cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5683/sp3/r3i2if
title: A novel multi-agent scheduling mechanism for adaptation of production plans in case of supply chain disruptions

description: Manufacturing companies typically use sophisticated production planning systems optimizing production steps, often delivering near-optimal solutions. As a downside for delivering a near-optimal schedule, planning systems have high computational demands resulting in hours of computation. Under normal circumstances this is not issue if there is enough buffer time before implementation of the schedule (e.g. at night for the next day). However, in case of unexpected disruptions such as delayed part deliveries or defectively manufactured goods, the planned schedule may become invalid and swift replanning becomes necessary. Such immediate replanning is unsuited for existing optimal planners due to the computational requirements. This paper proposes a novel solution that can effectively and efficiently perform replanning in case of different types of disruptions using an existing plan. The approach is based on the idea to adhere to the existing schedule as much as possible, adapting it based on limited local changes. For that purpose an agent-based scheduling mechanism has been devised, in which agents represent materials and production sites and use local optimization techniques and negotiations to generate an adapted (sufficient, but non-optimal) schedule. The approach has been evaluated using real production data from Huawei, showing that efficient schedules are produced in short time. The system has been implemented as proof of concept and is currently reimplemented and transferred to a production system based on the Jadex agent platform.
DOI: 10.5683/sp3/76rvf9
title: Agent-based, hybrid control architecture for optimized and flexible production scheduling and control in remanufacturing

description: Motivated by high ecological and economical potentials and driven by new laws, remanufacturing is receiving increasing attention as a process that puts used products into “as good as new or better” condition. Within this process, there are many challenges, which are unseen in manufacturing, such as the uncertainties resulting from unknown conditions of the used products. This places special demands on the control of the remanufacturing system (RS).
DOI: 10.5683/sp3/bpzpiz
title: A Multi-agent Hybrid Approach to Decision Support in Job Groups Handling

description: This study deals with scheduling groups of jobs, their arrival and delivery, and individual processing of each of them. All jobs in a group should be delivered at the same time after processing. One of the objectives is to minimize the average delivery time of the group containing that job (waiting period). The new way of modeling and solving the decision problem - a multi-agent hybrid approach is presented. This approach includes the design and implementation of two agent types: MP-agents (Mathematical Programming) and CLP-agents (Constraint Logic Programming). The iterative algorithm for solving the model under dynamic emergence of new orders is also included. This structure enables managers to ask all kinds of questions.
DOI: 10.5683/sp3/3zvlzo
title: Research on a Multi-Agent Job Shop Scheduling Method Based on Improved Game Evolution

description: multi-agent collaborative scheduling; game–genetic hybrid framework;
manufacturing workshops scheduling; intelligent optimization algorithm; energy consumption optimization
DOI: 10.5683/sp3/38mgma
title: A Hybrid MIP/CP Approach for Multi-activity Shift Scheduling

description: We propose a hybrid MIP/CP approach for solving multi-activity shift scheduling problems, based on regular languages that partially describe the set of feasible shifts. We use an aggregated MIP relaxation to capture the optimization part of the problem and to get rid of symmetry. Whenever the MIP solver generates a integer solution, we use a CP solver to check whether it can be turned into a feasible solution of the original problem. A MIP-based heuristic is also developed. Computational results are reported, showing that the proposed method is a promising alternative compared to the state-of-the-art.
DOI: 10.5683/sp3/uzz1ez
title: Dynamic Scheduling and Workforce Assignment in Open Source Software Development

description: A novel modeling framework is proposed for dynamic scheduling of projects and workforce assignment in open source software development (OSSD). The goal is to help project managers in OSSD distribute workforce to multiple projects to achieve high efficiency in software development (e.g. high workforce utilization and short development time) while ensuring the quality of deliverables (e.g. code modularity and software security). The proposed framework consists of two models: 1) a system dynamic model coupled with a meta-heuristic to obtain an optimal schedule of software development projects considering their attributes (e.g. priority, effort, duration) and 2) an agent based model to represent the development community as a social network, where development managers form an optimal team for each project and balance the workload among multiple scheduled projects based on the optimal schedule obtained from the system dynamic model. To illustrate the proposed framework, a software enhancement request process in Kuali foundation is used as a case study. Survey data collected from the Kuali development managers, project managers and actual historical enhancement requests have been used to construct the proposed models. Extensive experiments are conducted to demonstrate the impact of varying parameters on the considered efficiency and quality.
DOI: 10.5683/sp3/maqcek
title: Anytime and Efficient Coalition Formation with Spatial and Temporal Constraints

description: The Coalition Formation with Spatial and Temporal constraints Problem (CFSTP) is a multi-agent task scheduling problem where the tasks are spatially distributed, with deadlines and workloads, and the number of agents is typically much smaller than the number of tasks. Thus, the agents have to form coalitions in order to maximise the number of completed tasks. The state-of-the-art CFSTP solver, the Coalition Formation with Look-Ahead (CFLA) algorithm, has two main limitations. First, its time complexity is exponential with the number of agents. Second, as we show, its look-ahead technique is not effective in real-world scenarios, such as open multi-agent systems, where new tasks can appear at any time. In this work, we study its design and define an extension, called Coalition Formation with Improved Look-Ahead (
), which achieves better performance. Since we cannot eliminate the limitations of CFLA in 
, we also develop a novel algorithm to solve the CFSTP, the first to be simultaneously anytime, efficient and with convergence guarantee, called Cluster-based Task Scheduling (CTS). In tests where the look-ahead technique is highly effective, CTS completes up to 30% (resp. 10%) more tasks than CFLA (resp. 
) while being up to four orders of magnitude faster. Our results affirm CTS as the new state-of-the-art algorithm to solve the CFSTP.
DOI: 10.5683/sp3/9z9nau
title: ScheduleNet: Learn to solve multi-agent scheduling problems with reinforcement learning

description: We propose ScheduleNet, a RL-based real-time scheduler, that can solve various types of multi-agent scheduling problems. We formulate these problems as a semi-MDP with episodic reward (makespan) and learn ScheduleNet, a decentralized decision-making policy that can effectively coordinate multiple agents to complete tasks. The decision making procedure of ScheduleNet includes: (1) representing the state of a scheduling problem with the agent-task graph, (2) extracting node embeddings for agent and tasks nodes, the important relational information among agents and tasks, by employing the type-aware graph attention (TGA), and (3) computing the assignment probability with the computed node embeddings. We validate the effectiveness of ScheduleNet as a general learning-based scheduler for solving various types of multi-agent scheduling tasks, including multiple salesman traveling problem (mTSP) and job shop scheduling problem (JSP).
DOI: 10.5683/sp3/kjxo1d
title: Adaptive hybrid optimization for integrated project scheduling and staffing problem with time/resource trade-offs

description: • Proposes a dual-objective optimization model for the Integrated Project Scheduling and Personnel Staffing Problem (IPSPSP) with time/resource trade-offs.
•Introduces an adaptive hybrid algorithm combining NSGA-II and MOPSO to optimize project duration and personnel cost.
•Utilizes hybrid encoding for activity modes, activity priority lists, and personnel allocation plans to generate project schedules and personnel allocation plans.
•Employs a hypervolume-based adaptive search mechanism to improve solution quality.
•Experimental results show the proposed hybrid algorithm outperforms standalone NSGA-II and MOPSO in scheduling and resource optimization.
DOI: 10.5683/sp3/svxt3i
title: An Efficient Hybrid Job Scheduling Optimization (EHJSO) approach to enhance resource search using Cuckoo and Grey Wolf Job Optimization for cloud environment

description: Cloud computing has now evolved as an unavoidable technology in the fields of finance, education, internet business, and nearly all organisations. The cloud resources are practically accessible to cloud users over the internet to accomplish the desired task of the cloud users. The effectiveness and efficacy of cloud computing services depend on the tasks that the cloud users submit and the time taken to complete the task as well. By optimising resource allocation and utilisation, task scheduling is crucial to enhancing the effectiveness and performance of a cloud system. In this context, cloud computing offers a wide range of advantages, such as cost savings, security, flexibility, mobility, quality control, disaster recovery, automatic software upgrades, and sustainability. According to a recent research survey, more and more tech-savvy companies and industry executives are recognize and utilize the advantages of the Cloud computing. Hence, as the number of users of the Cloud increases, so did the need to regulate the resource allocation as well. However, the scheduling of jobs in the cloud necessitates a smart and fast algorithm that can discover the resources that are accessible and schedule the jobs that are requested by different users. Consequently, for better resource allocation and job scheduling, a fast, efficient, tolerable job scheduling algorithm is required. Efficient Hybrid Job Scheduling Optimization (EHJSO) utilises Cuckoo Search Optimization and Grey Wolf Job Optimization (GWO). Due to some cuckoo species’ obligate brood parasitism (laying eggs in other species’ nests), the Cuckoo search optimization approach was developed. Grey wolf optimization (GWO) is a population-oriented AI system inspired by grey wolf social structure and hunting strategies. Make span, computation time, fitness, iteration-based performance, and success rate were utilised to compare previous studies. Experiments show that the recommended method is superior.
DOI: 10.5683/sp3/wdlclf
title: Data for: Mn2+-doped CsPbBr3 Perovskite Supercrystals: Enhancing Morphology and Substrate Variation

description: Raw data from inductively coupled plasma-mass spectrometry, absorbance, photoluminescence, electron microscopy (size histograms), and powder X-ray diffraction experiments for supporting the publication: "Mn2+-doping CsPbBr3 Perovskite Supercrystals: Enhancing Morphology and Substrate Variation"
DOI: 10.15468/dl.abbndr
title: Occurrence Download

description: A dataset containing 590240 species occurrences available in GBIF matching the query:
{
 "GadmGid" : [
 "is IND.13_1"
 ]
}

 The dataset includes 590240 records from 308 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046906-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5683/sp3/akenxl
title: Multi-agent single machine scheduling

description: We consider the scheduling problems arising when several agents, each owning a
set of nonpreemptive jobs, compete to perform their respective jobs on one shared processing resource. Each agent wants to minimize a certain cost function, which depends on the
completion times of its jobs only. The cost functions we consider in this paper are maximum
of regular functions (associated with each job), number of late jobs and total weighted completion time. The different combinations of the cost functions of each agent lead to various
problems, whose computational complexity is analysed in this paper. In particular, we investigate the problem of finding schedules whose cost for each agent does not exceed a given
bound for each agent
DOI: 10.5683/sp3/aqkire
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/by4ah3
title: A multi-agent optimization algorithm for resource constrained project scheduling problem

description: In this paper, a multi-agent optimization algorithm (MAOA) is proposed for solving the resourceconstrained project scheduling problem (RCPSP). In the MAOA, multiple agents work in a grouped environment where each agent represents a feasible solution. The evolution of agents is achieved by using
four main elements in the MAOA, including social behavior, autonomous behavior, self-learning, and
environment adjustment. The social behavior includes the global one and the local one for performing
exploration. Through the global social behavior, the leader agent in every group is guided by the global
best leader. Through the local social behavior, each agent is guided by its own leader agent. Through the
autonomous behavior, each agent exploits its own neighborhood. Through the self-learning, the best
agent performs an intensified search to further exploit the promising region. Meanwhile, some agents
perform migration among groups to adjust the environment dynamically for information sharing. The
implementation of the MAOA for solving the RCPSP is presented in detail, and the effect of key parameters of the MAOA is investigated based on the Taguchi method of design of experiment. Numerical testing
results are provided by using three sets of benchmarking instances. The comparisons to the existing algorithms demonstrate the effectiveness of the proposed MAOA for solving the RCPSP.
DOI: 10.5683/sp3/xynxbz
title: LEARNING TO SCHEDULE COMMUNICATION IN MULTI-AGENT REINFORCEMENT LEARNING

description: Many real-world reinforcement learning tasks require multiple agents to make sequential decisions under the agents’ interaction, where well-coordinated actions
among the agents are crucial to achieve the target goal better at these tasks. One
way to accelerate the coordination effect is to enable multiple agents to communicate with each other in a distributed manner and behave as a group. In this paper,
we study a practical scenario when (i) the communication bandwidth is limited
and (ii) the agents share the communication medium so that only a restricted number of agents are able to simultaneously use the medium, as in the state-of-the-art
wireless networking standards. This calls for a certain form of communication
scheduling. In that regard, we propose a multi-agent deep reinforcement learning framework, called SchedNet, in which agents learn how to schedule themselves, how to encode the messages, and how to select actions based on received
messages. SchedNet is capable of deciding which agents should be entitled to
broadcasting their (encoded) messages, by learning the importance of each agent’s
partially observed information. We evaluate SchedNet against multiple baselines
under two different applications, namely, cooperative communication and navigation, and predator-prey. Our experiments show a non-negligible performance gap
between SchedNet and other mechanisms such as the ones without communication and with vanilla scheduling methods, e.g., round robin, ranging from 32% to
43%.
DOI: 10.5683/sp3/88ip8m
title: Comparison of agent-based scheduling to look-ahead heuristics for real-time transportation problems

description: We consider the real-time scheduling of full truckload transportation orders with time windows that arrive during schedule execution. Because a fast scheduling method is required, look-ahead heuristics are traditionally used to solve these kinds of problems. As an alternative, we introduce an agent-based approach where intelligent vehicle agents schedule their own routes. They interact with job agents, who strive for minimum transportation costs, using a Vickrey auction for each incoming order. This approach offers several advantages: it is fast, requires relatively little information and facilitates easy schedule adjustments in reaction to information updates. We compare the agent-based approach to more traditional hierarchical heuristics in an extensive simulation experiment. We find that a properly designed multi-agent approach performs as good as or even better than traditional methods. Particularly, the multi-agent approach yields less empty miles and a more stable service level.
DOI: 10.15468/dl.7fwdxf
title: Occurrence Download

description: A dataset containing 2899 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Plantae",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Country is Germany",
 "DecimalLongitude is greater than or equal to 7.09015127375792",
 "DecimalLongitude is less than 7.09321082694231",
 "DecimalLatitude is greater than or equal to 47.27011",
 "DecimalLatitude is less than or equal to 55.05864"
 ]
}

 The dataset includes 2899 records from 26 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046926-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5683/sp3/ecadi7
title: An agent-oriented approach to resolve scheduling optimization in intelligent manufacturing

description: Agent technology is considered as a promising approach for developing optimizing process plans in intelligent manufacturing. As a bridge between computer aided design (CAD) and computer aided manufacturing (CAM), the computer aided scheduling optimization (CASO) plays an important role in the computer integrated manufacturing (CIM) environment. In order to develop a multi-agent-based scheduling system for intelligent manufacturing, it is necessary to build various functional agents for all the resources and an agent manager to improve the scheduling agility. Identifying the shortcomings of traditional scheduling algorithm in intelligent manufacturing, the architecture of intelligent manufacturing system based on multi-agent is put forward, among which agent represents the basic processing entity. Multi-agent-based scheduling is a new intelligent scheduling method based on the theories of multi-agent system (MAS) and distributed artificial intelligence (DAI). It views intelligent manufacturing as composed of a set of intelligent agents, who are responsible for one or more activities and interacting with other related agents in planning and executing their responsibilities. In this paper, the proposed architecture consists of various autonomous agents that are capable of communicating with each other and making decisions based on their knowledge. The architecture of intelligent manufacturing, the scheduling optimization algorithm, the negotiation processes and protocols among the agents are described in detail. A prototype system is built and validated in an illustrative example, which demonstrates the feasibility of the proposed approach. The experiments prove that the implementation of multi-agent technology in intelligent manufacturing system makes the operations much more flexible, economical and energy efficient.
DOI: 10.5683/sp3/umzodx
title: An Agent-Based Approach for Scheduling Multiple Machines

description: We present a new agent-based solution approach for the problem of scheduling multiple non-identical machines in the face of sequence dependent setups, job machine restrictions, batch size preferences, fixed costs of assigning jobs to machines and downstream considerations. We consider multiple objectives such as minimizing (weighted) earliness and tardiness, and minimizing job-machine assignment costs. We use an agent-based architecture called Asynchronous Team (A-Team), in which each agent encapsulates a different problem solving strategy and agents cooperate by exchanging results. Computational experiments on large instances of real-world scheduling problems show that the results obtained by this approach are significantly better than any single algorithm or the scheduler alone. This approach has been successfully implemented in an industrial scheduling system.
DOI: 10.5683/sp3/omtqhm
title: Reactive scheduling using a multi-agent model: the SCEP framework

description: Multi-agent systems have been successfully applied to the scheduling problem for some time. However, their use often leads to poorly unsatisfactory disappointing results. A new multi-agent model, called supervisor, customers, environment, producers (SCEP), is suggested in this paper. This model, developed for all types of planning activities, introduces a dialogue between two communities of agents leading to a high level of co-operation. Its two main interests are the following: first it provides a more efficient control of the consequences generated by the local decisions than usual systems to each agent, then the adopted architecture and behaviour permit an easy co-operation between the different SCEP models, which can represent different production functions such as manufacturing, supply management, maintenance or different workshops. As a consequence, the SCEP model can be adapted to a great variety of scheduling/planning problems. This model is applied to the basic scheduling problem of flexible manufacturing systems, and it permits a natural co-habitation between infinite capacity scheduling processes, performed by the manufacturing orders, and finite capacity scheduling processes, performed by the machines. It also provides a framework in order to react to the disturbances occurring at different levels of the workshop.