Fuzzy goal programming approach for multi-objective multi-mode resource constrained project scheduling problem

This research proposes fuzzy goal programming model for multi-objective multi-mode resource constrained project scheduling problem. Objectives of the problem are minimization of the total time and the total cost of the project. Currently, objective in a multi-mode resource constrained project scheduling problem is often limited to a single objective function. Moreover, all elements of cost functions in a project are not included in the cost objective function. Incomplete total project cost causes errors in finding the project scheduling time. In this research there methods;1)goal programming, 2) fuzzy linear programming and 3) preemptive fuzzy goal programming are used to solve the multi-objective multi-mode resource constrained project scheduling problem. These methods can find the compromise solution of the problem. However, the proposed preemptive fuzzy goal programming is more flexible than the others because it can adjust to find variety of alternative solutions

Solving Tea Blending Problems Using Interactive Fuzzy Multi-Objective Linear Programming

Blending is a classical and well-known optimization problem that has been applied in the food, steel, and composite material industries. However, tea blending is more complicated than general problems due to the variety of products, processes, and sources of raw materials and semi-products. So, in this research, a fuzzy multi-objective model for the tea blending problem was proposed to minimize the total production cost and the deviation of quality target score; it provides a more robust and flexible method than existing models for complex real-world problems. Existing research works of a blending problem consider only raw material cost, but semi-product cost and processing cost are included in the proposed model that matches the actual case. Losses that occur during production are also incorporated. The selection of appropriate raw materials and semi-product sources can be obtained with the preferred levels of cost and quality by the proposed algorithm. The interactive fuzzy multi-objective programming to solve the problem has advantages over existing interactive programming methods. It is easy to manipulate interactively to obtain more efficient solutions than existing methods and both balanced and unbalanced solutions can be selected. The comparison of the results of an existing approach and the interactive fuzzy multi-objective programming algorithm for the tea industry is illustrated

AGGREGATE PRODUCTION PLANNING WITH FUZZY DEMAND AND VARIABLE SYSTEM CAPACITY BASED ON TOC MEASURES

Aggregate Production Planning (APP) model with fuzzy demand and variable system capacity is proposed in this research for a practical APP problem. A conventional APP problem assumes crisp market demands and also limited capacity by fixed hardware. In the proposed model, the difficulty in estimation crisp demands is relaxed by using fuzzy demand which also increases the flexibility of estimation and obtains the better production plan that can increase profit. The new approach to handle the fuzzy demand by integrating the possibility level of demand is proposed. Moreover, the limitation of system capacity is resolved by allowing additional investment in small machines and equipment. This investment can increase the necessary production capacity and eliminate the bottleneck of the system.  Three performance measures, based on the Theory of Constraints (TOC) concept, which are currently used in many organizations, are used to evaluate performance of the model. It is found that the proposed model can generate higher performance than conventional APP models

แบบจำลองสถานการณ์ลอจิสติกส์ภายในของระบบเอจีวีในแผนกประกอบโรงงานผลิตรถยนต์Internal Logistics Simulation Based on AGV System in Assembly Section of an Automotive Manufacturer

งานวิจัยนี้เป็นการศึกษาวิเคราะห์ลอจิสติกส์ภายในของระบบเอจีวีในแผนกประกอบโรงงานผลิตรถยนต์ด้วยแบบจำลองสถานการณ์ ปัจจุบันโรงงานกรณีศึกษาใช้แรงงานคนและรถขนของลากจูง (Trucky) ในการขนส่งวัตถุดิบ แต่ทางโรงงานกำลังพิจารณาปรับระบบการขนถ่ายวัสดุเป็นพาหนะลำเลียงวัสดุอัตโนมัติหรือเอจีวี อีกทั้งยังมีแผนที่จะเพิ่มกำลังการผลิตโดยเพิ่มจากเดิม 12% ดังนั้นงานวิจัยนี้จึงมีวัตถุประสงค์เพื่อวิเคราะห์เส้นทางการเดินรถเอจีวีในการขนถ่ายวัตถุดิบเข้าสายการประกอบเพื่อให้สามารถตอบสนองต่อความต้องการของลูกค้าที่สูงขึ้นได้ นอกจากนั้นยังหาจำนวนเอจีวีที่เหมาะสม จากการศึกษากำลังการผลิตในปัจจุบัน แผนผัง ระยะทางภายในผัง และคุณสมบัติของเอจีวี เส้นทางเอจีวีจึงได้ถูกออกแบบขึ้นโดยยึดหลักการของเส้นทางที่สั้นที่สุดโดยมีจุดตัดที่ต่ำและมีการเปลี่ยนแปลงผังให้น้อยที่สุด ได้เป็น 3 รูปแบบ หลังจากนั้นการจำลองสถานการณ์ด้วยคอมพิวเตอร์ถูกนำมาช่วยวิเคราะห์ผลเปรียบเทียบแทนการทดลองในระบบจริง จากผลการจำลองสถานการณ์พบว่า ผังแบบที่ 3 เป็นทางเลือกที่ดีที่สุดที่ไม่มีจุดตัด สามารถตอบสนองความต้องการที่ระดับการผลิตเดิมได้ด้วยเอจีวีเพียง 2 คันและสามารถรองรับการขยายกำลังการผลิตได้ถึง 21%This research aims to analyze the internal logistics in the assembly section of an automotive manufacturer by computer simulation. Currently, the case study factory uses human labor and truckies for the transport of raw materials or specific items. However, the Automated Guided Vehicle System (AGV) is currently under review to replace the material handling system and the productivity has also been planned to increase by 12%. Therefore, the objective of this research is to analyze the AGVs routing in the assembly section to serve increasing customer demand and to find the appropriate number of AGVs. According to the study of capacity, plant layout, plant spatial distance and the specification of an AGV, AGV routes were designed based on the shortest path with the minimum intersection points and the minimum layout changes. There were three patterns of AGV routes. Then, a computer simulation was used to compare these three patterns in preference to actual testing in the plant. The results from the simulation showed that the third route, with no intersection point is deemed the best option. This route can meet the requirements of the existing production capability with the use of only two AGVs. Overall it can support the manufacturing capacity expansion by 21%

Comparison of ANN and ANFIS Models for AF Diagnosis Using RR Irregularities

Classification of normal sinus rhythm (NSR), paroxysmal atrial fibrillation (PAF), and persistent atrial fibrillation (AF) is crucial in order to diagnose and effectively plan treatment for patients. Current classification models were primarily developed by electrocardiogram (ECG) signal databases, which may be unsuitable for local patients. Therefore, this research collected ECG signals from 60 local Thai patients (age 52.53 ± 23.92) to create a classification model. The coefficient of variance (CV), the median absolute deviation (MAD), and the root mean square of the successive differences (RMSSD) are ordinary feature variables of RR irregularities used by existing models. The square of average variation (SAV) is a newly proposed feature that extracts from the irregularity of RR intervals. All variables were found to be statistically different using ANOVA tests and Tukey’s method with a p-value less than 0.05. The methods of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were also tested and compared to find the best classification model. Finally, SAV showed the best performance using the ANFIS model with trapezoidal membership function, having the highest system accuracy (ACC) at 89.33%, sensitivity (SE), specificity (SP), and positive predictivity (PPR) for NSR at 100.00%, 94.00%, and 89.29%, PAF at 88.00%, 90.57%, and 81.48%, and AF at 80.00%, 96.00%, and 90.91%, respectively