Scheduling optimisation of a real flexible job shop including fixture availability and preventive maintenance

Thörnblad, Karin; Strömberg, Ann; Patriksson, Michael; Almgren, Torgny

doi:10.1504/ejie.2015.067451

Cited by 20 publications

(12 citation statements)

References 28 publications

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“…Scheduling can also find its application in timetable scheduling problems in educational institutes or universities to find the availability of teachers, students and classrooms. Thörnblad et al (2015) developed a time-indexed optimization system used in flexible job shop scheduling problem (FJSP) to schedule optimally the planned jobs on the resources which minimized the completion time and tardiness of the jobs. They also took into account the availability of fixtures and the prerequisite preventive maintenance activities.…”

Section: Scheduling Problemsmentioning

confidence: 99%

Ranking of sequencing rules in a job shop scheduling problem with preference selection index approach

Bari¹,

Karande²

2022

J. Decis. Anal. Int. Comp.

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Scheduling different jobs in an appropriate sequence are very important in manufacturing industries due to the influence of conflicting criteria. It becomes difficult to sequence the jobs as the job number increases due to numerous computations involved. In this article, six jobs are considered to be treated on a machine one by one. Seven different priority sequencing rules provide seven different sequencing options for the jobs which are assessed using a set of nine criteria. Preference selection index (PSI) approach, a multi-criterion decision-making (MCDM) technique is proposed to rank them from best to worst. The PSI approach, unlike other MCDM methods, does not require to find the relative significance of the criteria, which reduces work of finding weights of criteria hence is a very easy and effective tool for decision making. A benchmark problem from the previous literature is considered and solved using the PSI approach and the obtained results are found to be correct.

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Section: Scheduling Problemsmentioning

confidence: 99%

Ranking of sequencing rules in a job shop scheduling problem with preference selection index approach

Bari¹,

Karande²

2022

J. Decis. Anal. Int. Comp.

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“…That is, time-based and condition-based maintenance needed to be treated as a whole. For more recent information about maintenance scheduling, please refer to [13,16,31,37,41,43,51,53,60].…”

Section: Introductionmentioning

confidence: 99%

A three-agent scheduling problem for minimizing the flow time on two machines

Lee

Wang

2020

RAIRO-Oper. Res.

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This study introduces a two-machine three-agent scheduling problem. We aim to minimize the total tardiness of jobs from agent 1 subject to that the maximum completion time of jobs from agent 2 cannot exceed a given limit and that two maintenance activities from agent 3 must be conducted within two maintenance windows. Due to the NP-hardness of this problem, a genetic algorithm (named GA+) is proposed to obtain approximate solutions. On the other hand, a branch-and-bound algorithm (named B&B) is developed to generate the optimal solutions. When the problem size is small, we use B&B to verify the solution quality of GA+. When the number of jobs is large, a relative deviation is proposed to show the gap between GA+ and another ordinary genetic algorithm. Experimental results show that the proposed genetic algorithm can generate approximate solutions by consuming reasonable execution time.

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“…These tactical resource allocations (which are referred to in this text) should not be mistaken for the short-term resource allocations done when choosing between several available machining resources while scheduling. The latter is commonly addressed in industry (see an example from GKN, [2]). The tactical resource allocations identify which production processes that need to be developed.…”

Section: New Product Introductionmentioning

confidence: 99%

A Mathematical Optimization of the Tactical Resource Allocation of Machining Resources for an Efficient Capacity Utilization in Aerospace Component Manufacturing

Fotedar

Almgren

Cedergren

et al. 2019

Proceedings of the 10th Aerospace Technology Congress, October 8-9, 2019, Stockholm, Sweden

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In the aerospace industry, with low volumes and many products, there is a critical need to efficiently use available manufacturing resources. Currently, at GKN Aerospace, resource allocation decisions that in many cases will last for several years are to some extent made with a short-term focus so as to minimize machining time, which results in a too high load on the most capable machines, and too low load on the less capable ones. This creates an imbalance in capacity utilization that leads to unnecessary queuing at some machines, resulting in long lead times and in an increase in tied-up capital. Tactical resource allocation on the medium to long-range planning horizon (six months to several years) aims to address this issue by allocating resources to meet the predicted future demand as effectively as possible, in order to ensure long range profitability. Our intent is to use mathematical optimization to find the best possible allocations.

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Scheduling optimisation of a real flexible job shop including fixture availability and preventive maintenance

Cited by 20 publications

References 28 publications

Ranking of sequencing rules in a job shop scheduling problem with preference selection index approach

Ranking of sequencing rules in a job shop scheduling problem with preference selection index approach

A three-agent scheduling problem for minimizing the flow time on two machines

A Mathematical Optimization of the Tactical Resource Allocation of Machining Resources for an Efficient Capacity Utilization in Aerospace Component Manufacturing

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