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The problem of estimating the minimum forces extracted by robot fingers on the surface of a grasped rigid object is very crucial to guarantee the stability of the grip without causing defect or damage to the grasped object. Solving this problem is investigated in this paper. Moreover, the optimum sets of parameters used to tune the algorithm are also studied here.

Ant Colony Optimization (ACO), which is a swarm intelligence‐based method, is used in this work to solve this problem. The problem under scope is a complex, constraint optimization problem. We develop our own approach to calculate those minimum forces. Ants ability to reorganize and behave collectively is modelled here. The required forces are a result of the final ants distribution around the fingers contact points. Ants move from contact point to another following the maximum pheromone level direction until they settle on a solution that accomplishes the given criteria. Ants number on a contact point constitutes the total force exerted by a finger on that contact point. The process is repeated until optimum solution is found. Simulations are repeated to track down most suitable ACO parameters for this type of problems and with different fingers configurations.

The results show that ACO can find optimum fingers forces for grasping rigid objects. These objects could be any polygon with or without friction between the fingers tips and the object surface. The method is computationally acceptable and can be applied with different fingers configurations and with different friction coefficients. We found that the optimal set of parameters used to tune ACO is independent of the initial number of ants on each location.

In this paper we present a very original, new, and interesting technique used to solve the optimum grasping forces of rigid objects. It is a well‐known fact that standard optimization techniques have their own requirements and limitations. This technique is based on swarm intelligence. This work opens the door for further investigations on how nature based methods can be used to solve complex problems. ACO offers a simple, yet structure approach to solve nonlinear constraint optimization problems.

This chapter explores a descriptive theory of multidimensional travel behaviour, estimation of quantitative models and demonstration in an agent-based microsimulation.

A descriptive theory on multidimensional travel behaviour is conceptualised. It theorizes multidimensional knowledge updating, search start/stopping criteria and search/decision heuristics. These components are formulated or empirically modelled and integrated in a unified and coherent approach.

The theory is supported by empirical observations and the derived quantitative models are tested by an agent-based simulation on a demonstration network.

Based on artificially intelligent agents, learning and search theory and bounded rationality, this chapter makes an effort to embed a sound theoretical foundation for the computational process approach and agent-based micro-simulations. A pertinent new theory is proposed with experimental observations and estimations to demonstrate agents with systematic deviations from the rationality paradigm. Procedural and multidimensional decision-making are modelled. The numerical experiment highlights the capabilities of the proposed theory in estimating rich behavioural dynamics.

A finite element model for the elastohydrodynamic lubrication problem is presented. A coupling between the hydrodynamic equation and the foundation compliance equation is performed, then the resulting functional problem is given an ‘extended’ variational formulation. Some preliminary numerical results are also presented.

A class of exact solutions to the elastohydrodynamic problem to be used as test cases is presented. A numerical solution to the elastohydrodynamic problem according to the Petrov—Galerkin method is developed. The appearance of spurious numerical undulations in the film profile is examined. A comparison between analytical and numerical results is employed to determine which numerical schemes limit the outcome of numerical oscillations without compromising the solution accuracy.

Mesh Parameterization is central to reverse engineering, tool path planning, etc. This work synthesizes parameterizations with un-constrained borders, overall minimum angle plus area distortion. This study aims to present an assessment of the sensitivity of the minimized distortion with respect to weighed area and angle distortions.

A Mesh Parameterization which does not constrain borders is implemented by performing: isometry maps for each triangle to the plane Z = 0; an affine transform within the plane Z = 0 to glue the triangles back together; and a Levenberg–Marquardt minimization algorithm of a nonlinear F penalty function that modifies the parameters of the first two transformations to discourage triangle flips, angle or area distortions. F is a convex weighed combination of area distortion (weight: α with 0 ≤ α ≤ 1) and angle distortion (weight: 1 − α).

The present study parameterization algorithm has linear complexity [𝒪(n), n = number of mesh vertices]. The sensitivity analysis permits a fine-tuning of the weight parameter which achieves overall bijective parameterizations in the studied cases. No theoretical guarantee is given in this manuscript for the bijectivity. This algorithm has equal or superior performance compared with the ABF, LSCM and ARAP algorithms for the Ball, Cow and Gargoyle data sets. Additional correct results of this algorithm alone are presented for the Foot, Fandisk and Sliced-Glove data sets.

The devised free boundary nonlinear Mesh Parameterization method does not require a valid initial parameterization and produces locally bijective parameterizations in all of our tests. A formal sensitivity analysis shows that the resulting parameterization is more stable, i.e. the UV mapping changes very little when the algorithm tries to preserve angles than when it tries to preserve areas. The algorithm presented in this study belongs to the class that parameterizes meshes with holes. This study presents the results of a complexity analysis comparing the present study algorithm with 12 competing ones.

The purpose of this paper is to develop a closed-loop supply chain (CLSC) network equilibrium model which consists of manufactures, retailers and consumer markets engaged in a Cournot pricing game with heterogeneous multi-product.

The authors model the optimal behavior of the various decision makers and CLSC network equilibrium, and derive the equilibrium conditions based on variational inequality approach. The authors present a new Newton method to solve the proposed model.

The authors find that the algorithm converges to the solution rapidly for most cases. Besides, the authors discuss the effect of some parameters on the equilibrium solution of the model, and give some insights for policy makers, such as improving the technology level of the manufacturer, reducing the cost of waste disposal and increase the minimum ration of used product to total quantity.

The authors derive the network equilibrium conditions by the variational inequality formulation in order to obtain the computation of the equilibrium flows and prices. The authors present a new Newton method to solve the proposed model. The authors discuss the effect of some parameters on the equilibrium solution of the model, and give some managerial insights