Статті в журналах: "Optimisation MINLP"

1

Epelle, Emmanuel I., and Dimitrios I. Gerogiorgis. "A computational performance comparison of MILP vs. MINLP formulations for oil production optimisation." Computers & Chemical Engineering 140 (September 2020): 106903. http://dx.doi.org/10.1016/j.compchemeng.2020.106903.

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Diaby, Abdullatif Lacina, Lee Luong, Amer Yousef, and Jonas Addai Mensah. "A Review of Optimal Scheduling Cleaning of Refinery Crude Preheat Trains Subject to Fouling and Ageing." Applied Mechanics and Materials 148-149 (December 2011): 643–51. http://dx.doi.org/10.4028/www.scientific.net/amm.148-149.643.

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Refinery crude preheat train (CPHT) is prone to fouling and ageing effects due to the complexity of processed crude feedstock preheated prior to distillation. This has serious implications on the thermal and hydraulic performance of the CPHT. As a result, efficient performance of crude preheat trains is compromised and as such, optimal scheduling cleaning operations are required to restore performance. In this paper, we attempt to review the subject of fouling/ageing control and mitigation in crude preheat train network by optimal scheduling cleaning approach. Three prominent optimisation techniques/models namely Mathematical Models (Mixed integer linear programming (MILP) and Mixed integer non-linear programming (MINLP) models); Artificial Intelligence (AI) Models; and Heuristic Techniques used for achieving optimal cleaning are outlined.

3

Savola, Tuula, and Carl-Johan Fogelholm. "MINLP optimisation model for increased power production in small-scale CHP plants." Applied Thermal Engineering 27, no. 1 (January 2007): 89–99. http://dx.doi.org/10.1016/j.applthermaleng.2006.05.002.

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4

Yusuf, Noor, and Tareq Al-Ansari. "Current and Future Role of Natural Gas Supply Chains in the Transition to a Low-Carbon Hydrogen Economy: A Comprehensive Review on Integrated Natural Gas Supply Chain Optimisation Models." Energies 16, no. 22 (November 20, 2023): 7672. http://dx.doi.org/10.3390/en16227672.

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Natural gas is the most growing fossil fuel due to its environmental advantages. For the economical transportation of natural gas to distant markets, physical (i.e., liquefaction and compression) or chemical (i.e., direct and indirect) monetisation options must be considered to reduce volume and meet the demand of different markets. Planning natural gas supply chains is a complex problem in today’s turbulent markets, especially considering the uncertainties associated with final market demand and competition with emerging renewable and hydrogen energies. This review study evaluates the latest research on mathematical programming (i.e., MILP and MINLP) as a decision-making tool for designing and planning natural gas supply chains under different planning horizons. The first part of this study assesses the status of existing natural gas infrastructures by addressing readily available natural monetisation options, quantitative tools for selecting monetisation options, and single-state and multistate natural gas supply chain optimisation models. The second part investigates hydrogen as a potential energy carrier for integration with natural gas supply chains, carbon capture utilisation, and storage technologies. This integration is foreseen to decarbonise systems, diversify the product portfolio, and fill the gap between current supply chains and the future market need of cleaner energy commodities. Since natural gas markets are turbulent and hydrogen energy has the potential to replace fossil fuels in the future, addressing stochastic conditions and demand uncertainty is vital to hedge against risks through designing a responsive supply chain in the project’s early design stages. Hence, hydrogen supply chain optimisation studies and the latest works on hydrogen–natural gas supply chain optimisation were reviewed under deterministic and stochastic conditions. Only quantitative mathematical models for supply chain optimisation, including linear and nonlinear programming models, were considered in this study to evaluate the effectiveness of each proposed approach.

5

Gutierrez, G., and P. Vega. "PROCESS SYNTHESIS APPLIED TO ACTIVATED SLUDGE PROCESSES: A FRAMEWORK WITH MINLP OPTIMISATION MODELS." IFAC Proceedings Volumes 35, no. 1 (2002): 393–97. http://dx.doi.org/10.3182/20020721-6-es-1901.01458.

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6

Savola, Tuula, Tor-Martin Tveit, and Carl-Johan Fogelholm. "A MINLP model including the pressure levels and multiperiods for CHP process optimisation." Applied Thermal Engineering 27, no. 11-12 (August 2007): 1857–67. http://dx.doi.org/10.1016/j.applthermaleng.2007.01.002.

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7

Biscos, C., M. Mulholland, M. V. Le Lann, C. J. Brouckaert, R. Bailey, and M. Roustan. "Optimal operation of a potable water distribution network." Water Science and Technology 46, no. 9 (November 1, 2002): 155–62. http://dx.doi.org/10.2166/wst.2002.0228.

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This paper presents an approach to an optimal operation of a potable water distribution network. The main control objective defined during the preliminary steps was to maximise the use of low-cost power, maintaining at the same time minimum emergency levels in all reservoirs. The combination of dynamic elements (e.g. reservoirs) and discrete elements (pumps, valves, routing) makes this a challenging predictive control and constrained optimisation problem, which is being solved by MINLP (Mixed Integer Non-linear Programming). Initial experimental results show the performance of this algorithm and its ability to control the water distribution process.

8

Dkhili, Nouha, David Salas, Julien Eynard, Stéphane Thil, and Stéphane Grieu. "Innovative Application of Model-Based Predictive Control for Low-Voltage Power Distribution Grids with Significant Distributed Generation." Energies 14, no. 6 (March 23, 2021): 1773. http://dx.doi.org/10.3390/en14061773.

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In past decades, the deployment of renewable-energy-based power generators, namely solar photovoltaic (PV) power generators, has been projected to cause a number of new difficulties in planning, monitoring, and control of power distribution grids. In this paper, a control scheme for flexible asset management is proposed with the aim of closing the gap between power supply and demand in a suburban low-voltage power distribution grid with significant penetration of solar PV power generation while respecting the different systems’ operational constraints, in addition to the voltage constraints prescribed by the French distribution grid operator (ENEDIS). The premise of the proposed strategy is the use of a model-based predictive control (MPC) scheme. The flexible assets used in the case study are a biogas plant and a water tower. The mixed-integer nonlinear programming (MINLP) setting due to the water tower ON/OFF controller greatly increases the computational complexity of the optimisation problem. Thus, one of the contributions of the paper is a new formulation that solves the MINLP problem as a smooth continuous one without having recourse to relaxation. To determine the most adequate size for the proposed scheme’s sliding window, a sensitivity analysis is carried out. Then, results given by the scheme using the previously determined window size are analysed and compared to two reference strategies based on a relaxed problem formulation: a single optimisation yielding a weekly operation planning and a MPC scheme. The proposed problem formulation proves effective in terms of performance and maintenance of acceptable computational complexity. For the chosen sliding window, the control scheme drives the power supply/demand gap down from the initial one up to 38%.

9

Tanvir, M. S., and I. M. Mujtaba. "Optimisation of design and operation of MSF desalination process using MINLP technique in gPROMS." Desalination 222, no. 1-3 (March 2008): 419–30. http://dx.doi.org/10.1016/j.desal.2007.02.068.

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10

Brusis, D., J. Stichlmair, and F. F. Kuppinger. "MINLP Optimisation of a Distillation/Pervaporation Process for the Separation of a Ternary Azeotropic Mixture." Chemie Ingenieur Technik 73, no. 6 (June 2001): 624. http://dx.doi.org/10.1002/1522-2640(200106)73:6<624::aid-cite6242222>3.0.co;2-s.

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11

Amjath, Mohamed, Laoucine Kerbache, and James MacGregor Smith. "A Closed Queueing Networks Approach for an Optimal Heterogeneous Fleet Size of an Inter-Facility Bulk Material Transfer System." Logistics 8, no. 1 (March 4, 2024): 26. http://dx.doi.org/10.3390/logistics8010026.

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Background: This study addresses optimising fleet size in a system with a heterogeneous truck fleet, aiming to minimise transportation costs in interfacility material transfer operations. Methods: The material transfer process is modelled using a closed queueing network (CQN) that considers heterogeneous nodes and customised service times tailored to the unique characteristics of various truck types and their transported materials. The optimisation problem is formulated as a mixed-integer nonlinear programming (MINLP), falling into the NP-Hard, making exact solution computation challenging. A numerical approximation method, a modified sequential quadratic programming (SQP) method coupled with a mean value analysis (MVA) algorithm, is employed to overcome this challenge. Validation is conducted using a discrete event simulation (DES) model. Results: The proposed analytical model tested within a steel manufacturing plant’s material transfer process. The results showed that the analytical model achieved comparable optimisation of the heterogeneous truck fleet size with significantly reduced response times compared to the simulation method. Furthermore, evaluating performance metrics, encompassing response time, utilisation rate, and cycle time, revealed minimal discrepancies between the analytical and the simulation results, approximately ±8%, ±8%, and ±7%, respectively. Conclusions: These findings affirm the presented analytical approach’s robustness in optimising interfacility material transfer operations with heterogeneous truck fleets, demonstrating real-world applications.

12

Epelle, Emmanuel I., and Dimitrios I. Gerogiorgis. "Mixed-Integer Nonlinear Programming (MINLP) for production optimisation of naturally flowing and artificial lift wells with routing constraints." Chemical Engineering Research and Design 152 (December 2019): 134–48. http://dx.doi.org/10.1016/j.cherd.2019.09.042.

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13

Tveit, Tor-Martin, and Carl-Johan Fogelholm. "Multi-period steam turbine network optimisation. Part II: Development of a multi-period MINLP model of a utility system." Applied Thermal Engineering 26, no. 14-15 (October 2006): 1730–36. http://dx.doi.org/10.1016/j.applthermaleng.2005.11.004.

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14

Akgul, O., N. Mac Dowell, L. G. Papageorgiou, and N. Shah. "A mixed integer nonlinear programming (MINLP) supply chain optimisation framework for carbon negative electricity generation using biomass to energy with CCS (BECCS) in the UK." International Journal of Greenhouse Gas Control 28 (September 2014): 189–202. http://dx.doi.org/10.1016/j.ijggc.2014.06.017.

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15

Smith, E. "Global Optimisation of Nonconvex MINLPs." Computers & Chemical Engineering 21, no. 1-2 (1997): S791—S796. http://dx.doi.org/10.1016/s0098-1354(97)00146-4.

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16

Smith, Edward M. B., and Constantinos C. Pantelides. "Global optimisation of nonconvex MINLPs." Computers & Chemical Engineering 21 (May 1997): S791—S796. http://dx.doi.org/10.1016/s0098-1354(97)87599-0.

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17

Andrés, Beatriz, Raquel Sanchis, Raúl Poler, Manuel Díaz-Madroñero, and Josefa Mula. "A MILP for multi-machine injection moulding sequencing in the scope of C2NET Project." International Journal of Production Management and Engineering 6, no. 1 (January 31, 2018): 29. http://dx.doi.org/10.4995/ijpme.2018.8913.

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<p>The goal of C2NET European H2020 Funded Project is the creation of cloud-enabled tools for supporting the SMEs supply network optimization of manufacturing and logistic assets based on collaborative demand, production and delivery plans. In the scope of C2NET Project, and particularly in the Optimisation module (C2NET OPT), this paper proposes a novel holistic mixed integer linear programing (MILP) model to optimise the injection sequencing in a multi-machine case. The results of the MILP will support the production planner decision-making process in the calculation of (i) moulds setup in certain machines, and (ii) the amount of products to produce in order to minimise the setup, inventory, and backorders costs. The designed MILP takes part of the algorithms repository created in C2NET European Funded Project to solve realistic industry planning problems. The MILP is verified in realistic data considering three data sets with different sizes, in order to test it’s the computation efficiency.</p>

18

Yang, Yuqing, Stephen Bremner, Chris Menictas, and Merlinde Kay. "A Mixed Receding Horizon Control Strategy for Battery Energy Storage System Scheduling in a Hybrid PV and Wind Power Plant with Different Forecast Techniques." Energies 12, no. 12 (June 18, 2019): 2326. http://dx.doi.org/10.3390/en12122326.

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This paper presents a mixed receding horizon control (RHC) strategy for the optimal scheduling of a battery energy storage system (BESS) in a hybrid PV and wind power plant while satisfying multiple operational constraints. The overall optimisation problem was reformulated as a mixed-integer linear programming (MILP) problem, aimed at minimising the total operating cost of the entire system. The cost function of this MILP is composed of the profits of selling electricity, the cost of purchasing ancillary services for undersupply and oversupply, and the operation and maintenance cost of each component. To investigate the impacts of day-ahead and hour-ahead forecasting for battery optimisation, four forecasting methods, including persistence, Elman neural network, wavelet neural network and autoregressive integrated moving average (ARIMA), were applied for both day-ahead and hour-ahead forecasting. Numerical simulations demonstrated the significant increased efficiency of the proposed mixed RHC strategy, which improved the total operation profit by almost 29% in one year, in contrast to the day-ahead RHC strategy. Moreover, the simulation results also verified the significance of using more accurate forecasting techniques, where ARIMA can reduce the total operation cost by almost 5% during the whole year operation when compared to the persistence method as the benchmark.

19

Saber, Takfarinas, Joao Marques-Silva, James Thorburn, and Anthony Ventresque. "Exact and Hybrid Solutions for the Multi-Objective VM Reassignment Problem." International Journal on Artificial Intelligence Tools 26, no. 01 (February 2017): 1760004. http://dx.doi.org/10.1142/s0218213017600041.

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Machine Reassignment is a challenging problem for constraint programming (CP) and mixed integer linear programming (MILP) approaches, especially given the size of data centres. Hybrid solutions mixing CP and heuristic algorithms, such as, large neighbourhood search (CBLNS), also struggle to address the problem given its size and number of constraints. The multi-objective version of the Machine Reassignment Problem is even more challenging and it seems unlikely for CP, MILP or hybrid solutions to obtain good results in this context. As a result, the first approaches to address this problem have been based on other optimisation methods, including metaheuristics. In this paper we study three things: (i) under which conditions a mixed integer optimisation solver, such as IBM ILOG CPLEX, can be used for the Multi-objective Machine Reassignment Problem; (ii) how much of the search space can a well-known hybrid method such as CBLNS explore; and (iii) can we find a better hybrid approach combining MILP or CBLNS and an- other recent metaheuristic proposed for the problem (GeNePi). We show that MILP can handle only small or medium scale data centres, and with some relaxations, such as, an optimality tolerance gap and a limited number of directions explored in the search space. CBLNS on the other hand struggles with the problem in general but achieves reasonable performance for large instances of the problem. However, we show that our hybridisation improves both the quality of the set of solutions (CPLEX+GeNePi and CBLNS+GeNePi improve the solutions by +17.8% against CPLEX alone and +615% against CBLNS alone) and number of solutions (8.9 times more solutions than CPLEX alone and 56.76 times more solutions than CBLNS alone), while the processing time of CPLEX+GeNePi and CBLNS+GeNePi increases only by 6% and 16.4% respectively. Overall, the study shows that CPLEX+GeNePi is the best algorithm for small instances (CBLNS+GeNePi only gets 45.2% of CPLEX+GeNePi’s hypervolume) while CBLNS+GeNePi is better than the others on large instances (that CPLEX+GeNePi cannot address).

20

Ji, Xiaotong, Fan Xiao, Dan Liu, Ping Xiong, and Mingnian Zhang. "Distributionally Robust Collaborative Dispatch of Integrated Energy Systems with DNE Limits Considering Renewable and Contingency Uncertainties." Elektronika ir Elektrotechnika 29, no. 3 (June 27, 2023): 39–47. http://dx.doi.org/10.5755/j02.eie.33960.

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Collaborative optimisation of system reserves and utilisation of renewable energy is an efficient approach to achieving robust optimal dispatch of integrated energy systems (IES). However, conventional robust dispatch methods are often too conservative and lack the ability to consider uncertainties such as renewable energy and contingency probabilities. To address these limitations, this paper proposes a distributionally robust dispatch model that co-optimises reserves and do-not-exceed (DNE) limits while considering these uncertainties. First, a deterministic optimisation model of IES is established with a minimum operational cost objective and security constraints. Next, a two-stage robust collaborative optimisation framework of IES is built, based on the Wasserstein measure, with random equipment faults represented by an adjustable ambiguity set. Finally, to overcome the computational challenges associated with robust approaches, duality theory and Karush-Kuhn-Tucker (KKT) conditions are used to convert the formulation into a mixed integer linear programming (MILP) model. The Simulation results on the modified IEEE 33-bus system demonstrate the effectiveness of the proposed model and solution methodology.

21

Alvisi, S., M. Franchini, M. Gavanelli, and M. Nonato. "Near-optimal scheduling of device activation in water distribution systems to reduce the impact of a contamination event." Journal of Hydroinformatics 14, no. 2 (August 4, 2011): 345–65. http://dx.doi.org/10.2166/hydro.2011.147.

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This paper proposes an innovative procedure for identifying, in the event of accidental or intentional contamination of a water distribution system, the optimal scheduling of activation of a pre-selected set of flow control devices which will serve to minimise the volume of contaminated water consumed by users after the detection of the contaminant in the system. The constraints are represented by the number of available response teams and the maximum speed at which these teams can travel along the roadway. The optimal scheduling of device activation is sought by means of an optimisation process based on a genetic algorithm (GA) which interacts with a mixed integer linear programming (MILP) solver in order to ensure the feasibility of the scheduling identified. The optimisation procedure is coupled to a hydraulic and quality simulator, which enables a calculation of the volumes of contaminated water consumed by users, and a dynamic cache memory, which, by storing information on the system's behaviour as the optimisation process progresses, serves to limit the computational times. The application of the procedure to a highly complex real water distribution system shows that the optimisation process is robust and efficacious and produces a smaller volume of contaminated water consumed by the users than when the activation of all the devices was completed in the shortest amount of time.

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Schönberger, Manuel, Immanuel Trummer, and Wolfgang Mauerer. "Quantum-Inspired Digital Annealing for Join Ordering." Proceedings of the VLDB Endowment 17, no. 3 (November 2023): 511–24. http://dx.doi.org/10.14778/3632093.3632112.

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Finding the optimal join order (JO) is one of the most important problems in query optimisation, and has been extensively considered in research and practise. As it involves huge search spaces, approximation approaches and heuristics are commonly used, which explore a reduced solution space at the cost of solution quality. To explore even large JO search spaces, we may consider special-purpose software, such as mixed-integer linear programming (MILP) solvers, which have successfully solved JO problems. However, even mature solvers cannot overcome the limitations of conventional hardware prompted by the end of Moore's law. We consider quantum-inspired digital annealing hardware, which takes inspiration from quantum processing units (QPUs). Unlike QPUs, which likely remain limited in size and reliability in the near and mid-term future, the digital annealer (DA) can solve large instances of mathematically encoded optimisation problems today. We derive a novel, native encoding for the JO problem tailored to this class of machines that substantially improves over known MILP and quantum-based encodings, and reduces encoding size over the state-of-the-art. By augmenting the computation with a novel readout method, we derive valid join orders for each solution obtained by the (probabilistically operating) DA. Most importantly and despite an extremely large solution space, our approach scales to practically relevant dimensions of around 50 relations and improves result quality over conventionally employed approaches, adding a novel alternative to solving the long-standing JO problem.

23

Rai, Bharatendra K., Bimal Nepal, Angappa Gunasekaran, and Yuzhu Li. "Optimisation of process audit plan for minimising vehicle launch risk using MILP." International Journal of Procurement Management 6, no. 4 (2013): 379. http://dx.doi.org/10.1504/ijpm.2013.054742.

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24

Cerchio, Marco, Francesco Gullí, Maurizio Repetto, and Antonino Sanfilippo. "Hybrid Energy Network Management: Simulation and Optimisation of Large Scale PV Coupled with Hydrogen Generation." Electronics 9, no. 10 (October 20, 2020): 1734. http://dx.doi.org/10.3390/electronics9101734.

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The power production of electrical Renewable Energy Sources (RES), mainly PV and wind energy, is affected by their primary source of energy: solar radiation value or wind strength. Electrical networks with a large share of these sources must manage temporal imbalances of supply and demand. Hybrid Energy Networks (HEN) can mitigate the effects of this unbalancing by providing a connection between the electricity grid and and other energy vectors such as heat, gas or hydrogen. These couplings can activate synergies among networks that, all together, increase the share of renewable sources helping a decarbonisation of the energy sector. As the energy system becomes more and more complex, the need for simulation and optimisation tools increases. Mathematical optimisation can be used to look for a management strategy maximising a specific target, for instance economical, i.e. the minimum management cost, or environmental as the best exploitation or RES. The present work presents a Mixed Integer Linear Programming (MILP) optimisation procedure that looks for the minimum running cost of a system made up by a large-scale PV plant where hydrogen production, storage and conversion to electricity is present. In addition, a connection to a natural gas grid where hydrogen can be sold is considered. Different running strategies are studied and analysed as functions of electricity prices and other forms of electrical energy exploitation.

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Pettenati, M., N. Croiset, G. Picot-Colbeaux, J. Casanova, M. Azaroual, K. Besnard, and N. Rampnoux. "Optimisation of wastewater treatments through combined geomaterials and natural soil filter: modelling tools." Journal of Water Reuse and Desalination 2, no. 4 (December 1, 2012): 185–93. http://dx.doi.org/10.2166/wrd.2012.023.

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The main objective of this study is the establishment of innovative purification systems through the conceptualisation of reactive barriers in soil for artificial recharge of groundwater with treated wastewater. Numerical integration of hydrodynamics and biogeochemical processes controlling the effectiveness of this engineering system is applied to design soil column experiments. This leads to the elaboration of a combined aerobic/anaerobic environment to ensure the successive nitrification of rich ammonium wastewater and the denitrification mechanisms reducing NO3– according to heterotrophic denitrification and pyrite oxidation. A MIN3P reactive flow and transport model is used to reproduce an experimental flow-through column. Calculated concentrations of CH2O and NO3− are consistent with experimental results. Agreement between model and experimental results makes it possible to understand major processes taking place in the column and optimises future treatment experiments.

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Smith, E. M. B., and C. C. Pantelides. "A symbolic reformulation/spatial branch-and-bound algorithm for the global optimisation of nonconvex MINLPs." Computers & Chemical Engineering 23, no. 4-5 (May 1999): 457–78. http://dx.doi.org/10.1016/s0098-1354(98)00286-5.

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Veintimilla-Reyes, Jaime, Annelies De Meyer, Dirk Cattrysse, and Jos Van Orshoven. "From Linear Programming Model to Mixed Integer Linear Programming Model for the Simultaneous Optimisation of Water Allocation and Reservoir Location in River Systems." Proceedings 2, no. 11 (July 30, 2018): 594. http://dx.doi.org/10.3390/proceedings2110594.

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The allocation of water flowing through a river-with-reservoirs system to optimally meet spatially distributed and temporally variable demands can be conceived as a Network Flow Optimisation (NFO) problem and addressed by Linear Programming (LP). In this paper we present an extension of the strategic NFO-LP model to simultaneously optimise the allocation of water and the location of one or more reservoirs. The applicability of the MILP model has been illustrated by applying it to a hypothetical river network configuration consisting of seven candidate reservoir nodes and seven demand nodes, and by comparing the outcome (water levels in selected reservoir, penalties) with the values obtained by the original LP-model for the same network with six reservoirs present.

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Tausif, Ismail. "Last mile delivery Optimisation model for drone-enabled Vehicle Routing Problem." Emerging Minds Journal for Student Research 1 (July 11, 2023): 39–73. http://dx.doi.org/10.59973/emjsr.11.

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In light of e-commerce's exponential growth trajectory over recent years, it has never been more critical to evaluate the last-mile delivery segment for its efficiency and cost-effectiveness. Autonomous vehicles (drones) offer considerable promise as potential solutions with ongoing investigations into emerging technologies in this context. To address problems related to last mile-delivery in logistics operations, the practicality of adopting a hybrid truck-drone delivery system is examined through this study. The researchers utilized Mixed-integer linear programming (MILP)and Gurobi optimization solvers both for optimizing performance as well as facilitating execution. While testing a drone dataset of a well-known logistics company as part of their research using an optimization model, the findings suggested that were remarkable competitive advantages including significant gains in reduction of timing. Nevertheless, there are several constraints like maintenance, recharging, difficult weather conditions & traffic congestion-necessitating focused innovative AI-based approaches. In spite of these impediments, a hybrid truck-drone’s potential applicability can remarkably boost the efficiency of last-mile delivery operations.

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Chen, Yongnan, Songsong Liu, Lazaros G. Papageorgiou, Konstantinos Theofilatos, and Sophia Tsoka. "Optimisation Models for Pathway Activity Inference in Cancer." Cancers 15, no. 6 (March 15, 2023): 1787. http://dx.doi.org/10.3390/cancers15061787.

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Background: With advances in high-throughput technologies, there has been an enormous increase in data related to profiling the activity of molecules in disease. While such data provide more comprehensive information on cellular actions, their large volume and complexity pose difficulty in accurate classification of disease phenotypes. Therefore, novel modelling methods that can improve accuracy while offering interpretable means of analysis are required. Biological pathways can be used to incorporate a priori knowledge of biological interactions to decrease data dimensionality and increase the biological interpretability of machine learning models. Methodology: A mathematical optimisation model is proposed for pathway activity inference towards precise disease phenotype prediction and is applied to RNA-Seq datasets. The model is based on mixed-integer linear programming (MILP) mathematical optimisation principles and infers pathway activity as the linear combination of pathway member gene expression, multiplying expression values with model-determined gene weights that are optimised to maximise discrimination of phenotype classes and minimise incorrect sample allocation. Results: The model is evaluated on the transcriptome of breast and colorectal cancer, and exhibits solution results of good optimality as well as good prediction performance on related cancer subtypes. Two baseline pathway activity inference methods and three advanced methods are used for comparison. Sample prediction accuracy, robustness against noise expression data, and survival analysis suggest competitive prediction performance of our model while providing interpretability and insight on key pathways and genes. Overall, our work demonstrates that the flexible nature of mathematical programming lends itself well to developing efficient computational strategies for pathway activity inference and disease subtype prediction.

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Hong, Qiuyi, Fanlin Meng, and Jian Liu. "Customised Multi-Energy Pricing: Model and Solutions." Energies 16, no. 4 (February 20, 2023): 2080. http://dx.doi.org/10.3390/en16042080.

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With the increasing interdependence among energies (e.g., electricity, natural gas and heat) and the development of a decentralised energy system, a novel retail pricing scheme in the multi-energy market is demanded. Therefore, the problem of designing a customised multi-energy pricing scheme for energy retailers is investigated in this paper. In particular, the proposed pricing scheme is formulated as a bilevel optimisation problem. At the upper level, the energy retailer (leader) aims to maximise its profit. Microgrids (followers) equipped with energy converters, storage, renewable energy sources (RES) and demand response (DR) programs are located at the lower level and minimise their operational costs. Three hybrid algorithms combining metaheuristic algorithms (i.e., particle swarm optimisation (PSO), genetic algorithm (GA) and simulated annealing (SA)) with the mixed-integer linear program (MILP) are developed to solve the proposed bilevel problem. Numerical results verify the feasibility and effectiveness of the proposed model and solution algorithms. We find that GA outperforms other solution algorithms to obtain a higher retailer’s profit through comparison. In addition, the proposed customised pricing scheme could benefit the retailer’s profitability and net profit margin compared to the widely adopted uniform pricing scheme due to the reduction in the overall energy purchasing costs in the wholesale markets. Lastly, the negative correlations between the rated capacity and power of the energy storage and both retailer’s profit and the microgrid’s operational cost are illustrated.

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Brand, Cornelius, Martin Koutecký, and Sebastian Ordyniak. "Parameterized Algorithms for MILPs with Small Treedepth." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 14 (May 18, 2021): 12249–57. http://dx.doi.org/10.1609/aaai.v35i14.17454.

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Solving (mixed) integer (linear) programs, (M)I(L)Ps for short, is a fundamental optimisation task with a wide range of applications in artificial intelligence and computer science in general. While hard in general, recent years have brought about vast progress for solving structurally restricted, (non-mixed) ILPs: n-fold, tree-fold, 2-stage stochastic and multi-stage stochastic programs admit efficient algorithms, and all of these special cases are subsumed by the class of ILPs of small treedepth. In this paper, we extend this line of work to the mixed case, by showing an algorithm solving MILP in time f(a,d)poly(n), where a is the largest coefficient of the constraint matrix, d is its treedepth, and n is the number of variables. This is enabled by proving bounds on the denominators (fractionality) of the vertices of bounded-treedepth (non-integer) linear programs. We do so by carefully analysing the inverses of invertible sub-matrices of the constraint matrix. This allows us to afford scaling up the mixed program to the integer grid, and applying the known methods for integer programs. We then trace the limiting boundary of our "bounded fractionality" approach both in terms of going beyond MILP (by allowing non-linear objectives) as well as its usefulness for generalising other important known tractable classes of ILP. On the positive side, we show that our result can be generalised from MILP to MIP with piece-wise linear separable convex objectives with integer breakpoints. On the negative side, we show that going even slightly beyond such objectives or considering other natural related tractable classes of ILP leads to unbounded fractionality. Finally, we show that restricting the structure of only the integral variables in the constraint matrix does not yield tractable special cases.

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Hodencq, Sacha, Mathieu Brugeron, Jaume Fitó, Lou Morriet, Benoit Delinchant, and Frédéric Wurtz. "OMEGAlpes, an Open-Source Optimisation Model Generation Tool to Support Energy Stakeholders at District Scale." Energies 14, no. 18 (September 18, 2021): 5928. http://dx.doi.org/10.3390/en14185928.

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Energy modelling is key in order to face the challenges of energy transition. There is a wide variety of modelling tools, depending on their purpose or study phase. This article summarises their main characteristics and highlights ones that are relevant when it comes to the preliminary design of energy studies at district scale. It introduces OMEGAlpes, a multi-carrier energy modelling tool to support stakeholders in the preliminary design of district-scale energy systems. OMEGAlpes is a Mixed-Integer Linear Programming (MILP) model generation tool for optimisation. It aims at making energy models accessible and understandable through its open-source development and the integration of energy stakeholders and their areas of responsibility into the models. A library of use cases developed with OMEGAlpes is presented and enables the presentation of past, current, and future development with the tool, opening the way for future developments and collaborations.

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Laing, Harry, Chris O'Malley, Anthony Browne, Tony Rutherford, Tony Baines, and Mark J. Willis. "Development of a biogas distribution model for a wastewater treatment plant: a mixed integer linear programming approach." Water Science and Technology 82, no. 12 (August 4, 2020): 2761–75. http://dx.doi.org/10.2166/wst.2020.363.

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Abstract In this paper, we propose a realistic model for gas distribution of an advanced municipal wastewater treatment works and through minimisation of the total cost of gas distribution we perform retrospective optimisation (RO) using historical plant data. This site is the first in the UK with a mixed operational strategy for biomethane produced on site: to burn in combined heat and power (CHP) engines to create electricity, burn in steam boilers for onsite steam use or inject the biomethane into the National Grid. In addition, natural gas can be imported to make up shortfalls in biomethane if required. Implemented using a novel mixed integer linear programming (MILP) approach, to ensure a fast and robust solution, our results indicate the plant operated optimally within accepted tolerance 98% of the time. However, improving plant robustness (such as reducing unexpected breakdown incidents) could yield a significant increase in gas revenue of 7.8%.

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Ismail Adeyemi ADEYEMO, Oluwadare Olatunde AKINROGUNDE, Sunday Adeleke SALIMON, and Oluwaseyi Wasiu ADEBIYI. "Adaptive particle swarm optimization approach to simultaneous reconfiguration and shunt capacitor allocation in radial distribution network." Global Journal of Engineering and Technology Advances 12, no. 3 (September 30, 2022): 077–94. http://dx.doi.org/10.30574/gjeta.2022.12.3.0162.

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Simultaneous radial distribution network reconfiguration (RDNR) and shunt capacitor allocation (SCA) is one of the compensation techniques that are used for getting an improved radial structure with reduced real power loss and enhanced voltage stability. This study presents a novel adaptive particle swarm optimisation (APSO) technique for the simultaneous RDNR and SCA, which is a complex and nonlinear optimisation problem. Unlike the conventional particle swarm optimization (PSO) technique in which an initial population of particles is randomly generated, the fundamental loop concept is used to populate the search space of APSO with the candidate branches for each tie switch (open branch) in the loop. The candidate branches are preselected with the graph theory. This is done to mitigate infeasible configurations in the optimization process and also to ensure that the conditions for radiality of the network are satisfied. The effectiveness of the proposed APSO technique for simultaneous RDNR and SCA is demonstrated on the standard IEEE 33-bus and Nigerian Ayepe 34-bus RDNs using six event cases. The efficacy of the proposed APSO technique is further validated with the comparison of the observed simulation results with the reported results of similar work implemented with established algorithms like improved binary particle swarm optimization (IBPSO), modified pollinated flower algorithm (MFPA) and mixed integer linear programming (MILP). The result of the comparative study reveals that the proposed APSO technique outperforms the selected algorithms in most of the considered event cases.

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Cheng Seong, Khor. "A Model-Based Optimisation Approach for Process Synthesis of Olefins from Petroleum with Application to the Malaysian Petrochemical Industry." ASM Science Journal 12 (December 30, 2019): 1–15. http://dx.doi.org/10.32802/asmscj.2019.393.

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The shale gas revolution has rekindled interest in olefins production due to the abundance of ethane as a raw material resource. However, the main technology still revolves around the cost-intensive distillation operation. Hence this work aims to investigate the economic optimisation of olefins synthesis from petroleum in the light of recent developments. A model-based approach is applied to determine the optimal sequencing of separation and reaction processes for a multi-component hydrocarbon mixture feed to produce mainly ethylene and propylene. a mixed-integer linear program (MILP) is formulated based on a superstructure that captures numerous plausible synthesis alternatives. The model comprises linear mass balance reactor representation and simple sharp distillation based on split fractions for product recovery. Integer binary variablesis used for selecting the task for equipment and continuous variables for representing the flowrate of each task. To expedite converging to an optimal solution of a least total annualised cost configuration, the formulation is appended with logical constraints on the design and structural specifications derived from heuristics based on practical knowledge and experience. The modelling approach on actual case studies based on two such petrochemical facilities operating in Malaysia is implemented. Additionally, the solution analysis is enriched with the investigation on a second- and third-best (suboptimal) configurations obtained through appropriate integer cuts as constraints to the model. The results show good agreement with existing plant configurations, thus substantiating the value and verification of the proposed model-based optimisation approach.

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Luo, Jiaxiang, and Jiyin Liu. "An MILP model and clustering heuristics for LED assembly optimisation on high-speed hybrid pick-and-place machines." International Journal of Production Research 52, no. 4 (August 15, 2013): 1016–31. http://dx.doi.org/10.1080/00207543.2013.828173.

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Hadri, Mohamed, Vincenzo Trovato, Agnes Bialecki, Bruno Merk, and Aiden Peakman. "Assessment of High-Electrification UK Scenarios with Varying Levels of Nuclear Power and Associated Post-Fault Behaviour." Energies 14, no. 6 (March 23, 2021): 1780. http://dx.doi.org/10.3390/en14061780.

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Renewable integration into the electricity system of Great Britain (GB) is causing considerable demand for additional flexibility from plants. In particular, a considerable share of this flexibility may be dispatched to secure post-fault transient frequency dynamics. Pursuant to the unprecedented changes to the traditional portfolio of generation sources, this work presents a detailed analysis of the potential system-level value of unlocking flexibility from nuclear electricity production. A rigorous enhanced mixed integer linear programming (MILP) unit commitment formulation is adopted to simulate several generation-demand scenarios where different layers of flexibility are associated to the operation of nuclear power plants. Moreover, the proposed optimisation model is able to assess the benefit of the large contribution to the system inertial response provided by nuclear power plants. This is made possible by considering a set of linearised inertia-dependent and multi-speed constraints on post fault frequency dynamics. Several case studies are introduced considering 2050 GB low-carbon scenarios. The value of operating the nuclear fleet under more flexible paradigms is assessed, including environmental considerations quantified in terms of system-level CO2 emissions’ reduction.

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Luo, Jiaxiang, Jiyin Liu, and Yueming Hu. "An MILP model and a hybrid evolutionary algorithm for integrated operation optimisation of multi-head surface mounting machines in PCB assembly." International Journal of Production Research 55, no. 1 (June 21, 2016): 145–60. http://dx.doi.org/10.1080/00207543.2016.1200154.

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Chen, Qian Matteo, Alberto Finzi, Toni Mancini, Igor Melatti, and Enrico Tronci. "MILP, Pseudo-Boolean, and OMT Solvers for Optimal Fault-Tolerant Placements of Relay Nodes in Mission Critical Wireless Networks*." Fundamenta Informaticae 174, no. 3-4 (September 28, 2020): 229–58. http://dx.doi.org/10.3233/fi-2020-1941.

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In critical infrastructures like airports, much care has to be devoted in protecting radio communication networks from external electromagnetic interference. Protection of such mission-critical radio communication networks is usually tackled by exploiting radiogoniometers: at least three suitably deployed radiogoniometers, and a gateway gathering information from them, permit to monitor and localise sources of electromagnetic emissions that are not supposed to be present in the monitored area. Typically, radiogoniometers are connected to the gateway through relay nodes. As a result, some degree of fault-tolerance for the network of relay nodes is essential in order to offer a reliable monitoring. On the other hand, deployment of relay nodes is typically quite expensive. As a result, we have two conflicting requirements: minimise costs while guaranteeing a given fault-tolerance. In this paper, we address the problem of computing a deployment for relay nodes that minimises the overall cost while at the same time guaranteeing proper working of the network even when some of the relay nodes (up to a given maximum number) become faulty (fault-tolerance). We show that, by means of a computation-intensive pre-processing on a HPC infrastructure, the above optimisation problem can be encoded as a 0/1 Linear Program, becoming suitable to be approached with standard Artificial Intelligence reasoners like MILP, PB-SAT, and SMT/OMT solvers. Our problem formulation enables us to present experimental results comparing the performance of these three solving technologies on a real case study of a relay node network deployment in areas of the Leonardo da Vinci Airport in Rome, Italy.

40

Sigalo, Marvin B., Saptarshi Das, Ajit C. Pillai, and Mohammad Abusara. "Real-Time Economic Dispatch of CHP Systems with Battery Energy Storage for Behind-the-Meter Applications." Energies 16, no. 3 (January 25, 2023): 1274. http://dx.doi.org/10.3390/en16031274.

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The use of combined heat and power (CHP) systems has recently increased due to their high combined efficiency and low emissions. Using CHP systems in behind-the-meter applications, however, can introduce some challenges. Firstly, the CHP system must operate in load-following mode to prevent power export to the grid. Secondly, if the load drops below a predefined threshold, the engine will operate at a lower temperature and hence lower efficiency, as the fuel is only half-burnt, creating significant emissions. The aforementioned issues may be solved by combining CHP with a battery energy storage system (BESS); however, the dispatch of CHP and BESS must be optimised. Offline optimisation methods based on load prediction will not prevent power export to the grid due to prediction errors. Therefore, this paper proposes a real-time Energy Management System (EMS) using a combination of Long Short-Term Memory (LSTM) neural networks, Mixed Integer Linear Programming (MILP), and Receding Horizon (RH) control strategy. The RH control strategy is suggested to reduce the impact of prediction errors and enable real-time implementation of the EMS exploiting actual generation and demand data on the day. Simulation results show that the proposed method can prevent power export to the grid and reduce the operational cost by 8.75% compared to the offline method.

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Erichsen, Gerrit, Tobias Zimmermann, and Alfons Kather. "Effect of Different Interval Lengths in a Rolling Horizon MILP Unit Commitment with Non-Linear Control Model for a Small Energy System." Energies 12, no. 6 (March 14, 2019): 1003. http://dx.doi.org/10.3390/en12061003.

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In this paper, a fixed electricity producer park of both a short- and long-term renewable energy storage (e.g., battery, power to gas to power) and a conventional power plant is combined with an increasing amount of installed volatile renewable power. For the sake of simplicity, the grid is designed as a single copper plate with island restrictions and constant demand of 1000 MW; the volatile input is deducted from scaled 15-min input data of German grid operators. A mixed integer linear programming model is implemented to generate an optimised unit commitment (UCO) for various scenarios and configurations using CPLEX® as the problem solver. The resulting unit commitment is input into a non-linear control model (NLC), which tries to match the plan of the UCO as closely as possible. Using the approach of a rolling horizon the result of the NLC is fed back to the interval of the next optimisation run. The problem’s objective is set to minimise CO2 emissions of the whole electricity producer park. Different interval lengths are tested with perfect foresight. The results gained with different interval lengths are compared to each other and to a simple heuristic approach. As non-linear control model a characteristic line model is used. The results show that the influence of the interval length is rather small, which leads to the conclusion that realistic forecast lengths of two days can be used to achieve not only a sufficient quality of solutions, but shorter computational times as well.

42

Zhao, Xiancong, Hao Bai, Xin Lu, Qi Shi, and Jiehai Han. "A MILP model concerning the optimisation of penalty factors for the short-term distribution of byproduct gases produced in the iron and steel making process." Applied Energy 148 (June 2015): 142–58. http://dx.doi.org/10.1016/j.apenergy.2015.03.046.

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43

Papaleonidas, Christos, Dimitrios V. Lyridis, Alexios Papakostas, and Dimitris Antonis Konstantinidis. "An innovative decision support tool for liquefied natural gas supply chain planning." Maritime Business Review 5, no. 1 (January 8, 2020): 121–36. http://dx.doi.org/10.1108/mabr-09-2019-0036.

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Purpose The purpose of this paper is to improve the tactical planning of the stakeholders of the midstream liquefied natural gas (LNG) supply chain, using an optimisation approach. The results can contribute to enhance the proactivity on significant investment decisions. Design/methodology/approach A decision support tool (DST) is proposed to minimise the operational cost of a fleet of vessels. Mixed integer linear programming (MILP) used to perform contract assignment combined with a genetic algorithm solution are the foundations of the DST. The aforementioned methods present a formulation of the maritime transportation problem from the scope of tramp shipping companies. Findings The validation of the DST through a realistic case study illustrates its potential in generating quantitative data about the cost of the midstream LNG supply chain and the annual operations schedule for a fleet of LNG vessels. Research limitations/implications The LNG transportation scenarios included assumptions, which were required for resource reasons, such as omission of stochasticity. Notwithstanding the assumptions made, it is to the authors’ belief that the paper meets its objectives as described above. Practical implications Potential practitioners may exploit the results to make informed decisions on the operation of LNG vessels, charter rate quotes and/or redeployment of existing fleet. Originality/value The research has a novel approach as it combines the creation of practical management tool, with a comprehensive mathematical modelling, for the midstream LNG supply chain. Quantifying future fleet costs is an alternative approach, which may improve the planning procedure of a tramp shipping company.

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Oliveira, Miguel Castro, Susana M. Vieira, Muriel Iten, and Henrique A. Matos. "Optimisation of Water-Energy Networks in Process Industry: Implementation of Non-Linear and Multi-Objective Models." Frontiers in Chemical Engineering 3 (January 20, 2022). http://dx.doi.org/10.3389/fceng.2021.750411.

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The improvement of water and energy use in the industrial sector is an important concern to improve the overall techno-economic performance of single plants. The most recent EU strategy for energy system integration has been treating these issues in the redaction of its first pillar, which is based on the relations between the promotion of circular economy and energy efficiency and it has as specific objectives the promotion of waste heat recovery and energy recovery from wastewater. Although on the context of research and industrial appliance both waste heat recovery and water recycling and reuse have been extensively explored, it is still verifiable a lack of comprehension and application of methods to simultaneously improve the use of both water and energy in a plant. In this work, two approaches for the solving of an optimisation problem related to the improvement of water and energy use in a process industry plant (three water-using processes) are implemented. These approaches consist on the development of a mixed-integer non-linear programming (MINLP) model and a multi-objective programming (MOP) model using the Python language. In addition, a complementary approach based on the development of a non-linear programming (NLP) model for further heat integration is also developed. Within the three applied methodologies (MINLP, MOP and integrated MINLP and NLP), the integrated MINLP and NLP model was the one in which the most favourable results were obtained, with 33.7% freshwater savings, 73.2% energy savings and 67.2% total economic savings.

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Liu, Chao, Yingjie Ma, Dongda Zhang, and Jie Li. "A feasible path-based branch and bound algorithm for strongly nonconvex MINLP problems." Frontiers in Chemical Engineering 4 (September 19, 2022). http://dx.doi.org/10.3389/fceng.2022.983162.

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In this paper, a feasible path-based branch and bound (B&B) algorithm is proposed to solve mixed-integer nonlinear programming problems with highly nonconvex nature through integration of the previously proposed hybrid feasible-path optimisation algorithm and the branch and bound method. The main advantage of this novel algorithm is that our previously proposed hybrid steady-state and time-relaxation-based optimisation algorithm is employed to solve a nonlinear programming (NLP) subproblem at each node during B&B. The solution from a parent node in B&B is used to initialize the NLP subproblems at the child nodes to improve computational efficiency. This approach allows circumventing complex initialisation procedure and overcoming difficulties in convergence of process simulation. The capability of the proposed algorithm is illustrated by several process synthesis and intensification problems using rigorous models.

46

Kaplan, Z., C. Çetek, and T. Saraç. "A multi-objective nonlinear integer programming model for mixed runway operations within the TMAs." Aeronautical Journal, July 20, 2023, 1–31. http://dx.doi.org/10.1017/aer.2023.50.

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Abstract Global air traffic demand has shown rapid growth for the last three decades. This growth led to more delays and congestion within terminal manoeuvring areas (TMAs) around major airports. The efficient use of airport capacities through the careful planning of air traffic flows is imperative to overcome these problems. In this study, a mixed-integer nonlinear programming (MINLP) model with a multi-objective approach was developed to solve the aircraft sequencing and scheduling problem for mixed runway operations within the TMAs. The model contains fuel cost functions based on airspeed, altitude, bank angle, and the aerodynamic characteristics of the aircraft. The optimisation problem was solved by using the $\varepsilon$ -constraint method where total delay and total fuel functions were simultaneously optimised. We tested the model with different scenarios generated based on the real traffic data of Istanbul Sabiha Gökçen Airport. The results revealed that the average total delay and average total fuel were reduced by 26.4% and 6.7%, respectively.

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Al-Ashhab, Mohamed Sayed, Abdulrahman Fayez Alhejaili, and Shadi M. Munshi. "Developing a multi-objective flexible job shop scheduling optimization model using Lexicographic procedure considering transportation time." Journal of Umm Al-Qura University for Engineering and Architecture, March 1, 2023. http://dx.doi.org/10.1007/s43995-023-00017-1.

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AbstractA multi-objective flexible job shop scheduling problem (FJSSP) that considers transportation time using mathematical programming is proposed to optimise three conflicting objectives: minimising makespan, total cost, and total lateness. The proposed model was developed and verified in three stages. In the first stage, only one objective was considered. The minimisation of the makespan and total cost was considered separately in the first stage. In the second stage, only two objectives were considered. In this stage, the minimisation of the makespan and total cost was considered instantaneously. In the third stage, a model was developed to optimise the three objectives. The proposed model was formulated using mixed-integer nonlinear programming (MINLP) and solved using the DICOPT solver based on general algebraic modelling system (GAMS) optimisation software. This model includes the transportation times between machines in the FJSSP, and the problem is called the “flexible job shop scheduling problem with transportation time” (TT-FJSSP). The proposed model gave better results in comparison with the other recent developed models. The effect of changing the maximum allowable deviation when optimising the three objectives was studied to achieve more-practical results.

48

Klinge, Thomas, Hugues Talbot, Ian Paddick, Sebastien Ourselin, Jamie R. McClelland, and Marc Modat. "Toward semi-automatic biologically effective dose treatment plan optimisation for Gamma Knife radiosurgery." Physics in Medicine & Biology, August 12, 2022. http://dx.doi.org/10.1088/1361-6560/ac8965.

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Abstract Objective: Dose-rate effects in Gamma Knife radiosurgery treatments can lead to varying biologically effective dose (BED) levels for the same physical dose. The non-convex BED model depends on the delivery sequence and creates a non-trivial treatment planning problem. We investigate the feasibility of employing inverse planning methods to generate treatment plans exhibiting desirable BED characteristics using the per iso-centre beam-on times and delivery sequence. Approach: We implement two dedicated optimisation algorithms. One approach relies on mixed-integer linear programming (MILP) using a purposely developed convex underestimator for the BED to mitigate local minima issues at the cost of computational complexity. The second approach (local optimisation) is faster and potentially usable in a clinical setting but more prone to local minima issues. It sequentially executes the beam-on time (quasi-Newton method) and sequence optimisation (local search algorithm). We investigate the trade-off between time to convergence and solution quality by evaluating the resulting treatment plans’ objective function values and clinical parameters. We also study the treatment time dependence of the initial and optimised plans using BED95 (BED delivered to 95% of the target volume) values. Main results: When optimising the beam-on times and delivery sequence, the local optimisation approach converges several orders of magnitude faster than the MILP approach (minutes vs hours-days) while typically reaching within 1.2% (0.02-2.08%) of the final objective function value. The quality parameters of the resulting treatment plans show no meaningful difference between the local and MILP optimisation approaches. The presented optimisation approaches remove the treatment time dependence observed in the original treatment plans, and the chosen objectives successfully promote more conformal treatments. Significance: We demonstrate the feasibility of using an inverse planning approach within a reasonable timeframe to ensure BED-based objectives are achieved across varying treatment times and highlight the prospect of further improvements in treatment plan quality.

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Zhou, Yufeng, Ying Gong, Xiaoqing Hu, and Changshi Liu. "Casualty scheduling optimisation with facility disruptions under grey information in early stage of post-earthquake relief." Grey Systems: Theory and Application, December 6, 2022. http://dx.doi.org/10.1108/gs-08-2022-0090.

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PurposeThe purpose of this paper is to propose a new casualty scheduling optimisation problem and to effectively treat casualties in the early stage of post-earthquake relief.Design/methodology/approachDifferent from previous studies, some new characteristics of this stage are considered, such as the grey uncertainty information of casualty numbers, the injury deterioration and the facility disruption scenarios. Considering these new characteristics, we propose a novel casualty scheduling optimisation model based on grey chance-constrained programming (GCCP). The model is formulated as a 0–1 mixed-integer nonlinear programming (MINP) model. An improved particle swarm optimisation (PSO) algorithm embedded in a grey simulation technique is proposed to solve the model.FindingsA case study of the Lushan earthquake in China is given to verify the effectiveness of the model and algorithm. The results show that (1) considering the facility disruption in advance can improve the system reliability, (2) the grey simulation technology is more suitable for dealing with the grey uncertain information with a wider fluctuation than the equal-weight whitening method and (3) the authors' proposed PSO is superior to the genetic algorithm and immune algorithm.Research limitations/implicationsThe casualty scheduling problem in the emergency recovery stage of post-earthquake relief could be integrated with our study to further enhance the research value of this paper.Practical implicationsConsidering the facility disruption in advance is beneficial to treat more patients. Considering the facility disruption in the design stage of the emergency logistics network can improve the reliability of the system.Originality/value(1) The authors propose a new casualty scheduling optimisation problem based on GCCP in the early stage of post-earthquake relief. The proposed problem considers many new characteristics in this stage. To the best of the authors' knowledge, the authors are the first to use the GCCP to study the casualty scheduling problem under the grey information. (2) A MINP model is established to formulate the proposed problem. (3) An improved integer-encoded particle swarm optimisation (PSO) algorithm embedded grey simulation technique is designed in this paper.

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Anderson, Lovis, Mark Turner, and Thorsten Koch. "Generative deep learning for decision making in gas networks." Mathematical Methods of Operations Research, April 19, 2022. http://dx.doi.org/10.1007/s00186-022-00777-x.

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AbstractA decision support system relies on frequent re-solving of similar problem instances. While the general structure remains the same in corresponding applications, the input parameters are updated on a regular basis. We propose a generative neural network design for learning integer decision variables of mixed-integer linear programming (MILP) formulations of these problems. We utilise a deep neural network discriminator and a MILP solver as our oracle to train our generative neural network. In this article, we present the results of our design applied to the transient gas optimisation problem. The trained generative neural network produces a feasible solution in 2.5s, and when used as a warm start solution, decreases global optimal solution time by 60.5%.

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