cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5683/sp3/jl3sqd
title: Agent-Based Cooperative Scheduling

description: Cooperative scheduling is concerned with integrating
multiple problem-solving perspectives or objectives when
generating solutions to a scheduling problem. Cooperation may
involve interactions between humans,, between scheduling
agents, or between humans and agents. The purpose of this
paper is to focus on the later two types of cooperation iwhich a
team of software-based asynchronous agents play a major role in
identifying candidate scheduling alternatives. This so-called ATeam architecture is at the heart of a set of decision-support
tools that employ cooperative scheduling techniques for
scheduling the operations of real-world paper manufacturing
facilities. The IBM Paper Mill Scheduling System was
developed at the IBM T.J. Watson Research Center over the past
couple of years and is currently being sold world-wi
DOI: 10.5683/sp3/dle9gz
title: Proposal of Multi-Agent based Model for Dynamic Scheduling in Manufacturing

description: Some recent trends in manufacturing in particular and business in general, lead to new approaches
regarding the organisation and software architecture, mainly adopting distributed solutions. Such organisations
imply organisational and technological evolution through agility, distribution, decentralisation, reactivity and
flexibility. New organisational and technological paradigms are needed in order to reply to the modern
manufacturing systems challenges. The Multi-Agent paradigm represents one of the most promising approaches
to build complex, flexible, and cost-effective scheduling systems because of its distributed and dynamic nature.
Modelling the Scheduling of Manufacturing Systems by means of two technologies like Meta-Heuristics and
Multi-Agent Systems seems to be an interesting way to see Industrial Systems in the future. A multi-agent based
model for support dynamic scheduling in manufacturing environments is proposed.
DOI: 10.5683/sp3/icfjnn
title: A distributed multi-agent production planning and scheduling framework for mobile robots

description: Inspired by the new achievements in mobile robotics having as a result mobile robots able to execute different production tasks, we consider a factory producing a set of distinct products via or with the additional help of mobile robots. This particularly flexible layout requires the definition and the solution of a complex planning and scheduling problem. In order to minimize production costs, dynamic determination of the number of robots for each production task and the individual robot allocation are needed. We propose a solution in terms of a two-level decentralized Multi-Agent System (MAS) framework: at the first, production planning level, agents are tasks which compete for robots (resources at this level); at the second, scheduling level, agents are robots which reallocate themselves among different tasks to satisfy the requests coming from the first level. An iterative auction based negotiation protocol is used at the first level while the second level solves a Multi-Robot Task Allocation (MRTA) problem through a distributed version of the Hungarian Method. A comparison of the results with a centralized approach is presented.
DOI: 10.5683/sp3/af5nu2
title: Optimizing agent-based meeting scheduling through preference estimation

description: Meeting scheduling is a routine task that needs to be performed quite regularly and frequently within any organization. Unfortunately, this task can be quite tedious and time-consuming, potentially requiring a several rounds of negotiations among many people on the meeting date, time and place before a meeting can finally be confirmed. The objective of our research is to create an agent-based environment within which meeting scheduling can be performed and optimized. For meeting scheduling, we define optimality as the solution that has the highest average preference level among all the possible choices. Our model tries to mimic real life in that an individual's preferences are not made public. Without complete information, traditional optimal algorithms, such as A* will not work. In this paper, we present a novel “preference estimation” technique that allows us to find optimal solutions to negotiations problems without needing to know the exact preference models of all the meeting participants beforehand. Instead, their preferences are “estimated” and built on the fly based on observations of their responses during negotiation. Another unique contribution is the use of “preference rules” that allow preferences to change dynamical as scheduling decisions are made. This mimics changing preferences as schedule gets filled. This paper uses two negotiation algorithms to compare the effect of “preference estimation”—one that is based on negotiation through relaxation and the other that extends this with preference estimations. Simulations were then performed to compare these algorithms.
DOI: 10.5683/sp3/evo0z6
title: N∗—an agent-based negotiation algorithm for dynamic scheduling and rescheduling

description: This paper presents a generalized agent-based framework that uses negotiation to dynamically and optimally schedule events. Events can be created dynamically by any active agent in the environment. Each event may potentially require collaboration or resources from one or more other agents. The allocation of resources to the event will be negotiated iteratively until a compromise is found. The framework consists of a user preference model, an evaluation or utility function, and a negotiation protocol. The negotiation protocol is used to implement a distributed negotiation algorithm, called Nstar (N∗). N∗ is based conceptually on the A∗ algorithm for optimal search but extended for distributed negotiation. N∗ is a general distributed negotiation algorithm that makes use of negotiation strategies to find solutions that meet different negotiation objectives. For example, it can use a utility optimizing strategy to find the solution that maximizes average utilities of individual agents. Alternatively, it can select a time optimizing strategy to locate a ‘quick’ feasible solution. The negotiation protocol also performs conflict resolution using a form of iterative repair that renegotiates events which have conflicts. A special case of this framework was used in our MAFOA (mobile agents for office automation) environment to perform agent-based meeting scheduling. A computer simulation test bed was built to simulate the scheduling of hundreds of randomly generated meetings using our N∗ algorithm.
DOI: 10.5683/sp3/csgfd4
title: Supplemental materials: Ontario dairy veterinarians’ perspectives on down cow care, producer decision making, and implementation of recommended practices: A qualitative interview study

description: Supplemental materials: semi-structured veterinarian interview guide and codebook containing codes/themes from the interview transcripts.
DOI: 10.5683/sp3/lyufhr
title: A multi-agent-based approach for personnel scheduling in assembly centers

description: This paper presents a multi-agent-based approach for personnel scheduling problems in the context of a paced multi-product assembly center. Our purpose is to elaborate daily assignment of employees to workstations in order to minimize simultaneously the operational costs and personnel dissatisfactions. The proposed approach considers the individual competencies, mobility and preferences of each employee, as well as the personnel and competency requirements associated with each assembly activity given both the current master assembly schedule and the line balancing for each product. To benchmark the performance of the multi-agent approach, we use optimal solutions obtained through a linear programming model resolution using a commercial solver. Experimental results show that our multi-agent approach can produce high-quality and efficient solutions in a short computational time.
DOI: 10.5683/sp3/k6vowk
title: A market-based multi-agent system model for decentralized multi-project scheduling

description: We consider a multi-project scheduling problem, where each project is composed of a set of activities, with precedence relations, requiring specific amounts of local and shared (among projects) resources. The aim is to complete all the project activities, satisfying precedence and resource constraints, and minimizing each project schedule length. The decision making process is supposed to be decentralized, with as many local decision makers as the projects. A multi-agent system model, and an iterative combinatorial auction mechanism for the agent coordination are proposed. We provide a dynamic programming formulation for the combinatorial auction problem, and heuristic algorithms for both the combinatorial auction and the bidding process. An experimental analysis on the whole multi-agent system model is discussed.
DOI: 10.5683/sp3/rcrctt
title: Bridging the gap from peat to ponds: terrestrial and aquatic greenhouse gas emissions in an evolving permafrost-associated boreal peatland

description: Greenhouse gas fluxes, subsurface peat chemistry, and thermokarst pond water chemistry from a palsa-forming peatland (SAS2) in Northern QC (55.226551°, -77.695773°).
DOI: 10.5683/sp3/igedul
title: Replication data for: Liang et al 2025. Walking elicits muscle functional changes in the pectoral fin of Polypterus senegalus. JEB

description: This dataset contains all numerical data (electromyogrpahy and kinematics) and r-code required to replicate the analysis discussed in Liang et al. 2025. Walking elicits muscle functional change in the pectoral fin of Polypterus senegalus. doi:10.1242/jeb.250474.

The r-code (Liang_etal_2025_RProject.Rproj and tidyLinearAnalysis.R) works in conjunction with the three csv files in the accompanying tidyDataFiles folder. TO RUN CODE please ensure that the tidyDataFiles folder is in the same directory as the r-code files for proper functioning.
DOI: 10.5683/sp3/8giyfk
title: A Multi-Agent Infrastructure for Mobile Workforce Management in a Service Oriented Enterprise

description: With recent advances in mobile technologies and infrastructures, there are increasing demands for the support of mobile workforce management (MWM) across multiple platforms. Mobile users can interact with the MWM system through SMS messages or Web browsers from personal computers, PDA devices, or WAP phones. MWM typically involves tight collaboration, negotiation, and sophisticated business domain knowledge. Therefore, the main challenge of MWM for a service oriented enterprise is the integration of disparate business function for its mobile professional workforce and the management with a unified infrastructure, together with the provision of personalized assistance and automation. These requirements can be facilitated with the use of intelligent software agents, but has not been adequately studied before. As mobile devices become more powerful, intelligent software agents can now be deployed on these devices and hence also subject to mobility. Thus, MWM systems have to accommodate these requirements as well as the support of heterogeneous platforms. In this paper, we formulate a scalable, flexible, and intelligent multi-agent information system (MAIS) infrastructure for MWM with agent clusters in the context of a large service oriented enterprise. Each agent cluster comprise several types of agents to achieve the goal of each phase of the workforce management process, namely, task formulation, matchmaking, brokering, commuting, and service. We evaluate our approach against different stakeholders' perspective.
DOI: 10.5683/sp3/tyappv
title: Multi-agent deep Q-network-based metaheuristic algorithm for Nurse Rostering Problem

description: The Nurse Rostering Problem (NRP) aims to create an efficient and fair work schedule that balances both the needs of employees and the requirements of hospital operations. Traditional local search-based metaheuristic algorithms, such as adaptive neighborhood search (ANS) and variable neighborhood descent (VND), mainly focus on optimizing the current solution without considering potential long-term consequences, which may easily get stuck in local optima and limit the overall performance. Thus, we propose a multi-agent deep Q-network-based metaheuristic algorithm (MDQN-MA) for NRP to harness the strengths of various metaheuristics. Each agent encapsulates a metaheuristic algorithm, where its available actions represent different perspectives of the problem environment. By combining their strengths and various perspectives, these agents can work collaboratively to navigate and search for a broader range of potential solutions effectively. Furthermore, to improve the performance of an individual agent, we model its neighborhood search as a Markov Decision Process model and integrate a deep Q-network to consider long-term impacts for its neighborhood sequential decision-making. The experimental results clearly show that an individual agent in MDQN-MA can outperform ANS and VND, and multiple agents in MDQN-MA even perform better, achieving the best results among metaheuristic algorithms on the Second International Nurse Rostering Competition dataset.
DOI: 10.5683/sp3/qngnrb
title: A multi-agent based cooperative approach to scheduling and routing

description: In this paper, we propose a general agent-based distributed framework where each agent is implementing a different metaheuristic/local search combination. Moreover, an agent continuously adapts itself during the search process using a direct cooperation protocol based on reinforcement learning and pattern matching. Good patterns that make up improving solutions are identified and shared by the agents. This agent-based system aims to provide a modular flexible framework to deal with a variety of different problem domains. We have evaluated the performance of this approach using the proposed framework which embodies a set of well known metaheuristics with different configurations as agents on two problem domains, Permutation Flow-shop Scheduling and Capacitated Vehicle Routing. The results show the success of the approach yielding three new best known results of the Capacitated Vehicle Routing benchmarks tested, whilst the results for Permutation Flow-shop Scheduling are commensurate with the best known values for all the benchmarks tested.
DOI: 10.5683/sp3/i8paz4
title: Multi-agent-based agile scheduling

description: The multi-agent systems paradigm represents one of the most promising approaches to the development of agile scheduling systems in manufacturing. Innovative and balanced perspectives of multi-agent approaches to agile scheduling are discussed in this paper, and several achieved results and current developments are described. The description of the multi-agent framework emphasizes the use of negotiation mechanism to improve the scheduling flexibility, as well as the aspects of information integration, communication and coordination in a community of agents. For information integration and management various approaches are addressed and the utilization of a distributed/federated database management system is suggested. Finally, some innovative aspects for agile scheduling are introduced, particularly in the context of virtual enterprise environments.
DOI: 10.5683/sp3/hf2jop
title: 2018 methane emission quantification controlled release test at the Merced Vernal Pools and Grassland Reserve with sUAS

description: &lt;b&gt;Abstract&lt;/b&gt;&lt;br/&gt;&lt;p&gt;Environmental sensing using small unmanned aircraft systems (sUAS) has become more and more desirable due to the spatial temporal resolution and frequency of sampling. A methane controlled release campaign was conducted at the Merced Vernal Pools and Grassland Reserve for the purpose of testing the quantification performance of a sUAS-based methane measurement system using an open path laser spectrometer (OPLS). A single emission source was simulated, and mutiple flights were conducted downwind of the source to measure the emission using a mass-balance method. The wind, sUAS-trajectory, and concentration was measured for each flight. &lt;/p&gt;
description: <b>Dryad version number:</b> 3
<b>Version status:</b> submitted
<b>Dryad curation status:</b> Published
<b>Sharing link:</b> http://datadryad.org/dataset/doi:10.5061/dryad.612jm64g9
<b>Storage size:</b> 5484150
<b>Visibility:</b> public
DOI: 10.5683/sp3/tlwq2v
title: Agent-based project scheduling

description: Agent technology offers a new way of thinking about many of the classic problems in operations research. Among these are problems such as project scheduling subject to resource constraints. In this paper, we develop and experimentally evaluate eight agent-based algorithms for solving the multimode, resource-constrained project scheduling problem. Our algorithms differ in the priority rules used to control agent access to resources. We apply our approach to a 51-activity project originally published by Maroto and Tormos [1]. We solve the problem using two types of agent-based systems: (i) a system of simple, reactive agents that we call basic agents; and (ii) a system of more complex, deliberative agents that we call enhanced agents. Of the eight priority rules tested, we find that priority based on shortest processing time performs best in terms of schedule quality when applied by basic agents while the priority based on earliest due date performs best when applied by enhanced agents. In comparing agents across priority rules, we find that enhanced agents generate much better schedules (with makespans up to 66% shorter in some cases) and require only slightly more computation time.
DOI: 10.5683/sp3/jafo2y
title: Applications of agent-based models for optimization problems: A literature review

description: Agent based models (ABM) have been recently applied to solve optimization problems whose domains present several inter-related components in a distributed and heterogeneous environment.
In this work we illustrate the state of the art related to the use and to the application of ABM as optimization technique, given their peculiarity in dealing with the representation and the simulation of complex systems.
After a description of the approach and a comparison with classical heuristics, an extensive review aimed at evaluating the impact of these methodologies in the Operational Research/Management Science literature is provided.
DOI: 10.5683/sp3/tx7fpr
title: Optimizing agent-based meeting scheduling through preference estimation

description: Meeting scheduling is a routine task that needs to be performed quite regularly and frequently within any organization. Unfortunately, this task can be quite tedious and time-consuming, potentially requiring a several rounds of negotiations among many people on the meeting date, time and place before a meeting can finally be confirmed. The objective of our research is to create an agent-based environment within which meeting scheduling can be performed and optimized. For meeting scheduling, we define optimality as the solution that has the highest average preference level among all the possible choices. Our model tries to mimic real life in that an individual's preferences are not made public. Without complete information, traditional optimal algorithms, such as A* will not work. In this paper, we present a novel “preference estimation” technique that allows us to find optimal solutions to negotiations problems without needing to know the exact preference models of all the meeting participants beforehand. Instead, their preferences are “estimated” and built on the fly based on observations of their responses during negotiation. Another unique contribution is the use of “preference rules” that allow preferences to change dynamical as scheduling decisions are made. This mimics changing preferences as schedule gets filled. This paper uses two negotiation algorithms to compare the effect of “preference estimation”—one that is based on negotiation through relaxation and the other that extends this with preference estimations. Simulations were then performed to compare these algorithms.
DOI: 10.5683/sp3/9fdjwk
title: Wheat midge (Sitodiplosis mosellana) genomics on the Northern Great Plains of North America

description: This dataset accompanies the study “Wheat midge (Sitodiplosis mosellana) genomics on the Northern Great Plains of North America” and includes filtered SNP data (2,639 SNPs from 134 individuals) and supplementary tables with detailed sample metadata, BUSCO genome completeness results, and gene annotation summaries. These resources support analyses of population structure, connectivity, and comparative genomics of S. mosellana across the Canadian Prairies and northern Great Plains.
DOI: 10.5683/sp3/fqukiy
title: Population genomics infers seasonal, long-distance movement of diamondback moth (Plutella xylostella) on the Canadian Prairies

description: This dataset accompanies the study “Population genomics infers seasonal, long-distance movement of diamondback moth (Plutella xylostella) on the Canadian Prairies.” It includes whole-genome re-sequencing data used in the study. The dataset comprises filtered SNPs (10,285 SNPs from 178 individuals).
DOI: 10.5683/sp3/yo37tn
title: Supporting Data for "The Efficacy of Methane Leak Detection and Repair (LDAR) Programs in Practice"

description: Data to replicate figures in manuscript and supplemental information
DOI: 10.5683/sp3/zx8qmq
title: Diversity and Rainfall Treatment (DART) trials, Soil microbial environmental data

description: Soil data collected in 2024 associated with bacterial and fungal diversity, abundances, and activity across a precipitation gradient under a crop diversity and perenniality gradient.
DOI: 10.5683/sp3/sooim5
title: A MultiAgent Architecture for Distributed Course Timetabling

description: From Beck
DOI: 10.60707/2dnf-z260
title: GCxGC-MS Bundle - Total soluble organic analysis
DOI: 10.5683/sp3/supr8x
title: Replication Data for: Microwave-optical double-resonance vector magnetometry with warm Rb atoms

description: Data made available to accompany the manuscript Microwave-optical double-resonance vector magnetometry with warm Rb atoms