Mixed Integer Linear Programming Optimization of Heat Distribution within a Local Heating System

This paper presents a novel approach to the joint optimization of job scheduling and data allocation in grid computing environments. We formulate this joint optimization problem as a mixed integer quadratically constrained program. To tackle the nonlinearity in the constraint, we alternatively fix a subset of decision variables and optimize the remaining ones via Mixed Integer Linear Programming (MILP). We solve the MILP problem at each iteration via an off-the-shelf MILP solver. Our experimental results show that our method significantly outperforms existing heuristic methods, employing either independent optimization or joint optimization strategies. We have also verified the generalization ability of our method over grid environments with various sizes and its high robustness to the algorithm setting.

Predictive route choice control of destination coded vehicles with mixed integer linear programming optimization

International audience

Rapid online planning of train operation strategy in response to emergencies is a critical means of ensuring the safe and efficient operation for high-speed railways. However, it is difficult to achieve punctual, comfortable, and energy-saving operation under emergency scenarios due to the properties of high speeds, long inter-station distances, and the presence of long continuous gradients. This paper proposes an online planning approach of train operation curve based on dynamic programmingdeep Q network (DP-DQN). Firstly, the dynamic programming (DP) algorithm is used to optimize some train operation curves, and the state quadruples it contains are put into the memory buffer and incorporated into the reward function to guide and accelerate the network training. Then, the action mask from reinforcement learning is used to filter out the actions that cause the train speed to exceed the Automatic Train Protection speed, enabling fast and safe online planning of the speed curve under emergencies. Based on the actual data from a high-speed railway line, the proposed approach is evaluated in offline optimization. Experimental results show that, compared with mixed integer linear programming (MILP) and particle swarm optimization (PSO) algorithms, the DP-DQN approach yields small errors in train operation punctuality and stopping accuracy, better passenger comfort, and a reduction of energy consumption by 2.99% and 12.30%, respectively. Additionally, emergency scenarios involving temporary speed limit, traction/braking failure and emergency stop are simulated and analyzed. While DP-DQN results in slightly higher energy consumption than MILP in some scenarios, it significantly reduces train delays, ensures stopping accuracy, and completes online strategy planning within one second, which meets the real-time requirements of online planning.

A new local film heating system (LFHS) can precisely control the local mold wall temperature in the nanoinjection molding process.

Improving the detection of thermal insulation in buildings –which includes the development of models for heating and ventilation processes and fabric gain - could significantly increase building energy efficiency and substantially contribute to reductions in energy consumption and in the carbon footprints of domestic heating systems. Thermal insulation standards are now contractual obligations in new buildings, although poor energy efficiency is often a defining characteristic of buildings built before the introduction of those standards. Lighting, occupancy, set point temperature profiles, air conditioning and ventilation services all increase the complexity of measuring insulation efficiency. The identification of thermal insulation failure can help to reduce energy consumption in heating systems. Conventional methods can be greatly improved through the application of hybridized machine learning techniques to detect thermal insulation failures when a building is in operation. A three-step procedure is proposed in this paper that begins by considering the local building and heating system regulations as well as the specific features of the climate zone. Firstly, the dynamic thermal performance of different variables is specifically modelled, for each building type and climate zone. Secondly, Cooperative Maximum-Likelihood Hebbian Learning is used to extract the relevant features. Finally, neural projections and identification techniques are applied, in order to detect fluctuations in room temperatures and, in consequence, thermal insulation failures. The reliability of the proposed method is validated in three winter zone C cities in Spain. Although a great deal of further research remains to be done in this field, the proposed system is expected to outperform conventional methods described in Spanish building codes that are used to calculate energetic profiles in domestic and residential buildings.KeywordsFeature SelectionHeating SystemMachine IntelligenceImprove Energy EfficiencyIndoor TemperatureThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Thermal analysis of a new solar kang system

The decentralisation of existing district heating systems by network separation is a promising approach for district heating transformation. A district heating network is separated into several smaller district heating network structures that can be individually adapted, e.g. in relation to the existing network structure or concerning the properties of the local buildings supplied, such as the temperature requirements of their heating systems. In this work, an approach for the optimal separation of existing district heating networks is expanded, which enables the decentralisation of a specific district heating system and the partial transformation of the separated network depending on the heat source utilised and the local district heating system conditions. The approach first identifies coherent network areas through community detection and aggregation, then optimises network separation and the installation of heat pumps, and finally evaluates the arising district heating network systems through district heating network simulations. The individual steps of this approach are adapted to be applicable to large district heating network structures, e.g. to handle the great number of separation options. The approach is applied to two existing district heating systems with over four thousand buildings supplied. Two types of utilised waste heat sources are investigated, a sewage plant with fluctuating temperatures and an electrolyser with a dynamic operation schedule. The results show favourable transformation alternatives for existing district heating networks, as the separated network areas are transformed by reducing supply temperatures depending on the heat source potential utilised and the temperature requirements of the buildings supplied. • Separating district heating networks facilitates the utilisation of heat sources. • An optimisation model reveals the optimal separation of district heating networks. • The network separation considers available heat capacity and temperature level. • Heat pumps are installed in the separated network to utilise low-temperature heat. • A subsequent simulation evaluates the separated district heating networks.

Greenhouse gases emission assessment in residential sector through buildings simulations and operation optimization

Due to the complementarity of renewable energy sources, there has been a focus on technology hybridization in recent years. In the area of hybrid offshore power plants, the current research projects mostly focus on the combinational implementation of wind, solar, and wave energy technologies. Accordingly, considering the already existing offshore wind farms, there is the potential for the implementation of hybrid power plants by adding wave energy converters and floating photovoltaics. In this work, a stochastic sizing model is developed for the hybridization of existing offshore wind farms using wave energy converters and floating photovoltaics considering the export cable capacity limitation. The problem is modeled from an investor perspective to maximize the economic profits of the hybridization, while the costs and revenues regarding the existing units and the export cable are excluded. Furthermore, to tackle the uncertainties of renewable energy generation, as well as the energy price, a scenario generation method based on copula theory is proposed to consider the dependency structure between the different random variables. Altogether, the hybridization study is modeled in a mixed integer linear programming optimization framework considering the net present value of the project as the objective function. The results showed that hybrid-sources-based energy generation provided the highest economic profit in the studied cases in the different geographical locations. Furthermore, the technical specifications of the farms have also been considerably improved providing more stable energy generation, guaranteeing a minimum level of power in a high share of the time, and with a better utilization of the capacity of the cable while the curtailment of energy is maintained within the acceptable range.

Disruptions can cause demand fluctuation, thus overcrowding at hospital wards, and can make the waiting list of patients longer. Bed management at hospitals is one of the solutions to deal with overcrowding. In addition, Resilience Engineering (RE) is an approach that can help organizations to bounce back to their desired performance state after a disruption. In this paper, the concept of RE has been used to improve the bed management of hospitals during and after disruption. More precisely, bed sharing among hospital wards has been introduced as a collaboration strategy and its impact on the length of patients’ waiting list as the major performance index is investigated. Relationship priority between different wards, patients’ gender, patients’ length of stay and the number of rooms in every ward are the major factors considered in our modeling. A mixed integer linear programming optimization model with the objective of minimizing the patients’ waiting time in a hospital has been proposed for the real-world problems. The main contribution of the present paper is proposing a resiliency-based modeling of bed management in hospitals. Due to the complexity and making the proposed model applicable to the real world problems, a simulated annealing algorithm is used to solve the model and a new procedure for creating initial solution is presented. The results show that applying resilience strategy has a considerable impact on improving the hospital’s performance index.

This article proposes a modelling process to evaluate/optimise supply chain flows in a franchise network. We will study bakery networks composed of a supply chain producer and a retail outlet that sells the products made by the operator of the network in his own factories. Our modelling is a combination of two modelling processes: a first modelling reproduces the running supply chain through simulation and/or optimisation (Comelli, M., Fenies, P., and Tchernev, N., 2008. A combined financial and physical flows evaluation for logistic process and tactical production planning: application in a company supply chain. International Journal of Production Economics, 112 (1), 77–95); then data given by this model are used by a model (B) which reproduces the consequences of model (A) on the mixed franchise network thanks to a mixed integer linear programming optimisation based on the four management challenges. Then, for the opening of a new outlet, generated cash flow is analysed to choose between a company owned and a franchise. We show that the plural form is more efficient in generating cash flow for the operator. This form is, in fact, the best choice for an operator who wants to develop his network while balancing challenges such as growth, uniformity, local responsiveness and global adaptation. This kind of approach links together two research fields: a strategic one with the choice of the statutory form of the outlet in a mixed franchised network, and a tactical and operational one that optimises the cash flow in supply chains.

Small-scale hybrid energy systems are often composed by different power production technologies and adopted in mini-grids. In this work, a Mixed Integer Linear Programming optimization algorithm has been developed to compute the optimal scheduling of a micro-grid constituted by Internal Combustion Generators (ICGs) and a Storage System that can be either a conventional battery storage system or a Pumping Hydro energy Storage (PHES) based on Pump-as-Turbines. The algorithm computes the optimal energy generation scheduling of the micro-grid, minimizing a multi-objective fitness function constituted by the total costs of the energy system and the total CO2 and NOx emissions. In particular, the emissions are modelled with varying trends depending on the ICG load and not with constant values, which represents a simplification that is often adopted but that can induce misleading results. Furthermore, the algorithm takes into account all the physical constraints related to the generators and the storage system, such as maximum and minimum power generation, ramp-up and ramp-down limits and minimum up and down-time. The two energy storage technologies are compared and results show that a management strategy based on this algorithm can reduce significantly the total emissions of the system.

Effect of different economic support policies on the optimal synthesis and operation of a distributed energy supply system with renewable energy sources for an industrial area

Customary methods of managing the workforce are often inefficient, relying on simple forecasting methods, manual scheduling, and reactive actions to manage the expected workload․ Overstaffing, understaffing, and inefficient resource allocation are common issues․ This article explores the opportunities created by artificial intelligence and machine learning for workforce optimization in service-oriented industries. The three technical sub-areas are predictive demand forecasting approaches, clever skill-based scheduling, and the related performance, service quality and employee well-being analysis. Demand forecasting includes the modeling of seasonality using time series methods such as Long Short-Term Memory networks (LSTM) and Autoregressive Integrated Moving Average (ARIMA) models as well as the prediction of surges when unexpected demand spikes occur․ The skill-based scheduling problem gives an overview of multi-dimensional human competency management frameworks and mixed integer linear programming optimization approaches that focus on productivity, compliance and ergonomics. The recent peer-reviewed literature supports that the optimization of the workforce in a sustainable manner depends on a combination of technological capabilities and organizational culture, leadership commitment, and digital preparedness. For organizations to maximize productivity and ensure the health of their employees, human-centered design must be at the crux of AI predictions. The article concludes that AI-driven workforce optimization represents a model shift from administrative labor management to a performance-oriented labor management capability․