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    <description>recent bookmarks from Vaguery</description>
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  </channel><item rdf:about="https://arxiv.org/abs/2204.13501">
    <title>[2204.13501] The tropical and zonotopal geometry of periodic timetables</title>
    <dc:date>2025-04-16T13:38:33+00:00</dc:date>
    <link>https://arxiv.org/abs/2204.13501</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The Periodic Event Scheduling Problem (PESP) is the standard mathematical tool for optimizing periodic timetabling problems in public transport. A solution to PESP consists of three parts: a periodic timetable, a periodic tension, and integer periodic offset values. While the space of periodic tension has received much attention in the past, we explore geometric properties of the other two components, establishing novel connections between periodic timetabling and discrete geometry. Firstly, we study the space of feasible periodic timetables, and decompose it into polytropes, i.e., polytopes that are convex both classically and in the sense of tropical geometry. We then study this decomposition and use it to outline a new heuristic for PESP, based on the tropical neighbourhood of the polytropes. Secondly, we recognize that the space of fractional cycle offsets is in fact a zonotope. We relate its zonotopal tilings back to the hyperrectangle of fractional periodic tensions and to the tropical neighbourhood of the periodic timetable space. To conclude we also use this new understanding to give tight lower bounds on the minimum width of an integral cycle basis.
]]></description>
<dc:subject>scheduling heuristics looking-to-see rather-interesting combinatorics planning periodic-solutions geometry-of-search purdy-pitchers</dc:subject>
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<item rdf:about="https://arxiv.org/abs/2504.07779v1">
    <title>[2504.07779v1] Genetic Programming with Reinforcement Learning Trained Transformer for Real-World Dynamic Scheduling Problems</title>
    <dc:date>2025-04-16T13:34:31+00:00</dc:date>
    <link>https://arxiv.org/abs/2504.07779v1</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Dynamic scheduling in real-world environments often struggles to adapt to unforeseen disruptions, making traditional static scheduling methods and human-designed heuristics inadequate. This paper introduces an innovative approach that combines Genetic Programming (GP) with a Transformer trained through Reinforcement Learning (GPRT), specifically designed to tackle the complexities of dynamic scheduling scenarios. GPRT leverages the Transformer to refine heuristics generated by GP while also seeding and guiding the evolution of GP. This dual functionality enhances the adaptability and effectiveness of the scheduling heuristics, enabling them to better respond to the dynamic nature of real-world tasks. The efficacy of this integrated approach is demonstrated through a practical application in container terminal truck scheduling, where the GPRT method outperforms traditional GP, standalone Transformer methods, and other state-of-the-art competitors. The key contribution of this research is the development of the GPRT method, which showcases a novel combination of GP and Reinforcement Learning (RL) to produce robust and efficient scheduling solutions. Importantly, GPRT is not limited to container port truck scheduling; it offers a versatile framework applicable to various dynamic scheduling challenges. Its practicality, coupled with its interpretability and ease of modification, makes it a valuable tool for diverse real-world scenarios.
]]></description>
<dc:subject>scheduling industrial-engineering operations-research rather-interesting genetic-programming representation consider:rediscovery consider:representation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:2b9c1da6f7fa/</dc:identifier>
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<item rdf:about="https://arxiv.org/abs/1809.10428">
    <title>[1809.10428] Scheduling on (Un-)Related Machines with Setup Times</title>
    <dc:date>2020-05-04T11:56:32+00:00</dc:date>
    <link>https://arxiv.org/abs/1809.10428</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We consider a natural generalization of scheduling n jobs on m parallel machines so as to minimize the makespan. In our extension the set of jobs is partitioned into several classes and a machine requires a setup whenever it switches from processing jobs of one class to jobs of a different class. During such a setup, a machine cannot process jobs and the duration of a setup may depend on the machine as well as the class of the job to be processed next. 
For this problem, we study approximation algorithms for non-identical machines. We develop a polynomial-time approximation scheme for uniformly related machines. For unrelated machines we obtain an O(logn+logm)-approximation, which we show to be optimal (up to constant factors) unless NP⊂RP. We also identify two special cases that admit constant factor approximations.
]]></description>
<dc:subject>operations-research scheduling planning queueing-theory probability-theory to-write-about to-simulate consider:variability consider:visualization</dc:subject>
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<dc:identifier>https://pinboard.in/u:Vaguery/b:41affee1d179/</dc:identifier>
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<item rdf:about="https://arxiv.org/abs/1709.01670">
    <title>[1709.01670] Parameterized complexity of machine scheduling: 15 open problems</title>
    <dc:date>2017-09-25T12:05:50+00:00</dc:date>
    <link>https://arxiv.org/abs/1709.01670</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Machine scheduling problems are a long-time key domain of algorithms and complexity research. A novel approach to machine scheduling problems are fixed-parameter algorithms. To stimulate this thriving research direction, we propose 15 interesting open questions in this area.
]]></description>
<dc:subject>open-problems operations-research scheduling benchmarking to-write-about nudge-targets consider:looking-to-see consider:performance-measures</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:59257476c7a2/</dc:identifier>
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<item rdf:about="https://arxiv.org/abs/1704.00899">
    <title>[1704.00899] Dynamic Rank Maximal Matchings</title>
    <dc:date>2017-05-10T11:40:24+00:00</dc:date>
    <link>https://arxiv.org/abs/1704.00899</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We consider the problem of matching applicants to posts where applicants have preferences over posts. Thus the input to our problem is a bipartite graph G = (A U P,E), where A denotes a set of applicants, P is a set of posts, and there are ranks on edges which denote the preferences of applicants over posts. A matching M in G is called rank-maximal if it matches the maximum number of applicants to their rank 1 posts, subject to this the maximum number of applicants to their rank 2 posts, and so on. 
We consider this problem in a dynamic setting, where vertices and edges can be added and deleted at any point. Let n and m be the number of vertices and edges in an instance G, and r be the maximum rank used by any rank-maximal matching in G. We give a simple O(r(m+n))-time algorithm to update an existing rank-maximal matching under each of these changes. When r = o(n), this is faster than recomputing a rank-maximal matching completely using a known algorithm like that of Irving et al., which takes time O(min((r + n, r*sqrt(n))m).
]]></description>
<dc:subject>operations-research optimization rostering scheduling dynamic-optimization to-write-about consider:representation consider:multiobjective-optimization</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:e87228e1a5c1/</dc:identifier>
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<item rdf:about="http://arxiv.org/abs/1505.01005">
    <title>[1505.01005] Approximation Ratio of LD Algorithm for Multi-Processor Scheduling and the Coffman-Sethi Conjecture</title>
    <dc:date>2016-02-27T12:03:33+00:00</dc:date>
    <link>http://arxiv.org/abs/1505.01005</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Coffman and Sethi proposed a heuristic algorithm, called LD, for multi-processor scheduling, to minimize makespan over flowtime-optimal schedules. LD algorithm is a natural extension of a very well-known list scheduling algorithm, Longest Processing Time (LPT) list scheduling, to our bicriteria scheduling problem. Moreover, in 1976, Coffman and Sethi conjectured that LD algorithm has precisely the following worst-case performance bound: 54−34(4m−1), where m is the number of machines. In this paper, utilizing some recent work by the authors and Huang, from 2013, which exposed some very strong combinatorial properties of various presumed minimal counterexamples to the conjecture, we provide a proof of this conjecture. The problem and the LD algorithm have connections to other fundamental problems (such as the assembly line-balancing problem) and to other algorithms.
]]></description>
<dc:subject>scheduling planning operations-research heuristics proof nudge-targets consider:looking-to-see</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:aecbcc483730/</dc:identifier>
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</item>
<item rdf:about="http://arxiv.org/abs/1506.07964#">
    <title>[1506.07964] A Framework for a Multiagent-based Scheduling of Parallel Jobs</title>
    <dc:date>2015-09-17T12:10:03+00:00</dc:date>
    <link>http://arxiv.org/abs/1506.07964#</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[This paper presents a multiagent approach as a paradigm for scheduling parallel jobs in a parallel system. Scheduling parallel jobs is performed as a means to balance the load of a system in order to improve the performance of a parallel application. Parallel job scheduling is presented as a mapping between two graphs: one represents the dependency of jobs and the other represents the interconnection among processors. The usual implementation of parallel job scheduling algorithms is via the master-slave paradigm. The master-slave paradigm has inherent communication bottleneck that reduces the performance of the system when more processors are needed to process the jobs. The multiagent approach attempts to distribute the communication latency among the processors which improves the performance of the system as the number of participating processors increases. Presented in this paper is a framework for the behavior of an autonomous agent that cooperates with other agents to achieve a community goal of minimizing the processing time. Achieving this goal means an agent must truthfully share information with other agents via {\em normalization}, {\em task sharing}, and {\em result sharing} procedures. The agents consider a parallel scientific application as a finite-horizon game where truthful information sharing results into performance improvement for the parallel application. The performance of the multiagent-based algorithm is compared to that of an existing one via a simulation of the wavepacket dynamics using the quantum trajectory method (QTM) as a test application. The average parallel cost of running the QTM using the multiagent-based system is lower at higher number of processors.
]]></description>
<dc:subject>parallel-processing scheduling rather-interesting planning coordination software-development agent-based game-theory nudge-targets consider:robustness</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:a918a54e0b81/</dc:identifier>
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</item>
<item rdf:about="http://arxiv.org/abs/1411.7101">
    <title>[1411.7101] The robust single machine scheduling problem with uncertain release and processing times</title>
    <dc:date>2015-09-13T21:47:10+00:00</dc:date>
    <link>http://arxiv.org/abs/1411.7101</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In this work, we study the single machine scheduling problem with uncertain release times and processing times of jobs. We adopt a robust scheduling approach, in which the measure of robustness to be minimized for a given sequence of jobs is the worst-case objective function value from the set of all possible realizations of release and processing times. The objective function value is the total flow time of all jobs. We discuss some important properties of robust schedules for zero and non-zero release times, and illustrate the added complexity in robust scheduling given non-zero release times. We propose heuristics based on variable neighborhood search and iterated local search to solve the problem and generate robust schedules. The algorithms are tested and their solution performance is compared with optimal solutions or lower bounds through numerical experiments based on synthetic data.
]]></description>
<dc:subject>optimization scheduling machine-learning algorithms robustness nudge-targets consider:rediscovery consider:robustness</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:c892e8e629b1/</dc:identifier>
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<item rdf:about="http://arxiv.org/abs/1409.7186">
    <title>[1409.7186] Feature-based tuning of simulated annealing applied to the curriculum-based course timetabling problem</title>
    <dc:date>2014-12-14T14:37:10+00:00</dc:date>
    <link>http://arxiv.org/abs/1409.7186</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We consider the university course timetabling problem, which is one of the most studied problems in educational timetabling. In particular, we focus our attention on the formulation known as curriculum-based course timetabling problem (CB-CTT), which has been tackled by many researchers and has many available benchmarks. 
The contributions of this paper are twofold. On the one side, we propose an effective and robust single-stage simulated annealing search method for solving the problem. On the other side, we design and apply an extensive and statistically-principled analysis methodology for the algorithm parameter tuning procedure. The outcome of this analysis is a linear regression model between instance features and search method parameters, that allows us to set the parameters for unseen instances on the basis of a simple inspection of the instance itself. Using this method, our algorithm, despite its apparent simplicity, has been able to achieve very good experimental results on a set of popular benchmark testbeds. 
Finally, we propose many new, real-world instances, that could be used as ground for future comparisons.
]]></description>
<dc:subject>timetabling operations-research optimization scheduling metaheuristics multiobjective-optimization constraint-satisfaction nudge-targets note-NFL-mention</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:4426c22c10de/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:timetabling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:operations-research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:optimization"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:metaheuristics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:multiobjective-optimization"/>
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</item>
<item rdf:about="http://arxiv.org/abs/1309.7145">
    <title>[1309.7145] Propagating Regular Counting Constraints</title>
    <dc:date>2014-04-15T11:24:50+00:00</dc:date>
    <link>http://arxiv.org/abs/1309.7145</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Constraints over finite sequences of variables are ubiquitous in sequencing and timetabling. Moreover, the wide variety of such constraints in practical applications led to general modelling techniques and generic propagation algorithms, often based on deterministic finite automata (DFA) and their extensions. We consider counter-DFAs (cDFA), which provide concise models for regular counting constraints, that is constraints over the number of times a regular-language pattern occurs in a sequence. We show how to enforce domain consistency in polynomial time for atmost and atleast regular counting constraints based on the frequent case of a cDFA with only accepting states and a single counter that can be incremented by transitions. We also prove that the satisfaction of exact regular counting constraints is NP-hard and indicate that an incomplete algorithm for exact regular counting constraints is faster and provides more pruning than the existing propagator from [3]. Regular counting constraints are closely related to the CostRegular constraint but contribute both a natural abstraction and some computational advantages.
]]></description>
<dc:subject>constraint-satisfaction planning scheduling algorithms automata representation nudge-targets consider:representation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:1968fe189a4a/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:representation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1310.1553">
    <title>[1310.1553] A Workflow-Forecast Approach To The Task Scheduling Problem In Distributed Computing Systems</title>
    <dc:date>2013-12-19T13:17:12+00:00</dc:date>
    <link>http://arxiv.org/abs/1310.1553</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The aim of this paper is to provide a description of deep-learning-based scheduling approach for academic-purpose high-performance computing systems. The share of academic-purpose distributed computing systems (DCS) reaches 17.4 percents amongst TOP500 supercomputer sites (15.6 percents in performance scale) that makes them a valuable object of research. The core of this approach is to predict the future workflow of the system depending on the previously submitted tasks using deep learning algorithm. Information on predicted tasks is used by the resource management system (RMS) to perform efficient schedule.
]]></description>
<dc:subject>deep-learning operations-research scheduling algorithms prediction nudge-targets consider:symbolic-regression</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:9f240161d7a6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:deep-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:operations-research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:scheduling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:symbolic-regression"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1309.3285">
    <title>[1309.3285] A tabu search algorithm with efficient diversification strategy for high school timetabling problem</title>
    <dc:date>2013-09-17T17:22:30+00:00</dc:date>
    <link>http://arxiv.org/abs/1309.3285</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The school timetabling problem can be described as scheduling a set of lessons (combination of classes, teachers, subjects and rooms) in a weekly timetable. This paper presents a novel way to generate timetables for high schools. The algorithm has three phases. Pre-scheduling, initial phase and optimization through tabu search. In the first phase, a graph based algorithm used to create groups of lessons to be scheduled simultaneously; then an initial solution is built by a sequential greedy heuristic. Finally, the solution is optimized using tabu search algorithm based on frequency based diversification. The algorithm has been tested on a set of real problems gathered from Iranian high schools. Experiments show that the proposed algorithm can effectively build acceptable timetables.
]]></description>
<dc:subject>timetabling scheduling operations-research tabu-search heuristics optimization algorithms nudge-targets multiobjective-optimization still-not-out-of-the-box</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:a929e90bb262/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:timetabling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:scheduling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:operations-research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:tabu-search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:heuristics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:multiobjective-optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:still-not-out-of-the-box"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1202.5755">
    <title>[1202.5755] Balancing Work and Size with Bounded Buffers</title>
    <dc:date>2013-09-08T14:17:06+00:00</dc:date>
    <link>http://arxiv.org/abs/1202.5755</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We consider the fundamental problem of managing a bounded size queue buffer where traffic consists of packets of varying size, where each packet requires several rounds of processing before it can be transmitted from the queue buffer. The goal in such an environment is to maximize the overall size of packets that are successfully transmitted. This model is motivated by the ever-growing ubiquity of network processors architectures, which must deal with heterogeneously-sized traffic, with heterogeneous processing requirements. Our work addresses the tension between two conflicting algorithmic approaches in such settings: the tendency to favor packets with fewer processing requirements, thus leading to fast contributions to the accumulated throughput, as opposed to preferring packets of larger size, which imply a large increase in throughput at each step. We present a model for studying such systems, and present competitive algorithms whose performance depend on the maximum size a packet may have, and maximum amount of processing a packet may require. We further provide lower bounds on algorithms performance in such settings.
]]></description>
<dc:subject>scheduling operations-research engineering-design algorithms nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:36d379ec37c2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:scheduling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:operations-research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:engineering-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1301.7134">
    <title>[1301.7134] A general variable neighborhood search for single-machine total tardiness scheduling problem with step-deteriorating jobs</title>
    <dc:date>2013-04-08T20:32:14+00:00</dc:date>
    <link>http://arxiv.org/abs/1301.7134</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In this article, we study a single-machine scheduling problem of minimizing the total tardiness for a set of independent jobs. The processing time of a job is modeled as a step function of its starting time and a specific deteriorating date. A mixed integer programming model was applied to the problem and validated. Since the problem is known to be NP-hard, we proposed a heuristic named simple weighted search procedure (SWSP) and a general variable neighborhood search algorithm (GVNS). 
A perturbation procedure with 3-opt is embedded within the GVNS process in order to explore broader spaces. Extensive numerical experiments are carried out on some randomly generated test instances so as to investigate the performance of the proposed algorithms. By comparing to the results of the CPLEX optimization solver, the heuristic SWSP and the standard variable neighborhood search, it is shown that the proposed GVNS algorithm can provide better solutions within a reasonable running time.]]></description>
<dc:subject>operations-research scheduling complicated nudge-targets relaxing-the-assumptions interesting algorithms</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:4e63df3bbaec/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:operations-research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:scheduling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:complicated"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:relaxing-the-assumptions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1303.7015">
    <title>[1303.7015] A Multiobjective State Transition Algorithm for Single Machine Scheduling</title>
    <dc:date>2013-04-08T19:34:50+00:00</dc:date>
    <link>http://arxiv.org/abs/1303.7015</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In this paper, a discrete state transition algorithm is introduced to solve a multiobjective single machine job shop scheduling problem. In the proposed approach, a non-dominated sort technique is used to select the best from a candidate state set, and a Pareto archived strategy is adopted to keep all the non-dominated solutions. Compared with the enumeration and other heuristics, experimental results have demonstrated the effectiveness of the multiobjective state transition algorithm.]]></description>
<dc:subject>operations-research scheduling nudge-targets algorithms representation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:ba5de5349a28/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:operations-research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:scheduling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:representation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1301.3535">
    <title>[1301.3535] Airport Gate Scheduling for Passengers, Aircraft, and Operation</title>
    <dc:date>2013-02-03T14:12:19+00:00</dc:date>
    <link>http://arxiv.org/abs/1301.3535</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Passengers' experience is becoming a key metric to evaluate the air transportation system's performance. Efficient and robust tools to handle airport operations are needed along with a better understanding of passengers' interests and concerns. Among various airport operations, this paper studies airport gate scheduling for improved passengers' experience. Three objectives accounting for passengers, aircraft, and operation are presented. Trade-offs between these objectives are analyzed, and a balancing objective function is proposed. The results show that the balanced objective can improve the efficiency of traffic flow in passenger terminals and on ramps, as well as the robustness of gate operations.]]></description>
<dc:subject>multiobjective-optimization operations-research scheduling algorithms nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:ae0a01269df3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:multiobjective-optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:operations-research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:scheduling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1205.2200">
    <title>[1205.2200] A Greedy Double Swap Heuristic for Nurse Scheduling</title>
    <dc:date>2012-06-08T14:53:38+00:00</dc:date>
    <link>http://arxiv.org/abs/1205.2200</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["One of the key challenges of nurse scheduling problem (NSP) is the number of constraints placed on preparing the timetable, both from the regulatory requirements as well as the patients' demand for the appropriate nursing care specialists. In addition, the preferences of the nursing staffs related to their work schedules add another dimension of complexity. Most solutions proposed for solving nurse scheduling involve the use of mathematical programming and generally considers only the hard constraints. However, the psychological needs of the nurses are ignored and this resulted in subsequent interventions by the nursing staffs to remedy any deficiency and often results in last minute changes to the schedule. In this paper, we present a staff preference optimization framework which is solved with a greedy double swap heuristic. The heuristic yields good performance in speed at solving the problem. The heuristic is simple and we will demonstrate its performance by implementing it on open source spreadsheet software."]]></description>
<dc:subject>scheduling operations-research heuristics performance-measure nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:366f70dd460b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:scheduling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:operations-research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:heuristics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performance-measure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1203.3203">
    <title>[1203.3203] An efficient algorithm for generating AoA networks</title>
    <dc:date>2012-03-18T10:17:55+00:00</dc:date>
    <link>http://arxiv.org/abs/1203.3203</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["The activities, in project scheduling, can be represented graphically in two different ways, by either assigning the activities to the nodes 'AoN' directed acyclic graph (dag) or to the arcs 'AoA dag'. In this paper, a new algorithm is proposed for generating, for a given project scheduling problem, an Activity-on-Arc dag starting from the Activity-on-Node dag using the concepts of line graphs of graphs."]]></description>
<dc:subject>scheduling operations-research algorithms graph-theory</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:318a7966d149/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:scheduling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:operations-research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:graph-theory"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1110.1580">
    <title>[1110.1580] A Polylogarithmic-Competitive Algorithm for the k-Server Problem</title>
    <dc:date>2011-10-10T12:11:37+00:00</dc:date>
    <link>http://arxiv.org/abs/1110.1580</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["We give the first polylogarithmic-competitive randomized online algorithm for the $k$-server problem on an arbitrary finite metric space. In particular, our algorithm achieves a competitive ratio of O(log^3 n log^2 k log log n) for any metric space on n points. Our algorithm improves upon the deterministic (2k-1)-competitive algorithm of Koutsoupias and Papadimitriou [J.ACM'95] whenever n is sub-exponential in k."]]></description>
<dc:subject>scheduling operations-research algorithms nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:adae8c4aa37a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:scheduling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:operations-research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1110.1590">
    <title>[1110.1590] PSA: The Packet Scheduling Algorithm for Wireless Sensor Networks</title>
    <dc:date>2011-10-10T12:09:46+00:00</dc:date>
    <link>http://arxiv.org/abs/1110.1590</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["The main cause of wasted energy consumption in wireless sensor networks is packet collision. The packet scheduling algorithm is therefore introduced to solve this problem. Some packet scheduling algorithms can also influence and delay the data transmitting in the real-time wireless sensor networks. This paper presents the packet scheduling algorithm (PSA) in order to reduce the packet congestion in MAC layer leading to reduce the overall of packet collision in the system The PSA is compared with the simple CSMA/CA and other approaches using network topology benchmarks in mathematical method. The performances of our PSA are better than the standard (CSMA/CA). The PSA produces better throughput than other algorithms. On other hand, the average delay of PSA is higher than previous works. However, the PSA utilizes the channel better than all algorithms."]]></description>
<dc:subject>sensor-networks distributed-processing scheduling routing operations-research algorithms nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:f5523f24b00b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:sensor-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:distributed-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:scheduling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:routing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:operations-research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1107.1866">
    <title>[1107.1866] Priority-based task reassignments in hierarchical 2D mesh-connected systems using tableaux</title>
    <dc:date>2011-10-04T12:57:17+00:00</dc:date>
    <link>http://arxiv.org/abs/1107.1866</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["Task reassignments in 2D mesh-connected systems (2D-MSs) have been researched and simulated for several decades. We propose a hierarchical 2D mesh-connected system (2D-HMS) in order to exploit the regular nature of a 2D-MS. In our approach priority-based task assignments and reassignments in a 2D-HMS are represented by tableaux and their algorithms. We provide examples of priority-based task reassignments in a 2D-HMS in which task relocations are simply reduced to a jeu de taquin slide."]]></description>
<dc:subject>scheduling operations-research algorithms grid-computing optimization nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:968e55a58682/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:scheduling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:operations-research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:grid-computing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1007.0683">
    <title>[1007.0683] Scheduling Periodic Real-Time Tasks with Heterogeneous Reward Requirements</title>
    <dc:date>2010-08-03T11:54:23+00:00</dc:date>
    <link>http://arxiv.org/abs/1007.0683</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["We study the problem of scheduling periodic real-time tasks so as to meet their individual minimum reward requirements. A task generates jobs that can be given arbitrary service times before their deadlines. A task then obtains rewards based on the service times received by its jobs. We show that this model is compatible to the imprecise computation models and the increasing reward with increasing service models. In contrast to previous work on these models, which mainly focus on maximize the total reward in the system, we aim to fulfill different reward requirements by different tasks, which offers better fairness and allows fine-grained tradeoff between tasks. We first derive a necessary and sufficient condition for a system, along with reward requirements of tasks, to be feasible. We also obtain an off-line feasibility optimal scheduling policy.…"
]]></description>
<dc:subject>scheduling operations-research nudge-targets algorithms simulation optimization</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:e951d73c6621/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:scheduling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:operations-research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:optimization"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.agileagenda.com/about/">
    <title>AgileAgenda - Project Scheduling made simple</title>
    <dc:date>2008-07-01T21:03:42+00:00</dc:date>
    <link>http://www.agileagenda.com/about/</link>
    <dc:creator>Vaguery</dc:creator><dc:subject>agility GSD via:dunrie GTD scheduling task management planning software</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:d4cb0dc62578/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:agility"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:GSD"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:via:dunrie"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:GTD"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:scheduling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:task"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:management"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:planning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:software"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>