Relevant bibliographies by topics / 0–1 integer linear programming (ILP)

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic '0–1 integer linear programming (ILP)'

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Published: 3 June 2025
Last updated: 19 July 2025

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Journal articles on the topic "0–1 integer linear programming (ILP)"

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Arefin, Md Rajib, Touhid Hossain, and Md Ainul Islam. "Additive Algorithm for Solving 0-1 Integer Linear Fractional Programming Problem." Dhaka University Journal of Science 61, no. 2 (2013): 173–78. http://dx.doi.org/10.3329/dujs.v61i2.17066.

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In this paper, we present additive algorithm for solving a class of 0-1 integer linear fractional programming problems (0-1 ILFP) where all the coefficients at the numerator of the objective function are of same sign. The process is analogous to the process of solving 0-1 integer linear programming (0-1 ILP) problem but the condition of fathoming the partial feasible solution is different from that of 0-1 ILP. The procedure has been illustrated by two examples. DOI: http://dx.doi.org/10.3329/dujs.v61i2.17066 Dhaka Univ. J. Sci. 61(2): 173-178, 2013 (July)
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Ahmed, Waleed, and Ali Muhammad Ali Rushdi. "1 A Novel Minimization Method for Sensor Deployment Via Heuristic 2-Sat Solution." Sir Syed Research Journal of Engineering & Technology 1, no. 1 (2018): 7. http://dx.doi.org/10.33317/ssurj.v1i1.37.

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The tasks of guard placement or sensor deployment in an art gallery, a museum or in the corridors of public and security buildings pose the same problem, which requires placing the guards or sensors so as to cover a specified set of nodes with a minimum number of sensors or guards, thereby reducing the overall cost of the system as well assist power consumption. Generally, minimization can be done using optimization techniques such as linear programming, but in case of sensor deployment or guard placement there is a need either to place or not to place the sensor or guard, and hence only Boolean or binary values are used. Therefore, in order to optimize such a problem, we use the special case of linear integer programming known as Boolean integer linear programming (0-1 ILP). Other algorithms like Pseudo-Boolean SAT Solvers can also be used for the minimization purpose. In this paper, we introduce these minimization algorithms for the sensor deployment problem. We also contribute a greed-based heuristic, which utilizes the fact that the pertinent propositional formulas have variables of purely un-complemented literals. This heuristic has much less computational cost compared to those of 0-1 ILP and the Pseudo-Boolean SAT Solvers.
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Ahmed, Waleed, and Ali Muhammad Ali Rushdi. "A Novel Minimization Method for Sensor Deployment Via Heuristic 2-Sat Solution." Sir Syed University Research Journal of Engineering & Technology 7, no. 1 (2018): 7. http://dx.doi.org/10.33317/ssurj.v7i1.37.

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The tasks of guard placement or sensor deployment in an art gallery, a museum or in the corridors of public and security buildings pose the same problem, which requires placing the guards or sensors so as to cover a specified set of nodes with a minimum number of sensors or guards, thereby reducing the overall cost of the system as well assist power consumption. Generally, minimization can be done using optimization techniques such as linear programming, but in case of sensor deployment or guard placement there is a need either to place or not to place the sensor or guard, and hence only Boolean or binary values are used. Therefore, in order to optimize such a problem, we use the special case of linear integer programming known as Boolean integer linear programming (0-1 ILP). Other algorithms like Pseudo-Boolean SAT Solvers can also be used for the minimization purpose. In this paper, we introduce these minimization algorithms for the sensor deployment problem. We also contribute a greed-based heuristic, which utilizes the fact that the pertinent propositional formulas have variables of purely un-complemented literals. This heuristic has much less computational cost compared to those of 0-1 ILP and the Pseudo-Boolean SAT Solvers.
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Ahmed, Waleed, and Ali Muhammad Ali Rushdi. "A Novel Minimization Method for Sensor Deployment Via Heuristic 2-Sat Solution." Sir Syed University Research Journal of Engineering & Technology 7, no. 1 (2018): 7. http://dx.doi.org/10.33317/ssurj.37.

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The tasks of guard placement or sensor deployment in an art gallery, a museum or in the corridors of public and security buildings pose the same problem, which requires placing the guards or sensors so as to cover a specified set of nodes with a minimum number of sensors or guards, thereby reducing the overall cost of the system as well assist power consumption. Generally, minimization can be done using optimization techniques such as linear programming, but in case of sensor deployment or guard placement there is a need either to place or not to place the sensor or guard, and hence only Boolean or binary values are used. Therefore, in order to optimize such a problem, we use the special case of linear integer programming known as Boolean integer linear programming (0-1 ILP). Other algorithms like Pseudo-Boolean SAT Solvers can also be used for the minimization purpose. In this paper, we introduce these minimization algorithms for the sensor deployment problem. We also contribute a greed-based heuristic, which utilizes the fact that the pertinent propositional formulas have variables of purely un-complemented literals. This heuristic has much less computational cost compared to those of 0-1 ILP and the Pseudo-Boolean SAT Solvers.
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Ahmed, Osman, Sagahyroon Assim, Aburukba Raafat, and Aloul Fadi. "Optimization of energy consumption in cloud computing datacenters." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 1 (2021): 686–98. https://doi.org/10.11591/ijece.v11i1.pp686-698.

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Cloud computing has emerged as a practical paradigm for providing IT resources, infrastructure and services. This has led to the establishment of datacenters that have substantial energy demands for their operation. This work investigates the optimization of energy consumption in cloud datacenter using energy efficient allocation of tasks to resources. The work seeks to develop formal optimization models that minimize the energy consumption of computational resources and evaluates the use of existing optimization solvers in testing these models. Integer linear programming (ILP) techniques are used to model the scheduling problem. The objective is to minimize the total power consumed by the active and idle cores of the servers’ CPUs while meeting a set of constraints. Next, we use these models to carry out a detailed performance comparison between a selected set of Generic ILP and 0-1 Boolean satisfiability based solvers in solving the ILP formulations. Simulation results indicate that in some cases the developed models have saved up to 38% in energy consumption when compared to common techniques such as round robin. Furthermore, results also showed that generic ILP solvers had superior performance when compared to SAT-based ILP solvers especially as the number of tasks and resources grow in size.
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YILDIZ, Çağdaş, and Adem TÜZEMEN. "A Solution Proposal to Vehicle Routing Problem with Integer Linear Programming: A Distributor Company Sample." International Journal of Contemporary Economics and Administrative Sciences 9, no. 1 (2019): 46–78. https://doi.org/10.5281/zenodo.3262231.

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<strong>Abstract </strong> It was aimed to minimize the total distance of the routes under the capacity constraint of the routes that a distributor company has drawn in the direction of the demands. To this end, a route to Gebze-based steel production and distribution was drawn up to meet all the demands of a fabrication plant. In order to determine the minimum total distance routes, the solution recommendation by adapting the Capacity Constrained Vehicle Routing Problem (CVRP) which is one of the basic route problems using Branch and Cut algorithm of 0-1 Integer Linear Programming (ILP) was introduced. Distances between the nodes that make up the route are measured via Google Maps. Optimal solutions were obtained by using LINDO computer software to solve the problem.
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Osman, Ahmed, Assim Sagahyroon, Raafat Aburukba, and Fadi Aloul. "Optimization of energy consumption in cloud computing datacenters." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 1 (2021): 686. http://dx.doi.org/10.11591/ijece.v11i1.pp686-698.

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Cloud computing has emerged as a practical paradigm for providing IT resources, infrastructure and services. This has led to the establishment of datacenters that have substantial energy demands for their operation. This work investigates the optimization of energy consumption in cloud datacenter using energy efficient allocation of tasks to resources. The work seeks to develop formal optimization models that minimize the energy consumption of computational resources and evaluates the use of existing optimization solvers in testing these models. Integer linear programming (ILP) techniques are used to model the scheduling problem. The objective is to minimize the total power consumed by the active and idle cores of the servers’ CPUs while meeting a set of constraints. Next, we use these models to carry out a detailed performance comparison between a selected set of Generic ILP and 0-1 Boolean satisfiability based solvers in solving the ILP formulations. Simulation results indicate that in some cases the developed models have saved up to 38% in energy consumption when compared to common techniques such as round robin. Furthermore, results also showed that generic ILP solvers had superior performance when compared to SAT-based ILP solvers especially as the number of tasks and resources grow in size.
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SAGAHYROON, ASSIM, FADI A. ALOUL, and ALEXANDER SUDNITSON. "USING SAT-BASED TECHNIQUES IN LOW POWER STATE ASSIGNMENT." Journal of Circuits, Systems and Computers 20, no. 08 (2011): 1605–18. http://dx.doi.org/10.1142/s0218126611007980.

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Power consumption of synchronous sequential circuits can be reduced by careful encoding of the states of the circuit. The idea is to reduce the average number of bit changes per state transition by finding an optimal state assignment. This state assignment problem is NP-hard, and existing techniques rely mainly on heuristic-based methods. The primary goal of this work is to assess the suitability of using complete advanced Boolean Satisfiability and Integer Linear Programming (ILP) methods in finding an optimized solution. We formulate the problem as a 0-1 ILP instance with power minimization being the objective. Using generic and commercial solvers, the proposed approach was tested on sample circuits from the MCNC benchmark suite. Furthermore, in an effort to accelerate the search process, circuits were checked for symmetries and symmetry breaking predicates were added whenever applicable. The experimental results provide a pragmatic insight into the problem and basis for further exploration.
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CHOI, YOONSEO, and TAEWHAN KIM. "MEMORY ACCESS DRIVEN STORAGE ASSIGNMENT FOR VARIABLES IN EMBEDDED SYSTEM DESIGN." Journal of Circuits, Systems and Computers 15, no. 02 (2006): 145–68. http://dx.doi.org/10.1142/s0218126606003003.

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In this paper, we address the DRAM storage assignment problems of variables with an objective of maximizing the number of page/burst mode accesses. Specifically, (1) we solve the problem of variable alignment to memory for maximizing the use of page accesses by proposing a technique, called zone_alignment, which is based on the combination of an efficient 0-1 ILP (integer linear programming) formulation and the utilization of temporal locality of variables' accesses in code and (2) the approach used in (1) is then applied to solve the problem of variable alignment for maximizing the use of burst accesses. Surprisingly, the execution of the ILP formulation used in (2) is relatively much faster than that used in (1) and nevertheless, the (optimal) formulation in (2) produces much better results than that by the existing techniques whereas the (optimal) formulation in (1) produces almost the same or slightly improved results, indicating that the problem with page accesses and the problem with burst accesses must be treated completely separately. (3) Finally, by utilizing the techniques in (1) and (2), we propose solutions to two extended DRAM architectures: (a) DRAMs with the capability of both of page and burst access modes and (b) DRAMs with multiple banks. We provide a set of experimental data to show the effectiveness of the proposed techniques.
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Di, Zhen, Shihui Zhang, Ayijiang Babayi, Yuhang Zhou, and Shenghu Zhang. "Eco-Driving Optimization with the Traffic Light Countdown Timer in Vehicle Navigation and Its Impact on Fuel Consumption." Sustainability 17, no. 8 (2025): 3354. https://doi.org/10.3390/su17083354.

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For most drivers of fuel-powered vehicles who do not have specialized eco-driving knowledge, simple and practical strategies are the most effective way to encourage eco-driving habits. By incorporating traffic light countdown timers from vehicle navigation systems, this paper develops a 0–1 integer linear programming (ILP) model to determine the optimal speed curve and further provide actionable, easy-to-implement eco-driving recommendations. Specifically, time is discretized into one-second intervals, with speed and acceleration also discretized. Pre-calculating instantaneous fuel consumption under various speed and acceleration combinations ensures the linearity of the objective function. For a specified road and a given time duration, the optimal speed profile problem for approaching intersections is transformed into a series of speed and acceleration selections. Through the analysis of multiple application scenarios, this study proposes practical and easily adoptable eco-driving strategies, which can effectively reduce vehicle fuel consumption, thereby contributing to the sustainable development of urban traffic.
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Dissertations / Theses on the topic "0–1 integer linear programming (ILP)"

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Lazic, Jasmina. "New variants of variable neighbourhood search for 0-1 mixed integer programming and clustering." Thesis, Brunel University, 2010. http://bura.brunel.ac.uk/handle/2438/4602.

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Many real-world optimisation problems are discrete in nature. Although recent rapid developments in computer technologies are steadily increasing the speed of computations, the size of an instance of a hard discrete optimisation problem solvable in prescribed time does not increase linearly with the computer speed. This calls for the development of new solution methodologies for solving larger instances in shorter time. Furthermore, large instances of discrete optimisation problems are normally impossible to solve to optimality within a reasonable computational time/space and can only be tackled with a heuristic approach. In this thesis the development of so called matheuristics, the heuristics which are based on the mathematical formulation of the problem, is studied and employed within the variable neighbourhood search framework. Some new variants of the variable neighbourhood searchmetaheuristic itself are suggested, which naturally emerge from exploiting the information from the mathematical programming formulation of the problem. However, those variants may also be applied to problems described by the combinatorial formulation. A unifying perspective on modern advances in local search-based metaheuristics, a so called hyper-reactive approach, is also proposed. Two NP-hard discrete optimisation problems are considered: 0-1 mixed integer programming and clustering with application to colour image quantisation. Several new heuristics for 0-1 mixed integer programming problem are developed, based on the principle of variable neighbourhood search. One set of proposed heuristics consists of improvement heuristics, which attempt to find high-quality near-optimal solutions starting from a given feasible solution. Another set consists of constructive heuristics, which attempt to find initial feasible solutions for 0-1 mixed integer programs. Finally, some variable neighbourhood search based clustering techniques are applied for solving the colour image quantisation problem. All new methods presented are compared to other algorithms recommended in literature and a comprehensive performance analysis is provided. Computational results show that the methods proposed either outperform the existing state-of-the-art methods for the problems observed, or provide comparable results. The theory and algorithms presented in this thesis indicate that hybridisation of the CPLEX MIP solver and the VNS metaheuristic can be very effective for solving large instances of the 0-1 mixed integer programming problem. More generally, the results presented in this thesis suggest that hybridisation of exact (commercial) integer programming solvers and some metaheuristic methods is of high interest and such combinations deserve further practical and theoretical investigation. Results also show that VNS can be successfully applied to solving a colour image quantisation problem.
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Book chapters on the topic "0–1 integer linear programming (ILP)"

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Günlük, Oktay, Jon Lee, and Janny Leung. "A Polytope for a Product of Real Linear Functions in 0/1 Variables." In Mixed Integer Nonlinear Programming. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1927-3_18.

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Carstensen, Patricia J., Katta G. Murty, and Clovis Perin. "Intermediate feasibility in 0–1 integer linear systems." In Mathematical Programming Essays in Honor of George B. Dantzig Part I. Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/bfb0121052.

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Hoen, Alexander, Andy Oertel, Ambros Gleixner, and Jakob Nordström. "Certifying MIP-Based Presolve Reductions for $$0$$–$$1$$ Integer Linear Programs." In Integration of Constraint Programming, Artificial Intelligence, and Operations Research. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-60597-0_20.

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Xia, Jun, Li Luo, and Xuejun Yang. "A 0-1 Integer Linear Programming Based Approach for Global Locality Optimizations." In Advances in Computer Systems Architecture. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11859802_23.

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Marinescu, Radu, and Rina Dechter. "AND/OR Branch-and-Bound Search for Pure 0/1 Integer Linear Programming Problems." In Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11757375_14.

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Benferhat, Salem, Khaoula Boutouhami, Faiza Khellaf, and Farid Nouioua. "An Integer 0-1 Linear Programming Approach for Computing Inconsistency Degree in Product-Based Possibilistic DL-Lite." In Lecture Notes in Computer Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67582-4_23.

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"Linear Integer Programming Techniques for Portfolio Design Problem." In Algorithms for Solving Financial Portfolio Design Problems. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1882-3.ch004.

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This chapter introduces Integer Linear Programming (ILP) approaches for solving efficiently a ðnancial portfolio design problem. The authors proposed a matricial model in Chapter 3, which is a mathematical quadratic model. A linearization step is considered necessary to apply linear programming techniques. The corresponding matricial model shows clearly that the problem is strongly symmetrical. The row and column symmetries are easily handled by adding a negligible number of new constraints. The authors propose two linear models, which are given in detail and proven. These models represent the problem as linear constraint systems with 0-1 variables, which will be implemented in ILP solver. Experimental results in non-trivial instances of portfolio design problem are given.
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Elfouly, Fatma H., Rabie A. Ramadan, Mohamed I. Mahmoud, and Moawad I. Dessouky. "Efficient Data Reporting in a Multi-Object Tracking Using WSNs." In Robotic Systems. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1754-3.ch034.

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Object tracking is one of the most important applications in wireless sensor networks (WSNs). Many recent articles have been dedicated to localization of objects; however, few of these articles were concentrated on the reliability of network data reporting along with objects localization. In this work, the authors propose an efficient data reporting method for object tracking in WSNs. This paper aims to achieve both minimum energy consumption in reporting operation and balanced energy consumption among sensor nodes for WSN lifetime extension. Furthermore, data reliability is considered in the authors' model where the sensed data can reach the sink node in a more reliable way. This work first formulates the problem as 0/1 Integer Linear Programming (ILP) problem, and then proposes a SWARM intelligence for solving the optimization problem. Through simulation, the performance of proposed method to report information about the detected objects to the sink is compared with the previous works related to the authors' topic, such as LR-based object tracking algorithm, SEB, EPWSN, and ACO.
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"Chapter 2 Improved solution method for the 0-1 GAP model." In Linear Integer Programming. De Gruyter, 2021. http://dx.doi.org/10.1515/9783110703023-002.

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Floudas, Christodoulos A. "Mixed-Integer Linear Optimization." In Nonlinear and Mixed-Integer Optimization. Oxford University Press, 1995. http://dx.doi.org/10.1093/oso/9780195100563.003.0010.

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This chapter provides an introduction to the basic notions in Mixed-Integer Linear Optimization. Sections 5.1 and 5.2 present the motivation, formulation, and outline of methods. Section 5.3 discusses the key ideas in a branch and bound framework for mixed-integer linear programming problems. A large number of optimization models have continuous and integer variables which appear linearly, and hence separably, in the objective function and constraints. These mathematical models are denoted as Mixed-Integer Linear Programming MILP problems. In many applications of MILP models the integer variables are 0 — 1 variables (i.e., binary variables), and in this chapter we will focus on this sub-class of MILP problems. A wide range of applications can be modeled as mixed-integer linear programming MILP problems. These applications have attracted a lot of attention in the field of Operations Research and include facility location and allocation problems, scheduling problems, and fixed-charge network problems. The excellent books of Nemhauser and Wolsey (1988), and Parker and Rardin (1988) provide not only an exposition to such applications but also very thorough presentation of the theory of discrete optimization. Applications of MILP models in Chemical Engineering have also received significant attention particularly in the areas of Process Synthesis, Design, and Control. These applications include (i) the minimum number of matches in heat exchanger synthesis (Papoulias and Grossmann, 1983; see also chapter 8) (ii) heat integration of sharp distillation sequences (Andrecovich and Westerberg, 1985); (iii) multicomponent multiproduct distillation column synthesis (Floudas and Anastasiadis, 1988); (iv) multiperiod heat exchanger network, and distillation system synthesis (Floudas and Grossmann, 1986; Paules and Floudas, 1988); flexibility analysis of chemical processes (Grossmann and Floudas, 1987); (v) structural properties of control systems (Georgiou and Floudas, 1989, 1990); (vi) scheduling of batch processes (e.g., Rich and Prokapakis, 1986, 1986; Kondili et al., 1993; Shah et al, 1993; Voudouris and Grossmann, 1992, 1993); and (vii) planning and scheduling of batch processes (Shah and Pantelides, 1991, Sahinidis et al, 1989, Sahinidis and Grossmann, 1991). In addition to the above applications, MILP models are employed as subproblems in the mixed-integer nonlinear optimization approaches which we will discuss in the next chapter. In this section, we will present the formulation of Mixed-Integer Linear Programming MILP problems, discuss the complexity issues, and provide a brief overview of the solution methodologies proposed for MILP models.
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Conference papers on the topic "0–1 integer linear programming (ILP)"

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Chen, Xingjun, and Hongquan Shi. "Multi-Platform Air Defence Scheduling Based on 0-1 Integer Linear Programming." In 2017 4th International Conference on Information Science and Control Engineering (ICISCE). IEEE, 2017. http://dx.doi.org/10.1109/icisce.2017.107.

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Zhao, Yu-Ying, George Maroulis, and Theodore E. Simos. "0–1 Integer Linear Programming Model for Weighted Drug Enzyme-Target Identification Problem." In COMPUTATIONAL METHODS IN SCIENCE AND ENGINEERING: Advances in Computational Science: Lectures presented at the International Conference on Computational Methods in Sciences and Engineering 2008 (ICCMSE 2008). AIP, 2009. http://dx.doi.org/10.1063/1.3225445.

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Monton, Aaron Kyle, Vanessa Larioza, and Michael Pacis. "An Optimal Phasor Measurement Unit (PMU) Placement Algorithm with (N-1) Contingency Using Integer Linear Programming (ILP)." In 2021 IEEE International Conference on Automatic Control & Intelligent Systems (I2CACIS). IEEE, 2021. http://dx.doi.org/10.1109/i2cacis52118.2021.9495901.

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Lei, Zhen, Yan Sun, Yun-han Song, and Dong Li. "The Quantitative Analysis of Equipment Operational Test Credibility Based on Dynamic Programming and 0-1 Integer Linear Programming." In 2015 International Conference on Industrial Informatics - Computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII). IEEE, 2015. http://dx.doi.org/10.1109/iciicii.2015.111.

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Bahri, Susila. "0-1 integer linear programming model for location selection of fire station: A case study in Indonesia." In SYMPOSIUM ON BIOMATHEMATICS (SYMOMATH 2015). AIP Publishing LLC, 2016. http://dx.doi.org/10.1063/1.4945062.

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huang, xinxin, gang xu, yunfei chen, guoyi wang, jiawei chen, and wei peng. "The optimization algorithm of power capacity increase in industrial park based on 0-1 integer linear programming." In International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), edited by Ke Chen, Nan Lin, Romeo Meštrović, Teresa A. Oliveira, Fengjie Cen, and Hong-Ming Yin. SPIE, 2022. http://dx.doi.org/10.1117/12.2627904.

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Chaipornkaew, L., C. Pipatchatchawal, S. Kosawantana, et al. "Optimization Well Targeting, Platform Placement and Path Refinement to Enhance Mature and Marginal Field Values." In ADIPEC. SPE, 2024. http://dx.doi.org/10.2118/222165-ms.

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Enhancing hydrocarbon recovery from compartmentalized reservoirs in the Gulf of Thailand require a significant number of production wells, presenting challenges in finding an economically viable development strategy. To address these challenges, regional operators have perfected their operational expertise by adopting a statistical drilling approach, as opposed to an object-based drilling method because the reservoirs are so thin that they cannot be tracked reliably with seismic anomalies, and also known to be distributed almost randomly (Ionnikoff, et al. 2011). This strategy maximizes the likelihood of encountering hydrocarbons, following the Gulf of Thailand (GoT) Petroleum Model, while minimizing development costs through the Well Factory Standard (WFS) Drilling Approach (Kaeng, et al. 2016, Ionnikoff, et al. 2011, PTTEP 2020). It involves multi-stack targeting and commingled production plans, aiming to increase hydrocarbon encounters by drilling near faults (high and tight) and within statistically inferred intervals indicating new sands. Additionally, the modified-S well path enables quick drilling and cluster development, reducing costs (Figure 1). This GoT/WFS development strategy been effectively employed to enable marginal field development program (FDP) planning over the past few decades, turning a marginal prospect into an economic project. However, as production areas in the Gulf of Thailand continue to mature and the most accessible sands have already been extracted, advancements in automation and optimization are necessary to develop the remaining resources and reserves within the economic cutoffs. Manual preparation for a safe and resourceful FDP in mature development fields is often time-consuming and yields unoptimized outcomes. In recent years, our team has translated in-house knowledge and operational best-practice from the GoT/WFS model into mathematical equations, integrating them into our optimized and automated workflow. The core algorithm leverages Integer Linear Programming (ILP) to incorporate geological features and drilling criteria, to eventually provide a range of FDP scenarios. As projects advance to mature development stages, we developed optimization specifically for infill targeting functionality, which is the latest addition to our workflow (Chaipornkaew, et al. 2023). This represents a significant expansion from the initial focus on optimizing wellhead platform (WHP) placement only. The earlier work aimed to align optimal locations that are free of shallow anomalies with reachable targets, ensuring the shortest possible well paths (Ekkawong, et al. 2021). The combined efforts of the Chaipornkaew et al. (2023) studies and Ekkawong et al. (2021) studies, referred to as step-1 and step-2, have completed the automated workflow for FDP in mature fields, revolutionizing the FDP planning process in in the Gulf of Thailand. This system was tested in real pilot areas and has proven to be an efficient and optimized solution in complex regions with challenging fault systems and numerous existing wells. This paper focuses on enhancing our in-house and innovative workflow into a complete end-to-end solution. We focused on two key areas: algorithm improvement and plugin development, enabling our tooling to be fully functional across large-scale production areas. The algorithm improvements include automating fault polygon generation for input preparation and refining well paths for added output flexibility (referred to as step-0 and step-3 respectively). Furthermore, we tackled the challenges of managing data flow between common subsurface interpretation software and our tooling by developing plugin connectors. This is crucial for successful deployment and easy adoption, to successfully transition from a manual to a fully automated FDP process. Our tooling has gone through many iterations of algorithm improvements in the early phase of deployment through geologically diverse dataset. These solutions are now applicable to conceptual FDP in both exploration and development projects, significantly enhancing the value of mature and marginal fields across assets in the Gulf of Thailand.
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Quan, Ning, and Harrison Kim. "A Tight Upper Bound for Grid-Based Wind Farm Layout Optimization." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-59712.

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This paper uses the method developed by Billionnet et al. (1999) to obtain tight upper bounds on the optimal values of mixed integer linear programming (MILP) formulations in grid-based wind farm layout optimization. The MILP formulations in grid-based wind farm layout optimization can be seen as linearized versions of the 0-1 quadratic knapsack problem (QKP) in combinatorial optimization. The QKP is NP-hard, which means the MILP formulations remain difficult problems to solve, especially for large problems with grid sizes of more than 500 points. The upper bound method proposed by Billionnet et al. is particularly well-suited for grid-based wind farm layout optimization problems, and was able to provide tight optimality gaps for a range of numerical experiments with up to 1296 grid points. The results of the numerical experiments also suggest that the greedy algorithm is a promising solution method for large MILP formulations in grid-based layout optimization that cannot be solved using standard branch and bound solvers.
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