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Journal articles on the topic 'Mixed-integer model'

Author: Grafiati

Published: 3 June 2025

Last updated: 13 July 2025

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1

Westerlund, Joakim, Mattias Hästbacka, Sebastian Forssell, and Tapio Westerlund. "Mixed-Time Mixed-Integer Linear Programming Scheduling Model." Industrial & Engineering Chemistry Research 46, no. 9 (2007): 2781–96. http://dx.doi.org/10.1021/ie060991a.

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2

Wu, Jie, and Zhixiang Zhou. "A mixed-objective integer DEA model." Annals of Operations Research 228, no. 1 (2011): 81–95. http://dx.doi.org/10.1007/s10479-011-0938-8.

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3

Östermark, Ralf, Hans Skrifvars, and Tapio Westerlund. "A nonlinear mixed integer multiperiod firm model." International Journal of Production Economics 67, no. 2 (2000): 183–99. http://dx.doi.org/10.1016/s0925-5273(00)00019-0.

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4

Navarro, H., S. Nooshabadi, and J. A. Montiel-Nelson. "Adder model for mixed integer linear programming." Electronics Letters 45, no. 7 (2009): 348. http://dx.doi.org/10.1049/el.2009.3637.

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5

Teunissen, Peter J. G. "Ambiguity-Resolved Model Tests for Carrier-Phase GNSS." Applied Sciences 15, no. 7 (2025): 3531. https://doi.org/10.3390/app15073531.

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Although the theory of mixed-integer inference is well developed for GNSS parameter estimation, such is not yet the case for the validation and monitoring of mixed-integer GNSS carrier-phase models. It is the goal of this research to contribute to this field by introducing a class of mixed-integer model (MIM) tests for carrier-phase GNSS. Members from this class and their distributional properties are worked out for different model validation applications relevant to GNSS, such as detection, identification, significance testing, and integer testing. The power performance of the various tests is characterized, thereby showing how they are capable of significantly outperforming the customary ambiguity-float tests.

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6

Walker, H. Douglas, and Stephen W. Preiss. "Operational Planning Using Mixed Integer Programming." Forestry Chronicle 64, no. 6 (1988): 485–88. http://dx.doi.org/10.5558/tfc64485-6.

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A mathematical model was constructed and used to help plan five-year timber harvesting and delivery activities from an industrially managed public forest in Ontario. Harvest systems, harvest levels, and wood flows from compartments within the forest to various mills and delivery points were scheduled to minimize costs. The mathematical structure of the model may suggest applications to related forest planning problems. The model was useful in addressing the planning problem, and model results were used within the company's planning process. Data accuracy problems precluded assessing definitively the expected cost savings resulting from model use.

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7

Williams, H. P., and R. G. Jeroslow. "Logic-Based Decision Support: Mixed Integer Model Formulation." Journal of the Operational Research Society 41, no. 4 (1990): 357. http://dx.doi.org/10.2307/2583807.

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8

Williams, H. P. "Logic-Based Decision Support: Mixed Integer Model Formulation." Journal of the Operational Research Society 41, no. 4 (1990): 357–58. http://dx.doi.org/10.1057/jors.1990.58.

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9

Sayeed, Q. A., and E. C. De Meter. "Mixed-Integer Programming Model for Fixture Layout Optimization." Journal of Manufacturing Science and Engineering 121, no. 4 (1999): 701–8. http://dx.doi.org/10.1115/1.2833111.

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Workpiece deformation during machining is a significant source of machined feature geometric error. This paper presents a linear, mixed integer programming model for determining the optimal locations of locator buttons, supports, and their opposing clamps for minimizing the affect of static workpiece deformation on machined feature geometric error. This model operates on discretized candidate regions as opposed to continuous candidate regions. In addition it utilizes a condensed FEA model of the workpiece in order to minimize model size and computation expense. This model has two advantages over existing nonlinear programming (NLP) formulations. The first is its ability to solve problems in which fixture elements can be placed over multiple regions. The second is that a global optimal solution to this model can be obtained using commercially available software.

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10

Xu, Gang, and Lazaros G. Papageorgiou. "A mixed integer optimisation model for data classification." Computers & Industrial Engineering 56, no. 4 (2009): 1205–15. http://dx.doi.org/10.1016/j.cie.2008.07.012.

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11

Srinivasan, K., T. R. Neelakantan, P. Shyam Narayan, and C. Nagarajukumar. "Mixed-Integer Programming Model for Reservoir Performance Optimization." Journal of Water Resources Planning and Management 125, no. 5 (1999): 298–301. http://dx.doi.org/10.1061/(asce)0733-9496(1999)125:5(298).

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12

Achterberg, Tobias, Robert E. Bixby, Zonghao Gu, Edward Rothberg, and Dieter Weninger. "Presolve Reductions in Mixed Integer Programming." INFORMS Journal on Computing 32, no. 2 (2020): 473–506. http://dx.doi.org/10.1287/ijoc.2018.0857.

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Mixed integer programming has become a very powerful tool for modeling and solving real-world planning and scheduling problems, with the breadth of applications appearing to be almost unlimited. A critical component in the solution of these mixed integer programs is a set of routines commonly referred to as presolve. Presolve can be viewed as a collection of preprocessing techniques that reduce the size of and, more importantly, improve the “strength” of the given model formulation, that is, the degree to which the constraints of the formulation accurately describe the underlying polyhedron of integer-feasible solutions. As our computational results will show, presolve is a key factor in the speed with which we can solve mixed integer programs and is often the difference between a model being intractable and solvable, in some cases easily solvable. In this paper we describe the presolve functionality in the Gurobi commercial mixed integer programming code. This includes an overview, or taxonomy of the different methods that are employed, as well as more-detailed descriptions of several of the techniques, with some of them appearing, to our knowledge, for the first time in the literature.

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13

Kwon, Obin, Seunghyun Lee, and Jaeho Son. "Advanced Time-Cost Trade-Off Model using Mixed Integer Programming." Korean Journal of Construction Engineering and Management 16, no. 6 (2015): 53–62. http://dx.doi.org/10.6106/kjcem.2015.16.6.053.

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14

Melachrinoudis, Emanuel, David Rumpf, and Ramon Venegas. "Mixed integer programming improves spray operation planning." Canadian Journal of Forest Research 17, no. 12 (1987): 1602–8. http://dx.doi.org/10.1139/x87-245.

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The spruce budworm is the most destructive pest of spruce and fir forests in the northern United States and Canada. To counter the insect, the Maine Forest Service has, for the last decade (1975–1985), conducted a large-scale spray operation in Maine's northern and eastern forests. The operation involves the use of several types of aircraft flying from several airfields to spray hundreds of target areas. A minimum cost mixed integer programming model was developed to determine the airfield locations, the number of types of aircraft, and the flight plans to best conduct the spray operation. The model extends and complements a microcomputer linear programming model previously developed for and used by the Maine Forest Service. The savings obtained by the mixed integer programming model can be seen by the included comparison of their results.

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15

Shulzhenko, Sergii. "GENERATION UNIT COMMITMENT MIXED INTEGER LINEAR MODEL FOR SIMULTANEOUS HEAT AND ELECTRIC DAILY LOAD COVERING." System Research in Energy 2023, no. 1 (2023): 25–34. http://dx.doi.org/10.15407/srenergy2023.01.025.

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The unit commitment problem nowadays is widely used in the electric power sector. The problem was first time formulated in the 1940-s and still developing both methodologically and by including an additional number of technologies each of which has a different unique mathematical treatment corresponding to the specific technology's behavior. The common characteristic of the problem such as that is dedicated to the electricity production sector, hence the mathematical formulation is following pure electricity sector transformation but during the last years the Power-to-X technologies are implemented and their further development is expected in the future. This requires the advancement or at least modification of the problem formulation to meet possible exchange and usage between different types of energy within one integrated power system. The goal of the article is to further development of the existing versions of the unit commitment problem, which are dedicated to the operation of the generation in the power system by implementing additional equations allowing contemplation of the heat energy-producing technologies which are dedicated to cover a heat-energy load of the district heating systems. This should allow for conducting comprehensive studies of the simultaneous operation of electric- and heat-generating technologies to meet the energy demand of local energy systems, which is important for designing distributed generation mix, for example at a municipal level. The proposed mixed integer linear generation unit commitment model for simultaneous heat and electric daily load covering is described in the article. The proposed model in addition to the pure electric power balance also meets heat load using only-heat technologies (fuel boilers), combined heat and power units, and also industrial-scale electric boilers - which are converting electricity to heat energy. Keywords: mixed integer linear model, unit commitment problem, integrated power system, electric boilers, power-to-X technologies, conventional electricity generating technologies.

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16

Sarker, B. R., and H. Pan. "Designing a mixed-model, open-station assembly line using mixed-integer programming." Journal of the Operational Research Society 52, no. 5 (2001): 545–58. http://dx.doi.org/10.1057/palgrave.jors.2601118.

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17

Sarker, BR, and H. Pan. "Designing a mixed-model, open-station assembly line using mixed-integer programming." Journal of the Operational Research Society 52, no. 5 (2001): 545–58. http://dx.doi.org/10.1038/sj.jors.2601118.

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18

SUN, MINGHE. "A MIXED INTEGER PROGRAMMING MODEL FOR MULTIPLE-CLASS DISCRIMINANT ANALYSIS." International Journal of Information Technology & Decision Making 10, no. 04 (2011): 589–612. http://dx.doi.org/10.1142/s0219622011004476.

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A mixed integer programming model is proposed for multiple-class discriminant and classification analysis. When multiple discriminant functions, one for each class, are constructed with the mixed integer programming model, the number of misclassified observations in the sample is minimized. This model is an extension of the linear programming models for multiple-class discriminant analysis but may be considered as a generalization of mixed integer programming formulations for two-class classification analysis. Properties of the model are studied. The model is immune from any difficulties of many mathematical programming formulations for two-class classification analysis, such as nonexistence of optimal solutions, improper solutions, and instability under linear data transformation. In addition, meaningful discriminant functions can be generated under conditions where other techniques fail. Examples are provided. Results on publically accessible datasets show that this model is very effective in generating powerful discriminant functions.

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19

Bahiense, L., G. C. Oliveira, M. Pereira, and S. Granville. "A Mixed Integer Disjunctive Model for Transmission Network Expansion." IEEE Power Engineering Review 21, no. 8 (2001): 60. http://dx.doi.org/10.1109/mper.2001.4311560.

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20

RAMLOGAN, R. N., and I. C. GOULTER. "MIXED INTEGER MODEL FOR RESOURCE ALLOCATION IN PROJECT MANAGEMENT." Engineering Optimization 15, no. 2 (1989): 97–111. http://dx.doi.org/10.1080/03052158908941145.

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21

Martin, R. Kipp. "Using separation algorithms to generate mixed integer model reformulations." Operations Research Letters 10, no. 3 (1991): 119–28. http://dx.doi.org/10.1016/0167-6377(91)90028-n.

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22

Sigurdardottir, Silja R., Agust Valfells, Halldor Palsson, and Hlynur Stefansson. "Mixed integer optimization model for utilizing a geothermal reservoir." Geothermics 55 (May 2015): 171–81. http://dx.doi.org/10.1016/j.geothermics.2015.01.006.

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23

Lee, Amy H. I., and He‐Yau Kang. "A mixed 0‐1 integer programming for inventory model." Kybernetes 37, no. 1 (2008): 66–82. http://dx.doi.org/10.1108/03684920810850998.

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24

Bahiense, L., G. C. Oliveira, M. Pereira, and S. Granville. "A mixed integer disjunctive model for transmission network expansion." IEEE Transactions on Power Systems 16, no. 3 (2001): 560–65. http://dx.doi.org/10.1109/59.932295.

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25

Khezrimotlagh, Dariush, Shaharuddin Salleh, and Zahra Mohsenpour. "A new robust mixed integer-valued model in DEA." Applied Mathematical Modelling 37, no. 24 (2013): 9885–97. http://dx.doi.org/10.1016/j.apm.2013.05.031.

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26

Gourdin, Eric. "A Mixed Integer Model for the Sparsest Cut problem." Electronic Notes in Discrete Mathematics 36 (August 2010): 111–18. http://dx.doi.org/10.1016/j.endm.2010.05.015.

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27

Contreras, J., H. Lara, and G. Nouel-Borges. "A mixed integer nonlinear programming model for biomass production." Operational Research 19, no. 1 (2016): 39–57. http://dx.doi.org/10.1007/s12351-016-0283-4.

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28

Nam, K., A. Chaudhury, and H. Raghav Rao. "A mixed integer model of bidding strategies for outsourcing." European Journal of Operational Research 87, no. 2 (1995): 257–73. http://dx.doi.org/10.1016/0377-2217(94)00147-5.

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29

Glen, J. J. "A mixed integer programming model for fertiliser policy evaluation." European Journal of Operational Research 35, no. 2 (1988): 165–71. http://dx.doi.org/10.1016/0377-2217(88)90025-2.

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30

Shaftel, Timothy L., and Beverley M. Wilson. "A mixed-integer linear programming decision model for aquaculture." Managerial and Decision Economics 11, no. 1 (1990): 31–38. http://dx.doi.org/10.1002/mde.4090110105.

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31

Levin, Todd, and Valerie M. Thomas. "A mixed-integer optimization model for electricity infrastructure development." Energy Systems 4, no. 1 (2012): 79–98. http://dx.doi.org/10.1007/s12667-012-0067-8.

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32

King, Marvin L., David R. Galbreath, Alexandra M. Newman, and Amanda S. Hering. "Combining regression and mixed-integer programming to model counterinsurgency." Annals of Operations Research 292, no. 1 (2019): 287–320. http://dx.doi.org/10.1007/s10479-019-03420-x.

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33

Febianti, Fevi, and Ihda Hasbiyati. "OPTIMAL INVENTORY POLICY USING ASSIGNMENT MODEL AS MIXED INTEGER LINEAR PROGRAMMING PROBLEM." Journal of Mathematical Sciences and Optimization 1, no. 1 (2024): 17–24. http://dx.doi.org/10.31258/jomso.1.1.17-24.

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This article discusses the assignment model as a mixed integer linear programming problem used in determining inventory policy for the multi-item case with backorder using the branch-and-bound method. The assignment model used must meet the specified constraints. The LINGO 18.0 application is used to solve mixed integer linear programming problems from the inventory problem assignment model. The optimal solution obtained from the assignment model uses the LINGO 18.0 application, which is based on the branch-and-bound method compared to the EOQ model with backorder. The results obtained from the comparison of these two models can be concluded to show that the inventory problem provides more optimal value using the assignment model as a mixed integer linear programming problem compared to using the EOQ model with backorder.

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34

Mortezaei, Navid, Zulkifli Norzima, S. H. Tang, and Mohd Yusuff Rosnah. "Lot Streaming and Preventive Maintenance in a Multiple Product Permutation Flow Shop with Intermingling." Applied Mechanics and Materials 564 (June 2014): 689–93. http://dx.doi.org/10.4028/www.scientific.net/amm.564.689.

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A mathematical model forlot streaming problem with preventive maintenance was proposed. A mixed-integer linear model for multiple-product lot streaming problems was also developed. Mixed-integer programming formulation was presented which will enable the user to identify optimal sublot sizes and sequences simultaneously. Two situations were considered:1) all machines were available, and 2) all machines needed preventive maintenance tasks. For both situations a new mixed-integer formulation was developed. To demonstrate the practicality of the proposed model, numerical example was used. It showed that the percentage of make span reduction due to lot streaming in permutation flow shop is 54% when compared to consistent sublots with intermingling case.

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35

Molina, Alexander, Oscar Danilo Montoya, and Walter Gil-González. "Exact minimization of the energy losses and the CO2 emissions in isolated DC distribution networks using PV sources." DYNA 88, no. 217 (2021): 178–84. http://dx.doi.org/10.15446/dyna.v88n217.93099.

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This paper addresses the optimal location and sizing of photovoltaic (PV) sources in isolated direct current (DC) electrical networks, considering time-varying load and renewable generation curves. The mathematical formulation of this problem corresponds to mixed-integer nonlinear programming (MINLP), which is reformulated via mixed-integer convex optimization: This ensures the global optimum solving the resulting optimization model via branch & bound and interior-point methods. The main idea of including PV sources in the DC grid is to minimize the daily energy losses and greenhouse emissions produced by diesel generators in isolated areas. The GAMS package is employed to solve the MINLP model, using mixed and integer variables; also, the CVX and MOSEK solvers are used to obtain solutions from the proposed mixed-integer convex model in the MATLAB. Numerical results demonstrate important reductions in the daily energy losses and the harmful gas emissions when PV sources are optimally integrated into DC grid.

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36

Obaid, Hamoud Bin, Theodore B. Trafalis, Mastoor M. Abushaega, Abdulhadi Altherwi, and Ahmed Hamzi. "Optimizing Dynamic Evacuation Using Mixed-Integer Linear Programming." Mathematics 13, no. 1 (2024): 12. https://doi.org/10.3390/math13010012.

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This study presents a new approach to optimize the dynamic evacuation process through a dynamic traffic assignment model formulated using mixed-integer linear programming (MILP). The model approximates the travel time for evacuee groups with a piecewise linear function that accounts for variations in travel time due to load-dependent factors. Significant delays are transferred to subsequent groups to simulate delay propagation. The primary objective is to minimize the network clearance time—the total time required for the last group of evacuees to reach safety from the start of the evacuation. Given the model’s computational intensity, a simplified version is introduced for comparison. Both the original and simplified models are tested on small networks and benchmarked against the Cell Transmission Model, a well-regarded method in dynamic traffic assignment literature. Additional objectives, including average travel time and average evacuation time, are explored. A sensitivity analysis is conducted to assess how varying the number of evacuee groups impacts model outcomes.

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37

Ferber, Aaron, Bryan Wilder, Bistra Dilkina, and Milind Tambe. "MIPaaL: Mixed Integer Program as a Layer." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 02 (2020): 1504–11. http://dx.doi.org/10.1609/aaai.v34i02.5509.

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Machine learning components commonly appear in larger decision-making pipelines; however, the model training process typically focuses only on a loss that measures average accuracy between predicted values and ground truth values. Decision-focused learning explicitly integrates the downstream decision problem when training the predictive model, in order to optimize the quality of decisions induced by the predictions. It has been successfully applied to several limited combinatorial problem classes, such as those that can be expressed as linear programs (LP), and submodular optimization. However, these previous applications have uniformly focused on problems with simple constraints. Here, we enable decision-focused learning for the broad class of problems that can be encoded as a mixed integer linear program (MIP), hence supporting arbitrary linear constraints over discrete and continuous variables. We show how to differentiate through a MIP by employing a cutting planes solution approach, an algorithm that iteratively tightens the continuous relaxation by adding constraints removing fractional solutions. We evaluate our new end-to-end approach on several real world domains and show that it outperforms the standard two phase approaches that treat prediction and optimization separately, as well as a baseline approach of simply applying decision-focused learning to the LP relaxation of the MIP. Lastly, we demonstrate generalization performance in several transfer learning tasks.

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38

Sadowski, Krzysztof L., Dirk Thierens, and Peter A. N. Bosman. "GAMBIT: A Parameterless Model-Based Evolutionary Algorithm for Mixed-Integer Problems." Evolutionary Computation 26, no. 1 (2018): 117–43. http://dx.doi.org/10.1162/evco_a_00206.

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Learning and exploiting problem structure is one of the key challenges in optimization. This is especially important for black-box optimization (BBO) where prior structural knowledge of a problem is not available. Existing model-based Evolutionary Algorithms (EAs) are very efficient at learning structure in both the discrete, and in the continuous domain. In this article, discrete and continuous model-building mechanisms are integrated for the Mixed-Integer (MI) domain, comprising discrete and continuous variables. We revisit a recently introduced model-based evolutionary algorithm for the MI domain, the Genetic Algorithm for Model-Based mixed-Integer opTimization (GAMBIT). We extend GAMBIT with a parameterless scheme that allows for practical use of the algorithm without the need to explicitly specify any parameters. We furthermore contrast GAMBIT with other model-based alternatives. The ultimate goal of processing mixed dependences explicitly in GAMBIT is also addressed by introducing a new mechanism for the explicit exploitation of mixed dependences. We find that processing mixed dependences with this novel mechanism allows for more efficient optimization. We further contrast the parameterless GAMBIT with Mixed-Integer Evolution Strategies (MIES) and other state-of-the-art MI optimization algorithms from the General Algebraic Modeling System (GAMS) commercial algorithm suite on problems with and without constraints, and show that GAMBIT is capable of solving problems where variable dependences prevent many algorithms from successfully optimizing them.

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39

Berthold, Timo, and Gregor Hendel. "Learning To Scale Mixed-Integer Programs." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (2021): 3661–68. http://dx.doi.org/10.1609/aaai.v35i5.16482.

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Many practical applications require the solution of numerically challenging linear programs (LPs) and mixed integer programs (MIPs). Scaling is a widely used preconditioning technique that aims at reducing the error propagation of the involved linear systems, thereby improving the numerical behavior of the dual simplex algorithm and, consequently, LP-based branch-and-bound. A reliable scaling method often makes the difference whether these problems can be solved correctly or not. In this paper, we investigate the use of machine learning to choose at the beginning of the solution process between two common scaling methods: Standard scaling and Curtis-Reid scaling. The latter often, but not always, leads to a more robust solution process, but may suffer from longer solution times. Rather than training for overall solution time, we propose to use the attention level of a MIP solution process as a learning label. We evaluate the predictive power of a random forest approach and a linear regressor that learns the (square-root of the) difference in attention level. It turns out that the resulting classification not only reduces various types of numerical errors by large margins, but it also improves the performance of the dual simplex algorithm. The learned model has been implemented within the FICO Xpress MIP solver and it is used by default since release 8.9, May 2020, to determine the scaling algorithm Xpress applies before solving an LP or a MIP.

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40

Benkherouf, Lakdere, and Dalal Boushehri. "Optimal Policies for a Finite-Horizon Production Inventory Model." Advances in Operations Research 2012 (2012): 1–16. http://dx.doi.org/10.1155/2012/768929.

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This paper is concerned with the problem of finding the optimal production schedule for an inventory model with time-varying demand and deteriorating items over a finite planning horizon. This problem is formulated as a mixed-integer nonlinear program with one integer variable. The optimal schedule is shown to exist uniquely under some technical conditions. It is also shown that the objective function of the nonlinear obtained from fixing the integrality constraint is convex as a function of the integer variable. This in turn leads to a simple procedure for finding the optimal production plan.

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41

Luo, X. G., Z. C. Zhang, C. K. Kwong, and J. F. Tang. "Share-of-Surplus Product Line Optimisation with Price Levels." Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/389749.

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Kraus and Yano (2003) established the share-of-surplus product line optimisation model and developed a heuristic procedure for this nonlinear mixed-integer optimisation model. In their model, price of a product is defined as a continuous decision variable. However, because product line optimisation is a planning process in the early stage of product development, pricing decisions usually are not very precise. In this research, a nonlinear integer programming share-of-surplus product line optimization model that allows the selection of candidate price levels for products is established. The model is further transformed into an equivalent linear mixed-integer optimisation model by applying linearisation techniques. Experimental results in different market scenarios show that the computation time of the transformed model is much less than that of the original model.

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42

Bajcinca, N., M. V. A. Pedrosa, and V. Yfantis. "Mixed-Integer Model Predictive Control of Hybrid Impulsive Linear Systems." IFAC-PapersOnLine 53, no. 2 (2020): 6775–80. http://dx.doi.org/10.1016/j.ifacol.2020.12.327.

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43

Park, Yong Kuk, Min Goo Lee, Kyung Kwon Jung, and Young-Jin Won. "Design of Mixed Integer Linear Programming Model for Transportation Planning." Journal of the Institute of Electronics and Information Engineers 53, no. 11 (2016): 166–74. http://dx.doi.org/10.5573/ieie.2016.53.11.166.

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44

Khoo, Wooi Chen, Seng Huat Ong, and Biswas Atanu. "Coherent Forecasting for a Mixed Integer-Valued Time Series Model." Mathematics 10, no. 16 (2022): 2961. http://dx.doi.org/10.3390/math10162961.

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In commerce, economics, engineering and the sciences, quantitative methods based on statistical models for forecasting are very useful tools for prediction and decision. There is an abundance of papers on forecasting for continuous-time series but relatively fewer papers for time series of counts which require special consideration due to the integer nature of the data. A popular method for modelling is the method of mixtures which is known for its flexibility and thus improved prediction capability. This paper studies the coherent forecasting for a flexible stationary mixture of Pegram and thinning (MPT) process, and develops the likelihood-based asymptotic distribution. Score functions and the Fisher information matrix are presented. Numerical studies are used to assess the performance of the forecasting methods. Also, a comparison is made with existing discrete-valued time series models. Finally, the practical application is illustrated with two sets of real data. It is shown that the mixture model provides good forecasting performance.

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45

Chang, Leiya, Xiufang Liu, Dehui Wang, Yingchuan Jing, and Chenlong Li. "First-order random coefficient mixed-thinning integer-valued autoregressive model." Journal of Computational and Applied Mathematics 410 (August 2022): 114222. http://dx.doi.org/10.1016/j.cam.2022.114222.

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46

Kim, Jaehee, and Hwandon Jun. "Restricted Water Supply Planning by Using Mixed Integer Programming Model." Korean Society of Hazard Mitigation 17, no. 3 (2017): 97–108. http://dx.doi.org/10.9798/kosham.2017.17.3.97.

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47

Efratani Damanik, Sarintan, Agus Purwoko, and Rahmat Hidayat. "A mixed integer programming model for forest harvest scheduling problem." Journal of Physics: Conference Series 1175 (March 2019): 012044. http://dx.doi.org/10.1088/1742-6596/1175/1/012044.

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48

Kaufman, David E., Jason Nonis, and Robert L. Smith. "A mixed integer linear programming model for dynamic route guidance." Transportation Research Part B: Methodological 32, no. 6 (1998): 431–40. http://dx.doi.org/10.1016/s0191-2615(98)00013-7.

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Tao, Shaohui, Yanjun Xue, Shuguang Xiang, Weiwei Jiang, and Rongqiang Li. "Tighter Mixed-Integer Quadratic Programming Model for Process Data Rectification." Industrial & Engineering Chemistry Research 59, no. 21 (2020): 10061–71. http://dx.doi.org/10.1021/acs.iecr.0c00025.

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Gökċen, Hadı, and Erdal Erel. "BINARY INTEGER FORMULATION FOR MIXED-MODEL ASSEMBLY LINE BALANCING PROBLEM." Computers & Industrial Engineering 34, no. 2 (1998): 451–61. http://dx.doi.org/10.1016/s0360-8352(97)00142-3.

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