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The Product Test Scheduling Problem

Megan Wydick Martin et al.

International Journal of Applied Management and Technology2022https://doi.org/10.5590/ijmat.2022.21.1.01article
ABDC C
Weight
0.37

What the paper says

This research focused on product test scheduling in the presence of in-process and at-completion inspection constraints. Such testing arises in the context of the manufacture of products that must perform reliably in extreme environmental conditions. Often, these products must receive a certification from prescribed regulatory agencies at the successful completion of a predetermined series of tests. Operational efficiency is enhanced by determining the optimal order and start times of tests so as to minimize the makespan while ensuring that technicians are available when needed to complete in-process and at-completion inspections. We refer to this as the product test scheduling problem. We first formulated a mixed-integer linear programming (MILP) model to identify the optimal solution to this problem and solve it using a commercial optimization package. We also present a genetic algorithm (GA) solution methodology that is implemented and solved in Microsoft Excel. Computational results are presented demonstrating the merits and consistency of the MILP and GA solution approaches across a number of scenarios.

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https://doi.org/https://doi.org/10.5590/ijmat.2022.21.1.01

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@article{megan2022,
  title        = {{The Product Test Scheduling Problem}},
  author       = {Megan Wydick Martin et al.},
  journal      = {International Journal of Applied Management and Technology},
  year         = {2022},
  doi          = {https://doi.org/https://doi.org/10.5590/ijmat.2022.21.1.01},
}

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.05 × 0.4 = 0.02
M · momentum0.80 × 0.15 = 0.12
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.