Competition-based neural network for the multiple travelling salesmen problem with minmax objective

Somhom, Samerkae; Modares, Abdolhamid; Enkawa, Takao

doi:10.1016/s0305-0548(98)00069-0

Cited by 100 publications

(74 citation statements)

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“…SOM-based approaches have been used for solving the minmax m-TSP, where the objective is to minimise the path of the longest agent. The approach creates an individual network for each agent, and the adaptation prefers neurons from the currently shortest tours when allocating tasks to the individual agents [24]. A similar idea has been considered for multi-agent coverage of a polygonal world with obstacles [16].…”

Section: Related Workmentioning

confidence: 99%

Multi-robot path planning for budgeted active perception with self-organising maps

Best

Faigl

2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-We propose a self-organising map (SOM) algorithm as a solution to a new multi-goal path planning problem for active perception and data collection tasks. We optimise paths for a multi-robot team that aims to maximally observe a set of nodes in the environment. The selected nodes are observed by visiting associated viewpoint regions defined by a sensor model. The key problem characteristics are that the viewpoint regions are overlapping polygonal continuous regions, each node has an observation reward, and the robots are constrained by travel budgets. The SOM algorithm jointly selects and allocates nodes to the robots and finds favourable sequences of sensing locations. The algorithm has polynomial-bounded runtime independent of the number of robots. We demonstrate feasibility for the active perception task of observing a set of 3D objects. The viewpoint regions consider sensing ranges and selfocclusions, and the rewards are measured as discriminability in the ensemble of shape functions feature space. Simulations were performed using a 3D point cloud dataset from a real robot in a large outdoor environment. Our results show the proposed methods enable multi-robot planning for budgeted active perception tasks with continuous sets of candidate viewpoints and long planning horizons.

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Section: Related Workmentioning

confidence: 99%

Multi-robot path planning for budgeted active perception with self-organising maps

Best

Faigl

2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…The problem can be constructed as a minimum cost flow (MCF) problem taking the inspiration from a paper which formulates the MCF problem for AGV routing in container terminals [13]. In Figure 2 G=(N,A), each initial AGV location can be considered as a source node.…”

Section: Problem Overviewmentioning

confidence: 99%

Routing of Mobile Resources with PSO using Chaotic Randomness (Chaotic-PSO) for Unexpected Delivery Failures in Manufacturing

Özpınar

Kucukasci

2016

Mar. J. Pure Appl. Sci.

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Dealing with short term and long term production planning and scheduling has already been solved with different optimization and artificial methods and approaches. Under normal manufacturing conditions supply and demand progress controlled and supported by decision support systems, ERP and MRP software packages aiming maximum utilization of resources and minimizing the stocks aiming for JIT. These software packages becoming even more intelligent and proactive based on the data in the database systems. However all these systems start with initial assumption of under normal conditions of flow time ordering to delivering. In case of unplanned stops, failures, malfunctions, shortages of unplanned inventory levels alter these initial conditions and progress to a diverging outcomes and consequences. This paper aiming a dynamic allocation and routing of mobile resources in a manufacturing plant by reallocating them by using a modified Particle Swarm Optimization using Chaotic Randomness.

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“…A self-organizing NN for the VRP based on an enhanced mTSP NN model is due to (Torki et al, 1997). Recently, Somhom et al, 1999) have developed a self-organizing NN approach for the mTSP with a minmax objective function, which minimizes the cost of the most expensive route. Utilizing GA for the solution of mTSP seems to be first due to (Zhang et al, 1999).…”

Section: Heuristic Solution Approaches For Mtspmentioning

confidence: 99%

Traveling Salesman Problem: an Overview of Applications, Formulations, and Solution Approaches

Matai¹,

Singh²,

Mittal³

2010

Traveling Salesman Problem, Theory and Applications

198

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Competition-based neural network for the multiple travelling salesmen problem with minmax objective

Cited by 100 publications

References 0 publications

Multi-robot path planning for budgeted active perception with self-organising maps

Multi-robot path planning for budgeted active perception with self-organising maps

Routing of Mobile Resources with PSO using Chaotic Randomness (Chaotic-PSO) for Unexpected Delivery Failures in Manufacturing

Traveling Salesman Problem: an Overview of Applications, Formulations, and Solution Approaches

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