Optimal control model for finite capacity continuous MRP with deteriorating items

Abstract

A general model for continuous material requirements planning problem is proposed which contains of reworking of returned items along with deterioration of items. In the proposed model there are separated stocks for manufactured, returned and reworked items and also it is possible to consider returned items from both inventories of manufactured and reworked items. A general finite time linear quadratic optimal control problem is presented to attain the goal values for inventories, demands and productions. The goal values for inventories, demands and production can be considered as the capacity of stocks, scheduled demand and capacity of transportation respectively. Since the time is considered as a continuous parameter, the carrying cost of production process is more real than the periodic approach wherein time is considered as a discrete parameter. Finally a solution method is presented and numerical simulations are provided to validate the approach. KeywordsContinuous material requirements planning (CMRP) • Finite capacity MRP • Deterioration • Linear quadratic optimal control • Pontryagin minimum principle B Alireza Pooya

Optimal adaptive inspection and maintenance planning for deteriorating structural systems

“…The proposed adaptive planning is an on-line optimization at the level of array W * and is greedy since the strategy optimization is always performed assuming that it is the last opportunity to optimize the strategy. Multi-step-based improvement greedy techniques have demonstrated to perform much better than a single initial optimization, as shown empirically with a number of algorithms (Silver and Veness, 2010;Pooya and Pakdaman, 2018;Efroni et al, 2018).…”

“…Figure 2 retraces the steps of this strategy improvement. In the context of production and supply optimization, a similar approach is known as model predictive control (MPC) (Pooya and Pakdaman, 2018). The principle is also adopted to improve reinforcement learning algorithms (Kahn et al, 2017), and is key to the Monte Carlo Tree Search POMCP algorithm presented in (Silver and Veness, 2010).…”

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Finite Capacity Material Requirement Planning System for a Three-Stage Supply Chain Under Manufacturer-Managed Scheduling Concept

“…The hybrid algorithm combined a genetic and tabu search algorithms. Recently, Pooya and Pakdaman (2019) introduced a production inventory optimal control model for a continuous-time MRP (CMRP) environment with deteriorating items to determine the optimal quantities of production and purchasing at each moment of the planning horizon considering finite capacity constraints.…”