ANALISA PERANCANGAN KAPASITAS PENYIMPANAN OPTIMAL GUDANG BARANG JADI (STUDI KASUS: PT. SAGATRADE MURNI SAMARINDA)

2017 ◽  
Vol 12 (01) ◽  
pp. 1685-1691
Author(s):  
Andrie Andrie
Keyword(s):  
Number Sequence ◽  
Space Potential ◽  
Sequence Storage

Meningkatnya permintaan pasar akan suatu produk memaksa perusahaan untuk lebih meningkatkan kuantitas produksinya. Seiring meningkatnya kuantitas produksi, maka perusahaan memikirkan hal terpenting mengenai kebutuhan adanya tempat penyimpanan yang baik dan optimal. Penelitian ini memberikan gambaran bagaimana menganalisa kapasitas penyimpanan produk jadi pada perusahaan tersebut dengan menggunakan metode penyimpanan storage policy yaitu dedicated storage policy serta bagaimana menganalisa jalur lintas yang ada pada gudang. Hasil observasi lapangan memberikan hasil mengenai stok produk dari segi pengiriman dan pengeluarannya serta dimensi pengemasannya. Sistem penyimpanan mengacu pada metode dedicated storage policy yang bersifat part number sequence storage. Selanjutnya, untuk penempatan area penyimpanan seluruh varian yang ada seluas 224 m2, dan gambaran hasil analisa untuk penempatan area dan penyimpanan produk pada bulan November 2016 adalah 145,38 m2. Hasil ini menunjukkan bahwa penempatan penyimpanan dapat memberikan perluasan area penyimpanan seluas 78,62 m2. Sedangkan hasil jalur lintas dengan menentukan aisle space potential ratio menghasilkan presentase penambahan sebanyak 15 % dari jalur yang ada. Hasil ini merupakan analisa jalur lintas yang dilalui dua kendaran forklift.

ARCHITECTURAL DESIGN KEYWORDS OF INCEPTION SPACE

2019 ◽  
Vol 6 (2) ◽  
pp. 61
Author(s):  
Muhammad Heru Arie Edytia ◽  
Zulhadi Sahputra ◽  
Mirza Mirza
Keyword(s):  
Architectural Design ◽  
Positive Emotions ◽  
Main Design ◽  
Architectural Space ◽  
Space Design ◽  
People With Dementia ◽  
Space Potential ◽  
Design Mechanism ◽  
Christopher Nolan ◽  
And Behavior

This paper explains the idea of inception space from Inception (2010), a movie directed by Christopher Nolan, to explore the inception space potential in designing architectural space. Inception space is an architectural space design mechanism that translates the essential experience of space users as an effort to implant idea in the form of positive emotions. In other words, the architectural space is a medium of inception to a space user or a target (mark). The main purpose of inception space design is to affect the target (mark) by planting the idea ‘secretly’. The target is unaware of the intervention and considers the idea presented itself. This process becomes the beginning of an idea to grow in one's mind the beginning of mindset and behavior change. In other words, architects or planners can apply this mechanism to design and influence users so that the design success rate can be improved. The main design keywords as part of the inception process are perception, memory, scenario, layer, and labyrinth. The development of design methods of inception space can be explored and applied to different targets and contexts by applying these design keywords. For example, this design mechanism can be applied to people with dementia who experience memory and visuospatial deficit through wayfinding programming.

The Natural Numbers

2018 ◽  
Author(s):  
Øystein Linnebo
Keyword(s):  
Natural Number ◽  
Peano Arithmetic ◽  
Number Sequence ◽  
Natural Numbers ◽  
Ordinal Numbers ◽  
Cardinal Numbers

How are the natural numbers individuated? That is, what is our most basic way of singling out a natural number for reference in language or in thought? According to Frege and many of his followers, the natural numbers are cardinal numbers, individuated by the cardinalities of the collections that they number. Another answer regards the natural numbers as ordinal numbers, individuated by their positions in the natural number sequence. Some reasons to favor the second answer are presented. This answer is therefore developed in more detail, involving a form of abstraction on numerals. Based on this answer, a justification for the axioms of Dedekind–Peano arithmetic is developed.

scholarly journals Deep ConvNet: Non-Random Weight Initialization for Repeatable Determinism, Examined with FSGM

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4772
Author(s):  
Richard N. M. Rudd-Orthner ◽  
Lyudmila Mihaylova
Keyword(s):  
Cross Validation ◽  
Color Image ◽  
Validation Dataset ◽  
Number Sequence ◽  
Random Weight ◽  
Fast Gradient ◽  
Weight Initialization ◽  
Fitting In ◽  
Sequence Substitution ◽  
Sign Method

A repeatable and deterministic non-random weight initialization method in convolutional layers of neural networks examined with the Fast Gradient Sign Method (FSGM). Using the FSGM approach as a technique to measure the initialization effect with controlled distortions in transferred learning, varying the dataset numerical similarity. The focus is on convolutional layers with induced earlier learning through the use of striped forms for image classification. Which provided a higher performing accuracy in the first epoch, with improvements of between 3–5% in a well known benchmark model, and also ~10% in a color image dataset (MTARSI2), using a dissimilar model architecture. The proposed method is robust to limit optimization approaches like Glorot/Xavier and He initialization. Arguably the approach is within a new category of weight initialization methods, as a number sequence substitution of random numbers, without a tether to the dataset. When examined under the FGSM approach with transferred learning, the proposed method when used with higher distortions (numerically dissimilar datasets), is less compromised against the original cross-validation dataset, at ~31% accuracy instead of ~9%. This is an indication of higher retention of the original fitting in transferred learning.

scholarly journals A Generalized Dynamic Potential Energy Model for Multiagent Path Planning

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Liu He ◽  
Haoning Xi ◽  
Tangyi Guo ◽  
Kun Tang
Keyword(s):  
Path Planning ◽  
Potential Energy ◽  
Multiagent System ◽  
Small Time ◽  
Energy Model ◽  
Dijkstra Algorithm ◽  
Energy Field ◽  
Space Potential ◽  
Dynamic Potential ◽  
Planning Decision

Path planning for the multiagent, which is generally based on the artificial potential energy field, reflects the decision-making process of pedestrian walking and has great importance on the field multiagent system. In this paper, after setting the spatial-temporal simulation environment with large cells and small time segments based on the disaggregation decision theory of the multiagent, we establish a generalized dynamic potential energy model (DPEM) for the multiagent through four steps: (1) construct the space energy field with the improved Dijkstra algorithm, and obtain the fitting functions to reflect the relationship between speed decline rate and space occupancy of the agent through empirical cross experiments. (2) Construct the delay potential energy field based on the judgement and psychological changes of the multiagent in the situations where the other pedestrians have occupied the bottleneck cell. (3) Construct the waiting potential energy field based on the characteristics of the multiagent, such as dissipation and enhancement. (4) Obtain the generalized dynamic potential energy field by superposing the space potential energy field, delay potential energy field, and waiting potential energy field all together. Moreover, a case study is conducted to verify the feasibility and effectiveness of the dynamic potential energy model. The results also indicate that each agent’s path planning decision such as forward, waiting, and detour in the multiagent system is related to their individual characters and environmental factors. Overall, this study could help improve the efficiency of pedestrian traffic, optimize the walking space, and improve the performance of pedestrians in the multiagent system.

An anthropomorphic method for number sequence problems

2013 ◽  
Vol 22-23 ◽  
pp. 27-34 ◽  
Author(s):  
Claes Strannegård ◽  
Mehrdad Amirghasemi ◽  
Simon Ulfsbäcker
Keyword(s):  
Number Sequence
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