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Traveling Salesman Problem. IEOR 4405 Production Scheduling Professor Stein Sally Kim James Tsai April 30, 2009. TSP Defined. Given a list of cities and their pairwise distances, find the shortest tour that visits each city exactly once

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traveling salesman problem

Traveling Salesman Problem

IEOR 4405 Production Scheduling

Professor Stein

Sally Kim

James Tsai

April 30, 2009

tsp defined
TSP Defined

Given a list of cities and their pairwise distances, find the shortest tour that visits each city exactly once

Well-known NP-hard combinatorial optimization problem

Used to model planning, logistics, and even genome sequencing

project objectives
Project Objectives

Perform a literature search of the TSP

Find interesting, real-life applications

Discover algorithms uncovering optimal solutions

fuzzy multi objective lp approach
Fuzzy Multi-objective LP Approach
  • “Fuzzy Multi-objective Linear Programming Approach for Traveling Salesman Problem” (Rehmat, Amna; 2007)
  • Ideal solution would solve every TSP to optimality
    • Proven not only to be difficult, but also unrealistic
    • Impossible to have all constraints and resources in exact form – always vagueness
    • “Fuzzy Logic”: vague or imprecise data off which decisions are made
multi objective lp
Multi-objective LP
  • Takes a general linear multiple criteria decision making model and represents it as follows:
    • Find a vector xT = [x1, x2, … ,xn] which maximizes k objective functions, with n variables and m constraints

Opt Z = CX

s.t. AX <= b

Z = (z1, z2,…,zn) is the vector of objectives, C is a K x N matrix of constants and X is an Nx1 vector of decision variables, A is an M x N matrix of constants and b is a Mx1 vector of constants

fuzzy multi objective lp approach6
Fuzzy Multi-objective LP Approach
  • Modify the multi-objective LP formulation to:

Max Cx >=~Z0

s.t. AX<=~b

Where Z0=(z10,z20,…zn0) are aspiration levels and >=~ are fuzzy inequalities

  • Consider a case of TSP with 3 objectives: minimize cost, time, and overall distance
ant colony optimization
Ant Colony Optimization
  • ACO is a population based probabilistic technique for solving NP-hard combinatorial problems

“An interactive simulation and analysis software for solving TSP using Ant Colony Optimization algorithms” (Ugur, Aybars; 2008)

ant colony optimization8
Ant Colony Optimization

Simulation and analysis software are developed for solving TSP using ACO algorithm

Web-based tool employing virtual ants and interactive graphics to produce near-optimal solutions to the TSP

Artificial ants build solutions and exchange them with others via a communication scheme

ant colony optimization9
Ant Colony Optimization

ConstructSolutions: each ant starts at a particular state, then traverses the states one by one

ApplyLocalSearch: before updating the ant’s trail, a local search can be applied on each solution constructed

UpdateTrails: after the solutions are constructed and calculated, pheromone levels increase and decrease on paths according to favorability

ant colony optimization10
Ant Colony Optimization
  • Simulator TSPAntSim provides analysis of algorithms textually and graphically
    • Best tour-so-far represents the best found thus far
    • Tour best represents the best any tour length after
    • Standard deviation illustrates the evolution of the standard deviation of populations’ tour length
conclusions
Conclusions

While finding the exact solution is often desired in problems of optimality, this is sometimes not realistic

Relaxation and modification are some ways to approach a NP-hard problem that is otherwise difficult to solve