scholarly journals Fuzzy Approach to Decision Support System Design for Inventory Control and Management

2019 ◽  
Vol 28 (4) ◽  
pp. 549-557
Author(s):  
Mahuya Deb ◽  
Prabjot Kaur ◽  
Kandarpa Kumar Sarma
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Inventory Control ◽  
Inventory Management ◽  
Support System ◽  
Large Scale ◽  
Fuzzy Inference ◽  
Neural System ◽  
Inference System ◽  
Neuro Fuzzy

Abstract The ubiquitous nature of inventory and its reliance on a reliable decision support system (DSS) is crucial for ensuring continuous availability of goods. The DSS needs to be designed in a manner that enables it to highlight its present status. Further, the DSS should be able to provide indications about subtle and large-scale variations that are likely to occur in the supply chain within the context of the decision-making framework and inventory management. However, while dealing with the parameters of the system, it is observed that its operations and mechanisms are surrounded by uncertain, imprecise, and vague environments. Fuzzy-based approaches are best suited for such situations; however, these require assistance from learning systems like artificial neural network (ANN) to facilitate automated decision support. When ANN and fuzzy are combined, the fuzzy neural system and the neuro-fuzzy system (NFS) are formulated. The model of the DSS reported here is based on a framework commonly known as adaptive neuro-fuzzy inference system (ANFIS), which is a version of NFS. The configured model has the advantages of both the ANN and fuzzy systems, and has been tested for the design of a DSS for use as part of inventory control. In this work, we report the design of an ANFIS-based DSS configured to work as DSS for inventory management. The system accepts demand as input and generates procurement, ordering, and holding cost to control production and supply. The system deals with a certain profitability rating required to quantify the changes in the input and is combined with the day-to-day inventory records and demand-available cycle. The effectiveness of the system has been checked in terms of number and types of membership used, accuracy generated, and computational efficiency accounted by the computation cycles required.

scholarly journals Intelligent Decision Support System for Home Automation - ANFIS Based Approach

2018 ◽  
Vol 7 (2.24) ◽  
pp. 421
Author(s):  
Reena K ◽  
Veeramuth Venkatesh
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Fuzzy Inference ◽  
Home Automation ◽  
Simple Method ◽  
Inference System ◽  
Intelligent Decision Support System ◽  
Neuro Fuzzy ◽  
Intelligent Decision Support

Internet of Things (IoT) deals with the idea of remotely multiplexing and monitoring real-world things via Internet. This idea can be incorporated at home appliances to make it smarter, safer and automated. This design focuses on constructing a smart, wireless home security system that gives alerts to the respective user through the Internet. This paper gives a smart decision support system to various home automation services using fuzzy logic. The proposed system is built on Adaptive Neuro-Fuzzy Inference System (ANFIS). It’s a kind of artificial neural network that is used here for deciding on the changes taking place in the sensing unit. Through this simple method, various parameters such as temperature level, light intensity and human presenc eare monitored and controlled. 

scholarly journals Neuro-fuzzy inference systems approach to decision support system for economic order quantity

2019 ◽  
Vol 32 (1) ◽  
pp. 1114-1137 ◽  
Author(s):  
Siniša Sremac ◽  
Edmundas Kazimieras Zavadskas ◽  
Bojan Matić ◽  
Miloš Kopić ◽  
Željko Stević
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Systems Approach ◽  
Fuzzy Inference ◽  
Economic Order Quantity ◽  
Order Quantity ◽  
Fuzzy Inference Systems ◽  
Neuro Fuzzy ◽  
Inference Systems

scholarly journals PENENTUAN JUMLAH PRODUKSI SHAMPO DENGAN FUZZY INFERENCE SYSTEM SUGENO: STUDI KASUS PT. GUARDIAN PHARMATAMA TANGERANG

2017 ◽  
Vol 1 (2) ◽  
Author(s):  
Ri Sabti Septarini
Keyword(s):  
Fuzzy Logic ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Fuzzy Inference ◽  
Production Plan ◽  
Inference System ◽  
Fuzzy Variables ◽  
Fuzzy Input ◽  
A Company

ABSTRACTHuman are always faced with taking a decision. It also happens to a company in the process of determining which employees. In determination the production plan required a lot of considerations in case of taking decisions. Beside that, the number of employees in a company is to determine who get the production plan of the achievement. System is made to determine employees who will get benefits achievement based on the some criteria have been determined by the company. These criterias will be used as fuzzy input which also process a called fuzzy variables. In this research will construct decision support system by using fuzzy logic with fuzzy variables input that are productivity, quality tabbing and discipline. In of fuzzy logic method there are three stages, namely stage fuzzification, inference and deffuzification. At this stage of the fuzzy inference used the Sugeno method. The results of this experiments has performed that the system is able to display the production planning data for the calculation of the value of production that have been determined based on fuzzy logic with fuzzy variables. Keyword: Decision Support System, Fuzzy Logic,  Sugeno.

scholarly journals Decision Support System for Green Supplier Selection Using the Fuzzy Inference System Method in Abrasive Companies

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Defi Norita ◽  
Ririn Regiana Dwi Satya ◽  
Andary Asvaroza Munita ◽  
Asep Endih Nurhidayat
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Fuzzy Inference System ◽  
Supplier Selection ◽  
Fuzzy Inference ◽  
Inference System ◽  
Green Supplier Selection ◽  
System Method ◽  
Green Suppliers

The development of information and communication technology makes it easier for users in the industrial world to make decisions in choosing environmentally friendly suppliers more easily. This study aims to determine the selection of green suppliers of all the criteria that have been determined and make a decision support system for selecting green suppliers with the Fuzzy Inference System method. The method used in making identification of green supplier selection is to create criteria based on fuzzy rules and to make digital business modeling using business process modeling notation. Decision support there are 4 criteria used, namely price, reject quality, late delivery and environmental management. Based on the results of research conducted it is known that with the fuzzy inference system method that is assisted using matlab software, the optimization results on the fuzzy inference system show that prices are 20.5%, quality is 5.5%, environment is 5.5%, and material delays are 3%, then supplier performance in selecting green suppliers with a decision making system of 55% so that green supplier selection is obtained at abrasive companies.

scholarly journals Semi-Automatic Generation of a Fuzzy Inference System in a Reshoring Context

2020 ◽  
Author(s):  
Anders Adlemo ◽  
Per Hilletofth
Keyword(s):  
Fuzzy Logic ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Automatic Generation ◽  
Inference Rules ◽  
Inference System ◽  
Product Delivery

Reshoring can be regarded as offshoring in reverse. While offshoring mainly has been driven by cost aspects, reshoring considers multiple aspects, such as higher quality demands, faster product delivery and product mass-customization. Where to locate manufacturing is usually a purely manual activity that relies on relocation experts, hence, an automated decision-support system would be extremely useful. This paper presents a decision-support system for reshoring decision-making building a fuzzy inference system. The construction and functionality of the fuzzy inference system is briefly outlined and evaluated within a high-cost environment considering six specific reshoring decision criteria, namely cost, quality, time, flexibility, innovation and sustainability. A challenge in fuzzy logic relates to the construction of the so called fuzzy inference rules. In the relocation domain, fuzzy inference rules represent the knowledge and competence of relocation experts and are usually generated manually by the same experts. This paper presents a solution where fuzzy inference rules are automatically generated applying one hundred reshoring scenarios as input data. Another important aspect in fuzzy logic relates to the membership functions. These are mostly manually defined but, in this paper, a semi-automatic approach is presented. The reshoring decision recommendations produced by the semi-automatically configured fuzzy inference system are shown to be as accurate as those of a manually configured fuzzy inference system.

scholarly journals Development of decision support system for employee selection using Adaptive Neuro Fuzzy Inference System

2018 ◽  
Vol 154 ◽  
pp. 01077 ◽  
Author(s):  
Abdullah ‘Azzam ◽  
Sri Indrawati
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Day Care ◽  
Service Industry ◽  
Fuzzy Inference ◽  
Selection Process ◽  
Employee Selection ◽  
Inference System ◽  
The Right

The number of children day care is increasing from year to year. Children day care is categorized as service industry that help parents in caring and educate children. This type of service industry plays a substitute for the family at certain hours, usually during work hours. The common problems in this industry is related to the employee performance. Most of employees have a less understanding about the whole job. Some employees only perform a routine task, i.e. feeding, cleaning and putting the child to sleep. The role in educating children is not performed as well as possible. Therefore, the employee selection is an important process to solve a children day care problem. An effective decision support system is required to optimize the employee selection process. Adaptive neuro fuzzy inference system (ANFIS) is used to develop the decision support system for employee selection process. The data used to build the system is the historical data of employee selection process in children day care. The data shows the characteristic of job applicant that qualified and not qualified. From that data, the system can perform a learning process and give the right decision. The system is able to provide the right decision with an error of 0,00016249. It means that the decision support system that developed using ANFIS can give the right recommendation for employee selection process.

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