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Record W4399516745 · doi:10.33366/rekabuana.v7i2.4439

Optimasi Produktivitas Alat Berat dengan Metode Simpleks LINGO (Heavy-Duty Productivity Optimization Using LINGO Simplex Method)

2022· article· en· W4399516745 on OpenAlex
Heru Setiyo Cahyono, Apif M. Hajji, Aisyah Larasati, Imam Alfianto

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueReka Buana Jurnal Ilmiah Teknik Sipil dan Teknik Kimia · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Optimization Techniques
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsHeavy dutyProductivityMathematicsEngineeringAutomotive engineeringEconomics

Abstract

fetched live from OpenAlex

One of the areas that are now the location for clearing residential land is in Karang Widoro Village, which is now a plantation and fruit farm and has been purchased by the developer Podo Joyo Masyhur (PJM) Group who then runs the project "The OZ Tidar Housing, Malang" where the land being worked on has a total area of 47,000 m2. Construction work involving digging, hauling, and leveling equipment requires consideration so that the work can be carried out following the target volume of work and the specified time allocation. So it is necessary to carry out project control by considering aspects that affect excavation and backfill work by heavy equipment in the field. Several choices of methods in optimizing calculations and selecting tools to increase work productivity of heavy equipment are the Linear Simplex Method Program. Then combined with the use of currently widely developed software, this method is more effective in finding the best solution for a function with several variables from existing problems. LINGO can be a solution for optimizing work execution so that work targets can be completed on time with minimum operational costs and minimizing the number of units obtained. The results of the analysis of several factors that influence productivity are calculated using a linear program to achieve an optimum choice. The Simplex Method Lienar Program Optimization Model is: Minimization Z = 1836000 x1 + 483000 x2 + 712200 x3 (in IDR/day).ABSTRAKSalah satu area yang sekarang menjadi lokasi pembukaan lahan perumahan adalah di Desa Karang Widoro yang sekarang menjadi perkebunan dan pertanian buah serta telah dibeli oleh pengembang Podo Joyo Masyhur (PJM) Group yang kemudian menjalankan proyek “Perumahan The OZ Tidar, Malang” dimana lahan yang dikerjakan memiliki luas total 47.000 m2. Pada pekerjaan konstruksi yang mengikutsertakan alat gali-muat, angkut, dan perata tanah memerlukan pertimbangan agar pekerjaan dapat dilaksanakan sesuai dengan target volume pekerjaan dan alokasi waktu yang ditetapkan. Maka perlu dilakukan pengendalian proyek dengan mempertimbangkan aspek-aspek yang berpengaruh terhadap jalannya pekerjaan galian dan urukan oleh alat berat di lapangan. Beberapa pilihan metode dalam perhitungan optimasi dan pemilihan alat untuk peningkatan produktivitas kerja alat berat adalah Program Linear Metode Simpleks. Lalu digabungkan dengan penggunaan perangkat lunak yang saat ini banyak berkembang, metode ini lebih efektif dalam pencarian solusi terbaik suatu fungsi dengan beberapa variabel dari permasalahan yang ada. LINGO dapat menjadi solusi dalam optimasi pelaksanaan pekerjaan sehingga target pekerjaan dapat selesai tepat waktu dengan biaya operasional yang minimum serta meminimalkan jumlah unit yang didapatkan. Hasil analisis terhadap beberapa faktor yang berpengaruh pada produktivitas dan dihitung menggunakan program linear sehingga mampu dicapai pilihan yang optimum. Model Optimasi Program Lienar Metode Simpleks yang ditetapkan adalah : Minimasi Z =1836000 x1 + 483000 x2 + 712200 x3 (dalam Rp/hari)

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.524
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0030.000
Scholarly communication0.0020.004
Open science0.0020.002
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.031
GPT teacher head0.278
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it