Application of Neuro-Fuzzy system to solve Traveling Salesman Problem

This paper presents the application of adaptive neuro-fuzzy inference system (ANFIS) in solving the traveling salesman problem. Takagi-Sugeno-Kang neuro-fuzzy architecture model is used for this purpose. TSP, although, simple to describ

Traveling Salesman Problem for Surveillance Mission Using Particle Swarm Optimization

The surveillance mission requires aircraft to fly from a starting point through defended terrain to targets and return to a safe destination (usually the starting point). The process of selecting such a flight path is known as the Mission Route Planning (MRP) Problem and is a three-dimensional, multi-criteria (fuel expenditure, time required, risk taken, priority targeting, goals met, etc.) path search. Planning aircraft routes involves an elaborate search through numerous possibilities, which can severely task the resources of the system being used to compute the routes. Operational systems can take up to a day to arrive at a solution due to the combinatoric nature of the problem. This delay is not acceptable because timeliness of obtaining surveillance information is critical in many surveillance missions. Also, the information that the software uses to solve the MRP may become invalid during computation. An effective and efficient way of solving the MRP with multiple aircraft and multiple targets is desired. One approach to finding solutions is to simplify and view the problem as a two-dimensional, minimum path problem. This approach also minimizes fuel expenditure, time required, and even risk taken. The simplified problem is then the Traveling Salesman Problem (TSP)

Autonomous Flight, Fault, and Energy Management of the Flying Fish Solar-Powered Seaplane.

The Flying Fish autonomous unmanned seaplane is designed and built for persistent ocean surveillance. Solar energy harvesting and always-on autonomous control and guidance are required to achieve unattended long-term operation. This thesis describes the Flying Fish avionics and software systems that enable the system to plan, self-initiate, and autonomously execute drift-flight cycles necessary to maintain a designated watch circle subject to environmentally influenced drift. We first present the avionics and flight software architecture developed for the unique challenges of an autonomous energy-harvesting seaplane requiring the system to be: waterproof, robust over a variety of sea states, and lightweight for flight. Seaplane kinematics and dynamics are developed based on conventional aircraft and watercraft and upon empirical flight test data. These models serve as the basis for development of flight control and guidance strategies which take the form of a cyclic multi-mode guidance protocol that smoothly transitions between nested gain-scheduled proportional-derivative feedback control laws tuned for the trim conditions of each flight mode. A fault-tolerant airspeed sensing system is developed in response to elevated failure rates arising from pitot probe water ingestion in the test environment. The fault-tolerance strategy utilizes sensor characteristics and signal energy to combine redundant sensor measurements in a weighted voting strategy, handling repeated failures, sensor recovery, non-homogenous sensors, and periods of complete sensing failure. Finally, a graph-based mission planner combines models of global solar energy, local ocean-currents, and wind with flight-verified/derived aircraft models to provide an energy-aware flight planning tool. An NP-hard asymmetric multi-visit traveling salesman planning problem is posed that integrates vehicle performance and environment models using energy as the primary cost metric. A novel A* search heuristic is presented to improve search efficiency relative to uniform cost search. A series of cases studies are conducted with surface and airborne goals for various times of day and for multi-day scenarios. Energy-optimal solutions are identified except in cases where energy harvesting produces multiple comparable-cost plans via negative-cost cycles. The always-on cyclic guidance/control system, airspeed sensor fault management algorithm, and the nested-TSP heuristic for A* are all critical innovation required to solve the posed research challenges.Ph.D.Aerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91453/1/eubankrd_1.pd

Reject or Embrace Electric Cargo Bikes? An investigation of the Sustainability Effects of a Transition from Electric Vans to Electric Cargo Bikes in Last-Mile Delivery

In the recent period, there has been a massive increase in e-commerce. This has vastly changed Norwegian customers' shopping behavior and provided an increase in last-mile delivery. Simultaneously, the focus on sustainability has become more significant, and the Bergen Municipality is looking for ways to reduce traffic in the city center. This thesis investigates how a transition from e-vans to e-cargo bikes will affect sustainability in a city center. Both the Vehicle Routing Problem (VRP) and the Traveling Salesman Problem have been used to solve the route optimization problem, where it is assumed that all deliveries are made with either e-vans or e-cargo bikes. A mathematical model was developed from the beginning, but due to the large number of addresses, each address was assigned to a cluster. Then, Google OR-Tools was used to solve the VRP. The mathematical model and the Google OR-Tools model proved to perform equally well when compared using a small number of addresses. The main findings show that sustainability in a city center will be positively affected by a transition from e-vans to e-cargo bikes in last-mile delivery. This is because the social, environmental, and economic sustainability pillar will be positively affected due to lower noise levels, lower environmentally harmful emissions, and the potential for lower costs. The sensitivity analysis shows, however, that the number of meters traveled with e-cargo bikes increases faster than for e-vans when the demand for parcels increases. We believe that our findings will apply to cities with the same characteristics as Bergen, such as population and the size of the city center.nhhma

Towards a multilevel ant colony optimization

Masteroppgave i Informasjons- og kommunikasjonsteknologi IKT590 Universitetet i Agder 2014Ant colony optimization is a metaheuristic approach for solving combinatorial optimization problems which belongs to swarm intelligence techniques. Ant colony optimization algorithms are one of the most successful strands of swarm intelligence which has already shown very good performance in many combinatorial problems and for some real applications. This thesis introduces a new multilevel approach for ant colony optimization to solve the NP-hard problems shortest path and traveling salesman. We have reviewed different elements of multilevel algorithm which helped us in construction of our proposed multilevel ant colony optimization solution. We for comparison purposes implemented our own multi-threaded variant Dijkstra for solving shortest path to compare it with single level and multilevel ant colony optimization and reviewed different techniques such as genetic algorithms and Dijkstra’s algorithm. Our proposed multilevel ant colony optimization was developed based on the single level ant colony optimization which we both implemented. We have applied the novel multilevel ant colony optimization to solve the shortest path and traveling salesman problem. We show that the multilevel variant of ant colony optimization outperforms single level. The experimental results conducted demonstrate the overall performance of multilevel in comparison to the single level ant colony optimization, displaying a vast improvement when employing a multilevel approach in contrast to the classical single level approach. These results gave us a better understanding of the problems and provide indications for further research

A Ferroelectric Compute-in-Memory Annealer for Combinatorial Optimization Problems

Computationally hard combinatorial optimization problems (COPs) are ubiquitous in many applications, including logistical planning, resource allocation, chip design, drug explorations, and more. Due to their critical significance and the inability of conventional hardware in efficiently handling scaled COPs, there is a growing interest in developing computing hardware tailored specifically for COPs, including digital annealers, dynamical Ising machines, and quantum/photonic systems. However, significant hurdles still remain, such as the memory access issue, the system scalability and restricted applicability to certain types of COPs, and VLSI-incompatibility, respectively. Here, a ferroelectric field effect transistor (FeFET) based compute-in-memory (CiM) annealer is proposed. After converting COPs into quadratic unconstrained binary optimization (QUBO) formulations, a hardware-algorithm co-design is conducted, yielding an energy-efficient, versatile, and scalable hardware for COPs. To accelerate the core vector-matrix-vector (VMV) multiplication of QUBO formulations, a FeFET based CiM array is exploited, which can accelerate the intended operation in-situ due to its unique three-terminal structure. In particular, a lossless compression technique is proposed to prune typically sparse QUBO matrix to reduce hardware cost. Furthermore, a multi-epoch simulated annealing (MESA) algorithm is proposed to replace conventional simulated annealing for its faster convergence and better solution quality. The effectiveness of the proposed techniques is validated through the utilization of developed chip prototypes for successfully solving graph coloring problem, indicating great promise of FeFET CiM annealer in solving general COPs.Comment: 39 pages, 12 figure

Ant Colony Optimization

Ant Colony Optimization (ACO) is the best example of how studies aimed at understanding and modeling the behavior of ants and other social insects can provide inspiration for the development of computational algorithms for the solution of difficult mathematical problems. Introduced by Marco Dorigo in his PhD thesis (1992) and initially applied to the travelling salesman problem, the ACO field has experienced a tremendous growth, standing today as an important nature-inspired stochastic metaheuristic for hard optimization problems. This book presents state-of-the-art ACO methods and is divided into two parts: (I) Techniques, which includes parallel implementations, and (II) Applications, where recent contributions of ACO to diverse fields, such as traffic congestion and control, structural optimization, manufacturing, and genomics are presented

Operations Management

Global competition has caused fundamental changes in the competitive environment of the manufacturing and service industries. Firms should develop strategic objectives that, upon achievement, result in a competitive advantage in the market place. The forces of globalization on one hand and rapidly growing marketing opportunities overseas, especially in emerging economies on the other, have led to the expansion of operations on a global scale. The book aims to cover the main topics characterizing operations management including both strategic issues and practical applications. A global environmental business including both manufacturing and services is analyzed. The book contains original research and application chapters from different perspectives. It is enriched through the analyses of case studies