Economic Analysis of an Integrated Production–Inventory System under Stochastic Production Capacity and Energy Consumption

Abstract

Expensive power cost is a significant concern in today’s manufacturing world. Reduction in energy consumption is an ultimate measure towards achieving manufacturing efficiency and emissions control. In the existing literature of scheduling problems, the consumption of energy is considered uncertain under the dimensions of uncertain demand and supply. In reality, it is a random parameter that also depends on production capacity, manufacturing technology, and operational condition of the manufacturing system. As the unit production cost varies with production rate and reliability of the manufacturing system, the energy consumption of the system also varies accordingly. Therefore, this study investigated an unreliable manufacturing system under stochastic production capacities and energy consumption. A stochastic production–inventory policy is developed to optimize production quantity, production rate, and manufacturing reliability under variable energy consumption costs. As energy consumption varies in different operational states of manufacturing, we consider three specific states of power consumption, namely working, idle, and repair time, for an integrated production–maintenance model. The considered production system is subjected to stochastic failure and repair time, where productivity and manufacturing reliability is improved through additional technology investment. The robustness of the model is shown through numerical example, comparative study, and sensitivity analysis of model parameters. Several graphical illustrations are also provided to obtain meaningful managerial insights.

An Automated Smart EPQ-Based Inventory Model for Technology-Dependent Products under Stochastic Failure and Repair Rate

“…This work is extended to the case of unreliable manufacturing system by [3] where unit production cost is considered as a function of production rate and reliability parameter. Recently, Asghar et al [6] developed an unreliable EPQ-based inventory model which considers production rate as decision variable. The model considers the unit production cost as a function of production rate, manufacturing reliability, and manufacturing yield.…”

“…Therefore, this study investigates the need for considering technological improvement/advancements in the production process and corresponding costs for product innovation. This study outperforms the work of [6] as it considers the demand variability for technology innovation for an unreliable production system, and limits the per-unit technology investment for products τ as (< α% ) of the smart production technology development investment (χ). -Due to the stochastic nature of breakdowns and repair times, the study extended the production inventory model to an integrated smart production maintenance model.…”

“…Energy costs are considered as the biggest cost component in the production for complex and highly automated production systems. Asghar et al [6] showed that energy consumption for an unreliable production system is a variable factor for different production states and specific energy consumption costs for an automated flexible production system can be reduced with improved manufacturing reliability. The study can be further extended to the concept [34] of imperfect production process for an unreliable manufacturing system as the production of defectives is an inevitable phenomena.…”

Stochastic Control for Intra-Region Probability Maximization of Multi-Machine Power Systems Based on the Quasi-Generalized Hamiltonian Theory

“…Substituting the OCL into (5) and solving the function by Monte Carlo method, the results are as shown in Figure 9. Figure 9, the analytical results fit well with the Monte Carlo results, which verify the accuracy of the stochastic averaging method.…”

“…More and more scholars are beginning to study the effects of these random disturbances [4][5][6][7][8][9][10][11][12][13]. Most of current research focuses on the power systems steady state.…”