Determination of mass, damping coefficient, and stiffness of production system using convolution integral

Zanwar, D.R.; Deshpande, Vinayak; Modak, J. P.; Gupta, M.M.; Agrawal, Kavita

doi:10.1080/00207543.2014.998787

Cited by 1 publication

(1 citation statement)

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“…Based on a previous work [51], and [52] which applies second-order differential equation analogizing production system with mechanical vibration systems, here the approach is to propose an energy based analogy to obtain the dynamic equation for APP in production-inventory systems. In order to develop a consistent energy based analogy for APP, the following energy based function for APP, where E APP is the total energy of the APP system which is proposed: …”

Section: Mathematical Modeling Of Appmentioning

confidence: 99%

Optimal Control Approaches to the Aggregate Production Planning Problem

Davizón¹,

Martínez-Olvera

Soto

et al. 2015

Sustainability

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Abstract:In the area of production planning and control, the aggregate production planning (APP) problem represents a great challenge for decision makers in production-inventory systems. Tradeoff between inventory-capacity is known as the APP problem. To address it, static and dynamic models have been proposed, which in general have several shortcomings. It is the premise of this paper that the main drawback of these proposals is, that they do not take into account the dynamic nature of the APP. For this reason, we propose the use of an Optimal Control (OC) formulation via the approach of energy-based and Hamiltonian-present value. The main contribution of this paper is the mathematical model which integrates a second order dynamical system coupled with a first order system, incorporating production rate, inventory level, and capacity as well with the associated cost by work force in the same formulation. Also, a novel result in relation with the Hamiltonian-present value in the OC formulation is that it reduces the inventory level compared with the pure energy based approach for APP. A set of simulations are provided which verifies the theoretical contribution of this work.

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Section: Mathematical Modeling Of Appmentioning

confidence: 99%