Integer Linear Programming Framework Research Articles

Knowledge-Infused Abstractive Summarization of Clinical Diagnostic Interviews: Framework Development Study.

BackgroundIn clinical diagnostic interviews, mental health professionals (MHPs) implement a care practice that involves asking open questions (eg, “What do you want from your life?” “What have you tried before to bring change in your life?”) while listening empathetically to patients. During these interviews, MHPs attempted to build a trusting human-centered relationship while collecting data necessary for professional medical and psychiatric care. Often, because of the social stigma of mental health disorders, patient discomfort in discussing their presenting problem may add additional complexities and nuances to the language they use, that is, hidden signals among noisy content. Therefore, a focused, well-formed, and elaborative summary of clinical interviews is critical to MHPs in making informed decisions by enabling a more profound exploration of a patient’s behavior, especially when it endangers life.ObjectiveThe aim of this study is to propose an unsupervised, knowledge-infused abstractive summarization (KiAS) approach that generates summaries to enable MHPs to perform a well-informed follow-up with patients to improve the existing summarization methods built on frequency heuristics by creating more informative summaries.MethodsOur approach incorporated domain knowledge from the Patient Health Questionnaire-9 lexicon into an integer linear programming framework that optimizes linguistic quality and informativeness. We used 3 baseline approaches: extractive summarization using the SumBasic algorithm, abstractive summarization using integer linear programming without the infusion of knowledge, and abstraction over extractive summarization to evaluate the performance of KiAS. The capability of KiAS on the Distress Analysis Interview Corpus-Wizard of Oz data set was demonstrated through interpretable qualitative and quantitative evaluations.ResultsKiAS generates summaries (7 sentences on average) that capture informative questions and responses exchanged during long (58 sentences on average), ambiguous, and sparse clinical diagnostic interviews. The summaries generated using KiAS improved upon the 3 baselines by 23.3%, 4.4%, 2.5%, and 2.2% for thematic overlap, Flesch Reading Ease, contextual similarity, and Jensen Shannon divergence, respectively. On the Recall-Oriented Understudy for Gisting Evaluation-2 and Recall-Oriented Understudy for Gisting Evaluation-L metrics, KiAS showed an improvement of 61% and 49%, respectively. We validated the quality of the generated summaries through visual inspection and substantial interrater agreement from MHPs.ConclusionsOur collaborator MHPs observed the potential utility and significant impact of KiAS in leveraging valuable but voluminous communications that take place outside of normally scheduled clinical appointments. This study shows promise in generating semantically relevant summaries that will help MHPs make informed decisions about patient status.

Risk-averse optimal bidding strategy for a wind energy portfolio manager including EV parking lots for imbalance mitigation

In this study, an optimal bidding strategy for a wind energy portfolio manager (WEPM) including electricvehicle parking lots (EVPLs) that aims to maximize profits by trading in the day-ahead (DA) market and balancingmarket (BM) and through bilateral contracts, taking into account line capacities and risk management is proposed. Thementioned structure is modeled in mixed integer linear programming (MILP) framework, and the uncertainties regardingelectric vehicle (EV) behavior, electricity market data, wind power generation are captured via a stochastic approach. Todemonstrate the effectiveness of the model, several case studies are carried out considering Sweden and Turkey electricitymarket prices with different risk aversion factors and bilateral contract situations. In this manner, the important resultsas well as useful findings regarding the economic impacts of the proposed concept are analyzed in detail.

Polyhedral properties of the induced cluster subgraphs

A cluster graph is a graph whose every connected component is a complete graph. Given a simple undirected graph G, a subset of vertices inducing a cluster graph is called an independent union of cliques (IUC), and the IUC polytope associated with G is defined as the convex hull of the incidence vectors of all IUCs in the graph. The Maximum IUC problem, which is to find a maximum-cardinality IUC in a graph, finds applications in network-based data analysis. In this paper, we derive several families of facet-defining valid inequalities for the IUC polytope. We also give a complete description of this polytope for some special classes of graphs. We establish computational complexity of the separation problem for most of the considered families of valid inequalities and explore the effectiveness of employing the corresponding cutting planes in an integer (linear) programming framework for the Maximum IUC problem through computational experiments.

Harnessing the Flexibility of District Heating System for Integrating Extensive Share of Renewable Energy Sources in Energy Systems

Lately, the European Union has reinforced the targets set to cut back carbon emissions. The energy generation sector and particularly, the district heating (DH) system, is still prevailed by combustion of fossil fuels that heavily contributes to such emissions. This paper presents a system-based approach to study the coupling between electricity and DH sector for effective mitigation of emissions. A mixed integer linear programming framework is proposed that aims to exploit the flexibility of electricity cogeneration together with partial electrification of the DH system by investing in renewable technologies. The objective is to simultaneously minimize the investment cost and emissions. Both the electricity and DH load profiles are segregated into critical and flexible types. Comprehensive demand response (DR) framework of thermostatically controlled loads and electric vehicles is considered while preserving the chronology. The framework is applied to the Finnish energy system considering the generation mix. Results prove that coordinating the electricity cogeneration with renewable generation combined with partly shifting from DH to electrified heating has a great potential in reducing the emissions. For an average weather scenario under DR, the least-cost solution guarantees an annual emission reduction of 12.04% relative to the total emissions of Finland against the total investment of €13.24Bn in wind and solar power generation.

A reinforcement learning-based algorithm for the aircraft maintenance routing problem

With recent developments in the airline industry worldwide, the competition among the industry has increased largely with many key players in the market. In order to generate profits, the industry has paid much attention to generate optimal routes that are maintenance feasible. The main aim of operational aircraft maintenance routing problem (OAMRP) is to generate these optimal routes for each aircraft that are maintenance feasible and follow the constraints defined by the Federal Aviation Administration (FAA). In this paper, the OAMRP is studied with two main objectives. First, to propose a formulation of a network flow-based Integer Linear Programming (ILP) framework for the OAMRP that considers three main maintenance constraints simultaneously: maximum flying-hour, limit on the number of take-offs between two consecutive maintenance checks and the work-force capacity. Second, to develop a new reinforcement learning-based algorithm which can be used to solve the problem, quickly and efficiently, as compared to commonly available optimization software. Finally, the evaluation of the proposed algorithm on real case datasets obtained from a major airline located in the Middle East verifies that the algorithm generates high-quality solutions quickly for both medium and large-scale flight schedule dataset.

Carbon capture and storage from energy and industrial emission sources: A Europe-wide supply chain optimisation

Power, steel, cement and refining sectors are currently responsible for the largest shares of carbon dioxide emissions from stationary sources. Carbon capture and storage is envisioned as a key player for decarbonising the power and industry sectors. To achieve a significant penetration of carbon capture and storage technologies, supply chain optimisation has emerged as a crucial research task for designing such complex systems. A Europe-wide carbon capture and storage supply chain is here optimised via a mixed integer linear programming framework. The most significant carbon dioxide emitters (242 power plants, 25 steel mills, 111 cement plants and 59 refineries) are identified on exact geographic coordinates and included as candidates for capture. Capture plants are thoroughly represented in techno-economic terms, considering scale effects and different technological options. Transport and sequestration stages are implemented for either onshore or offshore operation. Different case studies are taken into account to assess carbon capture and storage policies and results determine optimal configurations in terms of costs, scale effects, technology options and network complexity. The minimum CO2 avoidance cost is 52 €/t, which increases by 9% if power plants are excluded from carbon sources. If offshore storage is preferred to onshore, cost raises by about 40%.

Optimal µPMU Placement Based on Hybrid Current Channels Selection for Distribution Grids

This article presents a new method for the optimal microphasor measurement units placement (OµPP) for distribution grids. A general integer linear programming framework has been defined by dividing the distribution grid into two parts: nonbranching bus series (NBS) and overlapped buses. An objective function is defined for each part associated with observability constraints. Also, a region matrix is used to describe a grid configuration instead of defining a much larger grid connectivity matrix. Based on the region matrix definition, the search space reduction technique is used for the NBS region that results in a reduction of the number of decision variables and the number of constraints is reduced. The main objective goal of the optimization problem is to find the optimal number of units and the current channels used by each unit for the distribution grid. To evaluate the efficiency of installing the microphasor measurement units (µPMUs), which use different numbers of current channels, a cost model for the µPMU is defined and used for the optimization problem. The cost model of the unit is divided into development and running costs as a function of the current channels number. The proposed method is tested for the IEEE 33 and 69 bus distribution grid and compared with the conventional OµPP. Three cases are studied in the comparison considering redial, microgrid configuration, and current channels limitations. The proposed method shows the ability to find the optimal number of current channels with the same number of units of the conventical method.

A multi grid discrete time based framework for maritime distribution logistics & inventory planning for refinery products

In this paper, a mixed integer linear programming(MILP) framework is presented to rigorously represent a maritime inventory routing problem(MIRP). The MIRP discussed here involves multiple refinery liquid products that need to be transported from multiple supply ports to associated consumer ports using ships with undedicated compartments. The model is based on VMI(vendor managed inventory) system where the vendor decides the product delivery quantity and time of delivery by tracking the consumer inventory and consumption patterns. The proposed mathematical framework involves a multi grid discrete time formulation which rigorously represents several intricacies related to products loading/unloading, jetty activities, port inventory limitations and ship voyages. Several scenarios representative of the actual process are designed and the results demonstrates the efficacy of the proposed framework in finding an optimum ship and jetty schedule that minimizes the total transportation cost in a computationally efficient manner.

Performance Modeling and Analysis of WDM Optical Networks under Wavelength Continuity Constraint using MILP

Objective: Optical networks exploit the Wavelength Division Multiplexing (WDM) to meet the ever-growing bandwidth demands of upcoming communication applications. This is achieved by dividing the enormous transmission bandwidth of fiber into smaller communication channels. The major problem with WDM network design is to find an optimal path between two end users and allocate an available wavelength to the chosen path for the successful data transmission. Methods: This communication over a WDM network is carried out through lightpaths. The merging of all these lightpaths in an optical network generates a virtual topology which is suitable for the optimal network design to meet the increasing traffic demands. But, this virtual topology design is an NP-hard problem. This paper aims to explore Mixed Integer Linear Programming (MILP) framework to solve this design issue. Results: The comparative results of the proposed and existing mathematical models show that the proposed algorithm outperforms with the various performance parameters. Conclusion: Finally, it is concluded that network congestion is reduced marginally in the overall performance of the network.

Day‐ahead charging operation of electric vehicles with on‐site renewable energy resources in a mixed integer linear programming framework

The large-scale penetration of electric vehicles (EVs) into the power system will provoke new challenges needed to be handled by distribution system operators (DSOs). Demand response (DR) strategies play a key role in facilitating the integration of each new asset into the power system. With the aid of the smart grid paradigm, a day-ahead charging operation of large-scale penetration of EVs in different regions that include different aggregators and various EV parking lots (EVPLs) is propounded in this study. Moreover, the uncertainty of the related EV owners, such as the initial state-of-energy and the arrival time to the related EVPL, is taken into account. The stochasticity of PV generation is also investigated by using a scenario-based approach related to daily solar irradiation data. Last but not least, the operational flexibility is also taken into consideration by implementing peak load limitation (PLL) based DR strategies from the DSO point of view. To reveal the effectiveness of the devised scheduling model, it is performed under various case studies that have different levels of PLL, and for the cases with and without PV generation.

A unified ILP framework for core ancestral genome reconstruction problems.

One of the key computational problems in comparative genomics is the reconstruction of genomes of ancestral species based on genomes of extant species. Since most dramatic changes in genomic architectures are caused by genome rearrangements, this problem is often posed as minimization of the number of genome rearrangements between extant and ancestral genomes. The basic case of three given genomes is known as the genome median problem. Whole-genome duplications (WGDs) represent yet another type of dramatic evolutionary events and inspire the reconstruction of preduplicated ancestral genomes, referred to as the genome halving problem. Generalization of WGDs to whole-genome multiplication events leads to the genome aliquoting problem. In this study, we propose polynomial-size integer linear programming (ILP) formulations for the aforementioned problems. We further obtain such formulations for the restricted and conserved versions of the median and halving problems, which have been recently introduced to improve biological relevance of the solutions. Extensive evaluation of solutions to the different ILP problems demonstrates their good accuracy. Furthermore, since the ILP formulations for the conserved versions have linear size, they provide a novel practical approach to ancestral genome reconstruction, which combines the advantages of homology- and rearrangements-based methods. Code and data are available in https://github.com/AvdeevPavel/ILP-WGD-reconstructor. Supplementary data are available at Bioinformatics online.

Strategic biofuel supply chain network design and techno-economic-environmental analysis for an Indian scenario

To tackle the increasing demand, adverse environmental and financial impact of fossil fuels on Indian economy, Indian government has taken a green move towards blending of 20% bioethanol into gasoline and 20% biodiesel into diesel. A feasibility analysis of this kind of move has been studied by performing techno-economic-environmental analysis of a four-layered SCN using second generation lignocellulose bio mass as feedstock. Using supply chain flow decisions as continuous variables and the existence and connectivity between the nodes among nodes in several echelons as binary variables, overall net present value of the supply chain network is maximized using transport, storage, production and import as operating expenditure and infrastructure as capital expenditure. The overall objective function also considered the revenue generation from selling the final product and also from the carbon credits via greenhouse gas emission savings throughout the project life cycle assessment. External imports are allowed to meet the unmet demand and maintain the product quality in terms of research octane number. With ~80% increase in demand in bioethanol and ~37% increase in biodiesel over the 9-year planning horizon, a dynamically changing SC structure coming as an outcome of the aforementioned mixed integer linear programming framework shows overall ~88% increase in the newly added locations. Further analysis shows that at least 40% of the feed supply should be provided to meet the all-time demand and keep the project alive.

A mixed integer linear programming framework for optimising the extraction strategy of open pit – underground mining options and transitions

ABSTRACT The decision to exploit a mineral deposit that can be extracted by open pit and/or underground mining involves consideration of the following options: (a) independent open pit extraction; (b) independent underground extraction; (c) simultaneous open pit and underground (OPUG) extraction; (d) sequential OPUG extraction; and (e) combinations of (c) and (d). This paper investigates the extraction strategy for deposits using Mixed Integer Linear Programming (MILP) optimisation framework to maximise the net present value and determine the schedules for mining, processing, underground capital and operational developments, and 3D crown pillar position. The MILP framework is implemented for a gold deposit. The results showed a combined sequential and simultaneous open pit and underground (OPUG) mining with crown pillar as the optimal extraction option generating NPV that is 11% and 13% better than independent OP or UG mining, respectively.

Mapping Entity Sets in News Archives Across Time

We propose a novel way of utilizing and accessing information stored in news archives as well as a new style of investigating the history. Our idea is to automatically generate similar entity pairs given two sets of entities, one from the past and one representing the present. This allows performing entity-oriented mapping between different times. We introduce an effective method to solve the aforementioned task based on a concise integer linear programming framework. In particular, our model first conducts typicality analysis to estimate entity representativeness. It next constructs orthogonal transformation between the two entity collections. The result is a set of typical across-time comparables. We demonstrate the effectiveness of our approach on the New York Times dataset through both qualitative and quantitative tests.

Incorporating Location Aspects in Process Integration Methodology

The large potential for waste resource and heat recovery in industry has been motivating research toward increasing efficiency. Process integration methods have proven to be effective tools in improving industrial sites while decreasing their resource and energy consumption; however, location aspects and their impact are generally overlooked. This paper presents a method based on process integration, which considers the location of plants. The impact of the locations is included within the mixed integer linear programming framework in the form of heat losses, temperature and pressure drop, and piping cost. The objective function is selected as minimisation of the total cost of the system excluding piping cost and ϵ -constraints are applied on the piping cost to systematically generate multiple solutions. The method is applied to a case study with industrial plants from different sectors. First, the interaction between two plants and their utility integration are illustrated, depending on the piping cost limit which results in the heat pump and boiler on one site being gradually replaced by excess heat recovered from the other plant. Then, the optimisation of the whole system is carried out, as a large-scale application. At low piping cost allowances, heat is shared through high pressure steam in above-ground pipes, while at higher piping cost limits the system switches toward lower pressure steam sharing in underground pipes. Compared to the business-as-usual operation of the sites, the optimal solution obtained with the proposed method leads to 20% reduction in the overall cost of the system, including the piping cost. Further reduction in the cost is possible using a state of the art method but the technical and economic feasibility is not guaranteed. Thus, the present work provides a tool to find optimal industrial symbiosis solutions under different investment limits on the infrastructure between plants.

Maximization of Underwater Sensor Networks Lifetime via Fountain Codes

The severe nature of underwater channel poses a great challenge for prolonging underwater acoustic sensor networks (UASNs) lifetime and achieving a reliable communication performance. Traditional approaches to improve the reliability such as automatic repeat request (ARQ) negatively affect the network lifetime (NL) due to energy dissipation caused by ARQ retransmission. A forward error correction (FEC) technique called fountain codes (FCs) can solve the energy efficiency problem of ARQ by transmitting both the original packet and some redundant packets to ensure a targeted reliability with few or no retransmissions. In this paper, we investigate performances of both traditional ARQ- and FC-based FEC methods in terms of NL, end-to-end delay, energy consumption, and frame error rate (FER) for UASNs. In this context, we abstract energy dissipation characteristics of conventional ARQ- and FC-based FEC method at the link-layer. We propose an integer linear programming (ILP) framework that maximizes the NL, which is operated on top of developed link-layer energy consumption models. Our results reveal that FC-based FEC methods can prolong the NL at a minimum of 16% while end-to-end delay, energy consumption, and FER can be reduced at least by 11%, 14%, and 9% as compared to classical ARQ, respectively.

An Integer Linear Programming Framework for Energy Optimization of Non-Preemptive Real Time Tasks on Multiprocessors

An Integer Linear Programming Framework for Energy Optimization of Non-Preemptive Real Time Tasks on Multiprocessors

A robust mixed integer linear programming framework for underground cut-and-fill mining production scheduling

ABSTRACTA review of general optimization studies that have been proposed for underground mining shows that previous works lack flexibility, operability and practicality in relation to cut-and-fill mining production scheduling. This paper presents a robust mixed integer linear programming (MILP) formulation for underground cut-and-fill mining. The objective function of the model is to maximize the net present value (NPV) of the operation while meeting all mining and processing operational and technical constraints. The MILP model features stope development and extraction sequencing constraints, mining and processing tonnage fluctuation constraints, and extraction duration and active levels control constraints. These features make the model more practical and expandable. The MILP model is verified and validated with two case studies from an existing mine and the results are compared with the actual mining strategy. The comparison shows a 9% to 17% improved NPV in both case studies resulting from mining higher grades and processing less tonnes thereby generating a better cash flow.

Optimising data-driven network under limited resource: a partial diversification approach

This paper describes a cardinality constrained network flow structure whose special characteristics are used to analyse different risk aspects under an environment of uncertainty. The network structure developed is a suitable alternative to support financial planning and many other decision-making problems with limited resources. By setting a diversification level, we can manage systematic and non-systematic risks under a stochastic mixed integer linear programming framework. A dual decomposition method, Progressive Hedging (PH), is applied to more efficiently accommodate instances with large numbers of scenarios. We studied the impact of the level of the diversification on transaction costs and considered different factors that influence the performance of the algorithm. In particular, a Lagrangian bound is embedded to enhance the capacity of the method. Numerical results show the effectiveness of the proposed decision support approach.

Sizing a Hybrid Renewable Energy System to Reduce Energy Costs at Various Levels of Robustness for an Industrial Site

The rising cost of energy in Australia is causing industry to look for energy sources other than the conventional grid. Industry has been slow to adopt non-dispatchable renewable sources partly because of their inherent variability, however their inclusion can reduce greenhouse gas emissions and energy costs. By using an alternative fuel production facility as a case study an installation of a hybrid renewable energy system is explored. This paper studies the effects of varying the robustness of the design with respect to load and renewable variability separately. Lithium ion battery packs are also included to act as a buffer in the system. The installation is modelled using a robust Mixed Integer Linear Programming (MILP) framework and the potential cost savings are quantified at various levels of robustness. Broad sizing guidelines are drawn from the results for the case study that should result in a robust energy supply.