2022
DOI: 10.3390/machines10121138
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Analysis of a Collaborative Scheduling Model Applied in a Job Shop Manufacturing Environment

Abstract: Collaborative Manufacturing Scheduling (CMS) is not yet a properly explored decision making practice, although its potential for being currently explored, in the digital era, by combining efforts among a set of entities, either persons or machines, to jointly cooperate for solving some more or less complex scheduling problem, namely occurring in job shop manufacturing environments. In this paper, an interoperable scheduling system integrating a proposed scheduling model, along with varying kinds of solving alg… Show more

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Cited by 3 publications
(4 citation statements)
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“…Varela et al 9 studied the collaborative manufacturing scheduling problem. Through an industrial case study, they proposed an operational scheduling system that integrates the proposed scheduling model and various types of solution algorithms.…”
Section: Introductionmentioning
confidence: 99%
“…Varela et al 9 studied the collaborative manufacturing scheduling problem. Through an industrial case study, they proposed an operational scheduling system that integrates the proposed scheduling model and various types of solution algorithms.…”
Section: Introductionmentioning
confidence: 99%
“…Collaborative networks (CN), and global or group decision-making approaches (GDMA) are fundamental for enabling and promoting the interaction and sharing of knowledge among two or more collaborating entities [1][2][3][4][5]. Moreover, independently of sharing or not having the same goal, and/or resources, usually, interplaying entities do fall into some kind of business environment, for instance in the context of distributed or extended manufacturing systems (EMS) or agile/virtual enterprises (A/VE) [6][7][8].…”
Section: Introductionmentioning
confidence: 99%
“…A DMS problem [7,15], is one typical example of the need for using CN and GDMA for solving the scheduling problem among a set of participating companies, which may or may not further share manufacturing resources and be geographically dispersed, tending to be quite complex combinatorial optimization problems [5,15].…”
Section: Introductionmentioning
confidence: 99%
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