Application of NSGA-II framework to the travel planning problem using real-world travel data

Beirigo, Breno Alves; Santos, Andre Gustavo dos

doi:10.1109/cec.2016.7743866

Cited by 16 publications

(8 citation statements)

References 10 publications

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“…Classical methods to solve MOTSP, such as non-dominated sorting genetic algorithm-II (NSGA-II) [31] and multiobjective evolutionary algorithm (MOEA/D) [32], generate low quality solutions and are computationally expensive [33]. There has been significant progress made in solving combinatorial optimization problems such as TSP through deep learning methods [34,35,36].…”

Section: Coverage Planningmentioning

confidence: 99%

Far-UVC Disinfection with Robotic Mobile Manipulator

Mehta¹,

Hsueh²,

Kourtzanidis³

et al. 2022

Preprint

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The COVID-19 pandemic has demonstrated the need for a more effective and efficient disinfection approach to combat infectious diseases. Ultraviolet germicidal irradiation (UVGI) is a proven mean for disinfection and sterilization and has been integrated into handheld devices and autonomous mobile robots. Existing UVGI robots which are commonly equipped with uncovered lamps that emit intense ultraviolet radiation suffer from: inability to be used in human presence, shadowing of objects, and long disinfection time. These robots also have a high operational cost. This paper introduces a cost effective germicidal system that utilizes UVGI to disinfect pathogens, such as viruses, bacteria, and fungi, on high contact surfaces (e.g. doors and tables). This system is composed of a team of 5-DOF mobile manipulators with end-effectors that are equipped with far-UVC excimer lamps. The design of the system is discussed with emphasis on path planning, coverage planning, and scene understanding. Evaluations of the UVGI system using simulations and irradiance models are also included. Please see the project's website for videos and simulations of the robot.

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Section: Coverage Planningmentioning

confidence: 99%

Far-UVC Disinfection with Robotic Mobile Manipulator

Mehta¹,

Hsueh²,

Kourtzanidis³

et al. 2022

Preprint

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“…This superiority derives from their ability to obtain a set of solutions in a single run. The two most common MOEAs are NSGA-II and MOEA/D [6][7][8], both of which have been implemented and applied in many practical problems. Moreover, multiple handcrafted heuristics have been addressed, including the Lin-Kernighan heuristic and the 2-opt local search [9].…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning Model for Blood Bank Vehicle Routing Multi-Objective Optimization

Altaf¹,

Roshdy²,

AlSagri³

2022

Computers, Materials &Amp; Continua

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The overall healthcare system has been prioritized within development top lists worldwide. Since many national populations are aging, combined with the availability of sophisticated medical treatments, healthcare expenditures are rapidly growing. Blood banks are a major component of any healthcare system, which store and provide the blood products needed for organ transplants, emergency medical treatments, and routine surgeries. Timely delivery of blood products is vital, especially in emergency settings. Hence, blood delivery process parameters such as safety and speed have received attention in the literature, as well as other parameters such as delivery cost. In this paper, delivery time and cost are modeled mathematically and marked as objective functions requiring simultaneous optimization. A solution is proposed based on Deep Reinforcement Learning (DRL) to address the formulated delivery functions as Multi-objective Optimization Problems (MOPs). The basic concept of the solution is to decompose the MOP into a scalar optimization sub-problems set, where each one of these sub-problems is modeled as a separate Neural Network (NN). The overall model parameters for each sub-problem are optimized based on a neighborhood parameter transfer and DRL training algorithm. The optimization step for the subproblems is undertaken collaboratively to optimize the overall model. Paretooptimal solutions can be directly obtained using the trained NN. Specifically, the multi-objective blood bank delivery problem is addressed in this research. One major technical advantage of this approach is that once the trained model is available, it can be scaled without the need for model retraining. The scoring can be obtained directly using a straightforward computation of the NN layers in a limited time. The proposed technique provides a set of technical strength points such as the ability to generalize and solve rapidly compared to other multi-objective optimization methods. The model was trained and tested on 5 major hospitals in Saudi Arabia's Riyadh region, and the simulation results indicated that time and cost decreased by 35% and 30%, respectively.

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“…There is a wide variety of algorithms ranging from exact methods to evolution based methods to solve multi objective TSP [5]. Evolutionary algorithms like non-dominated sorting genetic algorithm-II (NSGA-II) [6] and multi-objective evolutionary algorithm (MOEA/D) [7] are a popular choice of methods to tackle multi objective TSP and other multi objective optimization problems. Many algorithms also use evolutionary algorithms coupled with local search heuristics * Dept.…”

Section: Introductionmentioning

confidence: 99%

Pareto Frontier Approximation Network (PA-Net) to Solve Bi-objective TSP

Mehta¹,

Saeedi²

2022

Preprint

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Travelling salesperson problem (TSP) is a classic resource allocation problem used to find an optimal order of doing a set of tasks while minimizing (or maximizing) an associated objective function. It is widely used in robotics for applications such as planning, scheduling etc. In this work, we solve TSP for two objectives using reinforcement learning. Often in multi objective optimization problems, the associated objective functions can be conflicting in nature. In such cases, the optimality is defined in terms of Pareto optimality. A set of these Pareto Optimal solutions in the objective space form a Pareto front (or frontier). Each solution has its own trade off. In this work, we present PA-Net, a network that generates good approximations of the Pareto front for the bi-objective travelling salesperson problem (BTSP). Firstly, BTSP is converted into a constrained optimization problem. We then train our network to solve this constrained problem using the Lagrangian relaxation and policy gradient. With PA-Net we are able to generate good quality Pareto fronts with fast inference times. Finally, we present the application of PA-Net to find optimal visiting order in a robotic navigation task/coverage planning 1 .

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Application of NSGA-II framework to the travel planning problem using real-world travel data

Cited by 16 publications

References 10 publications

Far-UVC Disinfection with Robotic Mobile Manipulator

Far-UVC Disinfection with Robotic Mobile Manipulator

Deep Reinforcement Learning Model for Blood Bank Vehicle Routing Multi-Objective Optimization

Pareto Frontier Approximation Network (PA-Net) to Solve Bi-objective TSP

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