Adjusting Trip Rates in the Cross-Classification Table by Using the Fuzzy Optimization Method

2003 ◽  
Vol 1836 (1) ◽  
pp. 76-82
Author(s):  
Shinya Kikuchi ◽  
Jongho Rhee
Keyword(s):  
Travel Demand ◽  
Fuzzy Optimization ◽  
Optimization Method ◽  
Fuzzy Linear Programming ◽  
Actual Number ◽  
Linear Programming Method ◽  
Real World Data ◽  
Cross Classification ◽  
Trip Production ◽  
Classification Table

Trip-production rates presented in cross-classification tables are essential data for the planner’s understanding of the travel characteristics of a region. Trip rates obtained from surveys, however, often show a pattern that is not consistent with what is expected by the analyst; for example, the greater the household size and auto ownership, the greater the number of trips generated. This pattern may not be found in the trip rates that are obtained directly by the survey. In such cases, analysts commonly adjust the irregularities manually. The way in which the values are adjusted affects the credibility of the trip table and, ultimately, the forecast travel demand. A method that adjusts the values of the trip table systematically is presented. The process uses the fuzzy linear programming method. The objective is to make the adjusted value as close to the observed value as possible. The constraints are to make the adjusted values adhere to the analyst’s general expectations about the pattern of the values in the table, and to match the number of trips estimated from the adjusted trip table with the actual number of trips surveyed. An application example that uses real-world data is given.

Multi-Objective Fuzzy Linear Programming for Land Allocation under Different Crops in Bhagwanpur Distributary

2019 ◽  
Vol 6 (04) ◽  
Author(s):  
ASHUTOSH UPADHYAYA
Keyword(s):  
Linear Programming ◽  
Crop Production ◽  
Fuzzy Linear Programming ◽  
Programming Method ◽  
Command Area ◽  
Linear Programming Method ◽  
Net Return ◽  
Multi Objective ◽  
Labour Requirement ◽  
Canal Command Area

A study was undertaken in Bhagwanpur distributary of Vaishali Branch Canal in Gandak Canal Command Area, Bihar to optimally allocate land area under different crops (rice and maize in kharif, wheat, lentil, potato in rabi and green gram in summer) in such a manner that maximizes net return, maximizes crop production and minimizes labour requirement employing simplex linear programming method and Multi-Objective Fuzzy Linear Programming (MOFLP) method. Maximum net return, maximum agricultural production, and minimum labour required under defined constraints (including 10% affinity level of farmers to rice and wheat crops) as obtained employing Simplex method were ` 3.7 × 108, 5.06 × 107 Kg and 66,092 man-days, respectively, whereas Multi-Objective Fuzzy Linear Programming (MOFLP) method yielded compromised solution with net return, crop production and labour required as ` 2.4 × 108, 3.3 × 107Kg and 1,79,313 man-days, respectively. As the affinity level of farmers to rice and wheat crops increased from 10% to 40%, maximum net return and maximum production as obtained from simplex linear programming method and MOFLP followed a decreasing trend and minimum labour required followed an increasing trend. MOFLP may be considered as one of the best capable ways of providing a compromised solution, which can fulfill all the objectives at a time.

scholarly journals Seeking optimal groundwater pumping strategies at Pinggu District in Beijing, China

2012 ◽  
Vol 15 (2) ◽  
pp. 607-619 ◽  
Author(s):  
A. L. Yang ◽  
G. H. Huang ◽  
X. S. Qin ◽  
L. Li ◽  
W. Li
Keyword(s):  
Groundwater Management ◽  
Fuzzy Optimization ◽  
Cost Effective ◽  
Optimization Method ◽  
Chance Constrained Programming ◽  
Groundwater Pumping ◽  
Simulation And Optimization ◽  
Study Results ◽  
Pumping Rates ◽  
Beijing China

A simulation-based fuzzy optimization method (SFOM) was proposed for regional groundwater pumping management in considering uncertainties. SFOM enhanced the traditional groundwater management models by incorporating a response matrix model (RMM) into a fuzzy chance-constrained programming (FCCP) framework. RMM was used to approximate the input–output relationship between pumping actions and subsurface hydrologic responses. Due to its explicit expression, RMM could be easily embedded into an optimization model to help seek cost-effective pumping solutions. A groundwater management case in Pinggu District of Beijing, China, was used to demonstrate the method's applicability. The study results showed that the obtained system cost and pumping rates would vary significantly under different confidence levels of constraints satisfaction. The decision-makers could identify the best groundwater pumping strategy through analyzing the tradeoff between the risk of violating the related water resources conservation target and the economic benefit. Compared with traditional approaches, SFOM was particularly advantageous in linking simulation and optimization models together, and tackling uncertainties using fuzzy chance constraints.

Application of Fuzzy Logic and Fuzzy Optimization Techniques in Medical Image Processing

Biometrics ◽  
2017 ◽  
pp. 907-932 ◽  
Author(s):  
Niladri Sekhar Datta ◽  
Himadri Sekhar Dutta ◽  
Koushik Majumder
Keyword(s):  
Mathematical Model ◽  
Fuzzy Logic ◽  
Optimization Technique ◽  
Fuzzy Optimization ◽  
Fuzzy Model ◽  
Optimization Method ◽  
Fuzzy Modeling ◽  
Optimization Techniques ◽  
Fuzzy Variables ◽  
Truth Value

Fuzzy logic deals with approximate rather than fixed and exact reasoning. Fuzzy variables may have a truth value that ranges in degree between 0 and 1; extended to handle the concept of partial truth where the truth value may range between completely true or completely false. This computational logic uses truth degrees as a mathematical model of the vagueness phenomenon while probability is a mathematical model of ignorance. A huge number of complex problems may be solve using Fuzzy logic specifically Fuzzy modeling and optimization method. Fuzzy modeling is the understanding of the problem and analysis of the Fuzzy information where the Fuzzy optimization solves Fuzzy model optimally using optimization techniques via membership functions. In this research article authors describe the Fuzzy rules and its application and the different types of well known problems solved by the Fuzzy optimization technique.

scholarly journals Disparities in travel times between car and transit: Spatiotemporal patterns in cities

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yuan Liao ◽  
Jorge Gil ◽  
Rafael H. M. Pereira ◽  
Sonia Yeh ◽  
Vilhelm Verendel
Keyword(s):  
Travel Time ◽  
Travel Demand ◽  
Time Of Day ◽  
Sao Paulo ◽  
São Paulo ◽  
Real World Data ◽  
Car Use ◽  
Real Time Traffic ◽  
Average Travel Time ◽  
Car Travel

AbstractCities worldwide are pursuing policies to reduce car use and prioritise public transit (PT) as a means to tackle congestion, air pollution, and greenhouse gas emissions. The increase of PT ridership is constrained by many aspects; among them, travel time and the built environment are considered the most critical factors in the choice of travel mode. We propose a data fusion framework including real-time traffic data, transit data, and travel demand estimated using Twitter data to compare the travel time by car and PT in four cities (São Paulo, Brazil; Stockholm, Sweden; Sydney, Australia; and Amsterdam, the Netherlands) at high spatial and temporal resolutions. We use real-world data to make realistic estimates of travel time by car and by PT and compare their performance by time of day and by travel distance across cities. Our results suggest that using PT takes on average 1.4–2.6 times longer than driving a car. The share of area where travel time favours PT over car use is very small: 0.62% (0.65%), 0.44% (0.48%), 1.10% (1.22%) and 1.16% (1.19%) for the daily average (and during peak hours) for São Paulo, Sydney, Stockholm, and Amsterdam, respectively. The travel time disparity, as quantified by the travel time ratio $$R$$R (PT travel time divided by the car travel time), varies widely during an average weekday, by location and time of day. A systematic comparison between these two modes shows that the average travel time disparity is surprisingly similar across cities: $$R < 1$$R<1 for travel distances less than 3 km, then increases rapidly but quickly stabilises at around 2. This study contributes to providing a more realistic performance evaluation that helps future studies further explore what city characteristics as well as urban and transport policies make public transport more attractive, and to create a more sustainable future for cities.

scholarly journals Fuzzy Optimization Modeling Approach for QFD-Based New Product Design

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Zhihui Yang ◽  
Yizeng Chen
Keyword(s):  
Customer Satisfaction ◽  
Product Design ◽  
Quality Function Deployment ◽  
Programming Model ◽  
Fuzzy Optimization ◽  
New Product ◽  
Optimal Level ◽  
Fuzzy Linear Programming ◽  
Target Market ◽  
Functional Relationships

Quality function deployment (QFD) is a planning and problem-solving tool for translating customer requirements (CRs) into the engineering characteristics (ECs) of a product. Owing to the typical vagueness of functional relationships in a new product, product planning is becoming more difficult under uncertainties. To tackle the vagueness or imprecision in QFD, numerous scholars have applied the fuzzy set theory to QFD and proposed various fuzzy QFD models. In this study, a fuzzy linear programming model is developed to determine the optimal level of ECs, where the objective function is the overall customer satisfaction and the cost constraint is fuzzified. Finally, we use a software product design as a numerical example, which demonstrates that the proposed methodology can help the QFD team realize the overall customer satisfaction of new products catching up with or exceeding the competitors in the target market.

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