Modified RK-EDA to Solve a Permutation-Based Spare Part Allocation Problem

2022 ◽  
pp. 310-318
Author(s):  
Nouf Alkaabi ◽  
Siddhartha Shakya ◽  
Adriana Gabor ◽  
Beum Seuk Lee ◽  
Gilbert Owusu
Keyword(s):  
Spare Part ◽  
Allocation Problem

An Application of EDA and GA for Permutation Based Spare Part Allocation Problem

2020 ◽  
pp. 393-399
Author(s):  
Nouf Alkaabi ◽  
Siddhartha Shakya ◽  
Adriana Gabor ◽  
Andrzej Stefan Sluzek ◽  
Beum Seuk Lee ◽  
...  
Keyword(s):  
Spare Part ◽  
Allocation Problem

Optimalisasi Perawatan Dan Pengoperasian Alat Keselamatan Sekoci Sebagai Penunjang Keselamatan Di Mv Kartini Baruna

2020 ◽  
Vol 2 (1) ◽  
pp. 197-202
Author(s):  
Rudiana ◽  
Rr. Retno Safitri ◽  
Retno Junita
Keyword(s):  
Spare Part ◽  
Maintenance System

Sekoci merupakan alat penolong yang dapat digunakan untuk evakuasi seluruh awak kapal karena memiliki konstruksi yang lebih kuat dari alat penolong lainnya. Masalah yang sering terjadi adalah kurang terampilnya ABK tentang perawatan dan pengoperasian sekoci yang sesuai dengan prosedur dan rendahnya  perawatan sekoci di atas MV Kartini Baruna. Kemudian tujuan penelitian ini yaitu untuk mengetahui penyebab kurang terampilnya ABK tentang perawatan dan pengoperasian sekoci yang sesuai dengan prosedur dan mengetahui penyebab rendahnya  perawatan sekoci di atas MV Kartini Baruna. Metode yang digunakan adalah deskriptif kualitatif. Dari hasil penelitian diperoleh kesimpulan bahwa faktor yang menyebabkan kurang optimalnya kinerja dari sekoci di MV. Kartini Baruna adalah kurangnya spare part, penerapan PMS (Plan Maintenance System) yang tidak sesuai ketentuan, kurang terampilnya anak buah kapal (ABK) dalam pengoperasian sekoci. Faktor yang menyebabkan perawatan lifeboat yang kurang baik terhadap crew di MV. Kartini Baruna adalah lambatnya proses pengoperasian sekoci, membahayakan keselamatan crew diatas kapal. Kemudian upaya yang dilakukan untuk mengoptimalkan perawatan terhadap lifeboat di MV.Kartini Baruna yaitu dengan menerapkan PMS (plan maintenance system) sesuai ketentuan atau panduan

Finite Methods for a Nonlinear Allocation Problem

1989 ◽  
Author(s):  
Alan R. Washburn
Keyword(s):  
Allocation Problem

Location and allocation problem for spare parts depots on integrated logistics support

2019 ◽  
Vol 30 (6) ◽  
pp. 1252-1259
Author(s):  
Meilin WEN ◽  
Bohan LU ◽  
Shuyu LI ◽  
Rui KANG
Keyword(s):  
Spare Parts ◽  
Allocation Problem ◽  
Integrated Logistics

scholarly journals A Fuzzy Optimization Model for the Berth Allocation Problem and Quay Crane Allocation Problem (BAP + QCAP) with n Quays

2021 ◽  
Vol 9 (2) ◽  
pp. 152
Author(s):  
Edwar Lujan ◽  
Edmundo Vergara ◽  
Jose Rodriguez-Melquiades ◽  
Miguel Jiménez-Carrión ◽  
Carlos Sabino-Escobar ◽  
...  
Keyword(s):  
Optimization Model ◽  
Fuzzy Optimization ◽  
Optimal Solution ◽  
Allocation Problem ◽  
Berth Allocation ◽  
Triangular Fuzzy Numbers ◽  
Berth Allocation Problem ◽  
Arrival Times ◽  
Decision Variables ◽  
Short Time

This work introduces a fuzzy optimization model, which solves in an integrated way the berth allocation problem (BAP) and the quay crane allocation problem (QCAP). The problem is solved for multiple quays, considering vessels’ imprecise arrival times. The model optimizes the use of the quays. The BAP + QCAP, is a NP-hard (Non-deterministic polynomial-time hardness) combinatorial optimization problem, where the decision to assign available quays for each vessel adds more complexity. The imprecise vessel arrival times and the decision variables—berth and departure times—are represented by triangular fuzzy numbers. The model obtains a robust berthing plan that supports early and late arrivals and also assigns cranes to each berth vessel. The model was implemented in the CPLEX solver (IBM ILOG CPLEX Optimization Studio); obtaining in a short time an optimal solution for very small instances. For medium instances, an undefined behavior was found, where a solution (optimal or not) may be found. For large instances, no solutions were found during the assigned processing time (60 min). Although the model was applied for n = 2 quays, it can be adapted to “n” quays. For medium and large instances, the model must be solved with metaheuristics.

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