16,096 research outputs found
Human-Machine Collaborative Optimization via Apprenticeship Scheduling
Coordinating agents to complete a set of tasks with intercoupled temporal and
resource constraints is computationally challenging, yet human domain experts
can solve these difficult scheduling problems using paradigms learned through
years of apprenticeship. A process for manually codifying this domain knowledge
within a computational framework is necessary to scale beyond the
``single-expert, single-trainee" apprenticeship model. However, human domain
experts often have difficulty describing their decision-making processes,
causing the codification of this knowledge to become laborious. We propose a
new approach for capturing domain-expert heuristics through a pairwise ranking
formulation. Our approach is model-free and does not require enumerating or
iterating through a large state space. We empirically demonstrate that this
approach accurately learns multifaceted heuristics on a synthetic data set
incorporating job-shop scheduling and vehicle routing problems, as well as on
two real-world data sets consisting of demonstrations of experts solving a
weapon-to-target assignment problem and a hospital resource allocation problem.
We also demonstrate that policies learned from human scheduling demonstration
via apprenticeship learning can substantially improve the efficiency of a
branch-and-bound search for an optimal schedule. We employ this human-machine
collaborative optimization technique on a variant of the weapon-to-target
assignment problem. We demonstrate that this technique generates solutions
substantially superior to those produced by human domain experts at a rate up
to 9.5 times faster than an optimization approach and can be applied to
optimally solve problems twice as complex as those solved by a human
demonstrator.Comment: Portions of this paper were published in the Proceedings of the
International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and
in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper
consists of 50 pages with 11 figures and 4 table
Welcome to OR&S! Where students, academics and professionals come together
In this manuscript, an overview is given of the activities done at the Operations Research and Scheduling (OR&S) research group of the faculty of Economics and Business Administration of Ghent University. Unlike the book published by [1] that gives a summary of all academic and professional activities done in the field of Project Management in collaboration with the OR&S group, the focus of the current manuscript lies on academic publications and the integration of these published results in teaching activities. An overview is given of the publications from the very beginning till today, and some of the topics that have led to publications are discussed in somewhat more detail. Moreover, it is shown how the research results have been used in the classroom to actively involve students in our research activities
Application of Quantum Annealing to Nurse Scheduling Problem
Quantum annealing is a promising heuristic method to solve combinatorial
optimization problems, and efforts to quantify performance on real-world
problems provide insights into how this approach may be best used in practice.
We investigate the empirical performance of quantum annealing to solve the
Nurse Scheduling Problem (NSP) with hard constraints using the D-Wave 2000Q
quantum annealing device. NSP seeks the optimal assignment for a set of nurses
to shifts under an accompanying set of constraints on schedule and personnel.
After reducing NSP to a novel Ising-type Hamiltonian, we evaluate the solution
quality obtained from the D-Wave 2000Q against the constraint requirements as
well as the diversity of solutions. For the test problems explored here, our
results indicate that quantum annealing recovers satisfying solutions for NSP
and suggests the heuristic method is sufficient for practical use. Moreover, we
observe that solution quality can be greatly improved through the use of
reverse annealing, in which it is possible to refine a returned results by
using the annealing process a second time. We compare the performance NSP using
both forward and reverse annealing methods and describe how these approach
might be used in practice.Comment: 20 pages, 13 figure
Toward an automaton Constraint for Local Search
We explore the idea of using finite automata to implement new constraints for
local search (this is already a successful technique in constraint-based global
search). We show how it is possible to maintain incrementally the violations of
a constraint and its decision variables from an automaton that describes a
ground checker for that constraint. We establish the practicality of our
approach idea on real-life personnel rostering problems, and show that it is
competitive with the approach of [Pralong, 2007]
Taxonomic classification of planning decisions in health care: a review of the state of the art in OR/MS
We provide a structured overview of the typical decisions to be made in resource capacity planning and control in health care, and a review of relevant OR/MS articles for each planning decision. The contribution of this paper is twofold. First, to position the planning decisions, a taxonomy is presented. This taxonomy provides health care managers and OR/MS researchers with a method to identify, break down and classify planning and control decisions. Second, following the taxonomy, for six health care services, we provide an exhaustive specification of planning and control decisions in resource capacity planning and control. For each planning and control decision, we structurally review the key OR/MS articles and the OR/MS methods and techniques that are applied in the literature to support decision making
The SEC-system : reuse support for scheduling system development
Recently, in a joint cooperation of Stichting VNA, SAL Apotheken, the Faculty of Management and Organization, and the University Centre for Pharmacy, University of Groningen in the Netherlands, a Ph.D-study started regarding Apot(he)ek, Organization and Management (APOM). The APOM-project deals with the structuring and steering of pharmacy organization. The manageability of the internal pharmacy organization, and the manageability of the direct environment of pharmacy organization is the subject matter. The theoretical background of the APOM-project is described. A literature study was made to find mixes of objectives. Three mixes of objectives in pharmacy organization are postulated; the product mix, the process mix, and the customer mix. The typology will be used as a basic starting point for the empirical study in the next phase of the APOM-project.
A hybrid constraint integer programming approach to solve nurse scheduling problems
The Nurse Scheduling Problem can be simply defined as assigning a series of shift sequences (schedules) to several nurses over a planning horizon according to some constraints and preferences. The inherent benefits of having higher-quality and more flexible schedules are a reduction in outsourcing costs and an increase of job satisfaction in health organizations. In this paper, we present a novel systematic hybrid algorithm, which combines Integer Programming (IP) and Constraint Programming (CP) to efficiently solve highly-constrained Nurse Scheduling Problems. Our focus is to exploit the problem-specific information to improve the performance of the algorithm, and therefore obtain high-quality solutions as well as strong lower bounds. We test our algorithm based on some real-world benchmark instances. Very competitive results are reported compared to the state-of-the-art algorithms from the recent literature, showing that the proposed algorithm is able to solve a wide variety of real-world instances with different complex structures
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