Value-driven Manufacturing Planning using Cloud-based Evolutionary Optimisation

Shuai Zhao; Piotr Dziurzanski; Leandro Soares Indrusiak

Value-driven Manufacturing Planning using Cloud-based Evolutionary Optimisation

Shuai Zhao, Piotr Dziurzanski, Leandro Soares Indrusiak

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

This paper considers manufacturing planning and scheduling of manufacturing orders whose value decreases over time. The value decrease is modelled with a so-called value curve. Two genetic-algorithm-based methods for multi-objective optimisation has been proposed, implemented and deployed to a cloud. The first proposed method allocates and schedules manufacturing of all the ordered elements optimising both the makespan and the total value, whereas the second method selects only the profitable orders for manufacturing. The proposed evolutionary optimisation has been performed for a set of real-world-inspired manufacturing orders. Both the methods yield a similar total value, but the latter method leads to a shorter makespan.

Original language	English
Title of host publication	10th International Conference on Manufacturing Science and Technology
Publication status	Accepted/In press - 18 Apr 2019
Event	10th International Conference on Manufacturing Science and Technology - Sabah, Malaysia Duration: 9 Aug 2019 → 11 Aug 2019 Conference number: 10 http://icmst.org/

Conference

Conference	10th International Conference on Manufacturing Science and Technology
Abbreviated title	ICMST 2019
Country/Territory	Malaysia
City	Sabah
Period	9/08/19 → 11/08/19
Internet address	http://icmst.org/

Keywords

cloud computing
evolutionary algorithms
value-based scheduling

Access to Document

Zhao et al ICMST 2019v5Accepted author manuscript, 805 KB

Cite this

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title = "Value-driven Manufacturing Planning using Cloud-based Evolutionary Optimisation",

abstract = "This paper considers manufacturing planning and scheduling of manufacturing orders whose value decreases over time. The value decrease is modelled with a so-called value curve. Two genetic-algorithm-based methods for multi-objective optimisation has been proposed, implemented and deployed to a cloud. The first proposed method allocates and schedules manufacturing of all the ordered elements optimising both the makespan and the total value, whereas the second method selects only the profitable orders for manufacturing. The proposed evolutionary optimisation has been performed for a set of real-world-inspired manufacturing orders. Both the methods yield a similar total value, but the latter method leads to a shorter makespan.",

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AU - Dziurzanski, Piotr

AU - Soares Indrusiak, Leandro

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AB - This paper considers manufacturing planning and scheduling of manufacturing orders whose value decreases over time. The value decrease is modelled with a so-called value curve. Two genetic-algorithm-based methods for multi-objective optimisation has been proposed, implemented and deployed to a cloud. The first proposed method allocates and schedules manufacturing of all the ordered elements optimising both the makespan and the total value, whereas the second method selects only the profitable orders for manufacturing. The proposed evolutionary optimisation has been performed for a set of real-world-inspired manufacturing orders. Both the methods yield a similar total value, but the latter method leads to a shorter makespan.

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KW - evolutionary algorithms

KW - value-based scheduling

M3 - Chapter

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Y2 - 9 August 2019 through 11 August 2019

ER -