Efficient approximate algorithms for a class of dynamic lot size problems under product substitution
نویسندگان
چکیده
Our investigation into the production substitution between products is motivated by a significant issue faced firms in practice: effective balance of setup and cost. Given that can adjust an operation policy to lower costs or both, manager should also know how best make adjustment. Thus, this study, we consider class dynamic lot sizing problems with one-way two-way product modes under durable perishable products. According some structural properties optimal solution, devise forward programming (DP) algorithm work out problem two polynomial time. Then, develop efficient approximate DP solve multiple Finally, on comprehensive test bed, gain useful insights impact total costs. We tested effectiveness algorithm.
منابع مشابه
Forecast Horizons for a Class of Dynamic Lot-Size Problems Under Discrete Future Demand
We present structural and computational investigations of a new class of weak forecast horizons – minimal forecast horizons under the assumption that future demands are integer multiples of a given positive real number – for a specific class of dynamic lot-size (DLS) problems. Apart from being appropriate in most practical instances, the discreteness assumption offers a significant reduction in...
متن کاملEfficient sampling in approximate dynamic programming algorithms
Dynamic Programming (DP) is known to be a standard optimization tool for solving Stochastic Optimal Control (SOC) problems, either over a finite or an infinite horizon of stages. Under very general assumptions, commonly employed numerical algorithms are based on approximations of the cost-to-go functions, by means of suitable parametric models built from a set of sampling points in the d-dimens...
متن کاملAn Efficient Neurodynamic Scheme for Solving a Class of Nonconvex Nonlinear Optimization Problems
By p-power (or partial p-power) transformation, the Lagrangian function in nonconvex optimization problem becomes locally convex. In this paper, we present a neural network based on an NCP function for solving the nonconvex optimization problem. An important feature of this neural network is the one-to-one correspondence between its equilibria and KKT points of the nonconvex optimizatio...
متن کاملAn efficient one-layer recurrent neural network for solving a class of nonsmooth optimization problems
Constrained optimization problems have a wide range of applications in science, economics, and engineering. In this paper, a neural network model is proposed to solve a class of nonsmooth constrained optimization problems with a nonsmooth convex objective function subject to nonlinear inequality and affine equality constraints. It is a one-layer non-penalty recurrent neural network based on the...
متن کاملA Framework for Adapting Population-Based and Heuristic Algorithms for Dynamic Optimization Problems
In this paper, a general framework was presented to boost heuristic optimization algorithms based on swarm intelligence from static to dynamic environments. Regarding the problems of dynamic optimization as opposed to static environments, evaluation function or constraints change in the time and hence place of optimization. The subject matter of the framework is based on the variability of the ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Industrial and Management Optimization
سال: 2023
ISSN: ['1547-5816', '1553-166X']
DOI: https://doi.org/10.3934/jimo.2023082