17 research outputs found
A Swarm of Salesmen: Algorithmic Approaches to Multiagent Modeling
This honors thesis describes the algorithmic abstraction of a problem modeling a swarm of Mars rovers, where many agents must together achieve a goal. The algorithmic formulation of this problem is based on the traveling salesman problem (TSP), and so in this thesis I offer a review of the mathematical technique of linear programming in the context of its application to the TSP, an overview of some variations of the TSP and algorithms for approximating and solving them, and formulations without solutions of two novel TSP variations which are useful for modeling the original problem
A Swarm of Salesmen: Algorithmic Approaches to Multiagent Modeling
This honors thesis describes the algorithmic abstraction of a problem modeling a swarm of Mars rovers, where many agents must together achieve a goal. The algorithmic formulation of this problem is based on the traveling salesman problem (TSP), and so in this thesis I offer a review of the mathematical technique of linear programming in the context of its application to the TSP, an overview of some variations of the TSP and algorithms for approximating and solving them, and formulations without solutions of two novel TSP variations which are useful for modeling the original problem
Makespan minimizing on multiple travel salesman problem with a learning effect of visiting time
-The multiple traveling salesman problem (MTSP) involves the assignment and sequencing procedure simultaneously. The assignment of a set of nodes to each visitors and determining the sequence of visiting of nodes for each visitor. Since specific range of process is needed to be carried out in nodes in commercial environment, several factors associated with routing problem are required to be taken into account. This research considers visitors’ skill and category of customers which can affect visiting time of visitors in nodes. With regard to learning-by-doing, visiting time in nodes can be reduced. And different class of customers which are determined based on their potential purchasing of power specifies that required time for nodes can be vary. So, a novel optimization model is presented to formulate MTSP, which attempts to ascertain the optimum routes for salesmen by minimizing the makespan to ensure the balance of workload of visitors. Since this problem is an NP-hard problem, for overcoming the restriction of exact methods for solving practical large-scale instances within acceptable computational times. So, Artificial Immune System (AIS) and the Firefly (FA) metaheuristic algorithm are implemented in this paper and algorithms parameters are calibrated by applying Taguchi technique. The solution methodology is assessed by an array of numerical examples and the overall performances of these metaheuristic methods are evaluated by analyzing their results with the optimum solutions to suggested problems. The results of statistical analysis by considering 95% confidence interval for calculating average relative percentage of deviation (ARPD) reveal that the solutions of proposed AIS algorithm has less variation and Its’ confidence interval of closer than to zero with no overlapping with that of FA. Although both proposed meta-heuristics are effective and efficient in solving small-scale problems, in medium and large scales problems, AIS had a better performance in a shorter average time. Finally, the applicability of the suggested pattern is implemented in a case study in a specific company, namely Kalleh
Robust Online Epistemic Replanning of Multi-Robot Missions
As Multi-Robot Systems (MRS) become more affordable and computing
capabilities grow, they provide significant advantages for complex applications
such as environmental monitoring, underwater inspections, or space exploration.
However, accounting for potential communication loss or the unavailability of
communication infrastructures in these application domains remains an open
problem. Much of the applicable MRS research assumes that the system can
sustain communication through proximity regulations and formation control or by
devising a framework for separating and adhering to a predetermined plan for
extended periods of disconnection. The latter technique enables an MRS to be
more efficient, but breakdowns and environmental uncertainties can have a
domino effect throughout the system, particularly when the mission goal is
intricate or time-sensitive. To deal with this problem, our proposed framework
has two main phases: i) a centralized planner to allocate mission tasks by
rewarding intermittent rendezvous between robots to mitigate the effects of the
unforeseen events during mission execution, and ii) a decentralized replanning
scheme leveraging epistemic planning to formalize belief propagation and a
Monte Carlo tree search for policy optimization given distributed rational
belief updates. The proposed framework outperforms a baseline heuristic and is
validated using simulations and experiments with aerial vehicles
A Systematic Review of Approximability Results for Traveling Salesman Problems leveraging the TSP-T3CO Definition Scheme
The traveling salesman (or salesperson) problem, short TSP, is a problem of
strong interest to many researchers from mathematics, economics, and computer
science. Manifold TSP variants occur in nearly every scientific field and
application domain: engineering, physics, biology, life sciences, and
manufacturing just to name a few. Several thousand papers are published on
theoretical research or application-oriented results each year. This paper
provides the first systematic survey on the best currently known
approximability and inapproximability results for well-known TSP variants such
as the "standard" TSP, Path TSP, Bottleneck TSP, Maximum Scatter TSP,
Generalized TSP, Clustered TSP, Traveling Purchaser Problem, Profitable Tour
Problem, Quota TSP, Prize-Collecting TSP, Orienteering Problem, Time-dependent
TSP, TSP with Time Windows, and the Orienteering Problem with Time Windows. The
foundation of our survey is the definition scheme T3CO, which we propose as a
uniform, easy-to-use and extensible means for the formal and precise definition
of TSP variants. Applying T3CO to formally define the variant studied by a
paper reveals subtle differences within the same named variant and also brings
out the differences between the variants more clearly. We achieve the first
comprehensive, concise, and compact representation of approximability results
by using T3CO definitions. This makes it easier to understand the
approximability landscape and the assumptions under which certain results hold.
Open gaps become more evident and results can be compared more easily
Modeling and optimization of Central Ring Transportation System (CRTS) in Turkish Land Forces
Cataloged from PDF version of article.This thesis shows how Turkish Land Forces can optimally meet delivery and
pick-up demands of its units via Central Ring Transportation System. A mixed
integer programming model is proposed, and for the implementation of the
model, mathematical modeling software GAMS is used. The model is
implemented for three different fleet sizes of vehicles (4-vehicle, 5-vehicle, 6-
vehicle) with taking eight-week data of 2002 into account. How transportation
costs are affected by the number of vehicles is investigated, and an ideal number
of vehicles and the optimal routes to be followed are proposed.Akmeşe, Hamdi ÜnalM.S