A recursive time aggregation-disaggregation heuristic for the multidimensional and multiperiod precedence-constrained knapsack problem: An application to the open-pit mine block sequencing problem

In todays uncertain economic environment, economic risk is inherent in making large investments on manufacturing facilities. It is, therefore, practically meaningful to divide investment over multiple periods, reducing the risk of investment. Then, the cash-flow over the entire planning horizon would comprise positive inflow and negative outflow. In this case, in general, evaluation by internal rate of return (IRR) is not feasible, because multiple IRRs are involved. This paper deals with a problem of evaluating profitability, as well as risk, of investment alternatives made in multiple times of investment over the entire horizon. Typically, an additional investment is required after the initial one, for expanding manufacturing capacity or other reasons. The paper pays attention to a unit cash-flow over two periods, decomposing the total cash-flow into a series of unit cash-flow patterns. It is easy to evaluate profitability of a unit cash-flow by using IRR. The total cash-flow can be decomposed into the series of two types of unit cash-flows: an investment type one (negative-positive) and the borrowing type one (positive-negative). This paper, therefore, proposes a method in which only the borrowing type unit cash-flow is eliminated in the series by converting total cash-flow using capital interest rate. Then, a unique IRR can be obtained and the profitability is evaluated. Thus, the paper extends the method of IRR so that it may help decision making in complicated cash-flow pattern observed in practice.

Greenhouse climate control is essential for optimizing crop growth while minimizing resource consumption in controlled environment agriculture. Traditional rule-based and fixed-action strategies often struggle to achieve a balance between these objectives. This paper proposes a reinforcement learning (RL) based framework for greenhouse climate control, integrating deep learning models to predict both crop growth and resource consumption. The framework enables an RL agent to optimize greenhouse control setpoints dynamically, maximizing crop yield while ensuring sustainable resource usage. The proposed system incorporates a Multi-Layer Perceptron (MLP) model to predict internal greenhouse climate conditions, a Long Short-Term Memory (LSTM) model for crop parameter estimation, and a separate LSTM model for forecasting daily resource consumption. These models collectively simulate a greenhouse environment where an RL agent learns to regulate temperature, CO2 concentration, and irrigation levels by interacting with the virtual environment. A custom reward function is designed to guide the agent, considering key crop parameters; stem elongation, stem thickness, and cumulative trusses; alongside resource consumption metrics, including heating, electricity, CO2, and irrigation costs. To enhance the adaptability of the RL agent, a feature-selection mechanism identified the most influential climate and control features, reducing observation complexity and accelerating convergence. Retraining under stochastic weather conditions strengthened robustness to dynamic environments, enabling the agent to consistently outperform fixed-action strategies. Evaluation revealed a stable Pareto frontier between yield and resource consumption, confirming that the framework accurately captured the productivity and sustainability trade-off and remained robust across varying reward-weight settings. Comparative analysis of multiple RL algorithms; Proximal Policy Optimization (PPO), Deep Deterministic Policy Gradient (DDPG), Soft Actor-Critic (SAC), and Twin Delayed Deep Deterministic Policy Gradient (TD3) demonstrated that TD3 outperforms other algorithms, achieving the highest cumulative rewards and reaching optimal policies faster. Experimental evaluations demonstrate that the proposed TD3 RL-based greenhouse control system achieves higher crop yield growth rates while optimizing resource usage, outperforming conventional greenhouse control strategies. This study presents a novel data-driven, adaptive greenhouse management approach, bridging the gap between crop growth modeling and autonomous climate control, contributing to sustainable and intelligent agricultural practices.

One of the main trends of chemistry and pharmaceutical present development is miniaturization technologies introduction, that allows to create mechatronics systems with rare technical and technological characteristics: minimal weight-size parameters, little energy and resources consumption, mild wear, maximum sensitivity, increased accuracy parameters and operation speed, reliability and operations security. This article contains estimation results of mechatronics current development trends. The author considers that today mechatronics is not just a simple aggregation of two sciences: it shall be analyzed as the new technology, whereby the device with mechanical operating actuators comprises software. Now the mechatronics development is essential to the development of countries. Mechatronics suggests direct use of intelligent systems paradigm.

Multi-robot system for manufacturing is an Industry Internet of Things (IIoT) paradigm with significant operational cost savings and productivity improvement, where Unmanned Aerial Vehicles (UAVs) are employed to control and implement collaborative productions without human intervention. This mission-critical system relies on 3-Dimension (3-D) scene recognition to improve operation accuracy in the production line and autonomous piloting. However, implementing 3-D point cloud learning, such as Pointnet, is challenging due to limited sensing and computing resources equipped with UAVs. Therefore, we propose a Digital Twin (DT) empowered Knowledge Distillation (KD) method to generate several lightweight learning models and select the optimal model to deploy on UAVs. With a digital replica of the UAVs preserved at the edge server, the DT system controls the model sharing network topology and learning model structure to improve recognition accuracy further. Moreover, we employ network calculus to formulate and solve the model sharing configuration problem toward minimal resource consumption, as well as convergence. Simulation experiments are conducted over a popular point cloud dataset to evaluate the proposed scheme. Experiment results show that the proposed model sharing scheme outperforms the individual model in terms of computing resource consumption and recognition accuracy.

Starting from the glass frit procedure developed to seal dye solar cells, research is being conducted at Fraunhofer ISE on optimizing and upscaling the principle of perovskite solar cells by an in-situ coating method. A process is proposed, in which the porous substrate films can be filled in sequence with the dissolved active materials of the perovskite solar cell. By varying the porosity and the particle size in the substrate films, the active materials can be deposited in a self-organised process by capillary forces, and form the solar cell after drying. Specially designed structures in the glass substrate make the transport and inert drying of the solvent feasible. The procedure will enable future perovskite solar modules to be produced with minimal consumption of resources and at very low costs. In this paper, we report and comment on our results from proof of principle and about the on-going work in this new area.

Hospital beds are one of the most critical medical resources. Large hospitals in China have caused bed utilization rates to exceed 100% due to long-term extra beds. To alleviate the contradiction between the supply of high-quality medical resources and the demand for hospitalization, in this paper, we address the decision of choosing a case mix for a respiratory medicine department. We aim to generate an optimal admission plan of elective patients with the stochastic length of stay and different resource consumption. We assume that we can classify elective patients according to their registration information before admission. We formulated a general integer programming model considering heterogeneous patients and introducing patient priority constraints. The mathematical model is used to generate a scientific and reasonable admission planning, determining the best admission mix for multitype patients in a period. Compared with model II that does not consider priority constraints, model I proposed in this paper is better in terms of admissions and revenue. The proposed model I can adjust the priority parameters to meet the optimal output under different goals and scenarios. The daily admission planning for each type of patient obtained by model I can be used to assist the patient admission management in large general hospitals.

Efficient digital design of ganglion cells in the retinal pathway

Circular economy is a concept that aims to improve resource efficiency by minimizing resource consumption and waste generation. The implementation of circular economy can be conducted at three levels: in single enterprise or group of enterprises, in a group of collocated firms and at the city or municipal level. Currently, implementation of circular economy in Malaysia is at firm level. There is a lack of legal framework on the implementation of circular economy in Malaysia. However, there are certain sections and regulations in Environmental Quality Act 1974, Solid Waste and Public Cleansing Management Act 2007 and in Environmental Quality (Scheduled Waste) Regulation 2005, respectively, that promote resource circulation. Nationwide initiatives taken to promote circular economy are inclusion of integration of sustainable production and consumption, reduction of 40% of greenhouse gas emissions intensity from GDP compared to 2005 level and 22% of recycling of MSW, in Eleventh Malaysian Plan. Additionally, SWCorp has launched SWCorp Strategic Plan to promote sustainable solid waste management services, and CIDB has initiated CITP that has a target of incorporating 20% of recycled construction and demolition waste (tonnage) by year 2020 from baseline of 2016. Also, there are guidelines on coprocessing and proposals on establishment of industrial ecology by DOE. A few case studies show implementation of circular economy in manufacturing industries. The benefits of these implementations were reduction in energy and resource consumption, reduction in waste generation, protection of environment and human health, cost savings by reusing or recycling waste and additional profit gains by selling waste to potential buyers. Several opportunities of sustainable waste management and resource circulation have been highlighted in this chapter such as manufacturing of bioproducts and butanol from biomass to coprocessing between ELVs and construction industry. To successfully implement circular economy, top-down and bottom-up approach is required, and currently, Malaysia does not have explicit top-down and bottom-up approaches.

While wireless sensor networks are often envisioned to be composed of a collection of nodes scattered inadvertently in an environment, the implications of random node placement in terms of system resource requirements remain mostly unexplored. This work studies resource consumption in wireless sensor networks in the context of decentralized detection. It is argued that, for most detection problems, the number of bits necessary to capture most of the information contained in an observation is small and insignificant when compared to the communication overhead associated with data transfer. Message compression therefore plays a limited role in minimizing resource consumption at each node. The communication rate and the positioning of individual nodes are likely to have a much greater impact overall. In particular, it is shown that for typical dispersive environments, the random placement of wireless nodes may result in an average transmit power that is 6 to 24 times the power required to communicate on a regular lattice. This excessive power consumption may affect the coverage of a system, its life expectancy, the probability of error at the fusion center, and the number of nodes required for a system to operate properly. These preliminary results underscore the importance of planning in the deployment of sensor systems

People need food, energy and water for survival and prosperity. However, it is a challenge to meet the demand for these necessities because urbanization and climate change impacts are destroying food, energy and water systems. Resource optimization in one sector can undermine security in another due to competition for the same resources. Therefore, multiobjective optimization approaches have been used to understand the complexity associated with the Food, Energy Water Nexus. However most of these approaches focus on either maximizing resource production or minimizing resource consumption in the FEW Nexus. Maximizing production and minimizing consumption of resources simultaneously are two conflicting objectives that have not been well addressed by existing multiobjective optimization approaches. Therefore, it is difficult for these approaches to recognize feedback and cascading effects occurring in the FEW Nexus due to the interdependencies of the food, energy and water. For Sustainability of the FEW Nexus to be achieved, sustainable consumption and production of the resources within it need to be emphasized. Therefore, a novel multiobjective optimization approach that can explore alternative ways of both maximizing production and minimizing consumption simultaneously is proposed. This approach can be implemented using an Agent Based Evolutionary Algorithm as future work.

Unlike many natural resources, mineral riches of the earth are non-renewal, so questions of its rational usage have a very big value on behalf of a development of a national economy. The rational usage of mineral resources requires the presence of econometric models, allowing to fulfill the strategic planning of a resources consumption. It's clear, that an accuracy of such models influences directly on the risk of decision making by strategic macroeconomic problems. Existing models' analysis shows that there aren't special computer models for mining industry. Thus, the differential approximated econometric model of a dynamics of different countries resources production and consumption has been developed by us. This model is described by the modified Forrester's differential equations system, in which instead of determining a big amount of interdependent variables, the identification of the model structure and unknown or non-observable parameters is made. Because of this it permits to recover nonvisible trajectories using special identification algorithms. Model results got attest about good work of algorithms and programs and prove that the Marquardt's method gives more accurate results for identification of unknown and non-visible parameters, which allows to minimize the decision making risk by forecasting results of this model. This article includes the mathematical description of econometric dynamic models designed, data chosen as the input information for models developing, identification algorithms for recovering non-visible trajectories, obtained results and its analysis. Risk Analysis II, C.A. Brebbia (Editor) © 2000 WIT Press, www.witpress.com, ISBN 1-85312-830-9

ObjectivesTo determine the best way to intervene for ureteric stones which still require treatment during the COVID‐19 pandemic, with respect to infection control. In this setting, in which resources are constrained, extracorporeal shockwave lithotripsy (SWL) has prima facie advantages over ureteroscopy (URS). It is also necessary to also consider posttreatment resource consumption in regards to complications and repeat procedures.Subjects and methodsThe ideal ureteric stone treatment during a pandemic such as COVID‐19 would involve minimum resource consumption and a minimum number of patient attendances. We compared all patients initially treated with SWL to those initially treated with URS for acute ureteral colic within the state of Victoria, Australia in 2017.ResultsA total of 2724 ureteric stones were analyzed, a cumulative “3‐month exposure and burden on the healthcare system” was calculated for each patient by their initial procedure type. The readmission rate for URS was significantly higher than for SWL, 0.92 readmissions/patient for URS versus 0.54 readmissions/patient for SWL (P < .001). The cumulative hospital stay per patient for these two procedures was 2.35 days for SWL versus 3.21 days for URS (P < .001). The number of procedures per patient was 1.52 for SWL versus 1.89 for URS (P = .0213).ConclusionsPatients with ureteric stones treated initially by SWL have shorter length of stay with fewer overall attendances and procedures at 3 months than those treated with URS. During a pandemic such as COVID‐19, SWL may have benefits in preserving hospital resources and limiting opportunity for virus transmission, compared to URS.

the air defense of ship formation cooperation decision-making is a multi-stage multi-objective system. Based on the concepts of the fuzzy weighted distance and membership degree, considering the collaborative relationship in different phase, put forward the concept of thermodynamic power allotment wreath of the cooperation degree,combined the fuzzy optimum selection theory with dynamic programming methodology,it provides a new method for solving the multi-stage multi-objective optimization based on collaboration to obtain the optimum balanced tactic. This method is applied to solve the collaborative decision-making optimization problem of colligation anti-missile by the air defense of ship formation cooperation and gives some instructive results. Ships formation cooperative anti-air is a multi-stage and multi-objective decision-making system based on collaborative. The anti-air combat effect and combat resource consumption is a pair of contradiction. Operational effectiveness use minimal resource consumption to gain the biggest operational effects. For different decision form different operational effectiveness, operational effectiveness on surface is different, have different resource allocation, it is a complicated problem. Comprehensive analysis of fleet anti-missile process, we can see that it is a multi-stage multi-objective system, and because there is synergy between the stages, so we need to search based on multi-stage multi-objective dynamic optimization method of synergy. Multi-stage system optimization objective is to seek to optimize the index of the whole process of the target or the decision strategy of each stage. Fuzzy distance and membership degree is presented in this paper on the basis of the concept of coordination degree on the basis of fire distribution ring measurement, the fuzzy optimization theory and dynamic programming theory organically, and based on coordination degree of fire distribution ring multi-stage multi-objective decision-making system fuzzy optimization dynamic programming model.

Cross-organization emergency resource allocation optimization problem is essential to guarantee a successful emergency disposal, and it has become a research focus of modern emergency management. Generally speaking, there are two possible types of resource allocation scenarios: (1) if the emergency resources are overallocated, on the one hand, parallel execution of independent emergency activities can be supported and the emergency disposal time is reduced; on the other hand, too many idle resources may cause low resource utilization rate, high scheduling overhead, and high cost; and (2) if emergency resources are underallocated, this may lead to resource conflicts and the need for some emergency activities to wait for others to complete, and finally the emergency disposal time may increase. Therefore, reasonable emergency resource allocation strategies are highly desired. To the best of our knowledge, there is no formal approach to support the cross-organization emergency resource allocation issue. To handle this problem, we propose a two-layered framework to facilitate the allocation of limited emergency resources to meet its time constraints with high efficiency. More specifically, a kind of Petri net extended with time, resource, and message information, denoted as CE-net, is presented to model cross-organization emergency response processes. Based on the obtained CE-net, the minimum resource requirements are obtained with corresponding algorithms. Then, Minimum Execution Time (MET) strategy and Minimum Resource Consumption (MRC) strategy with their corresponding estimated execution intervals are introduced to facilitate the stakeholder to determine which strategy is suitable according to the timing requirements. A cross-organization fire emergency case is applied to validate the proposed approaches throughout the whole paper.

This paper presents a methodology for the design of field-programmable gate array (FPGA)-based real-time simulators (RTSs) for power electronic circuits (PECs). The programmability of the simulator results from the use of an efficient and scalable overlay architecture (OA). The proposed OA relies on a latency-insensitive design (LID) paradigm. LID consists of connecting small processing units that automatically synchronize and exchange data when appropriate. The use of such data-driven architecture aims to ease the design process while achieving a higher computational efficiency. The benefits of the proposed approach is evaluated by assessing the performance of the proposed solver in the simulation of a two-stage AC–AC power converter. The minimum achievable time-step and FPGA resource consumption for a wide range of power converter sizes is also evaluated. The proposed overlays are parametrizable in size, they are cost-effective, they provide sub-microsecond time-steps, and they offer a high computational performance with a reported peak performance of 300 GFLOPS.