927 research outputs found
Submodular Function Maximization for Group Elevator Scheduling
We propose a novel approach for group elevator scheduling by formulating it
as the maximization of submodular function under a matroid constraint. In
particular, we propose to model the total waiting time of passengers using a
quadratic Boolean function. The unary and pairwise terms in the function denote
the waiting time for single and pairwise allocation of passengers to elevators,
respectively. We show that this objective function is submodular. The matroid
constraints ensure that every passenger is allocated to exactly one elevator.
We use a greedy algorithm to maximize the submodular objective function, and
derive provable guarantees on the optimality of the solution. We tested our
algorithm using Elevate 8, a commercial-grade elevator simulator that allows
simulation with a wide range of elevator settings. We achieve significant
improvement over the existing algorithms.Comment: 10 pages; 2017 International Conference on Automated Planning and
Scheduling (ICAPS
Taming Numbers and Durations in the Model Checking Integrated Planning System
The Model Checking Integrated Planning System (MIPS) is a temporal least
commitment heuristic search planner based on a flexible object-oriented
workbench architecture. Its design clearly separates explicit and symbolic
directed exploration algorithms from the set of on-line and off-line computed
estimates and associated data structures. MIPS has shown distinguished
performance in the last two international planning competitions. In the last
event the description language was extended from pure propositional planning to
include numerical state variables, action durations, and plan quality objective
functions. Plans were no longer sequences of actions but time-stamped
schedules. As a participant of the fully automated track of the competition,
MIPS has proven to be a general system; in each track and every benchmark
domain it efficiently computed plans of remarkable quality. This article
introduces and analyzes the most important algorithmic novelties that were
necessary to tackle the new layers of expressiveness in the benchmark problems
and to achieve a high level of performance. The extensions include critical
path analysis of sequentially generated plans to generate corresponding optimal
parallel plans. The linear time algorithm to compute the parallel plan bypasses
known NP hardness results for partial ordering by scheduling plans with respect
to the set of actions and the imposed precedence relations. The efficiency of
this algorithm also allows us to improve the exploration guidance: for each
encountered planning state the corresponding approximate sequential plan is
scheduled. One major strength of MIPS is its static analysis phase that grounds
and simplifies parameterized predicates, functions and operators, that infers
knowledge to minimize the state description length, and that detects domain
object symmetries. The latter aspect is analyzed in detail. MIPS has been
developed to serve as a complete and optimal state space planner, with
admissible estimates, exploration engines and branching cuts. In the
competition version, however, certain performance compromises had to be made,
including floating point arithmetic, weighted heuristic search exploration
according to an inadmissible estimate and parameterized optimization
Planning in probabilistic domains using a deterministic numeric planner
In the probabilistic track of the IPC5 - the last International planning competitions - a probabilistic planner based on combining deterministic planning with replanning - FF-REPLAN - out performed the other competitors. This probabilistic planning paradigm discarded the probabilistic information of the domain, just considering for each action its nominal effect as a deterministic effect
Models and algorithms for complex system optimization problems : applications to hospital layout and LED traffic signal maintenance
"December 2010.""A Thesis presented to the Faculty of the Graduate School at the University of Missouri In Partial Fulfillment of the Requirements for the Degree Master of Science, Industrial Engineering."Thesis supervisor: Dr. Mustafa Sir.Due to rising healthcare costs, it is increasingly important to design health care buildings to be efficient and effective. One aspect of a healthcare facility's design is the size and layout of the building and departments. In this paper we review hospital design and the various layout methods that can be applied to hospitals. We formulate a mixed-integer linear programming model to determine the optimal size (i.e. width and length of each floor and number of floors) and department layout of a hospital. The model has multiple objectives; we consider department size requirements to determine a cost-efficient facility size and then place departments to minimize inter-departmental flows. Finally, we use the model to design a multi-floor hospital with seven departments and test the computation time for a variety of scenarios. The Energy Policy Act of 2005 specifies that all traffic signals manufactured after January 1, 2006 must realize the energy efficiency achieved by LED technology [10]. These new LED traffic signals use less energy and last longer than their predecessors, but they deteriorate gradually and require customized maintenance schedules to optimize their useful life and maximize public safety. In the second half of this paper we review the advantages of LED traffic signals and the current literature on their maintenance. We present three models and algorithms to compute optimal maintenance schedules. The first model is designed to model routine maintenance and includes routing costs. The second model is an approximation of the first model that can be solved for scenarios which include very large quantities of traffic signals. The final model allows for two actions, inspection and replacement, and introduces stochastic deterioration. We test the computation time of each algorithm and assess the resulting schedules.Includes bibliographical references (pages 163-165)
Decision-theoretic planning with non-Markovian rewards
A decision process in which rewards depend on history rather than merely on the current state is called a decision process with non-Markovian rewards (NMRDP). In decision-theoretic planning, where many desirable behaviours are more naturally expressed a
Decision-Theoretic Planning with non-Markovian Rewards
A decision process in which rewards depend on history rather than merely on
the current state is called a decision process with non-Markovian rewards
(NMRDP). In decision-theoretic planning, where many desirable behaviours are
more naturally expressed as properties of execution sequences rather than as
properties of states, NMRDPs form a more natural model than the commonly
adopted fully Markovian decision process (MDP) model. While the more tractable
solution methods developed for MDPs do not directly apply in the presence of
non-Markovian rewards, a number of solution methods for NMRDPs have been
proposed in the literature. These all exploit a compact specification of the
non-Markovian reward function in temporal logic, to automatically translate the
NMRDP into an equivalent MDP which is solved using efficient MDP solution
methods. This paper presents NMRDPP (Non-Markovian Reward Decision Process
Planner), a software platform for the development and experimentation of
methods for decision-theoretic planning with non-Markovian rewards. The current
version of NMRDPP implements, under a single interface, a family of methods
based on existing as well as new approaches which we describe in detail. These
include dynamic programming, heuristic search, and structured methods. Using
NMRDPP, we compare the methods and identify certain problem features that
affect their performance. NMRDPPs treatment of non-Markovian rewards is
inspired by the treatment of domain-specific search control knowledge in the
TLPlan planner, which it incorporates as a special case. In the First
International Probabilistic Planning Competition, NMRDPP was able to compete
and perform well in both the domain-independent and hand-coded tracks, using
search control knowledge in the latter
Progress in AI Planning Research and Applications
Planning has made significant progress since its inception in the 1970s, in terms both of the efficiency and sophistication of its algorithms and representations and its potential for application to real problems. In this paper we sketch the foundations of planning as a sub-field of Artificial Intelligence and the history of its development over the past three decades. Then some of the recent achievements within the field are discussed and provided some experimental data demonstrating the progress that has been made in the application of general planners to realistic and complex problems. The paper concludes by identifying some of the open issues that remain as important challenges for future research in planning
The 2014 International Planning Competition: Progress and Trends
We review the 2014 International Planning Competition (IPC-2014), the eighth
in a series of competitions starting in 1998. IPC-2014 was held in three separate
parts to assess state-of-the-art in three prominent areas of planning research: the
deterministic (classical) part (IPCD), the learning part (IPCL), and the probabilistic
part (IPPC). Each part evaluated planning systems in ways that pushed the edge of
existing planner performance by introducing new challenges, novel tasks, or both.
The competition surpassed again the number of competitors than its predecessor,
highlighting the competition’s central role in shaping the landscape of ongoing
developments in evaluating planning systems
Advanced transport operating system software upgrade: Flight management/flight controls software description
The Flight Management/Flight Controls (FM/FC) software for the Norden 2 (PDP-11/70M) computer installed on the NASA 737 aircraft is described. The software computes the navigation position estimates, guidance commands, those commands to be issued to the control surfaces to direct the aircraft in flight based on the modes selected on the Advanced Guidance Control System (AGSC) mode panel, and the flight path selected via the Navigation Control/Display Unit (NCDU)
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