17,157 research outputs found
Models and Strategies for Variants of the Job Shop Scheduling Problem
Recently, a variety of constraint programming and Boolean satisfiability
approaches to scheduling problems have been introduced. They have in common the
use of relatively simple propagation mechanisms and an adaptive way to focus on
the most constrained part of the problem. In some cases, these methods compare
favorably to more classical constraint programming methods relying on
propagation algorithms for global unary or cumulative resource constraints and
dedicated search heuristics. In particular, we described an approach that
combines restarting, with a generic adaptive heuristic and solution guided
branching on a simple model based on a decomposition of disjunctive
constraints. In this paper, we introduce an adaptation of this technique for an
important subclass of job shop scheduling problems (JSPs), where the objective
function involves minimization of earliness/tardiness costs. We further show
that our technique can be improved by adding domain specific information for
one variant of the JSP (involving time lag constraints). In particular we
introduce a dedicated greedy heuristic, and an improved model for the case
where the maximal time lag is 0 (also referred to as no-wait JSPs).Comment: Principles and Practice of Constraint Programming - CP 2011, Perugia
: Italy (2011
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
Simulating Light-Weight Personalised Recommender Systems in Learning Networks: A Case for Pedagogy-Oriented and Rating-Based Hybrid Recommendation Strategies
Recommender systems for e-learning demand specific pedagogy-oriented and hybrid recommendation strategies. Current systems are often based on time-consuming, top down information provisioning combined with intensive data-mining collaborative filtering approaches. However, such systems do not seem appropriate for Learning Networks where distributed information can often not be identified beforehand. Providing sound way-finding support for lifelong learners in Learning Networks requires dedicated personalised recommender systems (PRS), that offer the learners customised advise on which learning actions or programs to study next. Such systems should also be practically feasible and be developed with minimized effort. Currently, such so called light-weight PRS systems are scarcely available. This study shows that simulation studies can support the analysis and optimisation of PRS requirements prior to starting the costly process of their development, and practical implementation (including testing and revision) during field experiments in real-life learning situations. This simulation study confirms that providing recommendations leads towards more effective, more satisfied, and faster goal achievement. Furthermore, this study reveals that a light-weight hybrid PRS-system based on ratings is a good alternative for an ontology-based system, in particular for low-level goal achievement. Finally, it is found that rating-based light-weight hybrid PRS-systems enable more effective, more satisfied, and faster goal attainment than peer-based light-weight hybrid PRS-systems (incorporating collaborative techniques without rating).Recommendation Strategy; Simulation Study; Way-Finding; Collaborative Filtering; Rating
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