Relevant bibliographies by topics / Heuristic problem

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Heuristic problem'

Author: Grafiati
Published: 28 May 2022
Last updated: 30 July 2025

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Journal articles on the topic "Heuristic problem"

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Helmert, Malte. "Landmark Heuristics for the Pancake Problem." Proceedings of the International Symposium on Combinatorial Search 1, no. 1 (2010): 109–10. http://dx.doi.org/10.1609/socs.v1i1.18176.

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We describe the gap heuristic for the pancake problem, which dramatically outperforms current abstraction-based heuristics for this problem. The gap heuristic belongs to a family of landmark heuristics that have recently been very successfully applied to planning problems.
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Drake, John H., Matthew Hyde, Khaled Ibrahim, and Ender Ozcan. "A genetic programming hyper-heuristic for the multidimensional knapsack problem." Kybernetes 43, no. 9/10 (2014): 1500–1511. http://dx.doi.org/10.1108/k-09-2013-0201.

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Purpose – Hyper-heuristics are a class of high-level search techniques which operate on a search space of heuristics rather than directly on a search space of solutions. The purpose of this paper is to investigate the suitability of using genetic programming as a hyper-heuristic methodology to generate constructive heuristics to solve the multidimensional 0-1 knapsack problem Design/methodology/approach – Early hyper-heuristics focused on selecting and applying a low-level heuristic at each stage of a search. Recent trends in hyper-heuristic research have led to a number of approaches being de
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Drake, John H., Ender Özcan, and Edmund K. Burke. "A Case Study of Controlling Crossover in a Selection Hyper-heuristic Framework Using the Multidimensional Knapsack Problem." Evolutionary Computation 24, no. 1 (2016): 113–41. http://dx.doi.org/10.1162/evco_a_00145.

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Hyper-heuristics are high-level methodologies for solving complex problems that operate on a search space of heuristics. In a selection hyper-heuristic framework, a heuristic is chosen from an existing set of low-level heuristics and applied to the current solution to produce a new solution at each point in the search. The use of crossover low-level heuristics is possible in an increasing number of general-purpose hyper-heuristic tools such as HyFlex and Hyperion. However, little work has been undertaken to assess how best to utilise it. Since a single-point search hyper-heuristic operates on
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Özcan, Ender, Mustafa Misir, Gabriela Ochoa, and Edmund K. Burke. "A Reinforcement Learning - Great-Deluge Hyper-Heuristic for Examination Timetabling." International Journal of Applied Metaheuristic Computing 1, no. 1 (2010): 39–59. http://dx.doi.org/10.4018/jamc.2010102603.

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Hyper-heuristics can be identified as methodologies that search the space generated by a finite set of low level heuristics for solving search problems. An iterative hyper-heuristic framework can be thought of as requiring a single candidate solution and multiple perturbation low level heuristics. An initially generated complete solution goes through two successive processes (heuristic selection and move acceptance) until a set of termination criteria is satisfied. A motivating goal of hyper-heuristic research is to create automated techniques that are applicable to a wide range of problems wi
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Ursani, Ziauddin, and David W. Corne. "Introducing Complexity Curtailing Techniques for the Tour Construction Heuristics for the Travelling Salesperson Problem." Journal of Optimization 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/4786268.

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In this paper, complexity curtailing techniques are introduced to create faster version of insertion heuristics, that is, cheapest insertion heuristic (CIH) and largest insertion heuristic (LIH), effectively reducing their complexities fromO(n3)toO(n2)with no significant effect on quality of solution. This paper also examines relatively not very known heuristic concept of max difference and shows that it can be culminated into a full-fledged max difference insertion heuristic (MDIH) by defining its missing steps. Further to this the paper extends the complexity curtailing techniques to MDIH to
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Sanggala, Ekra, and Muhammad Ardhya Bisma. "Perbandingan Savings Algorithm dengan Nearest Neighbour dalam Menyelesaikan Russian TSP Instances." Jurnal Media Teknik dan Sistem Industri 7, no. 1 (2023): 27. http://dx.doi.org/10.35194/jmtsi.v7i1.3039.

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Travelling Salesman Problem (TSP) is the problem for finding the shortest route starting from start node then visiting number of nodes exactly once and finally go back to start node. Several heuristics are popular for solving TSP, for example Savings Algorithm and Nearest Neighbour. Performance heuristics on solving TSP are diverse, so there is need of reference for choosing a heuristic. Comparing heuristics on solving instance can be a reference for choosing a heuristic. This paper will discuss about comparison Savings Algorithm and Nearest Neighbour on Solving Russian TSP Instances. For gene
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Yao, Shunyu, Fei Liu, Xi Lin, Zhichao Lu, Zhenkun Wang, and Qingfu Zhang. "Multi-Objective Evolution of Heuristic Using Large Language Model." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 25 (2025): 27144–52. https://doi.org/10.1609/aaai.v39i25.34922.

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Heuristics are commonly used to tackle various search and optimization problems. Design heuristics usually require tedious manual crafting with domain knowledge. Recent works have incorporated Large Language Models (LLMs) into automatic heuristic search, leveraging their powerful language and coding capacity. However, existing research focuses on the optimal performance on the target problem as the sole objective, neglecting other criteria such as efficiency and scalability, which are vital in practice. To tackle this challenge, we propose to model the heuristic search as a multi-objective opt
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Ursani, Ziauddin, and Ahsan Ahmad Ursani. "Augmented tour construction heuristics for the travelling salesman problem." International Journal of Industrial Optimization 4, no. 2 (2023): 131–44. http://dx.doi.org/10.12928/ijio.v4i2.7875.

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Tour construction heuristics serve as fundamental techniques in optimizing the routes of a traveling salesman. These heuristics remain significant as foundational methods for generating initial solutions to the Traveling Salesman Problem (TSP), facilitating subsequent applications of tour improvement heuristics. These heuristics effectively comprise the iterative application of city node selection and insertion. However, thus far, no attempts have been made to enhance the basic structure of tour construction heuristics to bring a better initial solution for the advanced heuristics. This study
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Rayner, D. Chris, Michael Bowling, and Nathan Sturtevant. "Euclidean Heuristic Optimization." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (2011): 81–86. http://dx.doi.org/10.1609/aaai.v25i1.7815.

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We pose the problem of constructing good search heuristics as an optimization problem: minimizing the loss between the true distances and the heuristic estimates subject to admissibility and consistency constraints. For a well-motivated choice of loss function, we show performing this optimization is tractable. In fact, it corresponds to a recently proposed method for dimensionality reduction. We prove this optimization is guaranteed to produce admissible and consistent heuristics, generalizes and gives insight into differential heuristics, and show experimentally that it produces strong heuri
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Adubi, Stephen A., Olufunke O. Oladipupo, and Oludayo O. Olugbara. "Evolutionary Algorithm-Based Iterated Local Search Hyper-Heuristic for Combinatorial Optimization Problems." Algorithms 15, no. 11 (2022): 405. http://dx.doi.org/10.3390/a15110405.

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Hyper-heuristics are widely used for solving numerous complex computational search problems because of their intrinsic capability to generalize across problem domains. The fair-share iterated local search is one of the most successful hyper-heuristics for cross-domain search with outstanding performances on six problem domains. However, it has recorded low performances on three supplementary problems, namely knapsack, quadratic assignment, and maximum-cut problems, which undermines its credibility across problem domains. The purpose of this study was to design an evolutionary algorithm-based i
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Dissertations / Theses on the topic "Heuristic problem"

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Liogys, Mindaugas. "Heuristic Algorithms for Nurse Rostering Problem." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2013. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2013~D_20130930_092436-21259.

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In the dissertation the nurse rostering problem is investigated. The formulation of the problem is based on real-world data of one of the largest healthcare centers in Lithuania. Most recent publications that tackle the nurse rostering problem and the methods for solving the nurse rostering problem are reviewed, the mathematical formulation of the single objective and the multi-objective nurse rostering problem is presented, the requirements for the roster are described and a new method for solving the single objective and the multi-objective nurse rostering problem is proposed in this dissert
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Keuthen, Ralf. "Heuristic approaches for routing optimisation." Thesis, University of Nottingham, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.275960.

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Chan, Evelyn Yu-San. "Heuristic optimisation for the minimum distance problem." Thesis, Nottingham Trent University, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.324569.

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Hariri, Ahmad. "Heuristic algorithms for the travelling deliveryman problem." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amslaurea.unibo.it/6803/.

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McCluskey, T. L. "Experience-driven heuristic acquisition in general problem solvers." Thesis, City University London, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.234449.

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Kadhem, Dhia. "Heuristic solution approaches to the solid assignment problem." Thesis, University of Essex, 2017. http://repository.essex.ac.uk/20907/.

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The 3-dimensional assignment problem, also known as the Solid Assignment Problem (SAP), is a challenging problem in combinatorial optimisation. While the ordinary or 2-dimensional assignment problem is in the P-class, SAP which is an extension of it, is NP-hard. SAP is the problem of allocating n jobs to n machines in n factories such that exactly one job is allocated to one machine in one factory. The objective is to minimise the total cost of getting these n jobs done. The problem is commonly solved using exact methods of integer programming such as Branch-and-Bound B&B. As it is intractable
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Whitford, Angela Tracy. "Heuristic approaches to solve the frequency assignment problem." Thesis, Goldsmiths College (University of London), 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.321956.

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Buckley, Mark C. "Extroversion and a comparison of two problem-solving heuristics." Virtual Press, 1995. http://liblink.bsu.edu/uhtbin/catkey/941716.

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The purpose of this experiment was to explore the relationship between the "Big Five" personality dimensions, training and problem-solving effectiveness. The second purpose of this study was to explore the effects of training upon the quantity and self-reported quality of solutions generated to ill-structured problems. Subjects generated solutions to a problem and then were trained in either brainstorming or the hierarchical method. Then they were asked to generate additional solutions and rate their solutions. Subjects returned after a month and completed the NEO-FFI and then generated soluti
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Samson, Duncan Alistair. "The heuristic significance of enacted visualisation." Thesis, Rhodes University, 2012. http://hdl.handle.net/10962/d1003434.

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This study is centred on an analysis of pupils' lived experience while engaged in the generalisation of linear sequences/progressions presented in a pictorial context. The study is oriented within the conceptual framework of qualitative research, and is anchored within an interpretive paradigm. A case study methodological strategy was adopted, the research participants being the members of a mixed gender, high ability Grade 9 class of 23 pupils at an independent school in South Africa. The analytical framework is structured around a combination of complementary multiple perspectives provided b
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Bingol, Levent. "A Lagrangian Heuristic for solving a network interdiction problem." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2001. http://handle.dtic.mil/100.2/ADA401595.

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Thesis (M.S. in Operations Research) Naval Postgraduate School, December 2001.<br>Thesis Advisor(s): Wood, R. Kevin. "December 2001." Includes bibliographical references (p. 35-36). Also Available online.
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Books on the topic "Heuristic problem"

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Moustakas, Clark E. Heuristic research: Design, methodology, and applications. Sage Publications, 1990.

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Kothari, Ravi. Sensitivity analysis for the single row facility layout problem. Indian Institute of Management, Ahmedabad, 2012.

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Nicol, David M. A multistage linear array assignment problem. ICASE, 1988.

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Luca, Console, ed. Diagnostic problem solving: Combining heuristic, approximate and causal reasoning. North Oxford Academic, 1989.

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Luca, Console, ed. Diagnostic problem solving: Combining heuristic, approximate, and causal reasoning. Van Nostrand Reinhold, 1989.

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Torasso, Pietro. Diagnostic problem solving: Combining heuristic, approximate and casual reasoning. North Oxford Academic, 1989.

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Wassenhove, Luk N. van. A set partitioning heuristic for the generalized assignment problem. INSEAD, 1991.

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Thomas, Paul Richard. Heuristic approaches to the constrained resource project scheduling problem. University of Birmingham, 1996.

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Sreejit, Chakravarty, ed. Parallel and serial heuristics for the minimum set cover problem. State University of New York at Buffalo, Dept. of Computer Science, 1990.

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B, Fogel David, ed. How to solve it: Modern heuristics. 2nd ed. Springer, 2004.

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Book chapters on the topic "Heuristic problem"

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Martí, Rafael, and Gerhard Reinelt. "Heuristic Methods." In The Linear Ordering Problem. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16729-4_2.

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Dasgupta, Pallab, P. P. Chakrabarti, and S. C. DeSarkar. "Multiobjective Problem Reduction Search." In Multiobjective Heuristic Search. Vieweg+Teubner Verlag, 1999. http://dx.doi.org/10.1007/978-3-322-86853-4_5.

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Domínguez-Marín, Patricia. "Heuristic Procedures." In The Discrete Ordered Median Problem. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-8511-8_5.

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Taillard, Éric D. "Heuristics Design." In Design of Heuristic Algorithms for Hard Optimization. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13714-3_11.

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AbstractThe last chapter of the book gives some advice on developing heuristics. It discusses the difficulty of modeling the problem and gives an example of decomposing the problem into a chain of more manageable sub-problems. Secondly, it proposes an approach for the design of a specific heuristic. Finally, some techniques for parameter tuning and comparing the efficiency of algorithms are reviewed.
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Taillard, Éric D. "Problem Modeling." In Design of Heuristic Algorithms for Hard Optimization. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13714-3_3.

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AbstractThis chapter shows how a problem can be modeled so that it can be handled efficiently by a heuristic algorithm. It gives some examples of transformations of data, constraints, and objectives. Finally, it introduces some notions of multi-objective optimization.
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Lang, Rainer, and Thomas Milius. "A Heuristic for the Nesting Problem." In Operations Research ’92. Physica-Verlag HD, 1993. http://dx.doi.org/10.1007/978-3-662-12629-5_36.

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Kaijar, Saifuddin, and S. Durga Bhavani. "Developing Heuristic for Subgraph Isomorphism Problem." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32129-0_10.

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Romanova, Tetyana, Yuri Stoian, Andrii Chuhai, Georgiy Yaskov, and Oksana Melashenko. "Fast Heuristic for Particle Packing Problem." In Smart Technologies in Urban Engineering. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46874-2_11.

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Garlick, F. J., and S. Thompson. "A Taxonomy of Heuristic Problem Solving." In Systems for Sustainability. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4899-0265-8_76.

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Souza, Filipe, Diarmuid Grimes, and Barry O’Sullivan. "Variable-Relationship Guided LNS for the Car Sequencing Problem." In Communications in Computer and Information Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26438-2_34.

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AbstractLarge Neighbourhood Search (LNS) is a powerful technique that applies the “divide and conquer” principle to boost the performance of solvers on large scale Combinatorial Optimization Problems. In this paper we consider one of the main hindrances to the LNS popularity, namely the requirement of an expert to define a problem specific neighborhood. We present an approach that learns from problem structure and search performance in order to generate neighbourhoods that can match the performance of domain specific heuristics developed by an expert. Furthermore, we present a new objective fu
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Conference papers on the topic "Heuristic problem"

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Hitomi, Nozomi, and Daniel Selva. "The Effect of Credit Definition and Aggregation Strategies on Multi-Objective Hyper-Heuristics." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-47445.

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Heuristics and meta-heuristics are often used to solve complex real-world problems such as the non-linear, non-convex, and multi-objective combinatorial optimization problems that regularly appear in system design and architecture. Unfortunately, the performance of a specific heuristic is largely dependent on the specific problem at hand. Moreover, a heuristic’s performance can vary throughout the optimization process. Hyper-heuristics is one approach that can maintain relatively good performance over the course of an optimization process and across a variety of problems without parameter retu
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Puentes, Lucas, Jonathan Cagan, and Christopher McComb. "Automated Heuristic Induction From Human Design Data." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22151.

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Abstract Through experience, designers develop guiding principles, or heuristics, to aid decision-making in familiar design domains. Generalized versions of common design heuristics have been identified across multiple domains and applied by novices to design problems. Previous work leveraged a sample of these common heuristics to assist in an agent-based design process, which typically lacks heuristics. These predefined heuristics were translated into sequences of specifically applied design changes that followed the theme of the heuristic. To overcome the upfront burden, need for human inter
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Yokoyama, Soichiro, Ikuo Suzuki, Masahito Yamamoto, and Masashi Furukawa. "A New Heuristic for Traveling Salesman Problem Based on LCO." In ASME/ISCIE 2012 International Symposium on Flexible Automation. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/isfa2012-7227.

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The Traveling Salesman Problem (TSP) is one of the most well known combinatorial optimization problem and has wide range of application. Since the TSP is NP-hard, many heuristics for the TSP have been developed. This study proposes a new heuristic for the TSP based on one of these heuristics named Local Clustering Optimization (LCO). LCO is a metaheuristic proposed by Furukawa at el. to give an accurate solution for large scale problems in a reasonable time. However, conventional LCO-based heuristics for the TSP is not suited to solving asymmetric instances. The proposed method iteratively ado
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Koriche, Frederic, Christophe Lecoutre, Anastasia Paparrizou, and Hugues Wattez. "Best Heuristic Identification for Constraint Satisfaction." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/258.

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In constraint satisfaction problems, the variable ordering heuristic takes a central place by selecting the variables to branch on during backtrack search. As many hand-crafted branching heuristics have been proposed in the literature, a key issue is to identify, from a pool of candidate heuristics, which one is the best for solving a given constraint satisfaction task. Based on the observation that modern constraint solvers are using restart sequences, the best heuristic identification problem can be cast in the context of multi-armed bandits as a non-stochastic best arm identification proble
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Trevizan, Felipe, Sylvie Thiébaux, and Patrik Haslum. "Operator Counting Heuristics for Probabilistic Planning." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/758.

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For the past 25 years, heuristic search has been used to solve domain-independent probabilistic planning problems, but with heuristics that determinise the problem and ignore precious probabilistic information. In this paper, we present a generalization of the operator-counting family of heuristics to Stochastic Shortest Path problems (SSPs) that is able to represent the probability of the actions outcomes. Our experiments show that the equivalent of the net change heuristic in this generalized framework obtains significant run time and coverage improvements over other state-of-the-art heurist
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Leila Grošelj, Ema Leila, and Tomaž Poljanšek. "Influence of Graph Characteristics on Solutions of Feedback Arc Set Problem." In 10th Student Computing Research Symposium. University of Maribor Press, 2024. https://doi.org/10.18690/um.feri.6.2024.1.

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In this article we present Feedback Arc Set problem and how certain graph characteristics impact the results of heuristic algorithms. We then inspect how the most promising characteristic (treewidth) helps in choosing the most appropriate heuristics for our graph.
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de Souza, Marcelo. "Automatic Design of Heuristic Algorithms for Binary Optimization Problems." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/672.

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In this work we present AutoBQP, a heuristic solver for binary optimization problems. It applies automatic algorithm design techniques to search for the best heuristics for a given optimization problem. Experiments show that the solver can find algorithms which perform better than or comparable to state-of-the-art methods, and can even find new best solutions for some instances of standard benchmark sets.
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Rana, Rattan, and Deepak Garg. "Heuristic Approaches for K-Center Problem." In 2009 IEEE International Advance Computing Conference (IACC 2009). IEEE, 2009. http://dx.doi.org/10.1109/iadcc.2009.4809031.

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Stern, Roni. "Domain-Dependent and Domain-Independent Problem Solving Techniques." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/898.

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Heuristic search is a general problem-solving method. Some heuristic search algorithms, like the well-known A* algorithm, are domain-independent, in the sense that their knowledge of the problem at-hand is limited to the (1) initial state, (2) state transition operators and their costs, (3) goal-test function, and (4) black-box heuristic function that estimates the value of a state. Prominent examples are A* and Weighted A*. Other heuristic search algorithms are domain-dependent, that is, customized to solve problems from a specific domain. A well-known example is conflict-directed A*, which i
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Siqueira, Gabriel, Alexsandro Oliveira Alexandrino, Andre Rodrigues Oliveira, and Zanoni Dias. "Heuristics based on Adjacency Graph Packing for DCJ Distance Considering Intergenic Regions." In Simpósio Brasileiro de Bioinformática. Sociedade Brasileira de Computação, 2024. https://doi.org/10.5753/bsb.2024.245554.

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In this work, we explore heuristics for the Adjacency Graph Packing problem, which can be applied to the Double Cut and Join (DCJ) Distance Problem. The DCJ is a rearrangement operation and the distance problem considering it is a well established method for genome comparison. Our heuristics will use the structure called adjacency graph adapted to include information about intergenic regions, multiple copies of genes in the genomes, and multiple circular or linear chromosomes. The only required property from the genomes is that it must be possible to turn one into the other with DCJ operations
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Reports on the topic "Heuristic problem"

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Kaku, Bharat K., Thomas E. Morton, and Gerald L. Thompson. A Heuristic Algorithm for the Facilities Layout Problem. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada196093.

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Franco, John, and Yuan C. Ho. Probabilistic Performance of a Heuristic for the Satisfiability Problem. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada185544.

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Sterns, Anthony, Ronni Sterns, Jeffrey Adler, Douglas Kline, and Scott Collins. The Neighborhood Covering Heuristic (NCH) Approach for the General Mixed Integer Programming Problem. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada421653.

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Franklin, R., and L. Harmon. Heuristics for Cooperative Problem Solving. Defense Technical Information Center, 1989. http://dx.doi.org/10.21236/ada206371.

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Raychev, Nikolay. Can human thoughts be encoded, decoded and manipulated to achieve symbiosis of the brain and the machine. Web of Open Science, 2020. http://dx.doi.org/10.37686/nsrl.v1i2.76.

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This article discusses the current state of neurointerface technologies, not limited to deep electrode approaches. There are new heuristic ideas for creating a fast and broadband channel from the brain to artificial intelligence. One of the ideas is not to decipher the natural codes of nerve cells, but to create conditions for the development of a new language for communication between the human brain and artificial intelligence tools. Theoretically, this is possible if the brain "feels" that by changing the activity of nerve cells that communicate with the computer, it is possible to "achieve
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Jacobson, Sheldon H. A Heuristic Design Information Sharing Framework for Hard Discrete Optimization Problems. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada467897.

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Sadeh, Norman M., and Mark S. Fox. Variable and Value Ordering Heuristics for the Job Shop Scheduling Constraint Satisfaction Problem. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada311303.

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8

Pico, Andres, and Yong H. Tan. Heuristics for Solving Problem of Evacuating Non-Ambulatory People in a Short-Notice Disaster. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada576323.

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Hoffman, Karla. Real Time Scheduling and Routing: Using Column Generation, Branch-and-Cut, and Modern Heuristics to Solve Difficult Combinatorial Optimizational Problems. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada422267.

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