Flexible Flow Shop Scheduling Method with Public Buffer

The Effect of Changes in Settings from Multiple Filling Points to a Single Filling Point of an Industry 4.0-Based Yogurt Filling Machine

In process optimization, a process is adjusted so as to optimize a set of parameters while meeting constraints, with the objective to either minimize the total processing time or maximize the throughput. This article focused on the process optimization of a fully automated yogurt and flavor-filling machine developed based on the industrial revolution 4.0 concept. Mathematical models were developed for minimizing the total processing time or maximizing the throughput of an Industry 4.0-based yogurt filling system with two different machine settings called Case-I and Case-II. In Case-I, the yogurt and flavors are filled at two distinct points while Case-II considers the filling of yogurt and flavors at a single point. The models were tested with real data and the results revealed that Case-II is faster than Case-I in processing a set of customer orders. The results were used as inputs for the single-dimension rules to check which one results in more intended outputs. Additionally, different performance measures were considered and the one with most importance to the management was selected.

Machine Learning Based Optimization Model for Energy Management of Energy Storage System for Large Industrial Park

Renewable energy represented by wind energy and photovoltaic energy is used for energy structure adjustment to solve the energy and environmental problems. However, wind or photovoltaic power generation is unstable which caused by environmental impact. Energy storage is an important method to eliminate the instability, and lithium batteries are an increasingly mature technique. If the capacity is too large, it would cause waste and cost would increase, but too small capacity cannot schedule well. At the same time, the size of energy storage capacity is also constrained by power consumption, whereas large-scale industrial power consumption is random and non-periodic. This is a complex problem which needs a model that can not only dispatch but also give a reasonable storage capacity. This paper proposes a model considering the cycle life of a lithium battery and the installation parameters of the battery, and the electricity consumption data and photovoltaic power generation data of an industrial park was used to establish an energy management model. The energy management system aimed to reduce operating costs and obtain optimal energy storage capacity, which is constrained by lithium battery performance and grid demand. With the operational cost and reasonable battery capacity as the optimization objectives, the Deep Deterministic Policy Gradient (DDPG) method, the greedy dynamic programming algorithm, and the genetic algorithm (GA) were adopted, where the performance of lithium battery and the requirement of power grid were the constraints. The simulation results show that compared with the current forms of energy, the three energy management methods reduced the cost of capacity and operating of the energy storage system by 18.9%, 36.1%, and 35.9%, respectively.

Special Issue on “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”

Complex industrial automation systems and processes, such as chemical processes, manufacturing systems, wireless network systems, power and energy systems, smart grids and so forth, have greatly contributed to our daily life [...]