| Feb. 13, 2024, 4:20 p.m. |
Created
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[{"model": "core.projectfund", "pk": 63440, "fields": {"project": 11644, "organisation": 2, "amount": 0, "start_date": "2018-10-01", "end_date": "2022-03-30", "raw_data": 179704}}]
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| Jan. 30, 2024, 4:24 p.m. |
Created
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[{"model": "core.projectfund", "pk": 56278, "fields": {"project": 11644, "organisation": 2, "amount": 0, "start_date": "2018-10-01", "end_date": "2022-03-30", "raw_data": 156597}}]
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| Jan. 2, 2024, 4:15 p.m. |
Created
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[{"model": "core.projectfund", "pk": 49091, "fields": {"project": 11644, "organisation": 2, "amount": 0, "start_date": "2018-10-01", "end_date": "2022-03-30", "raw_data": 134685}}]
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| Dec. 5, 2023, 4:24 p.m. |
Created
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[{"model": "core.projectfund", "pk": 41844, "fields": {"project": 11644, "organisation": 2, "amount": 0, "start_date": "2018-09-30", "end_date": "2022-03-29", "raw_data": 102612}}]
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| Nov. 27, 2023, 2:14 p.m. |
Added
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{"external_links": []}
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| Nov. 21, 2023, 4:38 p.m. |
Created
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[{"model": "core.projectfund", "pk": 34550, "fields": {"project": 11644, "organisation": 2, "amount": 0, "start_date": "2018-09-30", "end_date": "2022-03-29", "raw_data": 62338}}]
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| Nov. 21, 2023, 4:38 p.m. |
Created
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[{"model": "core.projectorganisation", "pk": 100762, "fields": {"project": 11644, "organisation": 11120, "role": "LEAD_ORG"}}]
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| Nov. 21, 2023, 4:38 p.m. |
Created
40
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[{"model": "core.projectperson", "pk": 63461, "fields": {"project": 11644, "person": 13084, "role": "SUPER_PER"}}]
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| Nov. 20, 2023, 2:04 p.m. |
Updated
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{"title": ["", "Artificial Intelligence based multi-objective optimisation for energy management in dynamic flexible manufacturing systems"], "description": ["", "\nThe main goal of this project is to address the multi-objective dynamic flexible job shop scheduling \nproblem for reducing energy consumption and its related costs. The project aims to develop a \nsystem that employs composite dispatching rules that include reduction of energy consumption as \nits main objective. It is intended that such a system could be implemented in a flexible production \nsystem in which job scheduling occurs at random or unpredictable times. The proposed dispatching \nrule would prioritise all the jobs waiting for processing on a machine in the manufacturing system \nwhile taking into account different attributes of the job and the machine, as well as time. \nThe manufacturing industry currently faces the dual challenge of increasing energy prices, and \nregulatory mandates intended to reduce carbon emissions, which can prove problematic for many \nenterprises. It is becoming increasingly necessary to explore the potential of reducing energy \nconsumption of industrial manufacturing at a system level, which has so far been largely ignored. At \nthis level, operational research methods can be employed as an effective energy-saving approach. In \nthe future, the requirement on manufacturing system flexibility within the system will be increased \nto realise mass customisation and personalisation. On-line decision making and optimisation \ntechniques to accommodate these uncertainties and to maintain robustness of the flexible \nmanufacturing system is becoming increasingly important within the background of industry 4.0. \nThe application of multi-objective algorithms shows a great deal of promise at addressing the issues \nfaced by the manufacturing industry. By producing fast and elitist multi-objective scheduling \nalgorithms, it will be possible to optimise a number of factors, such as cost, energy consumption and \nlead time. These optimisations will go a long way in improving the current state of manufacturing, \nboth by reducing environmental footprint and by reducing financial overheads.\nA great deal of research has been conducted into the development of elitist heuristics and \nevolutionary algorithms, and their operation is well understood. Existing dynamic scheduling \nalgorithms will be extended to address the uncertainties within the manufacturing system as a \nbenchmark. By targeting the practical shortcomings of these algorithms, it will be possible to \ndevelop a system which avoids these problems, most notably: the high computational complexity of \nthe sorting.\nA large part of the project will focus on expanding the past research into multi-objective \noptimization problems and evolutionary algorithms. By developing understanding of the function of \njobs within the manufacturing environment, and the implementation mathematical modelling of the \njob shop scheduling process. A focus on fast computation and implementation will be the heart of \nthis research, in order to maximise the applicability of the scheduling algorithms to a modern \nmanufacturing environment. In the context of a manufacturing environment, swift computation is of \nparamount importance.\n\nThis project can be seen as a continuation of the research conducted by Dr. Ying Liu into the field of \nmeta-heuristics and industrial job scheduling. As such, much of Dr. Liu's research will be examined, \nwith the final intention of expanding it into an industry application."\n\n"], "extra_text": ["", "\n\n\n\n"], "status": ["", "Closed"]}
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| Nov. 20, 2023, 2:04 p.m. |
Added
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{"external_links": [47487]}
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| Nov. 20, 2023, 2:04 p.m. |
Created
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[{"model": "core.project", "pk": 11644, "fields": {"owner": null, "is_locked": false, "coped_id": "e7223d4b-3c1d-41ad-9fce-a4d6ff8c4714", "title": "", "description": "", "extra_text": "", "status": "", "start": null, "end": null, "raw_data": 62321, "created": "2023-11-20T13:43:31.964Z", "modified": "2023-11-20T13:43:31.964Z", "external_links": []}}]
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