scholarly journals RANCANGAN MODEL PERFORMANSI RISIKO RANTAI PASOK AGROINDUSTRI SUSU DENGAN MENGGUNAKAN PENDEKATAN LOGIKA FUZZY (A Supply Chain Risk Performance Model for Local Raw Milk Agro-Industry Based on Fuzzy Logic Approach)

2015 ◽  
Vol 35 (01) ◽  
pp. 88 ◽  
Author(s):  
Winnie Septiani ◽  
Taufik Djatna
Keyword(s):  
Fuzzy Logic ◽  
Supply Chain ◽  
Dairy Product ◽  
Inference Engine ◽  
Risk Exposure ◽  
Rule Base ◽  
Time Cost ◽  
Exposure Risk ◽  
Second Stage ◽  
The Impact

The critical point of performance and risk supply chain in a dairy product agro-industries was found in the product’s perishable characteristics. Bacterial and antibiotic contaminations have been identified as the major risks. This risks arise from a series of activities strarting from farms, cooperatives and Milk Processing Industry that will affect the entire supply chain performance. This paper aimed to design performance and risk supply chain model for agroindustrial dairy product supply chain risks by using Fuzzy Associative Memories (FAMS) approach. The approach isused to translate a quantity that is expressed using the language (linguistic). The fuzzy logic system provides fourbasic elements, namely :  (i) rule base; (ii) inference engine; (iii) fuzzification; (iv) defuzzification. There are three proposed components in the model, namely : (i) performance profile; (ii) risk profile and (iii) risk exposure, which were expressed in time, cost and quality. Theinitial stage began with the analysis of invetible exposure risk exposure,including analysis of environmental and configuration characteristics, as well as dairy agro-industries supply chain. The second stage wasanalysis of evitable exposure risk, while the third stage was to change, risk exposure into time, cost and quality performance units. The second stage generateds risk magnitude of risk as a function of probability andseverity, the two value that were measured with Fuzzy AssociativeMemories (FAMS). The Model, therefore  showed the  impact of emerging risks damage to the agro-industry supply chain, which could be measured and be minimized in order to improve the robustnesss of the supply chain.Keywords: Performance, risk, fuzzy, supply chain, dairy agroindustry ABSTRAKTitik kritis dari performansi dan risiko rantai pasok agroindustri susu terletak pada karakteristik produknya yang mudah rusak. Risiko tertinggi yang teridentifikasi pada rantai ini adalah risiko susu terkontaminasi bakteri dan antibiotik. Risiko ini muncul dari rangkaian aktivitas yang terjadi mulai dari peternakan, koperasi dan Industri Pengolahan Susu (IPS) yang akan mempengaruhi performasi rantai pasok keseluruhan. Paper ini bertujuan untuk merancang model performansi dan risiko rantai pasok agroindustri susu dengan menggunakan pendekatan Fuzzy Assosiated Memories (FAMs). Logika fuzzy digunakan untuk menerjemahkan suatu besaran yang diekspresikan menggunakan bahasa (linguistic). Secara umum dalam sistem logika fuzzy terdapat empat buah elemen dasar, yaitu: basis kaidah (rule base), mekanisme pengambilan keputusan (inference engine), proses fuzzifikasi (fuzzification) dan proses defuzzifikasi (defuzzification). Ada tiga komponen yang dipertimbangkan dalam rancangan model yaitu profil performansi, profil risiko dan eksposurrisiko dalam ukuran waktu, biaya dan kualitas. Tahap pertama dimulai dengan menganalisis eksposur risiko yang tidak terhindarkan yang meliputi analisis karakteristik lingkungan dan konfigurasi serta karakteristik rantai pasok agroindustri susu. Tahap kedua adalah menganalisis eksposure risiko yang dapat dihindari. Tahap ketiga adalah mengubah eksposur risiko ke dalam ukuran performansi waktu, biaya dan kualitas. Pada tahap kedua dihasilkan magnitude risiko, yang merupakan fungsi dari nilai probabilitas dan severity yang dilakukan dengan menggunakan Fuzzy Assosiated Memories (FAMs).Dengan model ini diharapkan dampak kerusakan dari risiko yang muncul pada rantai pasok agroindustri susu dapat terukur dan dapat diminimasi sehingga dapat meningkatkan ketangguhan (robustnes) dari rantai pasok.Kata kunci: Performansi, risiko, fuzzy, rantai pasok, agroindustri susu

scholarly journals A model for measuring distruption risk in the prefabrication supply chain

2021 ◽  
Vol 11 (2) ◽  
pp. 69-88
Author(s):  
Somik Ghosh ◽  
◽  
Mustafa Hamad ◽  
Keyword(s):  
Supply Chain ◽  
Construction Projects ◽  
Risk Model ◽  
Exposure Model ◽  
Risk Exposure ◽  
Cost Overruns ◽  
Batch Sizes ◽  
Risk Managers ◽  
The Impact ◽  
Disruption Risks

Use of prefabrication in construction projects is increasing due to the benefits in cost, time, quality, and safety. However, utilizing prefabrication introduces uncertainties inherent with the supply chain of the process. These uncertainties, if not managed, can disrupt the prefabrication process and result in schedule delays and cost overruns. This study proposes a model to measure disruption risks in the prefabrication process. The model was used in measuring the disruption risks of prefabrication of headwalls in patients’ rooms for a healthcare project as a pilot study. The risk model could successfully identify the disruption risks originating anywhere in the supply chain based on input information such as required material quantity, batch sizes of material deliveries, production rates, and batch sizes of transporting the headwall units. Using the model, the project team identified two uncertainties that could lead to possible disruptions: the start of the prefabrication processes and the required production rate to meet the on-site schedule. This is a first step to developing a risk exposure model that can prove valuable to the risk managers to analyse and manage the impact of disruptions. This will help the risk managers in making informed decisions about where to focus their limited resources.

scholarly journals SOFT COMPUTING AS A SOLUTION TO TIME/COST DISTRIBUTOR

2014 ◽  
pp. 100-105
Author(s):  
Nabil M. Hewahi
Keyword(s):  
Fuzzy Logic ◽  
Soft Computing ◽  
Main Idea ◽  
System Response ◽  
Time Cost ◽  
Importance Value ◽  
Time Limits ◽  
Lower Priority ◽  
Second Stage ◽  
System Time

In this paper we present a theoretical model based on soft computing to distribute the time/cost among the industry/machine sensors or effectors based on the type of the application. One of the most unstudied significant work is to recognize which sensor in an industry for example has higher priority than others. This is important to know which sensor to be checked first and within time limits of the system response. The problem of such systems is their variant environmental situations. Based on these varied situations, the priority of the importance of each sensor might change from time to another. Due to this uncertainty and lack of some information, soft computing is considered to be one of the plausible solutions. The presented idea is based on initially training of the system and continuously exploiting the system experience of the degree of importance of the sensors. The proposed system has three main stages, the first stage is concerned with training the system to obtain the necessary system time to respond, the necessary time allocated to recognize which sensors to check (or which has higher priority), and the initial importance value for each sensor, which indicates the initial judgment about the sensor importance. The second stage is to use the system experience about the importance of the sensor using fuzzy logic to decide the final values of each sensor 's importance. Based on the output of the second stage and the output of the first stage, the system distributes the time/cost among the sensors (some sensors with lower priority might be neglected). The main idea of the proposed work is based on neurofuzzy.

scholarly journals A Mixed Rough Sets/Fuzzy Logic Approach for Modelling Systemic Performance Variability with FRAM

2020 ◽  
Vol 12 (5) ◽  
pp. 1918
Author(s):  
Hussein Slim ◽  
Sylvie Nadeau
Keyword(s):  
Fuzzy Logic ◽  
Rough Sets ◽  
Assessment Model ◽  
Rule Base ◽  
Data Sets ◽  
Mathematical Framework ◽  
Real World Data ◽  
Second Stage ◽  
Fuzzy Logic Approach ◽  
Input Variables

The task to understand systemic functioning and predict the behavior of today’s sociotechnical systems is a major challenge facing researchers due to the nonlinearity, dynamicity, and uncertainty of such systems. Many variables can only be evaluated in terms of qualitative terms due to their vague nature and uncertainty. In the first stage of our project, we proposed the application of the Functional Resonance Analysis Method (FRAM), a recently emerging technique, to evaluate aircraft deicing operations from a systemic perspective. In the second stage, we proposed the integration of fuzzy logic into FRAM to construct a predictive assessment model capable of providing quantified outcomes to present more intersubjective and comprehensible results. The integration process of fuzzy logic was thorough and required significant effort due to the high number of input variables and the consequent large number of rules. In this paper, we aim to further improve the proposed prototype in the second stage by integrating rough sets as a data-mining tool to generate and reduce the size of the rule base and classify outcomes. Rough sets provide a mathematical framework suitable for deriving rules and decisions from uncertain and incomplete data. The mixed rough sets/fuzzy logic model was applied again here to the context of aircraft deicing operations, keeping the same settings as in the second stage to better compare both results. The obtained results were identical to the results of the second stage despite the significant reduction in size of the rule base. However, the presented model here is a simulated one constructed with ideal data sets accounting for all possible combinations of input variables, which resulted in maximum accuracy. The same should be further optimized and examined using real-world data to validate the results.

Impact of the Covid Pandemic on Construction Organisations in India – a Case Study

2021 ◽  
pp. 1-6
Author(s):  
Kudrekodlu Venkatesh Prasad ◽  
Nikhil Bhat
Keyword(s):  
Supply Chain ◽  
Mitigation Strategies ◽  
Social Effects ◽  
Labour Migration ◽  
Project Delivery ◽  
Construction Sector ◽  
Time Cost ◽  
The Impact

The construction sector in India employs nearly 60 million people, so the unprecedented two-month lockdown to slow the spread of Covid-19 in 2020 had devastating economic and social effects. The reduced demand for projects slowed demand for downstream industries, increased labour migration to villages and reduced logistics support for supplies and resources. This paper reports on the challenges experienced by one of India’s leading construction organisations on a major metro contract in Mumbai. It describes the impact of the pandemic on project delivery, including time, cost and supply chain issues, and discusses the mitigation strategies adopted.

Implementation of Fuzzy Logic Control on Battery Charging System

2019 ◽  
Vol 3 (1) ◽  
pp. 118-126 ◽  
Author(s):  
Prihangkasa Yudhiyantoro
Keyword(s):  
Complex System ◽  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Expert Knowledge ◽  
Experimental Result ◽  
Rule Base ◽  
Battery Charging ◽  
Charging System ◽  
Logic Control ◽  
Charging Control

This paper presents the implementation fuzzy logic control on the battery charging system. To control the charging process is a complex system due to the exponential relationship between the charging voltage, charging current and the charging time. The effective of charging process controller is needed to maintain the charging process. Because if the charging process cannot under control, it can reduce the cycle life of the battery and it can damage the battery as well. In order to get charging control effectively, the Fuzzy Logic Control (FLC) for a Valve Regulated Lead-Acid Battery (VRLA) Charger is being embedded in the charging system unit. One of the advantages of using FLC beside the PID controller is the fact that, we don’t need a mathematical model and several parameters of coefficient charge and discharge to software implementation in this complex system. The research is started by the hardware development where the charging method and the combination of the battery charging system itself to prepare, then the study of the fuzzy logic controller in the relation of the charging control, and the determination of the parameter for the charging unit will be carefully investigated. Through the experimental result and from the expert knowledge, that is very helpful for tuning of the  embership function and the rule base of the fuzzy controller.

The Impact of a Potential Public/Private Initiative on a Defined Region of the South Suburban Chicago Metropolitan Area

2019 ◽  
pp. 14-32
Keyword(s):  
Supply Chain ◽  
Economic Impact ◽  
Impact Analysis ◽  
Census Data ◽  
Economic Benefits ◽  
Local Economy ◽  
Market Area ◽  
State And Local ◽  
The University ◽  
The Impact

The university is considered one of the engines of growth in a local economy or its market area, since its direct contributions consist of 1) employment of faculty and staff, 2) services to students, and supply chain links vendors, all of which define the University’s Market area. Indirect contributions consist of those agents associated with the university in terms of community and civic events. Each of these activities represent economic benefits to their host communities and can be classified as the economic impact a university has on its local economy and whose spatial market area includes each of the above agents. In addition are the critical links to the University, which can be considered part of its Demand and Supply chain. This paper contributes to the field of Public/Private Impact Analysis, which is used to substantiate the social and economic benefits of cooperating for economic resources. We use Census data on Output of Goods and Services, Labor Income on Salaries, Wages and Benefits, Indirect State and Local Taxes, Property Tax Revenue, Population, and Inter-Industry to measure economic impact (Implan, 2016).

ESTIMATING GAS AND ROCK OUTBURST RISK ON THE BASIS OF KNOWLEDGE AND EXPERIENCE – THE EXPERT SYSTEM BASED ON FUZZY LOGIC

2014 ◽  
Vol 59 (1) ◽  
pp. 41-52 ◽  
Author(s):  
Norbert Skoczylas
Keyword(s):  
Fuzzy Logic ◽  
Expert System ◽  
Mining Industry ◽  
Automatic Assessment ◽  
Science Field ◽  
Second Stage ◽  
Two Stages ◽  
Outburst Hazard

Abstract The Author endeavored to consult some of the Polish experts who deal with assessing and preventing outburst hazards as to their knowledge and experience. On the basis of this knowledge, an expert system, based on fuzzy logic, was created. The system allows automatic assessment of outburst hazard. The work was completed in two stages. The first stage involved researching relevant sources and rules concerning outburst hazard, and, subsequently, determining a number of parameters measured or observed in the mining industry that are potentially connected with the outburst phenomenon and can be useful when estimating outburst hazard. Then, the Author contacted selected experts who are actively involved in preventing outburst hazard, both in the industry and science field. The experts were anonymously surveyed, which made it possible to select the parameters which are the most essential in assessing outburst hazard. The second stage involved gaining knowledge from the experts by means of a questionnaire-interview. Subjective opinions on estimating outburst hazard on the basis of the parameters selected during the first stage were then systematized using the structures typical of the expert system based on fuzzy logic.

Sign in / Sign up

Export Citation Format

Share Document