Integer Linear Programming Algorithm Research Articles

Multi-objective application placement in fog computing using graph neural network-based reinforcement learning

We propose a framework designed to tackle a multi-objective optimization challenge related to the placement of applications in fog computing, employing a deep reinforcement learning (DRL) approach. Unlike other optimization techniques, such as integer linear programming or genetic algorithms, DRL models are applied in real time to solve similar problem situations after training. Our model comprises a learning process featuring a graph neural network and two actor-critics, providing a holistic perspective on the priorities concerning interconnected services that constitute an application. The learning model incorporates the relationships between services as a crucial factor in placement decisions: Services with higher dependencies take precedence in location selection. Our experimental investigation involves illustrative cases where we compare our results with baseline strategies and genetic algorithms. We observed a comparable Pareto set with negligible execution times, measured in the order of milliseconds, in contrast to the hours required by alternative approaches.

Research on optimization algorithms for localization and capacity determination of chargers considering the spatiotemporal distribution of electric vehicles

The optimized layout of electric vehicle (EV) chargers is not only crucial for users' convenience but also a key element in urban sustainable development, energy transition, and the promotion of new energy vehicles. In order to provide a basis for the problem of localization and capacity determination of chargers and compare the merits of several mainstream algorithms, this paper first establishes an optimization model with the objective of minimizing the total investment cost of all the chargers and the constraint of meeting the charging demands of all electric vehicles. Optimizations were performed using genetic algorithm (GA), surrogate optimization algorithm (SOA), and mixed integer linear programming (MILP) algorithm, respectively. In the case of using MILP, the original nonlinear optimization problem was transformed into a linear problem. In the planning of city-level EV chargers, MILP took 14182.57 s to calculate the minimum cost of 34.62 million yuan. After retaining only 10% of the original data amount, SOA took 87651.34 s to calculate the minimum cost of 3.01 million yuan. The results indicate that GA is prone to falling into local optima and is not suitable for large-scale optimization problems. SOA, on the other hand, requires significant memory consumption, so the issue of memory usage needs to be carefully considered when using it directly. Although MILP is only applicable to linear programming problems, it has the advantages of lower memory usage and higher reliability if the problem can be transformed into a linear one.

Multi-Objective Optimization of an Energy Community Powered by a Distributed Polygeneration System

This paper presents a multi-objective optimization model for the integration of polygeneration systems into energy communities (ECs), by analyzing a case study. The concept of ECs is increasingly seen as beneficial for reducing global energy consumption and greenhouse gas emissions. Polygeneration systems have the potential to play a crucial role in this context, since they are known for producing multiple energy services from a single energy resource, besides the possibility of being fed also by renewable energy sources. However, optimizing the configuration and operation of these systems within ECs presents complex challenges due to the variety of technologies involved, their interactions, and the dynamic behavior of buildings. Therefore, the aim of this work is developing a mathematical model using a mixed integer linear programming (MILP) algorithm to optimally design and operate polygeneration systems integrated into ECs. The model is applied to a case study of an EC comprising nine buildings in a small city in the northeast of Italy. The work rests on the single- and multi-objective optimization of the polygeneration systems taking into account the sharing of electricity among the buildings (both self-produced and/or the purchased from the grid), as well as the sharing of heating and cooling between the buildings through a district heating and cooling network (DHCN). The main results from the EC case study show the possibility of reducing the total annual CO2 emissions by around 24.3% (about 1.72 kt CO2/year) while increasing the total annual costs by 1.9% (about 0.09 M€/year) or reducing the total annual costs by 31.9% (about 1.47 M€/year) while increasing the total annual CO2 emissions by 2.2% (about 0.16 kt CO2/year). The work developed within this research can be adapted to different case studies, such as in the residential–commercial buildings and industrial sectors. Therefore, the model resulting from this work constitutes an effective tool to optimally design and operate polygeneration systems integrated into ECs.

Introducing storage operators for coordinating residential batteries in distribution networks under time-of-use tariffs and adaptive power limits

The transition towards Time of Use (ToU) tariffs has become a promising solution for addressing power system challenges resulting from increased installations of renewable energy systems. ToU tariffs encourage residential Battery Energy Storage System (BESS) adoption to reduce customers' bills through maximizing energy storage during low-price intervals (e.g., middle of the day). However, simultaneous BESS charging affects diversity of load, which may lead to the violation of distribution networks constraints. Traditional network management with conservative fixed and static power limits leads to inefficient network capacity use since they do not consider changes in network operating conditions and status of BESS facilities. Specially, these approaches do not allow higher import limits when proportion of BESS facilities are in idling state. To better allocate the capacity of distribution networks to active BESS facilities (charging/discharging), this work introduces an independent storage operator to coordinate BESS control actions by employing time-varying and adaptive power limits. For this purpose, a Mixed Integer Linear Programming (MILP) algorithm is proposed for storage operator to manage BESS facilities while respecting network constraints and customers' desired bills. At each time step, the algorithm decides power limits for active BESS facilities based on predefined linear functions. These functions are generated offline by using Optimal Power Flow (OPF) to establish relationships between power limits and number of active BESS. The application of the algorithm using a real Jordanian distribution network demonstrates its effectiveness to allow a larger number of customers achieving their desired bills compared to using fixed power limits.

Optimizing code allocation for hybrid on-chip memory in IoT systems

With the increasing application of IoT devices, the memory subsystem, as the performance and energy bottleneck of IoT systems, has received a lot of attention. One of the keys is on-chip memory which can bridge the performance gap between the CPU and main memory. While many off-the-shelf embedded processors utilize the hybrid on-chip memory architecture containing scratchpad memories (SPMs) and caches, most existing literature ignores the collaboration between caches and SPMs. This paper proposes static SPM allocation strategies for the architecture mentioned above in IoT systems, which try to minimize the overall instruction memory subsystem latency and/or energy consumption. We capture the intra- and inter-task cache conflict misses via a fine-grained temporal cache behavior model. Based on this cache conflict information, we propose an integer linear programming (ILP) algorithm to generate an optimal static function level SPM allocation for system performance. Furthermore, to improve the scalability of the proposed allocation scheme for an enormous task set, we offer the interference factor to calculate the interference impact quantitatively. Then, based on the interference factor, we present two approximate knapsack based heuristic algorithms to provide near optimal static allocation schemes at both function- and basic block-level granularities, which favors fast design space exploration. The experiment results demonstrate that the proposed solution achieves a 30.85% improvement in memory performance, and up to 31.39% reduction in energy consumption, compared to the existing SPM allocation scheme at the function level. In addition, the proposed basic block level allocation algorithm shows better performance than our function level allocation algorithm and other basic block level allocation algorithm.

A Joint Scheduling Scheme for WiFi Access TSN.

In the context of Industry 4.0, industrial production equipment needs to communicate through the industrial internet to improve the intelligence of industrial production. This requires the current communication network to have the ability of large-scale equipment access, multiple communication protocols/heterogeneous systems interoperability, and end-to-end deterministic low-latency transmission. Time-sensitive network (TSN), as a new generation of deterministic Ethernet communication technology, is the main development direction of time-critical communication technology applied in industrial environments, and Wi-Fi technology has become the main way of wireless access for users due to its advantages of high portability and mobility. Therefore, accessing WiFi in the TSN is a major development direction of the current industrial internet. In this paper, we model the scheduling problem of TSN and WiFi converged networks and propose a scheme based on a greedy strategy distributed estimation algorithm (GE) to solve the scheduling problem. Compared with the integer linear programming (ILP) algorithm and the Tabu algorithm, the algorithm implemented in this paper outperforms the other algorithms in being able to adapt to a variety of different scenarios and in scheduling optimization efficiency, especially when the amount of traffic to be deployed is large.

Topology optimization for flow machine rotor design considering resonance and low mass density flows

When designing structures that take part in rotating fluid flow machines, one important effect that may be taken into account is resonance, which is evaluated through the modal analysis. Under rotating conditions, the formulation and behavior of the structure changes, requiring a new formulation. Thus, this work formulates the fluid flow topology optimization for rotating resonance, while also considering a low mass density (compressible or incompressible) fluid flow. Therefore, three different effects are considered in the fluid flow-based design: from the modal analysis, from the solid structure, and from the low mass density fluid flow. The topology optimization problem is formulated for a simplified 2D model of a rotating fluid flow device, and a nodal design variable. The topology optimization is performed through an integer linear programming algorithm. Numerical examples are presented for the developed topology optimization formulation.

QoT-aware tree selection, routing, modulation, and spectrum assignment for filterless EONs over the C + L-band

Filterless optical networks (FONs) as an economical solution use passive couplers/splitters rather than expensive active filter devices. In this way, after intermediate and destination nodes, lightpaths are dropped and continued, which generates leakage signals in other links and wastes spectrum. Thus, designing efficient resource allocation in FONs by considering the leakage signals and their interfering effects is of utmost importance. On the other hand, a gradual transition from wavelength division multiplexing optical networks to elastic optical networks (EONs) is occurring due to their efficient utilization of spectrum. Furthermore, utilizing the L-band beside the conventional C-band for spectrum assignment offers a wide range of frequency resources. Therefore, in this paper, we propose an integer linear program (ILP) to solve quality of transmission (QoT)-aware tree selection, routing, modulation, and spectrum assignment problems in filterless EONs over the C+L-band. Furthermore, we provide heuristic algorithms to deal with complex large-scale networks. The performance gap of the proposed ILP and heuristic algorithms is evaluated over a small-scale (5-node) network. The results show that the ILP and heuristic algorithms have almost the same performance in terms of spectrum usage and assigned modulation format, and ILP has a slightly higher generalized signal-to-noise-ratio (GSNR) (0.23 dB or 0.8% at optimum launch power). Furthermore, the heuristic algorithms are also examined over a large-scale network (TID region A topology). The results reveal that the GSNR estimation method severely affects the performance in terms of spectrum usage, blocking, and outage. Furthermore, by using the proposed MX5 method, as long as there is a fill margin of approximately 2 dB, there is no outage or blocking over the C+L-band, up to a network throughput of 110 Tbps and conventional C-band transmission with lower throughput (i.e., 40 Tbps). Finally, our extensive numerical results provide a rule of thumb for balancing blocking, outage, spectrum usage, and the number of expensive L-band transponders by selecting the appropriate modulation assignment method.

An advanced control strategy for optimizing the operation state of chillers with cold storage technology in data center

Data centers facing huge energy consumption challenges, the chiller is one of the main energy expenditure equipment. This study proposed a novel efficient operation strategy for chillers integrated with cold water storage technology. An advanced model predictive control (MPC) was developed to regulate the running parameters of the chillers and cold water storage device, targeting the maximum energy efficiency of cooling system. Specifically, the mixed integer linear programming (MILP) algorithm was constructed in MPC with low computer calculation cost to solve the optimization problem. The performance of the MPC strategy was validated through an actual data center test located at Guangzhou city and further comprehensively assessed through annual simulations. The relative error in terms of refrigeration capacity and cooling capacity of cold water storage device between simulation and field test was less than 5 %. During the on-site testing, compared with a Baseline strategy, the coefficient of performance (COP) of the MPC strategy increased by 1.96 on average with the cooling system energy consumption reduced by 5.8 %, and the power usage effectiveness (PUE) was reduced by 0.013. The annual PUE decreased by 0.018 and the annual electricity cost decreased by 21 % when the IT power was 4570 kW based on the simulation. In addition, the effect of model mismatch was quantified by setting the deviation degree of the chiller partial load rate (PLR).

Spatio-temporal fragmentation-aware time-varying service provisioning in computing power networks based on model-assisted reinforcement learning

Widespread application of AI with high computing requirements has promoted the rapid development of cloud and edge computing. Ubiquitous computing resources need to be interconnected through high-performance IP and optical networks. Computing power networks (CPNs) have been studied as an IP/optical cross-layer architecture to provide on-demand computing services. The service provisioning in CPNs can be modeled as a virtual optical network embedding problem; however, the time-varying characteristics of service requirements increase the complexity. In particular, there can be stranded computing or bandwidth resources that are isolated in the spatio-temporal dimension and therefore difficult for other requests to utilize. This phenomenon is referred to as spatio-temporal resource fragmentation, which will lead to inefficient use of resources and higher energy consumption. To meet the time-varying requirements and achieve an energy-efficient CPN, in this paper, we first analyze the main reason for spatio-temporal resource fragmentation and excess energy consumption in cross-layer CPNs, which is the mismatched embedding scheme and the inappropriate scheduling order. Therefore, an auxiliary-graph-model-assisted edge-featured graph-attention-network-enabled DRL algorithm, DeepDefrag, is studied to solve the problem. Its core highlight is co-optimizing the scheduling and embedding of time-varying virtual networks along with the awareness of resource fragmentation. In addition, an integer linear programming algorithm is developed to determine the performance bounds. We validate DeepDefrag through a series of ablation studies and comparison algorithms under different numbers of requests. The results show that DeepDefrag can achieve >26.4% lower energy consumption for CPNs compared with the DRL-based baseline. Moreover, it is shown to generalize well across requests and networks.

Fusing logic rule-based hybrid variable graph neural network approaches to fault diagnosis of industrial processes

It is practical that industrial processes are regarded as hybrid systems involving both continuous and discrete variables. Particularly, process discrete variables are usually rather crucial to process normal operations. However, existing data-driven fault diagnosis methods have been rarely applied for industrial processes concerning hybrid system characteristics. In response to this issue, this paper proposes a novel industrial process fault diagnosis approach based on fusing logic rule-based hybrid variable graph neural networks (GNNs) able to learn hybrid variable spatial structures. Therein, continuous variables samples are regarded as learning objects, while logic rule information is utilized to guide the learning process. Specifically, a mixed 0–1 integer linear programming algorithm is used to extract logic rules with fault labels from discrete variable data. Subsequently, a rule classification method is used to classify the logic rules with fault labels into independent and interdependent rule classes which are filed in a rule class library using as the basis for topological connections among data samples. In addition, an edge pruning-based graph optimization is employed to reduce redundant connections of the nodes. It is found that the established hybrid variable GNNs is able to learn the spatial topological structure of hybrid industrial process variables, providing rich heuristic knowledge for process fault classifications so as to effectively improve the fault diagnose performance. Simulation experiments are carried out in an industrial coal gasification process platform and a storage tank systems simulation platform, achieving satisfactory results.

Low-Carbon Economic Dispatch of Electricity and Cooling Energy System

In response to the issue of the hydropower consumption of run-of-river hydropower stations in Southwest China, the district cooling system can provide regulation capacity for hydropower utilization and suppress fluctuations caused by the uncertainty of hydropower. The innovative method is to utilize the thermal characteristics of pipelines and buildings, as well as the thermal comfort elasticity to shift the cooling and electricity loads, which helps to consume the surplus hydroelectric power generation. Taking the minimum total cost of coal consumption in thermal power units, hydropower abandonment penalty, and the carbon trading cost as the objective function, models were established for power supply balance constraints, heat transport constraints, and unit output constraints. The hybrid integer linear programming algorithm was used to achieve the low-carbon economic dispatch of the electric-cooling system. The calculation examples indicate that compared to the traditional real-time balance of cooling supply, the comprehensive consideration of thermal characteristics in a cooling system and flexible thermal comfort have a better operational performance. The carbon trading cost, coal consumption cost, and abandoned hydropower rate of a typical day was reduced by 4.25% (approximately CNY 7.55 × 104), 4.47% (approximately CNY 22.23 × 104), and 3.66%, respectively. Therefore, the electric-cooling dispatch model considering the thermal characteristics in cooling networks, building thermal inertia, and thermal comfort elasticity is more conducive to the hydropower utilization of run-of-river stations.

An optimal load curtailment method for operational risk assessment of integrated electricity, natural gas and heating systems

The rapid development of gas-fired generators and combined heat and power (CHP) units promotes the wide application of integrated electricity, natural gas and heating systems (IEGHSs). IEGHSs bring many opportunities to energy supply systems, but also produce huge challenges for ensuring secure and reliable energy supply. Therefore, it is indispensable to investigate the risk assessment for IEGHS operations, especially considering the complexity of system operations. First, a steady-state system model of IEGHSs is developed and linearized taking into account system operation characteristics, including generator operation constraints and the flexibility of CHP units. To reduce computation complexity, an optimal load curtailment method using a mixed integer linear programming algorithm is proposed, which considers both energy types and consumer importance of IEGHSs. Moreover, a contingency set based on the N-1 guideline is constructed taking into account the failures of IEGHSs with high probability. Additionally, the risk indices of IEGHSs are developed in two hierarchies: contingency level and system level. Furthermore, the operational risk of IEGHSs under the constructed contingency set is quantitatively evaluated using the proposed load curtailment method and the developed risk indices. Finally, case studies demonstrate the necessity of operational risk assessment and the effectiveness of the proposed method.

One-step production and two-step assembly scheduling in identical factories

In this article, a production–assembly scheduling problem is presented in three steps. In the first step, the production operation is performed by dedicated parallel machines; in the second step, the assembly operation is performed by the same parallel machines, and in the third step, the post-assembly operation is performed by one machine, and these three steps exist in parallel factories. This problem is NP hard and cannot be solved in a reasonable time by mathematical models. Therefore, a mixed integer linear programming algorithm is presented for small-sized problems, and a new metaheuristic algorithm is presented for large-sized problems, by combining quantum-behaved particle swarm optimization (QPSO), shortest processing time (SPT) and dominance rules, which is called the hybrid QPSOSPT dominance rules (HQSD) algorithm. Accurate parameter adjustment was achieved by analysis of variance. The HQSD algorithm was shown to obtain better results than the other algorithms.

Bayesian Network Structure Learning Using Improved A* with Constraints from Potential Optimal Parent Sets

Learning the structure of a Bayesian network and considering the efficiency and accuracy of learning has always been a hot topic for researchers. This paper proposes two constraints to solve the problem that the A* algorithm, an exact learning algorithm, is not efficient enough to search larger networks. On the one hand, the parent–child set constraints reduce the number of potential optimal parent sets. On the other hand, the path constraints are obtained from the potential optimal parent sets to constrain the search process of the A* algorithm. Both constraints are proposed based on the potential optimal parent sets. Experiments show that the time efficiency of the A* algorithm can be significantly improved, and the ability of the A* algorithm to search larger Bayesian networks can be improved by the two constraints. In addition, compared with the globally optimal Bayesian network learning using integer linear programming (GOBNILP) algorithm and the max–min hill-climbing (MMHC) algorithm, which are state of the art, the A* algorithm enhanced by constraints still performs well in most cases.

RedPacketBike: A Graph-Based Demand Modeling and Crowd-Driven Station Rebalancing Framework for Bike Sharing Systems

Bike-sharing systems have been deployed globally. One of the key issues for high-quality bike-sharing systems is to rebalance city-wide stations to maintain bike availability. Traditional strategies usually operate in fixed paths and limited capacities, lacking the flexibility to cope with the highly dynamic and context dependent riding demands, and usually suffer from high costs and long delays. In this work, we propose RedPacketBike, an incentive-driven, crowd-based station rebalancing framework to effectively recruit participants from hybrid fleets based on the accurate forecast of bike demand leveraging deep learning techniques. First, we propose a spatiotemporal clustering method to extract bike demand hotspots from fluctuating bike usage data. Then, we build a context-aware deep neural network named BikeNet to forecast the trends of bike demand hotspots, simultaneously modeling the spatial correlations. Finally, we propose a reinforcement-learning-based method to find optimal station rebalancing schemes by generating station rebalancing tasks with an integer linear programming (ILP) algorithm and allocating tasks to participants from hybrid fleets with dynamic incentive designs and reward expectations. Experiments using real-world bike-sharing system data validate the performance of our framework, achieving a demand forecast error below 4.171 measured in MAE, and a 17.2% improvement of station availability, outperforming the state-of-the-art baselines.

Integration of Renewable Energy and Electric Vehicles in Power Systems: A Review

Electric vehicles (EVs) represent a promising green technology for mitigating environmental impacts. However, their widespread adoption has significant implications for management, monitoring, and control of power systems. The integration of renewable energy sources (RESs), commonly referred to as green energy sources or alternative energy sources, into the network infrastructure is a sustainable and effective approach to addressing these matters. This paper provides a comprehensive review of the integration of RESs and EVs into power systems. The bibliographic analysis revealed that IEEE Access had the highest impact among journals. In order to enhance the classification of the reviewed literature, we have provided an analytical summary of the contributions made by each paper. The categorization facilitated the recognition of the primary objectives explored in the reviewed works, including the classification of EVs and RESs, the incorporation of RESs and EVs into power systems with an emphasis on emissions, the establishment of EV charging stations and parking facilities, EV batteries and battery energy storage systems, strategies for managing the integration of RESs with EVs, EV aggregators, and the financial implications. In order to provide researchers with a valuable synopsis of the implementation particulars, the papers were bifurcated into two primary classifications, namely mathematical algorithms and heuristic algorithms. The mixed integer linear programming algorithm and particle swarm optimization algorithm were commonly utilized formulations in optimization. MATLAB/Simulink was the primary platform used for executing a considerable portion of these algorithms, with CPLEX being the dominant optimization tool. Finally, this study offers avenues for further discourse and investigation regarding areas of research that remain unexplored.

FastInformer-HEMS: A Lightweight Optimization Algorithm for Home Energy Management Systems

In a smart home with distributed energy resources, the home energy management system (HEMS) controls the photovoltaic (PV) storage system by executing the optimization algorithm to achieve the lowest power cost. The existing mixed integer linear programming (MILP) algorithm is not suitable for execution on the end-user side due to its high computational complexity. The HEMS algorithm based on a long short-term memory neural network (LSTM-HEMS) can effectively solve the problem of the high computational complexity of MILP, but its optimization outcome is not high due to the accumulation of prediction errors. In order to achieve a better balance between computational complexity and optimization outcome, this paper proposes a lightweight optimization algorithm called the FastInformer-HEMS, which introduces the E-Attn attention mechanism based on Informer and uses global average pooling to extract the attention characteristics. Meanwhile, the proposed method introduces the maximum self-consumption algorithm as a backup strategy to ensure the safe operation of the system. The simulated results show that the computational complexity of the proposed FastInformer-HEMS is the lowest among the existing algorithms. Compared with the existing LSTM-HEMS, the proposed algorithm reduces the power consumption cost by 12.3% and 6.6% in the two typical scenarios, while the execution time decreases by 13.6 times.

Trans-population graph-based coverage optimization of allogeneic cellular therapy.

Pre-clinical development and in-human trials of 'off-the-shelf' immune effector cell therapy (IECT) are burgeoning. IECT offers many potential advantages over autologous products. The relevant HLA matching criteria vary from product to product and depend on the strategies employed to reduce the risk of GvHD or to improve allo-IEC persistence, as warranted by different clinical indications, disease kinetics, on-target/off-tumor effects, and therapeutic cell type (T cell subtype, NK, etc.). The optimal choice of candidate donors to maximize target patient population coverage and minimize cost and redundant effort in creating off-the-shelf IECT product banks is still an open problem. We propose here a solution to this problem, and test whether it would be more expensive to recruit additional donors or to prevent class I or class II HLA expression through gene editing. We developed an optimal coverage problem, combined with a graph-based algorithm to solve the donor selection problem under different, clinically plausible scenarios (having different HLA matching priorities). We then compared the efficiency of different optimization algorithms - a greedy solution, a linear programming (LP) solution, and integer linear programming (ILP) -- as well as random donor selection (average of 5 random trials) to show that an optimization can be performed at the entire population level. The average additional population coverage per donor decrease with the number of donors, and varies with the scenario. The Greedy, LP and ILP algorithms consistently achieve the optimal coverage with far fewer donors than the random choice. In all cases, the number of randomly-selected donors required to achieve a desired coverage increases with increasing population. However, when optimal donors are selected, the number of donors required may counter-intuitively decrease with increasing population size. When comparing recruiting more donors vs gene editing, the latter was generally more expensive. When choosing donors and patients from different populations, the number of random donors required drastically increases, while the number of optimal donors does not change. Random donors fail to cover populations different from their original populations, while a small number of optimal donors from one population can cover a different population. Graph-based coverage optimization algorithms can flexibly handle various HLA matching criteria and accommodate additional information such as KIR genotype, when such information becomes routinely available. These algorithms offer a more efficient way to develop off-the-shelf IECT product banks compared to random donor selection and offer some possibility of improved transparency and standardization in product design.

Optimization of auxiliary fuel tank layout to decrease the aircraft empty weight

The modern civil aircraft has the trend to utilize the auxiliary fuel tank to increase the range. This paper presents an optimization concept for fuel tank design to optimize auxiliary fuel tank placement in aircraft to decrease the aircraft empty weight. A model formulation has been created to satisfy the minimum fuel weight requirement and the aircraft center of gravity limit. To solve the fuel tank layout issue, the integer linear programming algorithm has been applied. In order to validate the concept a simulation model and different calculation cases have been built. The calculation results show that the method can effectively reduce the aircraft empty weight and can gain high benefits.