 4th International Conference on Information and Automation for Sustainability, 2008
    Co-Authors: Ayan Acharya, Deepyaman Maiti, Amit Konar, Ramadoss Janarthanan
    Abstract:

    Ant Colony Optimization (ACO) is a metaheuristic for solving difficult discrete optimization Problems. This paper presents a deterministic model based on differential equation to analyze the dynamics of basic Ant System algorithm. Traditionally, the deposition of pheromone on different parts of the tour of a particular ant is always kept unvarying. Thus the pheromone concentration remains uniform throughout the entire path of an ant. This article introduces an exponentially increasing pheromone deposition approach by artificial ants to improve the performance of basic Ant System algorithm. The idea here is to introduce an additional attracting force to guide the ants towards destination more easily by constructing an artificial potential field identified by increasing pheromone concentration towards the goal. Apart from carrying out analysis of Ant System dynamics with both traditional and the newly proposed deposition rules, the paper presents an exhaustive set of experiments performed to find out suitable parameter ranges for best performance of Ant System with the proposed deposition approach. Simulations reveal that the proposed deposition rule outperforms the traditional one by a large extent both in terms of solution quality and algorithm convergence. Thus, the contributions of the article can be presented as follows: i) it introduces differential equation and explores a novel method of analyzing the dynamics of ant system algorithms, ii) it initiates an exponentially increasing pheromone deposition approach by artificial ants to improve the performance of algorithm in terms of solution quality and convergence time, iii) exhaustive experimentation performed facilitates the discovery of an algebraic relationship between the parameter set of the algorithm and feature of the Problem Environment.

  • Extension of Ant System algorithms with exponential pheromone deposition rule for improved performance
    International Journal of Intelligent Defence Support Systems, 2008
    Co-Authors: Ayan Acharya, Amit Konar, Aritra Banerjee, Lakhmi C. Jain
    Abstract:

    The paper extends the classical Ant System (AS) algorithms by proposing a novel approach of exponential pheromone deposition by artificial ants ensuring a concentration gradient along solution paths. The stability analysis with a deterministic mathematical model based on differential equation yields the proper range of the parameters. A roadmap of connected cities, where the shortest path between a source-destination pair is to be determined, is taken as a Problem Environment. Exhaustive simulations confirm that the proposed deposition rule, with properly chosen parameter values, outperforms the traditional one with large margin both in terms of solution quality and algorithm convergence.

Gündüz Ulusoy - One of the best experts on this subject based on the ideXlab platform.

  • A combination of different resource management policies in a multi-project Environment
    2013
    Co-Authors: Umut Beşikci, Gündüz Ulusoy, Ümit Bilge
    Abstract:

    Multi-project Problem Environments are defined according to the way resources are managed in the Problem Environment which is called the resource management policy (RMP) in this study. Different resource management policies can be defined according to the characteristics of the projects and/or resources in the Problem Environment. The most common RMP encountered in the multi-project scheduling literature is the resource sharing policy (RSP) where resources can be shared among projects without any costs or limitations. This policy can be seen as an extreme case since there is a strong assumption of unconstrained resource sharing. Another RMP can be defined as the other extreme such that resources cannot be shared among projects which is called resource the dedication policy (RDP). The last RMP considered in this study is between these two policies where resources are dedicated but can be transferred among projects when a project finishes, the dedicated resources to this project can be transferred to another one starting after the finish of the corresponding project. This RPM is called the resource transfer policy (RTP). In this study we investigate a Problem Environment where all these three types of RPM are present. Additionally, the general resource capacities are taken as decision variables that are constrained by a given general budget. We call this multi-project Environment as the Generalized Resource Portfolio Problem (GRPP). We have investigated this Problem and proposed an iterative solution approach based on exact solution methods which determines the general resource capacities from the budget, resource dedications, resource sharing and resource transfer decisions and schedules the individual projects. Computational results for over forty test Problems are reported.

  • resource dedication Problem in a multi project Environment
    Flexible Services and Manufacturing Journal, 2013
    Co-Authors: Umut Beşikci, Ümit Bilge, Gündüz Ulusoy
    Abstract:

    There can be different approaches to the management of resources within the context of multi-project scheduling Problems. In general, approaches to multi-project scheduling Problems consider the resources as a pool shared by all projects. On the other hand, when projects are distributed geographically or sharing resources between projects is not preferred, then this resource sharing policy may not be feasible. In such cases, the resources must be dedicated to individual projects throughout the project durations. This multi-project Problem Environment is defined here as the resource dedication Problem (RDP). RDP is defined as the optimal dedication of resource capacities to different projects within the overall limits of the resources and with the objective of minimizing a predetermined objective function. The projects involved are multi-mode resource constrained project scheduling Problems with finish to start zero time lag and non-preemptive activities and limited renewable and nonrenewable resources. Here, the characterization of RDP, its mathematical formulation and two different solution methodologies are presented. The first solution approach is a genetic algorithm employing a new improvement move called combinatorial auction for RDP, which is based on preferences of projects for resources. Two different methods for calculating the projects’ preferences based on linear and Lagrangian relaxation are proposed. The second solution approach is a Lagrangian relaxation based heuristic employing subgradient optimization. Numerical studies demonstrate that the proposed approaches are powerful methods for solving this Problem.

  • A modied branch and cut procedure for resource portfolio Problem under relaxed resource dedication policy
    2012
    Co-Authors: Umut Beşikci, Ümit Bilge, Gündüz Ulusoy
    Abstract:

    Multi-project scheduling Problems are characterized by the way resources are managed in the Problem Environment. The general approach in multi-project scheduling literature is to consider resource capacities as a common pool that can be shared among all projects without any restrictions or costs. The way the resources are used in a multi-project Environment is called resource management policy and the aforementioned assumption is called Resource Sharing Policy in this study. The resource sharing policy is not a generalization for multi-project scheduling Environments and different resource management policies maybe defined to identify characteristics of different Problem Environments. In this study, we present a resource management policy which prevents sharing of resources among projects but allows resource transfers when a project starts after the completion of another one. This policy is called the Relaxed Resource Dedication (RRD) Policy in this study. The general resource capacities might or might not be decision variables. We will treat here the case where the general available amounts of resources are decision variables to be determined subject to a limited budget. We call this Problem as the Resource Portfolio Problem (RPP). In this study, RPP is investigated under RRD policy and a modified Branch and Cut (B&C)procedure based on CPLEX is proposed. The B&C procedure of CPLEX is modified with different branching strategies, heuristic solution approaches and valid inequalities. The computational studies presented demonstrate the effectiveness of the proposed solution approaches.

  • Different Resource Management Policies in Multi-Mode Resource Constrained Multi-Project Scheduling
    2011
    Co-Authors: Umut Beşikci, Ümit Bilge, Gündüz Ulusoy
    Abstract:

    This study investigates different resource management policies in resource constrained multi-project Problem Environments. The Problem Environment under investigation has alternative modes for activities, a set of renewable and nonrenewable resources used by activities and further considerations such as general resource budget. The characterization of the way resources are used by individual projects in the multiproject Environment is called resource management policy in this study. The solution approaches in the literature for multi-project Problems generally defines the resources as a pool that can be shared by all the projects which in fact creates a general assumption for the resource usage characteristics. This resource management policy is referred as resource sharing policy in this study. Resource sharing policy can be invalid in some certain cases where sharing assumption is not feasible because of some characteristics of resources and/or projects which require different resource management policies for the multi-project Environment. According to the characteristics of resources and projects, resource management policies such as resource dedication, relaxed resource dedication and generalized resource management policies can be defined. In this paper, these resource management policies will be defined and their mathematical formulations will be presented and discussed.

  • Resource preference based improvement heuristics for resource portfolioProblem
    2011
    Co-Authors: Umut Beşikci, Ümit Bilge, Gündüz Ulusoy
    Abstract:

    The multi-project Problem Environment under consideration involves multiple-projects with activities having alternative execution modes, a general resource budget and a resource management policy that does not allow sharing of resources among projects. The multi-project scheduling model for this Problem Environment is called Resource Portfolio Problem. There are three basic conceptual Problems in RPP: (i) determining the general resource capacities from the given general resource budget (general resource capacities determination); (ii) dedication of the general resource capacities to projects (resource dedication) and finally (iii) scheduling of individual projects with the given resource dedications. In this study, different preference based improvement heuristics are proposed for general resource capacities determination and resource dedication conceptual Problems. For general resource capacities determination, the current general resource capacity values are changed according to the resource preferences such that the resulting capacity state would be more preferable. Similarly for resource dedication, resource dedication values of projects are changed according to the preferences of projects for resources such that the resulting resource dedication state would be more preferable. These two improvement heuristics separates and couples the conceptual Problems. Different preference calculation methods are proposed employing Lagrangian relaxation and linear relaxation of MRCPSP formulation.