A comparative study of different pull control strategies in multi-product manufacturing systems using discrete event simulation

Pull production control strategies coordinate manufacturing operations based on actual demand. Up to now, relevant publications mostly examine manufacturing systems that produce a single type of a product. In this research, we examine the CONWIP, Base Stock, and CONWIP/Kanban Hybrid pull strategies in multi-product manufacturing systems. In a multi-product manufacturing system, several types of products are manufactured by utilizing the same resources. We develop queueing network models of multi-stage, multi-product manufacturing systems operating under the three aforementioned pull control strategies. Simulation models of the alternative production systems are implemented using an open-source software. A comparative evaluation of CONWIP, Base Stock and CONWIP/Kanban Hybrid in multi-product manufacturing is carried out in a series of simulation experiments with varying demand arrival rates, setup times and control parameters. The control strategies are compared based on average wait time of backordered demand, average finished products inventories, and average length of backorders queues. The Base Stock strategy excels when the manufacturing system is subjected to high demand arrival rates. The CONWIP strategy produced consistently the highest level of finished goods inventories. The CONWIP/Kanban Hybrid strategy is significantly affected by the workload that is imposed on the system.

Battery and Energy Metals: Future Drivers of the Minerals Industry?

A wide range of metals and minerals are currently used in battery and energy technology, meaning that an increasing number of these commodities are being considered as potentially viable primary products by the minerals industry. A select group of these minerals and elements that are vital for energy and battery technologies, including Al, Cr, Co, Cu, graphite, In, Li, Mn, Mo, the rare earth elements (REEs; primarily Dy and Nd), Ni, Ag, Ti, and V, are also likely to undergo rapid increases in demand as a result of the move toward low- and zero-CO2 energy and transportation technology (often termed the energy transition) driven by climate change mitigation and consumer and investor concerns and demands. Increased levels of mineral exploration, discovery, and production will be needed to meet this rising demand. However, several of these key metals and minerals are produced as co- and by-products of other elements. This means that their production is inherently linked to the production of main product elements that may not undergo similar increases in demand, creating issues related to security of supply. It is also not simple to just produce more metal and minerals given the environmental, social, and governmental challenges the global mining industry currently faces. Finally, there are uncertainties over exactly what technologies will dominate the energy transition, meaning that robust demand predictions are still relatively problematic. Quantifying these and other uncertainties and addressing issues over by-and coproduct supply will help ensure that mineral deposits are used sustainably. In addition, understanding the deportment and processing behavior of key critical metals and minerals that are produced as by- or coproducts of main metals such as copper will allow these to actually be extracted from mineral deposits being mined now and into the future rather than be lost to waste. Both of these are vital steps in terms of ensuring that future increases in metal and mineral demand can be met. The impact of these changes on metal and mineral demand and pricing also needs to be examined to ensure the economics of these changes relating to the energy transition are fully understood. All of this means that the mineral industry must act and plan for this transition accordingly in coordination with governments and other organizations. This is especially true given the long lead-in times related to the vast majority of mineral exploration and mining projects compared to the potentially rapid increase in demand for certain battery and energy metals and minerals. This is somewhat analogous to the technology sector, where software (analogous to battery and energy technology) can advance rapidly, creating significant demand that puts pressure on associated hardware (in this case, the development of new mines or changes in mineral processing) that advances more slowly. Failing to ensure mineral and metal supply meets increasing (and potentially rapidly varying) demand may lead to situations where demand far exceeds supply, causing preventable issues related to supply chain continuity and further delaying climate change mitigation, with potential global consequences.

An efficient dynamic power management model for a stand-alone DC Microgrid using CPIHC technique

The power generation using solar photovoltaic (PV) system in microgrid requires energy storage system due to their dilute and intermittent nature. The system requires efficient control techniques to ensure the reliable operation of the microgrid. This work presents dynamic power management using a decentralized approach. The control techniques in microgrid including droop controllers in cascade with proportional-integral (PI) controllers for voltage stability and power balance have few limitations. PI controllers alone will not ensure microgrid’s stability. Their parameters cannot be optimized for varying demand and have a slow transient response which increases the settling time. The droop controllers have lower efficiency. The load power variation and steady-state voltage error make the droop control ineffective. This paper presents a control scheme for dynamic power management by incorporating the combined PI and hysteresis controller (CPIHC) technique. The system becomes robust, performs well under varying demand conditions, and shows a faster dynamic response. The proposed DC microgrid has solar PV as an energy source, a lead-acid battery as the energy storage system, constant and dynamic loads. The simulation results show the proposed CPIHC technique efficiently manages the dynamic power, regulates DC link voltage and battery’s state of charge (SoC) compared to conventional combined PI and droop controller (CPIDC).

Multiple Means of Engagement Strategies for Maximising the Learning of Mathematics in Pandemic-regulated Classrooms

Many learners find mathematics learning challenging. In response to that actuality, this paper highlights mathematics teachers’ experiences of, and insights into how they adopted and implemented the principle of “Multiple Means of Engagement” (MME) to maximise learning in pandemic-regulated classrooms (in the context of the study, characterised by alternative weeks of attendance, social distancing and wearing of masks). The MME principle is one of the three universal design for learning (UDL) key principles, which guides on how diverse groups of learners can be effectively catered for. The empirical processes, premised on a phenomenological case study, commenced with focus group discussions with 8 high school mathematics teachers from a previously disadvantaged area, who have prior-training in MME. A free attitude interview (FAI) technique was used, to afford the teachers the opportunity to share their insights into the application of MME in their pandemic-regulated classrooms. The content analysis of the teachers’ reflections revealed the following aspects: clear instructions, step-by-step guides, checklists to enhance self-regulation, varying demand and resources to meet challenges, fostering collaboration, providing corrective feedback to sustain effort and persistence, addressing mathematical vocabulary and using real-life situations to recruit interest. These strategies were found not only essential in maximising learning in mathematics under normal circumstances, but also indispensable during the prevailing conditions of the pandemic. The findings therefore suggest MME as a suitable mathematical approach during this Covid19 period.

A Conceptual Framework for Minimizing Peak Load Electricity using Internet of Things

Electricity load demand converts from time to time frequently in a day. Encountering time-varying demand particularly in peak times is considered a big challenge that faces electric utilities. Persistent growth in peak load increases the prospect of power failure and increases the electricity equipping marginal cost. Therefore, balancing production and consumption of electricity or addressing peak load has become a key attention of utilities. Most previous works and researches were focused on applying Shave/Shift peak load to solve energy scarcity. In this study, we introduce four significant technologies and techniques for achieving peak load shaving, namely “Internet of Things (IoT) in Energy System”, “On-site Generation systems (Renewable Energy Resources)”, “Demand Side Management (DSM)” applications of control center and “Energy Storage Systems (ESSs)”. The impact of these four major methods for peak load shaving to the grid has been discussed in detail. Finally, we suggest a conceptual framework as guiding tool for illustrating the presented technologies of Shave/Shift peak load in energy systems.

An Application of a Deep Q-Network Based Dynamic Fare Bidding System to Improve the Use of Taxi Services during Off-Peak Hours in Seoul

The problem of structural imbalance in terms of supply and demand due to changes in traffic patterns by time zone has been continuously raised in the mobility market. In Korea, unlike large overseas cities, the waiting time tolerance increases during the daytime when supply far exceeds demand, resulting in a large loss of operating profit. The purpose of this study is to increase taxi demand and further improve driver’s profits through real-time fare discounts during off-peak daytime hours in Seoul, Korea. To this end, we propose a real-time fare bidding system among taxi drivers based on a dynamic pricing scheme and simulate the appropriate fare discount level for each regional time zone. The driver-to-driver fare competition system consists of simulating fare competition based on the multi-agent Deep Q-Network method after developing a fare discount index that reflects the supply and demand level of each region in 25 districts in Seoul. According to the optimal fare discount level analysis in the off-peak hours, the lower the OI Index, which means the level of demand relative to supply, the higher the fare discount rate. In addition, an analysis of drivers’ profits and matching rates according to the distance between the origin and destination of each region showed up to 89% and 65% of drivers who actively offered discounts on fares. The results of this study in the future can serve as the foundation of a fare adjustment system for varying demand and supply situations in the Korean mobility market.

Real-Time Control Strategy of Fuel Cell and Battery System for Electric Hybrid Boat Application

In this paper, an effective control strategy is proposed to manage energy distribution from fuel cells and batteries for hybrid electric boat applications. The main objectives of this real-time control are to obtain fast current tracking for the batteries’ system, the DC bus voltage stability by using a fuel cell, and energy load distribution for a hybrid electric boat under varying demand conditions. The proposed control strategy is based on a combination of frequency approach and current/voltage control of interleaved boost converters to reduce the hydrogen consumption by the fuel cell and improve the quality of energy transfer. The frequency approach was dedicated to managing the DC power-sharing between the load, the fuel cell, and the batteries’ storage system by extracting the power references. The closed loop control system utilized to control the energy is based on the DC/DC converters. The performance evaluation of the proposed control strategy has been tested through a real-time experimental test bench based on a dSPACE board (DS1104).

Profit maximization in an inventory system with time-varying demand, partial backordering and discrete inventory cycle

In this paper, an inventory problem where the inventory cycle must be an integer multiple of a known basic period is considered. Furthermore, the demand rate in each basic period is a power time-dependent function. Shortages are allowed but, taking necessities or interests of the customers into account, only a fixed proportion of the demand during the stock-out period is satisfied with the arrival of the next replenishment. The costs related to the management of the inventory system are the ordering cost, the purchasing cost, the holding cost, the backordering cost and the lost sale cost. The problem is to determine the best inventory policy that maximizes the profit per unit time, which is the difference between the income obtained from the sales of the product and the sum of the previous costs. The modeling of the inventory problem leads to an integer nonlinear mathematical programming problem. To solve this problem, a new and efficient algorithm to calculate the optimal inventory cycle and the economic order quantity is proposed. Numerical examples are presented to illustrate how the algorithm works to determine the best inventory policies. A sensitivity analysis of the optimal policy with respect to some parameters of the inventory system is developed. Finally, conclusions and suggestions for future research lines are given.