TSP is np- hard problem which has number of solution but it's difficult to find optimal solution . I gave here fast,easy and efficient solution on TSP using one algorithm with good explanation.Hope you understood very well.
TSP is np- hard problem which has number of solution but it's difficult to find optimal solution . I gave here fast,easy and efficient solution on TSP using one algorithm with good explanation.Hope you understood very well.
In computer science, divide and conquer is an algorithm design paradigm based on multi-branched recursion. A divide-and-conquer algorithm works by recursively breaking down a problem into two or more sub-problems of the same or related type until these become simple enough to be solved directly.
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
In computer science, divide and conquer is an algorithm design paradigm based on multi-branched recursion. A divide-and-conquer algorithm works by recursively breaking down a problem into two or more sub-problems of the same or related type until these become simple enough to be solved directly.
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
Linkedin - Analisi della User ExperienceMatteo Losi
Analisi della user experience di LinkedIn mediante analisi euristica esperta, interviste strutturate e task analisys al fine di un redesign mirato alla risoluzione delle criticità.
Unsteady MHD Flow Past A Semi-Infinite Vertical Plate With Heat Source/ Sink:...IJERA Editor
In the present paper a numerical attempt is made to study the combined effects of heat source and sink on unsteady laminar boundary layer flow of a viscous, incompressible, electrically conducting fluid along a semiinfinite vertical plate. A magnetic field of uniform strength is applied normal to the flow. The governing boundary layer equations are solved numerically, using Crank-Nicolson method. Graphical results of velocity and temperature fields, tabular values of Skin-friction and Nusselt are presented and discussed at various parametric conditions. From this study, it is found that the velocity and temperature of the fluid increase in the presence of heat source but they decrease in the presence of heat absorption parameter.
Fixed Position Constraints in a Travelling Salesman Problem (TSP) with Multip...dbpublications
In this paper, we have considered
fixed position constraints in the usual
travelling salesman problem with
multiple job facilities at each station.
With fixed position(s) constraints one
means that the station(s) (cities or nodes)
are visited in such a way that a particular
station(s) is to be visited in a certain
specific step(s).
The problem can be described as
follows:
There are ‘N’ stations to be
visited and ‘M’ distinct jobs to be
performed by a salesman. The distance
between each pair of stations and
facilities for jobs at each station, are
known. The salesman starts from a
station (home station denoted as ‘A0’)
and returns back to it after completing all
the jobs on the basis of performing the
jobs as early as possible. The objective is
to find a tour of the salesman by using
the fixed position constraints such that
the total distance traveled is minimum
while completing all the ‘M’ jobs, on the
basis of first come first serve.
Traveling Salesman Problem in Distributed Environmentcsandit
In this paper, we focus on developing parallel algorithms for solving the traveling salesman problem (TSP) based on Nicos Christofides algorithm released in 1976. The parallel algorithm
is built in the distributed environment with multi-processors (Master-Slave). The algorithm is installed on the computer cluster system of National University of Education in Hanoi,
Vietnam (ccs1.hnue.edu.vn) and uses the library PJ (Parallel Java). The results are evaluated and compared with other works.
TRAVELING SALESMAN PROBLEM IN DISTRIBUTED ENVIRONMENTcscpconf
In this paper, we focus on developing parallel algorithms for solving the traveling salesman
problem (TSP) based on Nicos Christofides algorithm released in 1976. The parallel algorithm
is built in the distributed environment with multi-processors (Master-Slave). The algorithm is
installed on the computer cluster system of National University of Education in Hanoi,
Vietnam (ccs1.hnue.edu.vn) and uses the library PJ (Parallel Java). The results are evaluated
and compared with other works.
Simulators play a major role in analyzing multi-modal transportation networks. As their complexity increases, optimization becomes an increasingly challenging task. Current calibration procedures often rely on heuristics, rules of thumb and sometimes on brute-force search. Alternatively, we provide a statistical method which combines a distributed, Gaussian Process Bayesian optimization method with dimensionality reduction techniques and structural improvement. We then demonstrate our framework on the problem of calibrating a multi-modal transportation network of city of Bloomington, Illinois. Our framework is sample efficient and supported by theoretical analysis and an empirical study. We demonstrate on the problem of calibrating a multi-modal transportation network of city of Bloomington, Illinois. Finally, we discuss directions for further research.
Fuzzy transform for high-resolution satellite images compressionTELKOMNIKA JOURNAL
Many compression methods have been developed until now, especially for very high-resolution satellites images, which, due to the massive information contained in them, need compression for a more efficient storage and transmission. This paper modifies Perfilieva's Fuzzy transform using pseudo-exponential function to compress very high-resolution satellite images. We found that very high-resolution satellite images can be compressed by F-transform with pseudo-exponential function as the membership function. The compressed images have good quality as shown by the PSNR values ranging around 59-66 dB. However, the process is quite time-consuming with average 187.1954 seconds needed to compress one image. These compressed images qualities are better than the standard compression methods such as CCSDS and Wavelet method, but still inferior regarding time consumption.
Contents of the presentation:
- ABOUT ME
- Bisection Method using C#
- False Position Method using C#
- Gauss Seidel Method using MATLAB
- Secant Mod Method using MATLAB
- Report on Numerical Errors
- Optimization using Golden-Section Algorithm with Application on MATLAB
How to Run Landing Page Tests On and Off Paid Social PlatformsVWO
Join us for an exclusive webinar featuring Mariate, Alexandra and Nima where we will unveil a comprehensive blueprint for crafting a successful paid media strategy focused on landing page testing.With escalating costs in paid advertising, understanding how to maximize each visitor’s experience is crucial for retention and conversion.
This session will dive into the methodologies for executing and analyzing landing page tests within paid social channels, offering a blend of theoretical knowledge and practical insights.
The Pearmill team will guide you through the nuances of setting up and managing landing page experiments on paid social platforms. You will learn about the critical rules to follow, the structure of effective tests, optimal conversion duration and budget allocation.
The session will also cover data analysis techniques and criteria for graduating landing pages.
In the second part of the webinar, Pearmill will explore the use of A/B testing platforms. Discover common pitfalls to avoid in A/B testing and gain insights into analyzing A/B tests results effectively.
Mastering Local SEO for Service Businesses in the AI Era is tailored specifically for local service providers like plumbers, dentists, and others seeking to dominate their local search landscape. This session delves into leveraging AI advancements to enhance your online visibility and search rankings through the Content Factory model, designed for creating high-impact, SEO-driven content. Discover the Dollar-a-Day advertising strategy, a cost-effective approach to boost your local SEO efforts and attract more customers with minimal investment. Gain practical insights on optimizing your online presence to meet the specific needs of local service seekers, ensuring your business not only appears but stands out in local searches. This concise, action-oriented workshop is your roadmap to navigating the complexities of digital marketing in the AI age, driving more leads, conversions, and ultimately, success for your local service business.
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Embrace AI for Local SEO: Learn to harness the power of AI technologies to optimize your website and content for local search. Understand the pivotal role AI plays in analyzing search trends and consumer behavior, enabling you to tailor your SEO strategies to meet the specific demands of your target local audience. Leverage the Content Factory Model: Discover the step-by-step process of creating SEO-optimized content at scale. This approach ensures a steady stream of high-quality content that engages local customers and boosts your search rankings. Get an action guide on implementing this model, complete with templates and scheduling strategies to maintain a consistent online presence. Maximize ROI with Dollar-a-Day Advertising: Dive into the cost-effective Dollar-a-Day advertising strategy that amplifies your visibility in local searches without breaking the bank. Learn how to strategically allocate your budget across platforms to target potential local customers effectively. The session includes an action guide on setting up, monitoring, and optimizing your ad campaigns to ensure maximum impact with minimal investment.
A.I. (artificial intelligence) platforms are popping up all the time, and many of them can and should be used to help grow your brand, increase your sales and decrease your marketing costs.In this presentation:We will review some of the best AI platforms that are available for you to use.We will interact with some of the platforms in real-time, so attendees can see how they work.We will also look at some current brands that are using AI to help them create marketing messages, saving them time and money in the process. Lastly, we will discuss the pros and cons of using AI in marketing & branding and have a lively conversation that includes comments from the audience.
Key Takeaways:
Attendees will learn about LLM platforms, like ChatGPT, and how they work, with preset examples and real time interactions with the platform. Attendees will learn about other AI platforms that are creating graphic design elements at the push of a button...pre-set examples and real-time interactions.Attendees will discuss the pros & cons of AI in marketing + branding and share their perspectives with one another. Attendees will learn about the cost savings and the time savings associated with using AI, should they choose to.
Short video marketing has sweeped the nation and is the fastest way to build an online brand on social media in 2024. In this session you will learn:- What is short video marketing- Which platforms work best for your business- Content strategies that are on brand for your business- How to sell organically without paying for ads.
The What, Why & How of 3D and AR in Digital CommercePushON Ltd
Vladimir Mulhem has over 20 years of experience in commercialising cutting edge creative technology across construction, marketing and retail.
Previously the founder and Tech and Innovation Director of Creative Content Works working with the likes of Next, John Lewis and JD Sport, he now helps retailers, brands and agencies solve challenges of applying the emerging technologies 3D, AR, VR and Gen AI to real-world problems.
In this webinar, Vladimir will be covering the following topics:
Applications of 3D and AR in Digital Commerce,
Benefits of 3D and AR,
Tools to create, manage and publish 3D and AR in Digital Commerce.
Videos are more engaging, more memorable, and more popular than any other type of content out there. That’s why it’s estimated that 82% of consumer traffic will come from videos by 2025.
And with videos evolving from landscape to portrait and experts promoting shorter clips, one thing remains constant – our brains LOVE videos.
So is there science behind what makes people absolutely irresistible on camera?
The answer: definitely yes.
In this jam-packed session with Stephanie Garcia, you’ll get your hands on a steal-worthy guide that uncovers the art and science to being irresistible on camera. From body language to words that convert, she’ll show you how to captivate on command so that viewers are excited and ready to take action.
How to Use AI to Write a High-Quality Article that Ranksminatamang0021
In the world of content creation, many AI bloggers have drifted away from their original vision, resulting in low-quality articles that search engines overlook. Don't let that happen to you! Join us to discover how to leverage AI tools effectively to craft high-quality content that not only captures your audience's attention but also ranks well on search engines.
Disclaimer: Some of the prompts mentioned here are the examples of Matt Diggity. Please use it as reference and make your own custom prompts.
First Things First: Building and Effective Marketing Strategy
Too many companies (and marketers) jump straight into activation planning without formalizing a marketing strategy. It may seem tedious, but analyzing the mindset of your targeted audiences and identifying the messaging points most likely to resonate with them is time well spent. That process is also a great opportunity for marketers to collaborate with sales leaders and account managers on a galvanized go-to-market approach. I’ll walk you through the methods and tools we use with our clients to ensure campaign success.
Key Takeaways:
-Recognize the critical role of strategy in marketing
-Learn our approach for building an actionable, effective marketing strategy
-Receive templates and guides for developing a marketing strategy
Mastering Multi-Touchpoint Content Strategy: Navigate Fragmented User JourneysSearch Engine Journal
Digital platforms are constantly multiplying, and with that, user engagement is becoming more intricate and fragmented.
So how do you effectively navigate distributing and tailoring your content across these various touchpoints?
Watch this webinar as we dive into the evolving landscape of content strategy tailored for today's fragmented user journeys. Understanding how to deliver your content to your users is more crucial than ever, and we’ll provide actionable tips for navigating these intricate challenges.
You’ll learn:
- How today’s users engage with content across various channels and devices.
- The latest methodologies for identifying and addressing content gaps to keep your content strategy proactive and relevant.
- What digital shelf space is and how your content strategy needs to pivot.
With Wayne Cichanski, we’ll explore innovative strategies to map out and meet the diverse needs of your audience, ensuring every piece of content resonates and connects, regardless of where or how it is consumed.
The session includes a brief history of the evolution of search before diving into the roles technology, content, and links play in developing a powerful SEO strategy in a world of Generative AI and social search. Discover how to optimize for TikTok searches, Google's Gemini, and Search Generative Experience while developing a powerful arsenal of tools and templates to help maximize the effectiveness of your SEO initiatives.
Key Takeaways:
Understand how search engines work
Be able to find out where your users search
Know what is required for each discipline of SEO
Feel confident creating an SEO Plan
Confidently measure SEO performance
It's another new era of digital and marketers are faced with making big bets on their digital strategy. If you are looking at modernizing your tech stack to support your digital evolution, there are a few can't miss (often overlooked) areas that should be part of every conversation. We'll cover setting your vision, avoiding siloes, adding a democratized approach to data strategy, localization, creating critical governance requirements and more. Attendees will walk away with actions they can take into initiatives they are running today and consider for the future.
In this presentation, Danny Leibrandt explains the impact of AI on SEO and what Google has been doing about it. Learn how to take your SEO game to the next level and win over Google with his new strategy anyone can use. Get actionable steps to rank your name, your business, and your clients on Google - the right way.
Key Takeaways:
1. Real content is king
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Most small businesses struggle to see marketing results. In this session, we will eliminate any confusion about what to do next, solving your marketing problems so your business can thrive. You’ll learn how to create a foundational marketing OS (operating system) based on neuroscience and backed by real-world results. You’ll be taught how to develop deep customer connections, and how to have your CRM dynamically segment and sell at any stage in the customer’s journey. By the end of the session, you’ll remove confusion and chaos and replace it with clarity and confidence for long-term marketing success.
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https://nidmindia.com/
1. Fuzzy multi objective linear programming for traveling salesman
problem
Guided by: Presented by:
Prof. T.R. Gulati Vishwadeep Gautam
Dept. of Mathematics, (12312032), MSM
I.I.T. Roorkee
2. Content
Introduction
Membership function and Linear Programming
Multi objective linear programming
Fuzzy multi objective linear programming(FMOLP)
FMOLP approach for Traveling Salesman Problem
Case study of Traveling Salesman Problem
Conclusion
References
3. Introduction
Traveling Salesman Problem (TSP) is an important real life problem in
artificial intelligence and operations research domain.
TSP is well-known among NP-hard combinatorial optimization
problems. It represents a class of problems which are analogous to
finding the least-cost sequence for visiting a set of cities, starting and
ending at the same city in such a way that each city is visited exactly
once
TSP in order to simultaneously minimize the cost, distance and time.
4. Why we use FMOLP
Information about real life systems is often available in the form of
vague descriptions. Hence, fuzzy methods are designed to handle
vague terms, and are most suited to finding optimal solutions to
problems with vague parameter.
Fuzzy multi-objective linear programming (FMOLP), an amalgamation
of fuzzy logic and multi objective linear programming, deals with
flexible aspiration levels or goals and fuzzy constraints with acceptable
deviations.
5. Membership Function and LP
A membership function µ 𝐴(x) is characterized by µ 𝐴 : X → [0,1], x ∈ X,
where x is a real number describing on object or it’s attribute, X is the
universal of discourse and A is subset of X.
A Fuzzy Membership function is mapped on interval [0,1]which is an
arbitrary grade of truth.
The general linear programming Problem model, for maximization, proposed
by Dantzig is:
Max Z = 𝑖=1
𝑛
𝐶𝑖 𝑋𝑖 (1)
Subject to 𝑖=1
𝑛
𝑎𝑖𝑗 𝑥𝑖 ≤ 𝑏𝑖 ; (j=1,2…,m) and 𝑥𝑖 ≥ 0
Where Z is the objective function, 𝒙𝒊 are the decision variables, m is
the number of constraints, n is the number of decision variables, and 𝒃𝒊 are
the given resources.
6. Multi Objective Linear Programming
Linear Programming is limited by the fact that it can deal only with
single objective function and does not incorporate soft constraints. So
we use multi objective linear programming .
A general Linear Multiple Criteria Decision Making model can be
represented as follows:
Find a vector such that 𝑋 𝑇= [𝑥1, 𝑥2, … , 𝑥 𝑛] which maximize k objective
functions with n variables and m constraints as:
𝑍𝑖 = 𝑗=1
𝑛
𝐶𝑖𝑗 𝑥𝑗 ; i=1,2,…,k., (2)
Subject to 𝑖=1
𝑛
𝑎𝑖𝑗 𝑥𝑖 ≤ 𝑏𝑗 ; j=1,2,…m., Where 𝑐𝑖𝑗 , 𝑎𝑖𝑗 𝑎𝑛𝑑 𝑏𝑗 are given
crisp values
7. Cont.
In precise form, multiple objective problems can be represented by
following Multi-Objective Linear Programming model:
optimize Z = CX (3)
Subject to AX ≤b.
Where, Z = [𝑧1, 𝑧2, … , 𝑧 𝑛] is vector of objectives, C is K ×N matrix of
constants and X is N ×1 vector of decision variables, A is M×N matrix
of constant and b is M × 1vector of constants.
8. Fuzzy multi-objective linear programming
Considering the following Multi-Objective Linear Programming model
max Z = CX (4)
Subject to AX ≤ b
Adopted Fuzzy model by Zimmerman is given by,
max 𝑍0 ≤ CX (5)
subjected to AX ≤ b
Where 𝑍0 = [𝑧1
0
,𝑧2
0
, … , 𝑧 𝑛
0] are goals. ≥ and ≤ are fuzzy inequalities that
are fuzzifications of ≥ and ≤ respectively.
9. Coun.
For measurement of satisfaction levels of objectives and constraints
Zimmerman suggested simplest type of Membership function given by,
𝜇1𝑘(𝐶 𝑘X)=
0, if 𝐶 𝑘X ≤ 𝑍 𝑘
0
−𝑡 𝑘
1 − (𝐶 𝑘X − 𝑍 𝑘
0
)/𝑡 𝑘 , if 𝑍 𝑘
0
− 𝑡 𝑘 ≤ 𝐶 𝑘X ≤ 𝑍 𝑘
0
1, if 𝐶 𝑘X ≥ 𝑍 𝑘
0
, k = 1,2,…,n.
(6)
Where 𝑡 𝑘 Represent tolerance for objective 𝑍 𝑘 which is decided by
decision maker.
In case of minimizing objective function, Fuzzy Membership function is,
10. 𝜇1𝑘(𝐶 𝑘X)=
0, if 𝐶 𝑘X ≥ 𝑍 𝑘
0
+ 𝑡 𝑘
1 − (𝑍 𝑘
0
−𝐶 𝑘X)/𝑡 𝑘 , if 𝑍 𝑘
0
≤ 𝐶 𝑘X ≤ 𝑍 𝑘
0
+𝑡 𝑘
1, if 𝐶 𝑘X ≤ 𝑍 𝑘
0
,
k = 1,2,…,n. (7)
Another class of Fuzzy Membership functions:
𝜇2𝑖(𝑎𝑖X)=
0, if 𝑎𝑖X ≥ 𝑏𝑖 + 𝑑𝑖
1 − (𝑎𝑖X−𝑏𝑖)/𝑑𝑖 , if 𝑏𝑖 ≤ 𝑎𝑖X ≤ 𝑏𝑖 + 𝑑𝑖
1, if 𝑎𝑖X ≤ 𝑏𝑖
,
I = 1,2,…,m. (8)
𝑑𝑖 is tolerance for fuzzy resource 𝑏𝑖 for 𝑖 𝑡ℎ
constraint.
11. Cont.
As a result objective function becomes
𝑚𝑎𝑥 𝑋 {𝜇11(𝐶1X), 𝜇12(𝐶2X),…, 𝜇1𝐾(𝐶 𝐾X), 𝜇21(𝑎1X), 𝜇22(𝑎2X),…,
𝜇2𝑚(𝑎 𝑚X)} (9)
According to Fuzzy Sets, membership function of intersection of any
two or more sets is minimum Membership function of these sets, so
objective function become
𝑚𝑎𝑥 𝑋min{𝜇11(𝐶1X), 𝜇12(𝐶2X),…, 𝜇1𝐾(𝐶 𝐾X), 𝜇21(𝑎1X), 𝜇22(𝑎2X),…,
𝜇2𝑚(𝑎 𝑚X)} (10)
From this representation we have
Max CX ≥ 𝑍0 (11)
subjected to 𝛼 ≤ 1 - (𝑍0-𝐶 𝑘X) / 𝑡 𝑘, K = 1,2,…,n.
𝛼 ≤ 1 - (𝑎𝑖X − 𝑏𝑖)/𝑑𝑖 , I = 1,2,…,m and 𝛼 ≥ 1 ,X ≥0 ,
where 𝛼 is overall satisfaction level achieved with respect to solution.
12. FMLOP Approach For TSP
Considering the situation when decision maker has to determine
optimal solution of TSP with min(cost, time, distance). Let 𝑋𝑖𝑗be the link
from city i to j and
𝑋𝑖𝑗 =
1, 𝑐𝑖𝑡𝑦 𝑖 → 𝑐𝑖𝑡𝑦(𝑗)
0, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
(12)
Let 𝐶𝑖𝑗 be the cost of traveling from city(i) to city(j), also let 𝑍1
0
be the
total estimated cost of entire route for TSP and 𝑡1is tolerance for
estimated cost, then objective function for minimization of cost is
given as
𝑍1: min 𝑖=1
𝑛
𝑗=1
𝑛
𝐶𝑖𝑗 𝑋𝑖𝑗 ≤ 𝑍1
0
. (13)
13. Cont.
Let 𝑑𝑖𝑗 be the distance from city(I) to city(j). , also let 𝑍2
0
be the total
estimated distance of route for TSP and 𝑡2 is tolerance for distance,
then objective function for minimization of distance is given as
𝑍2: min 𝑖=1
𝑛
𝑗=1
𝑛
𝑑𝑖𝑗 𝑋𝑖𝑗 ≤ 𝑍2
0
. (14)
Similarly let 𝑡𝑖𝑗 be the time spent in traveling from city(i) to city(j) and
𝑍3
0
be the corresponding aspiration level for objective function for
minimization of total time and 𝑡3 be tolerance. The objective function
is given as
𝑍3: min 𝑖=1
𝑛
𝑗=1
𝑛
𝑡𝑖𝑗 𝑋𝑖𝑗 ≤ 𝑍3
0
. (15)
14. Cont.
We have the restriction in TSP that every city should be visited from
exactly one its neighboring city, and vice versa. i.e.
𝑖=1
𝑛
𝑋𝑖𝑗 = 1, ∀ j . (16)
And
𝑗=1
𝑛
𝑋𝑖𝑗 = 1, ∀ i . (17)
A route can not be selected more than once, that is
𝑋𝑖𝑗+𝑋𝑗𝑖 ≤1, ∀ i,j
and non-negativity constraints is
𝑋𝑖𝑗 ≥ 0.
These constraints collectively are expressed in vector form and fuzzy
membership functions are defined for all objective functions.
15. Case Study for TSP
Assuming that salesman starts from his home city 0; has to visit the
three cities exactly once. A map of the cities to be visited is shown in
Figure and the cities listed along with
their cost, time and distance matrix in
table-1.
Let triplet (c,d,t) represents; cost in rupees,
distance in kilometers, and time in hours
Respectively for the corresponding couple
of cites.
16. Table:1
The matrix for time, cost and distance for each pair of cities
City 0
( C , d , t )
1
( C , d , t )
2
( C , d , t )
3
( C , d , t )
0 0 , 0 , 0 20 , 5 , 4 15 , 5 , 5 11 , 3 , 2
1 20 , 5 , 4 0 , 0 , 0 30 , 5 , 3 10 , 3 , 3
2 15 , 5 , 5 30 , 5 , 3 0 , 0 , 0 20 , 10 , 2
3 11 , 3 , 2 10 , 3 , 3 20 , 10 , 2 0 , 0 , 0
17. Cont.
Let links 𝑋𝑖𝑗 be the decision variable of selection of link (i, j) from city(i)
to city(j). The three objective function 𝑍1, 𝑍2 , 𝑍3 are formulated for
cost, distance and time respectively. Their Aspiration levels are set as 65,
16, 11 by solving each objective function subject to the given constraints
in the TSP and their corresponding tolerances are decided as 5, 2, 1.
Objective functions:
Min 𝑍1=
20𝑥01+15𝑥02+11𝑥03+20𝑥10+30𝑥12+10𝑥13+15𝑥20+30𝑥21+20𝑥23
+11𝑥30+10𝑥31+20𝑥32 ≤65 (18)
Tolerance 𝑡1= 5.
18. Cont.
Min 𝑍2 = 5𝑥01+5𝑥02+3𝑥03+5𝑥10+5𝑥12+3𝑥13+5𝑥20+5𝑥21+10𝑥23
+3𝑥30+3𝑥31+10𝑥32 ≤16 (19)
𝑡2=2.
Min 𝑍3 = 4𝑥01+5𝑥02+2𝑥03+4𝑥10+3𝑥12+3𝑥13+5𝑥20+3𝑥21+2𝑥23
+2𝑥30+3𝑥31+2𝑥32 ≤11 (20)
𝑡3=1.
The fuzzy membership function for cost, distance and time objective
function are given as under based on equation (18),(19) and (20).
24. Cont.
As given in table 2, only 𝑍1 and 𝑍2 are considered and 𝑍3 is omitted;
an optimal route with 𝜶 = 0.8 is obtained.
When 𝑍3 is also considered , solution becomes infeasible on these
tolerances. Again by relaxing tolerance in 𝑍3 to 4, solution becomes
feasible. In this case, the optimal path is achieved with 𝜶 = 0.55 .
By increasing tolerance in 𝑍3from 4 to 5, an optimal solution with
𝜶 = 0.62 is obtained.
These results show that by adjusting tolerance an optimal solution to
Multi-Criteria TSP can be determined.
25. Conclusion
In this work, the analysis of the symmetric TSP as a Fuzzy problem with
vague decision parameters .
general lesson can be taken from this study is:
Multi objective TSP exists in uncertain or vague environment where route
selection is done by exploiting these parameters.
The tolerances are introduced by the decision maker to accommodate this
vagueness.
By adjusting these tolerances, a range of solutions with different aspiration
level are found from which decision maker can choose the one that best
meets his satisfactory level within the given domain of tolerances.
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