Integer Linear Programming Approach Research Articles

Optimising the self-consumption and self-sufficiency: A novel approach for adequately sizing a photovoltaic plant with application to a metropolitan station

The recent trends in designing sustainable power systems emphasise the importance of self-consumption (SC) both at individual and community level. This new paradigm changes the way in which we design photovoltaic facilities for residential houses and for various municipality services as well.In this context, the paper aims to formulate several optimisation problems using criteria such as self-consumption, self-sufficiency (SS) and net present value (NPV) as objectives to provide an optimal photovoltaic (PV) plant size for a singular power system - a subway station. By using this multi-objective approach, the work emphasises how each criteria impacts the profitability and value of the overall investment, involving possible shareholders in the design process by choosing a desired solution from the Pareto-efficient set of configurations. Moreover, a global optimal solution is provided by formulating an optimisation problem through a single-objective Mixed Integer Linear Programming (MILP) approach involving an equivalent metric, the net-energy exchanged with the grid (NEEG).The proposed methodology is validated in a case study on the power system of a subway station in Bucharest (Romania), thus identifying a configuration that focuses on self-consumption maximisation and a solution that reduces the yearly energy bill of the respective power system by 25%.

Dynamic and adaptive grouping maintenance strategies: New scalable optimization algorithms

This paper focuses on new efficient and adaptive optimization algorithms to cope with the maintenance grouping problem for series, parallel, and complex systems. We propose a Particle Swarm Optimization approach to cope with small and medium problem sizes, and that will be used to benchmark existing heuristic solutions such as Genetic Algorithms. To address scalability and adaptability issues, we propose a new dynamic optimization algorithm based on a clustering technique. This clustering-based solution is formulated using an Integer Linear Programing approach to guarantee the convergence to global optimal solutions of the considered problem. We show the performance of the proposed approaches with a clear advantage to the clustering-based algorithm that we recommend for large industrial systems.

A decision analysis model for reducing carbon emission from coal-fired power plants and its compensatory units

The increasing carbon dioxide level in the earth's atmosphere and continuously changing climate creates a significant challenge to sustainability in the world. It is not easy to control pollution due to carbon dioxide emissions from coal-fired power plants into the atmosphere. However, carbon capture technology provides an advantage for capturing carbon from power plants. Various researchers suggested the non-linear optimization model with post-combustion carbon capture technology in coal-fired power plants to reduce carbon emission. However, in their research articles, most researchers did not include loss of power due to retrofitting carbon capture technology in power plants and carbon emission from the compensatory power plant. This paper proposes a linear optimization model that minimizes the emission release from the power plant and its compensatory plant by appropriate selection of carbon capture technology. Our proposed model incorporates loss of power due to adopting carbon capture technology and emission release from the power plant and compensatory power plant in the problem formulation. We have also generated the Pareto curve that determines the trade-off solutions between emission release and the overall electricity cost. The applicability of our model is illustrated through power sector data from two Indian states. The net reduction of emissions in the two states are 27.17 % and 26.29 %, achieved by a mixed integer linear programming approach in coal-fired power plants. The model developed is generic and provides a sustainable environment for the generation of electricity.

Sizing and Management of an Energy System for a Metropolitan Station with Storage and Related District Energy Community

Future renewable energy communities will reshape the paradigm in which we design and control efficient power systems at the district level. In this manner, the focus will be fundamentally shifted towards sustainable related concepts such as self-consumption, self-sufficiency and net energy exchanged with the grid. In this context, the paper presents a novel approach for optimally designing and controlling the photovoltaic plant and energy storage systems for a metro station in order to increase collective self-consumption and self-sufficiency at the district level. The methodology considers a community of several households connected to a subway station and focuses on the interaction between energy sources and consumers. Furthermore, the optimal solution is determined by using a Mixed Integer Linear Programming Approach, and the impact of different configurations on the overall district benefit is investigated by using several simulation scenarios. The work presents a detailed case study to underline the benefits and flexibility offered by the energy storage system in comparison with a scenario involving only a photovoltaic plant.

Tire noise optimization problem: a mixed integer linear programming approach

We present a Mixed Integer Linear Programming (MILP) approach in order to model the non-linear problem of minimizing the tire noise function. In a recent work, we proposed an exact solution for the Tire Noise Optimization Problem, dealing with an APproximation of the noise (TNOP-AP). Here we study the original non-linear problem modeling the EXact- or real-noise (TNOP-EX) and propose a new scheme to obtain a solution for the TNOP-EX. Relying on the solution for the TNOP-AP, we use a Branch&Cut framework and develop an exact algorithm to solve the TNOP-EX. We also take more industrial constraints into account. Finally, we compare our experimental results with those obtained by other methods.

The dual polyhedron to the chordal graph polytope and the rebuttal of the chordal graph conjecture

The integer linear programming approach to structural learning of decomposable graphical models led us earlier to the concept of a chordal graph polytope. An open mathematical question motivated by this research is what is the minimal set of linear inequalities defining this polytope, i.e. what are its facet-defining inequalities, and we came up in 2016 with a specific conjecture that it is the collection of so-called clutter inequalities. In this theoretical paper we give an implicit characterization of the minimal set of inequalities. Specifically, we introduce a dual polyhedron (to the chordal graph polytope) defined by trivial equality constraints, simple monotonicity inequalities and certain inequalities assigned to incomplete chordal graphs. Our main result is that the vertices of this polyhedron yield the facet-defining inequalities for the chordal graph polytope. We also show that the original conjecture is equivalent to the condition that all vertices of the dual polyhedron are zero-one vectors. Using that result we disprove the original conjecture: we find a vector in the dual polyhedron which is not in the convex hull of zero-one vectors from the dual polyhedron.

Improved inference of tandem domain duplications.

MotivationProtein domain duplications are a major contributor to the functional diversification of protein families. These duplications can occur one at a time through single domain duplications, or as tandem duplications where several consecutive domains are duplicated together as part of a single evolutionary event. Existing methods for inferring domain-level evolutionary events are based on reconciling domain trees with gene trees. While some formulations consider multiple domain duplications, they do not explicitly model tandem duplications; this leads to inaccurate inference of which domains duplicated together over the course of evolution.ResultsHere, we introduce a reconciliation-based framework that considers the relative positions of domains within extant sequences. We use this information to uncover tandem domain duplications within the evolutionary history of these genes. We devise an integer linear programming approach that solves our problem exactly, and a heuristic approach that works well in practice. We perform extensive simulation studies to demonstrate that our approaches can accurately uncover single and tandem domain duplications, and additionally test our approach on a well-studied orthogroup where lineage-specific domain expansions exhibit varying and complex domain duplication patterns.Availability and implementationCode is available on github at https://github.com/Singh-Lab/TandemDuplications.Supplementary information Supplementary data are available at Bioinformatics online.

Bi-objective optimal design of Hydrogen and Methane Supply Chains based on Power-to-Gas systems

This paper presents a methodological design framework for Hydrogen and Methane Supply Chains (HMSC) based on Power-to-Gas (PtG) systems. The novelty of the work is twofold, first considering a specific demand for hydrogen for electromobility in addition to the hydrogen demand required as a feedstock to produce synthetic methane from the methanation process and performing a bi-objective optimization of the HMSC to provide effective support for the study of deployment scenarios. The approach is based on a Mixed Integer Linear Programming (MILP) approach with augmented epsilon-constraint implemented in the GAMS environment according to a multi-period approach (2035–2050) with several available energy sources (wind, PV, hydro, national network) for hydrogen production. Carbon dioxide sources stem mainly from methanization and gasification processes. The objectives to be minimized simultaneously are the Total Annual Cost and the greenhouse gas emissions related to the whole HMSC over the entire period studied.

Tactical runway scheduling for demand and delay management

PurposeAirports are limited in terms of capacity. Particularly, runways can only accommodate a certain number of movements (arrivals and departures) while ensuring safety and determined operational requirements. In such a constrained operating environment, any reduction in system capacity results in major delays with significant costs for airlines and passengers. Therefore, the efficient operation of airports is a critical cornerstone for demand and delay management of the whole air transportation system. Runway scheduling deals with the sequencing of arriving and departing aircraft at airports such that a predefined objective is optimized subject to several operational constraints, like the dependency of separation on the leading and trailing aircraft type or the runway occupancy time. This study aims to develop a model that acts as a tactical runway scheduling methodology for reducing delays while managing runway usage.Design/methodology/approachBy considering real airport performance data with scheduled and actual movements, as well as arrival/departure delays, this study presents a robust model together with an optimization algorithm, which incorporates the knowledge of uncertainty into the tactical operational step. The approach transforms the planning problem into an assignment problem with side constraints. The coupled landing/take-off problem is solved to optimality by exploiting a time-indexed (0, 1) formulation for the problem. The Binary Integer Linear Programming approach allows to include multi-criteria and multi-constraints levels and, even with some major simplifications, provides fewer sequence changes and target time updates, when compared to the usual approach in which the plan is simply updated in case of infeasibility. Thus, the use of robust optimization leads to a protection against tactical uncertainties, reduces delays and achieves more stable operations.FindingsThis model has been validated with real data from a large international European airport in different traffic scenarios. Results are compared to the actual sequencing of flights and show that the algorithm can significantly contribute to the reduction of delay, while adhering as much as possible to the operative procedures and constraints, and to the objectives of the airport stakeholders. Computational experiments performed on the case study illustrate the benefits of this arrival/departure integrated approach: the proposed algorithm significantly reduces weighted aircraft delay and computes efficient runway schedule solutions within a few seconds and with little computational effort. It can be adopted as a decision-making tool in the tactical stage. Furthermore, this study presents operational insights regarding demand and delay management based on the results of this work.Originality/valueScheduling arrivals and departures at runways is a complex problem that needs to address diverse and often competing considerations among involved flights. In the context of the Airport Collaborative Decision Making programme, airport operators and air navigation service providers require arrival and departure management tools that improve aircraft flows at airports. Airport runway optimization, as the main element that combines airside and groundside operations, is an ongoing challenge for air traffic management.

A stochastic machine learning based approach for observability enhancement of automated smart grids

This paper develops a machine learning aggregated integer linear programming approach for the full observability of the automated smart grids by positioning of micro-synchrophasor units, taking into account the reconfigurable structure of the distribution systems. The proposed stochastic approach presents a strategy occurring in several stages to micro-synchrophasor unit positioning based on the load level and demand in the system and based on the pre-determined sectionalizing and tie switches. Such a technique can also deploy the zero-injection limitations of the model and reduce the search space of the problem. Moreover, a novel method based on whale optimization method (WOM) is introduced to simultaneously enhance the reliability indices in order to specify the optimum topology for each phase and reduce the costs of power losses and customer interruptions. Although the problem of micro-synchrophasor placement is formulated in an integer linear programming framework, the restructuring technique is resolved on the basis of the WOM heuristic approach. Considering the uncertainty due to the metering devices or forecast errors, a stochastic framework based on point estimation is deployed to handle the uncertainty effects. The simulation and numerical results on a real system verify that the proposed method assures visibility of the distribution network pre and post reconfiguration in the time horizon of the planning. Furthermore, the results show that the system observability can be guaranteed at different load levels even though the system experiences different reconfiguration and topologies.

A methodological design framework for hydrogen and methane supply chain with special focus on Power-to-Gas systems: Application to Occitanie region, France

This work presents a methodological design framework for Hydrogen and Methane Supply Chains (HMSC). An innovative approach is to focus on Power-to-Hydrogen (PtH) and Power-to-Methane (PtM) concepts, and their interactions with other technologies, and energy carriers (i.e., Steam Methane Reforming – SMR, and natural gas). The overall objective of this work is to perform single objective and multi-objective optimizations for HMSC design to provide effective support for deployment scenarios. The methodological framework developed is based on a Mixed Integer Linear Programming (MILP) approach with augmented ε-constraint implemented in the GAMS environment according to a multi-period approach (2035-2050). Several available energy sources (wind, PV, hydro, national power grid, and natural gas) for hydrogen production through electrolysis and SMR are included. Carbon dioxide sources stem mainly from methanization and gasification processes, which are used to produce methane through methanation. The objective to be minimised in the single optimization approach is the total annual cost considering the externality of greenhouse gas emissions through the carbon price for the whole HMSC over the entire period studied. The multi-objective optimization includes as objectives the total annual cost, greenhouse gas emissions, and the total methane production from methanation. The Levelized Cost of Energy (LCOE), and the greenhouse gas emissions for each energy carrier are also computed. The results show that renewable hydrogen from PtG can be competitive with SMR through the implementation of carbon prices below 0.27 €/kgCO2. In the case of synthetic methane, the available resources can meet the demand through PtG, and even if synthetic methane for natural gas network injection is thus far from competitive with natural gas, power-to-gas technologies have the potential to decarbonize the fossil economy and achieve a circular economy through CO2 recovery.

Scheduling for heterogeneous systems in accelerator-rich environments

The world is creating ever more data and the applications are required to deal with ever-increasing datasets. To process such datasets heterogeneous and manycore accelerators are being deployed in various computing systems to improve energy efficiency. In this work, we present a runtime management system designed for such heterogeneous systems with manycore accelerators. More specifically, we design a resource-based runtime management system that considers application characteristics and respective execution properties on the nodes and accelerators. We propose scheduling heuristics and run time environment solutions to achieve better throughput and reduced energy in computing systems with different accelerators. We give implementation details about our framework; show different scheduling algorithms, and present experimental evaluation of our system. We also compare our approaches with an optimal scheme where integer linear programming approach has been implemented for mapping applications on the heterogeneous system. While it is possible to extend the proposed framework to a wide variety of accelerators, our initial focus is on Graphics Processing Units (GPUs). Our experimental evaluations show that including accelerator support in the management framework improves energy consumption and execution time significantly. We believe that this approach has the potential to provide an effective solution for next generation accelerator-based computing systems.

Modelling and deploying multi-energy flexibility: The energy lattice framework

• New multi-energy lattice framework to model multi-energy systems (MES) flexibility. • Categorization of physical and market/regulatory flexibility features. • Two-step methodology to deploy MES flexibility for short-term operational planning. • Simultaneous participation in energy and frequency control markets. • Multi-energy lattice framework demonstrated on the Milan district heating system. This work proposes a novel modelling framework and an associated optimization methodology for short-term operational planning to deploy multi-energy system (MES) flexibility, with application to district energy systems and participation in energy and frequency control ancillary services (FCAS). In this paper, the proposed flexibility framework, based on the concept of multi-energy lattice , models a MES by several energy layers, each one associated with a specific energy carrier. The identified energy layers are linked by specific conversion nodes associated with coupling devices that operate across several energy carriers. After illustrating in detail the main features of the multi-energy lattice methodology and, particularly, how it enables to clearly describe and quantify how flexibility arises from both single-layer and cross-layer energy balancing, different features of the concept of multi-energy flexibility are defined and discussed. An associated operational optimization methodology to deploy multi-energy flexibility in a market environment is then introduced. This methodology is based on a two-step mixed integer linear programming approach, namely, (i) definition of a multi-energy baseline to cope with the energy demand across multiple energy vectors and (ii) identification of flexibility margins and economic convenience to offer different FCAS by deploying multi-energy flexibility. The multi-energy lattice concept is then demonstrated on the Milan district heating system. This MES plant exploits its flexibility for participation in multiple FCAS markets besides day-ahead energy trading, and it is shown how the proposed methodology is able to optimize the flexibility arising from different devices and optimally combine these contributions across multiple energy layers for business case purposes, in line with the general theory of the multi-energy lattice.

Analysis and Exploitation of Landforms for Improved Optimisation of Camera-Based Wildfire Detection Systems

Tower-mounted camera-based wildfire detection systems provide an effective means of early forest fire detection. Historically, tower sites have been identified by foresters and locals with intimate knowledge of the terrain and without the aid of computational optimisation tools. When moving into vast new territories and without the aid of local knowledge, this process becomes cumbersome and daunting. In such instances, the optimisation of final site layouts may be streamlined if a suitable strategy is employed to limit the candidate sites to landforms which offer superior system visibility. A framework for the exploitation of landforms for these purposes is proposed. The landform classifications at 165 existing tower sites from wildfire detection systems in South Africa, Canada and the USA are analysed using the geomorphon technique, and it is noted that towers are located at or near certain landform types. A metaheuristic and integer linear programming approach is then employed to search for optimal tower sites in a large area currently monitored by the ForestWatch wildfire detection system, and these sites are then classified according to landforms. The results support the observations made for the existing towers in terms of noteworthy landforms, and the optimisation process is repeated by limiting the candidate sites to selected landforms. This leads to solutions with improved system coverage, achieved within reduced computation times. The presented framework may be replicated for use in similar applications, such as site-selection for military equipment, cellular transmitters, and weather radar.

Solving Hydropower Unit Commitment Problem Using a Novel Sequential Mixed Integer Linear Programming Approach

Hydro Unit Commitment (HUC) is an important problem of power systems and when it is dealt with via a mathematical programming approach and optimization, it leads to the complicated class of mixed-integer nonlinear programming (MINLP). Many attempts have been made to solve the problem efficiently, while there is still ongoing research to come up with better solution schemes in terms of runtime and optimality. Highly nonlinear nature of the relationships and constraints in the optimization problem have forced the researchers to deal with the HUC problem in simplified manners which may result in impractical and unreliable solutions, i.e. schedules. Here in this paper we proposed a new method based on sequential mixed-integer linear programming (MILP) for solving a more realistic version of the HUC problem efficiently. We applied the proposed method to a cascade of two hydropower plants, Karun-3 and Karun-4, located in the Southwest of Iran. The sequential MILP approach was compared with several MINLP solvers of the GAMS optimization package. The results indicated that the proposed methodology outperformed the MINLP solvers in terms of efficiency, with solution time of less than 30 s, compared to 10 min that were given to the solvers, and in terms of optimality with more than 20 thousand cubic meters per day in water release. Additionally, we have explored the effect of penalizing the total number of startups on the total release, convergence of the algorithm, and the computation time. In all of the cases the total number of startups was reduced more than three times.

The in‐house logistics routing problem

AbstractIn this work, we consider the internal logistics processes in the car assembling company SEAT S.A (where SEAT is Sociedad Española de Automóviles de Turismo). We focus on the design of the supplying routes from the warehouse toward the workstations along a production line. The in‐house logistics routing problem (ILRP) can be seen as an extension of the classical vehicle routing problem considering special features such as long‐term fixed routes, unknown orders, backorders, and homogeneous fleet. To solve the ILRP, we propose an integer linear programming (ILP) model and a simulation‐based iterated local search (SimILS) algorithm. We conduct two computational experiments. The first experiment analyzes the methods' performance, and the second experiment compares the SimILS results with the current company's routes, considering real data provided by SEAT S.A. The results show that the SimILS provides the best overall results overcoming both the ILP approach and the current company's solution, leading to a significant improvement in the company's processes.

Multi-objective optimization model for airport gate assignment problem

PurposeThe purpose of this paper is to provide feasible and fast solutions for the multi-objective airport gate assignment problem (AGAP) considering both passenger-oriented and airline-oriented objectives, which is the total walking distance from gate to baggage carousels (TWD) and the total aircraft fuel consumption during taxi operations (TFC). In addition, obtaining feasible and near-optimal solutions in a short time reduces the gate planning time to be spent by air traffic controllers.Design/methodology/approachThe mixed integer linear programming (MILP) approach is implemented to solve the multi-objective AGAP. The weighted sum approach technique was applied in the model to obtain non-dominated solutions. Because of the complexity of the problem, the simulated annealing (SA) algorithm was used for the proposed model. The results were compared with baseline results, which were obtained from the algorithm using the fastest gate assignment and baggage carousel combinations without any conflict taking place at the gate assignments.FindingsThe proposed model noticeably decreased both the TWD and TFC. The improvement of the TWD and TFC changed from 22.8% to 46.9% and from 4.7% to 7.1%, respectively, according to the priorities of the objectives. Additionally, the average number of non-dominated solutions was calculated as 6.94, which presents many feasible solutions for air traffic controllers to manage ground traffic while taking the airline and passenger objectives into consideration.Practical implicationsThe proposed MILP model includes the objectives of different stakeholders: air traffic controllers, passengers and airlines. In addition, the proposed model can provide feasible gate and baggage carousel assignments together in a short time. Therefore, the model creates a flexibility for air traffic controllers to re-arrange assignments if any unexpected situations take place.Originality/valueThe proposed MILP model combines the TWD and TFC together for the AGAP problem using the SA. Moreover, the proposed model integrates passenger-oriented and airline-oriented objectives together and reveals the relationships between the objectives in only a short time.

Analisa Optimalisasi Keuntungan dengan Integer Linear Programming dan Metode Branch and Bound pada Toko Bunga QuinnaStory

QuinnaStory Flower Shop produces flower buckets with 2 types of sizes, namely small flower buckets and large flower buckets. With current problems in the form of limited resources, lack of automatic cutting machines, manpower and optimization of production planning. This study aims to determine how to solve the search for maximum profit from production. The optimal solution based on the problem at QuinnaStory Flower Shop can be solved by integer linear programming approach. The method used in this research is the simplex method to look for maximum benefits. The results obtained required the production of 36 small flower buckets and 31.5 large flower buckets to obtain a maximum profit of Rp. 2,790,000, - However, the results of product production / day are still in decimal form. Therefore, it is continued with the branch and bound method to find non-decimal results. The result of the settlement is required to produce 36 small flower buckets and 31 large flower buckets / day to get a maximum profit of IDR 2,764,000.

Optimizing Extensibility of CAN FD for Automotive Cyber-Physical Systems

Extensibility is an important optimization objective for the E/E architecture of automotive cyber-physical systems (ACPS), while little attention has paid to the extensibility-aware design of in-vehicle network. To address this problem, this paper formulates a trade-off problem that balances the bandwidth utilization and the extensibility from the initial design of CAN FD. We firstly propose a new extensibility model and the related evaluation metric, and then two optimization algorithms, namely, the mixed integer linear programming (MILP) approach and the simulated annealing (SA) based heuristic approach, are proposed to resolve the trade-off problem for mid-sized and industry sized signal sets, respectively. The experiment results show the efficiency of the proposed extensibility metric and the optimization algorithms. By comparing with state-of-the-art algorithm, the MILP reduces the increase range of the bandwidth utilization of the extended signal set by 18.17% to 57.64% averagely, and 49.22% to 89.40% maximally, with only 0.06% to 0.79% bandwidth utilization overhead; the SA approach can reduces the increase range of the bandwidth utilization of the extended signal set by 12.71% to 58.33% averagely, and 40.08% to 89.40% maximally, with only 0.06% to 0.8% bandwidth utilization overhead.

Discrete relaxation method for hybrid e-beam and triple patterning lithography layout decomposition

Hybrid electron beam lithography (EBL) and triple patterning lithography (TPL) is an advanced technology for IC manufacture. To solve the hybrid EBL and TPL layout decomposition problem (HETLD), we propose a discrete relaxation method in this paper. Existed works hard to evaluate the quality of the obtained results theoretically. Our discrete relaxation is the first approach which can be used to evaluate the solution quality. First, we introduce an extended minimum weight dominating set coloring problem (EMDSC). We show that the EMDSC problem is a discrete relaxation of the HETLD problem, which is solved by an integer linear programming (ILP) approach. Second, we legalize the relaxation solution to a feasible solution of the initial problem by inserting stitches and assigning EBLs. Experimental results show that our method achieves the near-optimal solution.