Traveling Salesman Problem

This book is a collection of current research in the application of evolutionary algorithms and other optimal algorithms to solving the TSP problem. It brings together researchers with applications in Artificial Immune Systems, Genetic Algorithms, Neural Networks and Differential Evolution Algorithm. Hybrid systems, like Fuzzy Maps, Chaotic Maps and Parallelized TSP are also presented. Most importantly, this book presents both theoretical as well as practical applications of TSP, which will be a vital tool for researchers and graduate entry students in the field of applied Mathematics, Computing Science and Engineering

An algorithm for dynamic order-picking in warehouse operations

Warehousing has been traditionally viewed as a non value-adding activity but in recent years a number of new developments have meant that supply chain logistics have become critical to profitability. This paper focuses specifically on order-picking which is a key factor affecting warehouse performance. Order picking is the operation of retrieving goods from specified storage locations based on customer orders. Today's warehouses face challenges for greater responsiveness to customer orders that require more flexibility than conventional strategies can offer. Hence, dynamic order-picking strategies that allow for changes of pick-lists during a pick cycle have attracted attention recently. In this paper we introduce an interventionist routing algorithm for optimising the dynamic order-picking routes. The algorithm is tested using a set of simulations based on an industrial case example. The results indicate that under a range of conditions, the proposed interventionist routing algorithm can outperform both static and heuristic dynamic order-picking routing algorithms.This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.ejor.2015.06.07

Algorithms for multi-robot systems on the cooperative exploration & last-mile delivery problems

La aparición de los vehículos aéreos no tripulados (UAVs) y de los vehículos terrestres no tripulados (UGVs) ha llevado a la comunidad científica a enfrentarse a problemas ideando paradigmas de cooperación con UGVs y UAVs. Sin embargo, no suele ser trivial determinar si la cooperación entre UGVs y UAVs es adecuada para un determinado problema. Por esta razón, en esta tesis, investigamos un paradigma particular de cooperación UGV-UAV en dos problemas de la literatura, y proponemos un controlador autónomo para probarlo en escenarios simulados. Primero, formulamos un problema particular de exploración cooperativa que consiste en alcanzar un conjunto de puntos de destino en un área de exploración a gran escala. Este problema define al UGV como una estación de carga móvil para transportar el UAV a través de diferentes lugares desde donde el UAV puede alcanzar los puntos de destino. Por consiguiente, proponemos el algoritmo TERRA para resolverlo. Este algoritmo se destaca por dividir el problema de exploración en cinco subproblemas, en los que cada subproblema se resuelve en una etapa particular del algoritmo. Debido a la explosión de la entrega de paquetes en las empresas de comercio electrónico, formulamos también una generalización del conocido problema de la entrega en la última milla. En este caso, el UGV actúa como una estación de carga móvil que transporta a los paquetes y a los UAVs, y estos se encargan de entregarlos. De esta manera, seguimos la estrategia de división descrita por TERRA, y proponemos el algoritmo COURIER. Este algoritmo replica las cuatro primeras etapas de TERRA, pero construye una nueva quinta etapa para producir un plan de tareas que resuelva el problema. Para evaluar el paradigma de cooperación UGV-UAV en escenarios simulados, proponemos el controlador autónomo ARIES. Este controlador sigue un enfoque jerárquico descentralizado de líder-seguidor para integrar cualquier paradigma de cooperación de manera distribuida. Ambos algoritmos han sido caracterizados para identificar los aspectos relevantes del paradigma de cooperación en los problemas relacionados. Además, ambos demuestran un gran rendimiento del paradigma de cooperación en tales problemas, y al igual que el controlador autónomo, revelan un gran potencial para futuras aplicaciones reales.The emergence of Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) has conducted the research community to face historical complex problems by devising UGV-UAV cooperation paradigms. However, it is usually not a trivial task to determine whether or not a UGV-UAV cooperation is suitable for a particular problem. For this reason, in this thesis, we investigate a particular UGV-UAV cooperation paradigm over two problems in the literature, and we propose an autonomous controller to test it on simulated scenarios. Driven by the planetary exploration, we formulate a particular cooperative exploration problem consisting of reaching a set of target points in a large-scale exploration area. This problem defines the UGV as a moving charging station to carry the UAV through different locations from where the UAV can reach the target points. Consequently, we propose the cooperaTive ExploRation Routing Algorithm (TERRA) to solve it. This algorithm stands out for splitting up the exploration problem into five sub-problems, in which each sub-problem is solved in a particular stage of the algorithm. In the same way, driven by the explosion of parcels delivery in e-commerce companies, we formulate a generalization of the well-known last-mile delivery problem. This generalization defines the same UGV’s and UAV’s rol as the exploration problem. That is, the UGV acts as a moving charging station which carries the parcels along several UAVs to deliver them. In this way, we follow the split strategy depicted by TERRA to propose the COoperative Unmanned deliveRIEs planning algoRithm (COURIER). This algorithm replicates the first four TERRA’s stages, but it builds a new fifth stage to produce a task plan solving the problem. In order to evaluate the UGV-UAV cooperation paradigm on simulated scenarios, we propose the Autonomous coopeRatIve Execution System (ARIES). This controller follows a hierarchical decentralized leader-follower approach to integrate any cooperation paradigm in a distributed manner. Both algorithms have been characterized to identify the relevant aspects of the cooperation paradigm in the related problems. Also, both of them demonstrate a great performance of the cooperation paradigm in such problems, and as well as the autonomous controller, reveal a great potential for future real applications

Quantum Computing in Logistics and Supply Chain Management an Overview

The work explores the integration of quantum computing into logistics and supply chain management, emphasising its potential for use in complex optimisation problems. The discussion introduces quantum computing principles, focusing on quantum annealing and gate-based quantum computing, with the Quantum Approximate Optimisation Algorithm and Quantum Annealing as key algorithmic approaches. The paper provides an overview of quantum approaches to routing, logistic network design, fleet maintenance, cargo loading, prediction, and scheduling problems. Notably, most solutions in the literature are hybrid, combining quantum and classical computing. The conclusion highlights the early stage of quantum computing, emphasising its potential impact on logistics and supply chain optimisation. In the final overview, the literature is categorised, identifying quantum annealing dominance and a need for more research in prediction and machine learning is highlighted. The consensus is that quantum computing has great potential but faces current hardware limitations, necessitating further advancements for practical implementation

Coverage & cooperation: Completing complex tasks as quickly as possible using teams of robots

As the robotics industry grows and robots enter our homes and public spaces, they are increasingly expected to work in cooperation with each other. My thesis focuses on multirobot planning, specifically in the context of coverage robots, such as robotic lawnmowers and vacuum cleaners. Two problems unique to multirobot teams are task allocation and search. I present a task allocation algorithm which balances the workload amongst all robots in the team with the objective of minimizing the overall mission time. I also present a search algorithm which robots can use to find lost teammates. It uses a probabilistic belief of a target robot’s position to create a planning tree and then searches by following the best path in the tree. For robust multirobot coverage, I use both the task allocation and search algorithms. First the coverage region is divided into a set of small coverage tasks which minimize the number of turns the robots will need to take. These tasks are then allocated to individual robots. During the mission, robots replan with nearby robots to rebalance the workload and, once a robot has finished its tasks, it searches for teammates to help them finish their tasks faster

Drone Base Station Trajectory Management for Optimal Scheduling in LTE-Based Sparse Delay-Sensitive M2M Networks

Providing connectivity in areas out of reach of the cellular infrastructure is a very active area of research. This connectivity is particularly needed in case of the deployment of machine type communication devices (MTCDs) for critical purposes such as homeland security. In such applications, MTCDs are deployed in areas that are hard to reach using regular communications infrastructure while the collected data is timely critical. Drone-supported communications constitute a new trend in complementing the reach of the terrestrial communication infrastructure. In this study, drones are used as base stations to provide real-time communication services to gather critical data out of a group of MTCDs that are sparsely deployed in a marine environment. Studying different communication technologies as LTE, WiFi, LPWAN and Free-Space Optical communication (FSOC) incorporated with the drone communications was important in the first phase of this research to identify the best candidate for addressing this need. We have determined the cellular technology, and particularly LTE, to be the most suitable candidate to support such applications. In this case, an LTE base station would be mounted on the drone which will help communicate with the different MTCDs to transmit their data to the network backhaul. We then formulate the problem model mathematically and devise the trajectory planning and scheduling algorithm that decides the drone path and the resulting scheduling. Based on this formulation, we decided to compare between an Ant Colony Optimization (ACO) based technique that optimizes the drone movement among the sparsely-deployed MTCDs and a Genetic Algorithm (GA) based solution that achieves the same purpose. This optimization is based on minimizing the energy cost of the drone movement while ensuring the data transmission deadline missing is minimized. We present the results of several simulation experiments that validate the different performance aspects of the technique

Coverage Path Planning for Autonomous Robots

Coverage Path Planning (CPP) is a problem of path computation with minimal length that guarantees to scan the entire area of interest. CPP finds its application in diverse fields like cartography, inspection, precision agriculture, milling, and demining. However, this thesis is a prominent step to solve CPP for real-world problems where environment poses multiple challenges. At first, four significant and pressing challenges for CPP in extreme environment are identified. Each challenge is formulated as a problem and its solution has been presented as a dedicated chapter in this thesis. The first problem, Goal-Oriented Sensor based CPP, focuses on cumbersome tasks like Nuclear Decommissioning, where the robot covers an abandoned site in tandem with the goal to reach a static target in minimal time. To meet the grave speeding-up challenge, a novel offline-online strategy is proposed that efficiently models the site using floor plans and grid maps as a priori information. The proposed strategy outperforms the two baseline approaches with reduction in coverage time by 45%- 82%. The second problem explores CPP of distributed regions, applicable in post-disaster scenarios like Fukushima Daiichi. Experiments are conducted at radiation laboratory to identify the constraints robot would be subjected to. The thesis is successfully able to diagnose transient damage in the robot’s sensor after 3 Gy of gamma radiation exposure. Therefore, a region order travel constraint known as Precedence Provision is imposed for successful coverage. The region order constraint allows the coverage length to be minimised by 65% in comparison to state-of-the-art techniques. The third problem identifies the major bottleneck of limited on-board energy that inhibits complete coverage of distributed regions. The existing approaches allow robots to undertake multiple tours for complete coverage which is impractical in many scenarios. To this end, a novel algorithm is proposed that solves a variant of CPP where the robot aims to achieve near-optimal area coverage due to path length limitation caused by the energy constraint. The proposed algorithm covers 23% - 35% more area in comparison to the state-of-the-art approaches. Finally, the last problem, an extension of the second and third problems, deals with the problem of CPP over a set of disjoint regions using a fleet of heterogeneous aerial robots. A heuristic is proposed to deliver solutions within acceptable time limits. The experiments demonstrate that the proposed heuristic solution reduces the energy cost by 15-40% in comparison to the state-of-the art solutions

Optimal Control of Two-Wheeled Mobile Robots for Patrolling Operations

Optimal Control of Two-Wheeled Mobile Robots for Patrolling Operations Walaaeldin Ahmed Ghadiry, Concordia University, 2015 This work studies the use of the two-wheeled mobile robots in patrolling operations, and provides the most distance-e�cient as well as time-e�cient trajectories to patrol a given area. Novel formulations in the context of constrained optimization are introduced which can be solved using existing software. The main concept of the problem is directly related to the well-known Traveling Salesman Problem (TSP) and its variants, where a salesman starts from a base city and visits a number of other cities with minimum travel distance while satisfying the constraint that each city has to be visited only once. Finally, the salesman returns back to the starting base city after completing the mission. Two di�erent patrolling con�gurations that are related to the TSP and its variants, namely the Single Depot multiple Traveling Salesman Problem (mTSP) and the Multidepot multiple Traveling Salesman Problem (MmTSP) are investigated. Novel algorithms are introduced for the trajectory planning of multiple two-wheeled mobile robots, either with two di�erential motors (which can turn on the spot) or with Dubins-like vehicles. The output trajectories for both types of wheeled robots are investigated by using a model predictive control scheme to ensure their kinematic feasibility for the best monitoring performance. The proposed formulations and algorithms are veri�ed by a series of simulations using e�cient programming and optimization software as well as experimental tests in the lab environment