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View Point Planning for Inspecting Static and Dynamic Scenes with MultiRobot TeamsBudhiraja, Ashish Kumar 05 September 2017 (has links)
We study the problem of viewpoint planning in static and dynamic scenes using multirobot teams. This work is motivated by two applications: bridge inspection and environmental monitoring using Unmanned Aerial Vehicles. For static scenes, we are given a set of target points in a polygonal environment that must be monitored using robots with cameras. The goal is to compute a tour for all the robots such that every target is visible from at least one tour. We solve this problem optimally by reducing it to Generalized Travelling Salesman Problem. For dynamic scenes, we study the multirobot assignment problem for multitarget tracking. The problem can be viewed as the mixed packing and covering problem. We optimally solve the problem using Mixed Quadratic Integer Linear Program to maximize the total number of targets covered. In addition to theoretical contribution, we also present our hardware system design and findings from field experiments. / Master of Science / We study the problem of viewpoint planning in static and dynamic scenes using multirobot teams. This work is motivated by two applications: bridge inspection and environmental monitoring using Unmanned Aerial Vehicles. For static scenes, we are given a set of target points in a static 2D or 3D environment such as a bridge. Target points are key locations that we are interested to monitor using cameras on the robots. The goal is to compute a tour for all the robots such that every target location is visible from at least one robot’s tour. We want to minimize the sum of lengths of all the robot’s tours combined. We find the best possible solution for this problem. For dynamic scenes, we study the multirobot trajectory assignment problem for multitarget tracking. Here, the target points may be moving, e.g., expanding plumes in an oil spill. The goal in this is to maximize the total number of targets covered at each time step. We provide the best possible solution in this case. In addition to theoretical contribution, we also present our hardware system design and findings from field experiments.

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Tight FlowBased Formulations for the Asymmetric Traveling Salesman Problem and Their Applications to some Scheduling ProblemsTsai, PeiFang 15 June 2006 (has links)
This dissertation is devoted to the development of new flowbased formulations for the asymmetric traveling salesman problem (ATSP) and to the demonstration of their applicability in effectively solving some scheduling problems. The ATSP is commonly encountered in the areas of manufacturing planning and scheduling, and transportation logistics. The integration of decisions pertaining to production and shipping, in the supply chain context, has given rise to an additional and practical relevance to this problem especially in situations involving sequencedependent setups and routing of vehicles. Our objective is to develop new ATSP formulations so that algorithms can be built by taking advantage of their relaxations (of integer variables, thereby, resulting in linear programs) to effectively solve largesize problems.
In view of our objective, it is essential to have a formulation that is amenable to the development of an effective solution procedure for the underlying problem. One characteristic of a formulation that is helpful in this regard is its tightness. The tightness of a formulation usually refers to the quality of its approximation to the convex hull of integer feasible solutions. Another characteristic is its compactness. The compactness of a formulation is measured by the number of variables and constraints that are used to formulate a given problem. Our formulations for the ATSP and the scheduling problems that we address are both tight and compact.
We present a new class of polynomial length formulations for the asymmetric traveling salesman problem (ATSP) by lifting an ordered pathbased model using logical restrictions in concert with the ReformulationLinearization Technique (RLT). We show that a relaxed version of this formulation is equivalent to a flowbased ATSP model, which, in turn, is tighter than the formulation based on the exponential number of DantzigFulkersonJohnson (DFJ) subtour elimination constraints. The proposed lifting idea is applied to derive a variety of new formulations for the ATSP, and a detailed analysis of these formulations is carried out to show that some of these formulations are the tightest among those presented in the literature. Computational results are presented to exhibit the relative tightness of our formulations and the efficacy of the proposed lifting process.>
While the computational results demonstrate the efficacy of employing the proposed theoretical RLT and logical lifting ideas, yet it remains of practical interest to take due advantage of the tightest formulations. The key requirement to accomplish this is the ability to solve the underlying LP relaxations more effectively. One approach, to that end, is to solve these LP relaxations to (near) optimality by using deflected subgradient methods on Lagrangian dual formulations. We solve the LP relaxation of our tightest formulation, ATSP6, to (near) optimality by using a deflected subgradient algorithm with average direction strategy (SA_ADS) (see Sherali and Ulular [69]). We also use two nondifferentiable optimization (NDO) methods, namely, the variable target value method (VTVM) presented by Sherali et al. [66] and the trust region target value method (TRTV) presented by Lim and Sherali [46], on the Lagrangian dual formulation of ATSP6. The preliminary results show that the nearoptimal values obtained by the VTVM on solving the problem in the canonical format are the closest to the target optimal values. Another approach that we use is to derive a set of strong valid inequalities based on our tighter formulations through a suitable surrogation process for inclusion within the more compact manageable formulations. Our computational results show that, when the dual optimal solution is available, the associated strong valid inequalities generated from our procedure can successfully lift the LP relaxation of a less tight formulation, such as ATSP2R¯, to be as tight as the tightest formulation, such as ATSP6.
We extend our new formulations to include precedence constraints in order to enforce a partial order on the sequence of cities to be visited in a tour. The presence of precedence constraints within the ATSP framework is encountered quite often in practice. Examples include: disassembly optimization (see Sarin et al. [62]), and scheduling of wafers/ ICs on automated testing equipments in a semiconductor manufacturing facility (see Chen and Hsia [17]); among others. Our flowbased ATSP formulation can very conveniently capture these precedence constraints. We also present computational results to depict the tightness of our precedenceconstrained asymmetric traveling salesman problem (PCATSP) formulations.
We, then, apply our formulations to the hot strip rolling scheduling problem, which involves the processing of hot steel slabs, in a prespecified precedence order, on one or more rollers. The singleroller hot strip rolling scheduling problem can be directly formulated as a PCATSP. We also consider the multipleroller hot strip rolling scheduling problem. This gives rise to the multipleasymmetric traveling salesman problem (mATSP). Not many formulations have been presented in the literature for the mATSP, and there are none for the mATSP formulations involving a precedence order among the cities to be visited by the salesmen, which is the case for the multipleroller hot strip rolling scheduling problem. To begin with, we develop new formulations for the mATSP and show the validity of our formulations, and present computational results to depict their tightness. Then, we extend these mATSP formulations to include a prespecified, special type of precedence order in which to process the slabs, and designate the resulting formulations as the restricted precedenceconstrained multipleasymmetric traveling salesman problem (rPCmATSP) formulations. We directly formulate the multipleroller hot strip rolling scheduling problem as a rPCmATSP. Furthermore, we consider the hot strip rolling scheduling problem with slab selection in which not all slabs need to be processed. We model the singleroller hot strip rolling scheduling problem with slab selection as a multipleasymmetric traveling salesman problem with exactly two traveling salesmen. Similarly, the multipleroller hot strip rolling scheduling problem with slab selection is modeled as a multipleasymmetric traveling salesman problem with (m+1) traveling salesmen.
A series of computational experiments are conducted to exhibit the effectiveness of our formulations for the solution of hot strip rolling scheduling problems. Furthermore, we develop two mixedinteger programming algorithms to solve our formulations. These are based on Benders΄ decomposition [13] and are designated Benders΄ decomposition and Modified Benders΄ methods. In concert with a special type of precedence order presented in the hot strip rolling scheduling problems, we further introduce an adjustable density ratio of the associated precedence network and we use randomly generated test problems to study the effect of various density ratios in solving these scheduling problems. Our experimentation shows the efficacy of our methods over CPLEX.
Finally, we present a compact formulation for the job shop scheduling problem, designated as JSCD (job shop conjunctivedisjunctive) formulation, which is an extension of our ATSP formulations. We use two test problems given in Muth and Thompson [53] to demonstrate the optimal schedule and the lower bound values obtained by solving the LP relaxations of our formulations. However, we observe that the lower bound values obtained by solving the LP relaxations of all variations of our JSCD formulation equal to the maximum total processing time among the jobs in the problem. / Ph. D.

43 
Computation of Mileage Limits for Traveling Salesmen by Means of Optimization TechniquesTorstensson, Johan January 2008 (has links)
Many companies have traveling salesmen that market and sell their products.This results in much traveling by car due to the daily customer visits. Thiscauses costs for the company, in form of travel expenses compensation, and environmentaleffects, in form of carbon dioxide pollution. As many companies arecertified according to environmental management systems, such as ISO 14001,the environmental work becomes more and more important as the environmentalconsciousness increases every day for companies, authorities and public.The main task of this thesis is to compute reasonable limits on the mileage ofthe salesmen; these limits are based on specific conditions for each salesman’sdistrict. The objective is to implement a heuristic algorithm that optimizes thecustomer tours for an arbitrary chosen month, which will represent a “standard”month. The output of the algorithm, the computed distances, will constitute amileage limit for the salesman.The algorithm consists of a constructive heuristic that builds an initial solution,which is modified if infeasible. This solution is then improved by a local searchalgorithm preceding a genetic algorithm, which task is to improve the toursseparately.This method for computing mileage limits for traveling salesmen generates goodsolutions in form of realistic tours. The mileage limits could be improved if theinput data were more accurate and adjusted to each district, but the suggestedmethod does what it is supposed to do.

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Computation of Mileage Limits for Traveling Salesmen by Means of Optimization TechniquesTorstensson, Johan January 2008 (has links)
<p>Many companies have traveling salesmen that market and sell their products.This results in much traveling by car due to the daily customer visits. Thiscauses costs for the company, in form of travel expenses compensation, and environmentaleffects, in form of carbon dioxide pollution. As many companies arecertified according to environmental management systems, such as ISO 14001,the environmental work becomes more and more important as the environmentalconsciousness increases every day for companies, authorities and public.The main task of this thesis is to compute reasonable limits on the mileage ofthe salesmen; these limits are based on specific conditions for each salesman’sdistrict. The objective is to implement a heuristic algorithm that optimizes thecustomer tours for an arbitrary chosen month, which will represent a “standard”month. The output of the algorithm, the computed distances, will constitute amileage limit for the salesman.The algorithm consists of a constructive heuristic that builds an initial solution,which is modified if infeasible. This solution is then improved by a local searchalgorithm preceding a genetic algorithm, which task is to improve the toursseparately.This method for computing mileage limits for traveling salesmen generates goodsolutions in form of realistic tours. The mileage limits could be improved if theinput data were more accurate and adjusted to each district, but the suggestedmethod does what it is supposed to do.</p>

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De l'optimisation pour l'aide à la décision : applications au problème du voyageur de commerce probabiliste et à l'approximation de données / Optimization for decisionmaking : applications to the probabilistic traveling salesman problem and spline approximation from real datasetsBenhida, Soufia 12 December 2018 (has links)
La 1ere partie de ce travail traite l'optimisation des tournées sous forme d'un problème d'optimisation nommé Le problème de Voyageur de Commerce. Dans cette partie nous nous intéressons à faire une riche présentation du problème de Voyageur de Commerce, ses variantes, puis nous proposons une stratégie de génération de contrainte pour la résolution du TSP. Ensuite on traite sa version stochastique : le problème de Voyageur de commerce Probabiliste. Nous proposons une formulation mathématique du PTSP et nous présentons des résultats numériques obtenus par résolution exacte pour une série d'instances de petite taille. Dans la seconde partie, nous proposons une méthode d'approximation générale permettant d'approcher différents type de données, d'abord nous traitons l'approximation d'un signal de vent (cas simple, ID), ensuite l'approximation d'un champ de vecteurs avec prise en compte de la topographie qui constitue la principale contribution de cette partie. / The first part of this work deals with route optimization in the form of an optimization problem named The Traveler's Business Problem. In this part we are interested to make a rich presentation of the problem of Traveler Commerce, its variants, then we propose a strategy of constraint generation for the resolution of the TSP. Then we treat its stochastic version : the probabilistic business traveler problem. We propose a mathematical formulation of the PTSP and we present numerical results obtained by exact resolution for a series of small instances. In the second part, we propose a method of general approximation to approximate different type of data, first we treat the approximation of a wind signal (simple case, 1D), then the approximation of a vector field taking into account the topography which is the main contribution of this part.

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Evaluating pheromone intensities and 2opt local search for the Ant System applied to the Dynamic Travelling Salesman Problem / Utvärdering av feromonintensiteter och 2opt lokalsökning i Ant System för det dynamiska handelsresandeproblemetSvensson, Erik R., Lagerqvist, Klas January 2017 (has links)
Ant Colony Optimization (ACO) algorithms have been successful in solving a wide variety of NPhard optimization problems. The Traveling Salesman Problem (TSP) has served as a benchmarking problem for many novel ACO algorithms. The slightly harder Dynamic Traveling Salesman Problem (DTSP) is more realistic in the sense that realtime changes happen in the graph belonging to a TSP instance. This thesis studied the original ACO algorithm: the Ant System, and how the amount of pheromone deposited by the ants within the algorithm aﬀected the performance when solving both TSP and DTSP problems. Additionally, 2opt local search was added to the algorithm, to see how it impacted the performance. We found that when the ants deposited a greater amount of pheromone, the performance for TSP increased, while the performance for DTSP decreased. We concluded that the Ant System in its original form is unsuitable for solving the DTSP. 2opt local search improved the performance in all instances. / Ant Colony Optimizationalgoritmer (ACO) har visat sig vara bra på att lösa många olika NPsvåra optimeringsproblem. För att mäta prestandan för nya ACOalgoritmer har i många fall Handelsresandeproblemet (eng. TSP) använts. Den dynamiska varianten av TSP (eng. DTSP), är ett något svårare problem då förändringar i grafen kan ske i realtid. Denna uppsats utredde hur olika mängder feromon som avges av myrorna inuti algoritmen Ant System, påverkade prestandan för både TSPoch DTSPinstanser. Utöver detta studerades hur den lokala sökningsheuristiken 2opt påverkade prestandan. Resultaten visade att om myrorna tilläts släppa mer feromoner, ökade prestantan för TSP, men minskade för DTSP. Därav drog vi slutsatsen att algoritmen Ant System i sin ursprungliga form ej är lämplig för att lösa DTSP. Den lokala söknigsheuristiken 2opt förbättrade prestandan i alla tester.

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Solution to a bay design and production sequencing problemCreswell, Steven Howard, 1961 January 1989 (has links)
This thesis addresses the problem of setting up a surface mount placement machine for production. The objective is to minimize the number of machine changeovers made during a production run consisting of a number of circuit cards. The solution to the problem involves two separate decisions. The first decision considers determining how to combine feeders together in "bays" or groups of feeders, and how to assign the bays to the circuit cards. The second decision considers the circuit card production sequence. A mathematical programming formulation is given, however, its solution is very difficult for problems of a realistic size. Several heuristic approaches are suggested and used to solve actual and test problems. The heuristic for bay design uses clustering techniques used in Group Technology while the sequencing problem is solved using heuristics based on solution techniques for the Traveling Salesman problem.

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The solution of a milktruck routing problem via traveling salesman analysis : the development of an alternative approachTurner, Walter Lynn January 2011 (has links)
Digitized by Kansas Correctional Industries

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MultiStop Routing Optimization: A Genetic Algorithm ApproachHommadi, Abbas 01 May 2018 (has links)
In this research, we investigate and propose new operators to improve Genetic Algorithm’s performance to solve the multistop routing problem. In a multistop route, a user starts at point x, visits all destinations exactly once, and then return to the same starting point. In this thesis, we are interested in two types of this problem. The first type is when the distance among destinations is fixed. In this case, it is called static traveling salesman problem. The second type is when the cost among destinations is affected by traffic congestion. Thus, the time among destinations changes during the day. In this case, it is called timedependent traveling salesman problem. This research proposes new improvements on genetic algorithm to solve each of these two optimization problems.
First, the Travelling Salesman Problem (TSP) is one of the most important and attractive combinatorial optimization problems. There are many metaheuristic algorithms that can solve this problem. In this paper, we use a Genetic Algorithm (GA) to solve it. GA uses different operators: selection, crossover, and mutation. Sequential Constructive Crossover (SCX) and Bidirectional Circular Constructive Crossover (BCSCX) are efficient to solve TSP. Here, we propose a modification to these crossovers. The experimental results show that our proposed adjustment is superior to SCX and BCSCX as well as to other conventional crossovers (e.g. Order Crossover (OX), Cycle Crossover (CX), and Partially Mapped Crossover (PMX)) in term of solution quality and convergence speed. Furthermore, the GA solver, that is improved by applying inexpensive local search operators, can produce solutions that have much better quality within reasonable computational time.
Second, the TimeDependent Traveling Salesman Problem (TDTSP) is an interesting problem and has an impact on reallife applications such as a delivery system. In this problem, time among destinations fluctuates during the day due to traffic, weather, accidents, or other events. Thus, it is important to recommend a tour that can save driver’s time and resources. In this research, we propose a MultiPopulation Genetic Algorithm (MGA) where each population has different crossovers. We compare the proposed MG against SinglePopulation Genetic Algorithm (SGA) in terms of tour time solution quality. Our finding is that MGA outperforms SGA. Our method is tested against realworld traffic data [1] where there are 200 different instances with different numbers of destinations. For all tested instances, MGA is superior on average by at least 10% (for instances with size less than 50) and 20% (for instances of size 50) better tour time solution compared to SGA with OX and SGA with PMX operators, and at least 4% better tour time compared toga with SCX operator.

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On Linear Programming, Integer Programming and Cutting PlanesEspinoza, Daniel G. 30 March 2006 (has links)
In this thesis we address three related topic in the field of Operations Research.
Firstly we discuss the problems and limitation of most common solvers for linear programming, precision.
We then present a solver that generate rational optimal solutions to linear programming problems by solving a succession of (increasingly more precise) floating point approximations of the original rational problem until the rational optimality conditions are achieved.
This method is shown to be (on average) only 20% slower than the common pure floating point approach, while returning true optimal solutions to the problems.
Secondly we present an extension of the Local Cut procedure introduced by Applegate et al, 2001, for the Symmetric Traveling Salesman Problem (STSP), to the general setting of MIP problems.
This extension also proves finiteness of the separation, facet and tilting procedures in the general MIP setting, and also provides conditions under which the separation procedure is guaranteed to generate cuts that separate the current fractional solution from the convex hull of the mixedinteger polyhedron.
We then move on to explore some configurations for local cuts, realizing extensive testing on the instances from MIPLIB.
Those results show that this technique may be useful in general MIP problems, while the experience of Applegate et al, shows that the ideas can be successfully applied to structures problems as well.
Thirdly we present an extensive computational experiment on the TSP and Domino Parity inequalities as introduced by Letchford, 2000.
This work also include a safeshrinking theorem for domino parity inequalities, heuristics to apply the planar separation algorithm introduced by Letchford to instances where the planarity requirement does not hold, and several practical speedups.
Our computational experience showed that this class of inequalities effectively improve the lower bounds from the best relaxations obtained with Concorde, which is one of the state of the art solvers for the STSP.
As part of these experience, we solved to optimality the (up to now) largest two STSP instances, both of them belong to the TSPLIB set of instances and they have 18,520 and 33,810 cities respectively.

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