Cucuk Nur Rosyidi

Prof. Dr. Cucuk Nur Rosyidi

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Academic Career Length

15Years (2012-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 January 2020 in Operations Research Perspectives
Verified
Journal Article
Open access
WJWakhid Ahmad Jauhari
Wakhid Ahmad Jauhari
NANiimas Ayu Frensilia Putri Adam
Niimas Ayu Frensilia Putri Adam
CRCucuk Nur Rosyidi
Cucuk Nur Rosyidi
IPI Nyoman Pujawan
I Nyoman Pujawan
NSNita H. Shah
Nita H. Shah
1 February 2021 in Heliyon
Verified
Journal Article
Open access
PPPhengky Pangestu
Phengky Pangestu
EPEko Pujiyanto
Eko Pujiyanto
CRCucuk Nur Rosyidi
Cucuk Nur Rosyidi
Sustainable manufacturing has grown widely owing to recent environmental issues. This study aims to develop a multi-objective multi-pass turning optimization model to determine the optimal cutting parameters, including spindle rotation speed, feed rate, depth of cut, and number of roughing passes. The optimization model considers several criteria in the key metrics of sustainable manufacturing, i.e., energy consumption, carbon emissions, production time, and production cost. A numerical example is provided to show the application of the model, including sensitivity analysis, to study the effects of several cutting parameters on the objective functions. The model can be used by manufacturing industries to improve their manufacturing process efficiency and simultaneously produce products that support sustainable manufacturing.
1 January 2017 in Cogent Engineering
Verified
Journal Article
Open access
ADAnindya Rachma Dwicahyani
Anindya Rachma Dwicahyani
WJWakhid Jauhari
Wakhid Jauhari
CRCucuk Nur Rosyidi
Cucuk Nur Rosyidi
Pringgo Widyo LaksonoPringgo Widyo Laksono
Pringgo Widyo Laksono
27 February 2021 in Journal of Industrial and Production Engineering
Verified
Journal Article
Closed access
CRCucuk Nur Rosyidi
Cucuk Nur Rosyidi
SHShanella Nestri Hapsari
Shanella Nestri Hapsari
WJWakhid Ahmad Jauhari
Wakhid Ahmad Jauhari
A large steel company needs to improve its efficiency by integrating the decision making from suppliers to buyers. Therefore, an optimization model is required to determine simultaneously the optimal raw material allocation, liquid steel allocation subject to maintenance schedule, and coils allocation from Hot Strip Mill (HSM) to five sub divisions under HSM. The model is aimed at maximizing total profit with several constraints such as production capacity at each sub division, maintenance cost, and inventory balance. We use the operation research methodology to solve the problem, start with problem definition, model development, find optimal solution, and sensitivity analysis. The model was solved using the Oracle Crystal Ball software. Sensitivity analysis results show that the objective function is sensitive to the changes of maintenance cost, raw material price, and product selling price. The allocation of coil, liquid steel, and the amount of raw material orders are sensitive to the maintenance cost and product selling price.
22 February 2025 in Supply Chain Analytics
Verified
Journal Article
Open access
DHDimas Nurbani Harefah
Dimas Nurbani Harefah
WJWakhid Ahmad Jauhari
Wakhid Ahmad Jauhari
CRCucuk Nur Rosyidi
Cucuk Nur Rosyidi
DUDana Marsetiya Utama
Dana Marsetiya Utama
24 August 2016 in Cogent Engineering
Verified
Journal Article
Open access
CRCucuk Nur Rosyidi
Cucuk Nur Rosyidi
RMRina Murtisari
Rina Murtisari
WJWakhid Ahmad Jauhari
Wakhid Ahmad Jauhari
The aim of this paper is to develop an optimization model to optimally select suppliers and allocate components to the selected suppliers. The objectibve function of the model is to minimize purchasing cost and fuzzy quality loss. Using fuzzy quality loss, the assembly quality may be divided into several grades allowing the fuzziness in the resulted assembly tolerance. The model considers several constraints such as production capacity, assembly quality, and process and technological capability of the suppliers. A numerical example is given in this paper to show the implementation of the model. Sensitivity analysis is performed to determine the effect of process capability to the supplier selection, components allocation and total cost. The increase of process capability in one of the suppliers affects the selection and component allocation mainly when the process capability increase from 1 to 1.5. Further, increasing the process capability index to 2 has no effect on the selection and allocation but affects the fuzzy quality loss and hence reduces the total cost.