276 research outputs found
Economic Lot-Sizing with Start-up Costs: The Convex Hull
A partial description of the convex hull of solutions to the economic lot-sizing problem with start-up costs (ELSS) has been derived recently. Here a larger class of valid inequalities is given and it is shown that these inequalities describe the convex hull of ELSS. This in turn proves that a plant location formulation as a linear program solves ELSS. Finally a separation algorithm is given
Changing Bases: Multistage Optimization for Matroids and Matchings
This paper is motivated by the fact that many systems need to be maintained
continually while the underlying costs change over time. The challenge is to
continually maintain near-optimal solutions to the underlying optimization
problems, without creating too much churn in the solution itself. We model this
as a multistage combinatorial optimization problem where the input is a
sequence of cost functions (one for each time step); while we can change the
solution from step to step, we incur an additional cost for every such change.
We study the multistage matroid maintenance problem, where we need to maintain
a base of a matroid in each time step under the changing cost functions and
acquisition costs for adding new elements. The online version of this problem
generalizes online paging. E.g., given a graph, we need to maintain a spanning
tree at each step: we pay for the cost of the tree at time
, and also for the number of edges changed at
this step. Our main result is an -approximation, where is
the number of elements/edges and is the rank of the matroid. We also give
an approximation for the offline version of the problem. These
bounds hold when the acquisition costs are non-uniform, in which caseboth these
results are the best possible unless P=NP.
We also study the perfect matching version of the problem, where we must
maintain a perfect matching at each step under changing cost functions and
costs for adding new elements. Surprisingly, the hardness drastically
increases: for any constant , there is no
-approximation to the multistage matching maintenance
problem, even in the offline case
Two-stage stochastic minimum s − t cut problems: Formulations, complexity and decomposition algorithms
We introduce the two‐stage stochastic minimum s − t cut problem. Based on a classical linear 0‐1 programming model for the deterministic minimum s − t cut problem, we provide a mathematical programming formulation for the proposed stochastic extension. We show that its constraint matrix loses the total unimodularity property, however, preserves it if the considered graph is a tree. This fact turns out to be not surprising as we prove that the considered problem is NP-hard in general, but admits a linear time solution algorithm when the graph is a tree. We exploit the special structure of the problem and propose a tailored Benders decomposition algorithm. We evaluate the computational efficiency of this algorithm by solving the Benders dual subproblems as max-flow problems. For many tested instances, we outperform a standard Benders decomposition by two orders of magnitude with the Benders decomposition exploiting the max-flow structure of the subproblems
Reinforcement Learning for Variable Selection in a Branch and Bound Algorithm
Mixed integer linear programs are commonly solved by Branch and Bound
algorithms. A key factor of the efficiency of the most successful commercial
solvers is their fine-tuned heuristics. In this paper, we leverage patterns in
real-world instances to learn from scratch a new branching strategy optimised
for a given problem and compare it with a commercial solver. We propose FMSTS,
a novel Reinforcement Learning approach specifically designed for this task.
The strength of our method lies in the consistency between a local value
function and a global metric of interest. In addition, we provide insights for
adapting known RL techniques to the Branch and Bound setting, and present a new
neural network architecture inspired from the literature. To our knowledge, it
is the first time Reinforcement Learning has been used to fully optimise the
branching strategy. Computational experiments show that our method is
appropriate and able to generalise well to new instances
Testing Tablet Computers in Nursing Education: A Comprehensive Evaluation Framework
Background: Tablet Computers (TCs) and other mobile digital devices are rapidly changing the way we communicate and access information in our personal and professional lives. Scarce research exists regarding their effectiveness in promoting the learning of health professionals. This paper describes the evaluation framework used in a study to test TCs in a post-diploma baccalaureate nursing program in the Gulf Cooperation Council (GCC) state of Qatar.Purpose: The evaluation framework was structured around 10 objectives designed to assess the impact of TC integration into the evidence-based practice (EBP) and reflective practice (RP) components of a scholarship course. Evaluation variables included perceptions of knowledge, confidence, comfort, satisfaction and technical skill before and after the 7-week TC implementation; students’ usage patterns and attitudes about the usefulness of TCs in promoting their learning related to EBP and RP were also examined; in addition, students’ views about the impact of TCs on the learning environment and their engagement in the learning process were sought.Methods: A mixed method descriptive design was used to assess outcomes of interest. Qualitative methods (focus groups, participant observation, field notes and reflective journals) were used to capture subjective perspectives of TC users. Quantitative methods (pre-test/posttest, activity logs and skills labs) were used to assess change in knowledge, attitude and technical proficiency over time.Results: The evaluation framework used to assess process and outcome variables in this study combined structural, philosophical, theoretical, pedagogical and methodological elements. These included the logic model, participatory action, theory-based course concepts, as well as a learning taxonomy involving cognitive, affective and psychomotor competencies.Conclusion: The value of a comprehensive evaluation plan executed in tandem with TC implementation is highlighted
On Budget-Feasible Mechanism Design for Symmetric Submodular Objectives
We study a class of procurement auctions with a budget constraint, where an
auctioneer is interested in buying resources or services from a set of agents.
Ideally, the auctioneer would like to select a subset of the resources so as to
maximize his valuation function, without exceeding a given budget. As the
resources are owned by strategic agents however, our overall goal is to design
mechanisms that are truthful, budget-feasible, and obtain a good approximation
to the optimal value. Budget-feasibility creates additional challenges, making
several approaches inapplicable in this setting. Previous results on
budget-feasible mechanisms have considered mostly monotone valuation functions.
In this work, we mainly focus on symmetric submodular valuations, a prominent
class of non-monotone submodular functions that includes cut functions. We
begin first with a purely algorithmic result, obtaining a
-approximation for maximizing symmetric submodular functions
under a budget constraint. We view this as a standalone result of independent
interest, as it is the best known factor achieved by a deterministic algorithm.
We then proceed to propose truthful, budget feasible mechanisms (both
deterministic and randomized), paying particular attention on the Budgeted Max
Cut problem. Our results significantly improve the known approximation ratios
for these objectives, while establishing polynomial running time for cases
where only exponential mechanisms were known. At the heart of our approach lies
an appropriate combination of local search algorithms with results for monotone
submodular valuations, applied to the derived local optima.Comment: A conference version appears in WINE 201
Perspective Cuts for the ACOPF with Generators
International audienceThe alternating current optimal power flow problem is a fundamental problem in the management of smart grids. In this paper we consider a variant which includes activation/deactivation of generators at some of the grid sites. We formulate the problem as a mathematical program, prove its NP-hardness w.r.t. ac-tivation/deactivation, and derive two perspective reformulations
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