Artykuły w czasopismach: „Heuristic programming”

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MALITSKY, YURI, i MEINOLF SELLMANN. "STOCHASTIC OFFLINE PROGRAMMING". International Journal on Artificial Intelligence Tools 19, nr 04 (sierpień 2010): 351–71. http://dx.doi.org/10.1142/s0218213010000236.

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We propose a framework which we call stochastic off-line programming (SOP). The idea is to embed the development of combinatorial algorithms in an off-line learning environment which helps the developer choose heuristic advisors that guide the search for satisfying or optimal solutions. In particular, we consider the case where the developer has several heuristic advisors available. Rather than selecting a single heuristic, we propose that one of the heuristics is chosen randomly whenever the heuristic guidance is sought. The task of the SOP is to learn favorable instance-specific distributions of the heuristic advisors in order to boost the average-case performance of the resulting combinatorial algorithm. Applying this methodology to a typical optimization problem, we show that substantial improvements can in fact be achieved when we perform learning in an instances specific manner.

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WHITE, DOUGLAS J. "Heuristic Programming". IMA Journal of Management Mathematics 2, nr 2 (1989): 173–88. http://dx.doi.org/10.1093/imaman/2.2.173.

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Drake, John H., Matthew Hyde, Khaled Ibrahim i Ender Ozcan. "A genetic programming hyper-heuristic for the multidimensional knapsack problem". Kybernetes 43, nr 9/10 (3.11.2014): 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 developed to automatically generate new heuristics from a set of heuristic components. A population of heuristics to rank knapsack items are trained on a subset of test problems and then applied to unseen instances. Findings – The results over a set of standard benchmarks show that genetic programming can be used to generate constructive heuristics which yield human-competitive results. Originality/value – In this work the authors show that genetic programming is suitable as a method to generate reusable constructive heuristics for the multidimensional 0-1 knapsack problem. This is classified as a hyper-heuristic approach as it operates on a search space of heuristics rather than a search space of solutions. To our knowledge, this is the first time in the literature a GP hyper-heuristic has been used to solve the multidimensional 0-1 knapsack problem. The results suggest that using GP to evolve ranking mechanisms merits further future research effort.

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Gebser, Martin, Benjamin Kaufmann, Javier Romero, Ramón Otero, Torsten Schaub i Philipp Wanko. "Domain-Specific Heuristics in Answer Set Programming". Proceedings of the AAAI Conference on Artificial Intelligence 27, nr 1 (30.06.2013): 350–56. http://dx.doi.org/10.1609/aaai.v27i1.8585.

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We introduce a general declarative framework for incorporating domain-specific heuristics into ASP solving. We accomplish this by extending the first-order modeling language of ASP by a distinguished heuristic predicate. The resulting heuristic information is processed as an equitable part of the logic program and subsequently exploited by the solver when it comes to non-deterministically assigning a truth value to an atom. We implemented our approach as a dedicated heuristic in the ASP solver clasp and show its great prospect by an empirical evaluation.

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Burke, Edmund K., Matthew R. Hyde, Graham Kendall i John Woodward. "Automating the Packing Heuristic Design Process with Genetic Programming". Evolutionary Computation 20, nr 1 (marzec 2012): 63–89. http://dx.doi.org/10.1162/evco_a_00044.

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The literature shows that one-, two-, and three-dimensional bin packing and knapsack packing are difficult problems in operational research. Many techniques, including exact, heuristic, and metaheuristic approaches, have been investigated to solve these problems and it is often not clear which method to use when presented with a new instance. This paper presents an approach which is motivated by the goal of building computer systems which can design heuristic methods. The overall aim is to explore the possibilities for automating the heuristic design process. We present a genetic programming system to automatically generate a good quality heuristic for each instance. It is not necessary to change the methodology depending on the problem type (one-, two-, or three-dimensional knapsack and bin packing problems), and it therefore has a level of generality unmatched by other systems in the literature. We carry out an extensive suite of experiments and compare with the best human designed heuristics in the literature. Note that our heuristic design methodology uses the same parameters for all the experiments. The contribution of this paper is to present a more general packing methodology than those currently available, and to show that, by using this methodology, it is possible for a computer system to design heuristics which are competitive with the human designed heuristics from the literature. This represents the first packing algorithm in the literature able to claim human competitive results in such a wide variety of packing domains.

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Soysal, Mehmet, Mustafa Çimen, Mine Ömürgönülşen i Sedat Belbağ. "Performance Comparison of Two Recent Heuristics for Green Time Dependent Vehicle Routing Problem". International Journal of Business Analytics 6, nr 4 (październik 2019): 1–11. http://dx.doi.org/10.4018/ijban.2019100101.

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This article concerns a green Time Dependent Capacitated Vehicle Routing Problem (TDCVRP) which is confronted in urban distribution planning. The problem is formulated as a Markovian Decision Process and a dynamic programming (DP) approach has been used for solving the problem. The article presents a performance comparison of two recent heuristics for the green TDCVRP that explicitly accounts for time dependent vehicle speeds and fuel consumption (emissions). These heuristics are the classical Restricted Dynamic Programming (RDP) algorithm, and the Simulation Based RDP that consists of weighted random sampling, RDP heuristic and simulation. The numerical experiments show that the Simulation Based Restricted Dynamic Programming heuristic can provide promising results within relatively short computational times compared to the classical Restricted Dynamic Programming for the green TDCVRP.

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Boston, Kevin, i Pete Bettinger. "An Analysis of Monte Carlo Integer Programming, Simulated Annealing, and Tabu Search Heuristics for Solving Spatial Harvest Scheduling Problems". Forest Science 45, nr 2 (1.05.1999): 292–301. http://dx.doi.org/10.1093/forestscience/45.2.292.

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Abstract Heuristics are commonly used to solve spatial harvest scheduling problems. They can generate spatially and temporally feasible solutions to large problems that traditional mathematical programming techniques are unable to solve. A common complaint about heuristics is that the quality of the solutions is unknown. We compared three heuristic techniques commonly used to solve spatial harvest scheduling problems: Monte Carlo integer programming, simulated annealing, and tabu search. Five hundred solutions to four problems, which had between 3000 to 5000 0-1 integer variables, were generated with each heuristic technique. In addition to the heuristic solutions, the optimal solution value was found to each problem using integer programming. Simulated annealing found the highest solution value for three of the four planning problems, and was less than 1% from the highest objective function value in the fourth problem. Tabu search located the best solution for the fourth planning problem. Monte Carlo integer programming had the lowest objective function for all four problems. Tabu search had the smallest range of solutions, followed by simulated annealing. Monte Carlo integer programming had the largest range of solutions. Using the Anderson-Darling statistics, the hypothesis that the solutions from each heuristic technique were distributed as a Weibull distribution was rejected for 10 of the 12 set of values. For the two solutions where the Weibull distribution was not rejected, the estimated optimal solution was found to be an unreliable estimate of the actual optimal solution. It appears that the reliability of using extreme value statistics to estimate the optimal solution is dependent on the quality of solutions generated by the heuristic procedure. For. Sci. 45(2):292-301.

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Ghaffariyan M, R., K. Stampfer, J. Sessions, T. Durston, CH Kanzian i M. Kuehmaier. "Road network optimization using heuristic and linear programming". Journal of Forest Science 56, No. 3 (1.04.2010): 137–45. http://dx.doi.org/10.17221/12/2009-jfs.

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To minimize the cost of logging, it is necessary to optimize the road density. The aim of this study was to determine optimal road spacing (ORS) in Northern Austria. The stepwise regression method was used in modelling. The production rate of tower yarder was 10.4 m3/PSHo (Productive system hours) and cost of 19.71 €.m–3. ORS was studied by calculating road construction cost, installation cost and yarding cost per m3 for different road spacing. The minimum total cost occurred at 39.15 €.m–3 and ORS would be 474 m assuming uphill and downhill yarding. The optimal road density and yarding distance are 21.1 m.ha–1 and 90 m, respectively. A sample logging area was used to plan different roads and, using network analysis, the best solution was found based on a modified shortest path algorithm. The network analysis results were very different from the optimal road spacing results that assumed roads and logging corridors could be located anywhere in the planning area at a constant cost. Mixed integer programming was also used to get a real optimal solution.

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Pommerening, Florian, Gabriele Röger, Malte Helmert i Blai Bonet. "LP-Based Heuristics for Cost-Optimal Planning". Proceedings of the International Conference on Automated Planning and Scheduling 24 (11.05.2014): 226–34. http://dx.doi.org/10.1609/icaps.v24i1.13621.

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Many heuristics for cost-optimal planning are based on linear programming. We cover several interesting heuristics of this type by a common framework that fixes the objective function of the linear program. Within the framework, constraints from different heuristics can be combined in one heuristic estimate which dominates the maximum of the component heuristics. Different heuristics of the framework can be compared on the basis of their constraints. With this new method of analysis, we show dominance of the recent LP-based state-equation heuristic over optimal cost partitioning on single-variable abstractions. We also show that the previously suggested extension of the state-equation heuristic to exploit safe variables cannot lead to an improved heuristic estimate. We experimentally evaluate the potential of the proposed framework on an extensive suite of benchmark tasks.

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Joshi, Vijay, i Prasad Modak. "Heuristic Algorithms for Waste Load Allocation in a River Basin". Water Science and Technology 21, nr 8-9 (1.08.1989): 1057–64. http://dx.doi.org/10.2166/wst.1989.0307.

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Waste load allocation for rivers has been a topic of growing interest. Dynamic programming based algorithms are particularly attractive in this context and are widely reported in the literature. Codes developed for dynamic programming are however complex, require substantial computer resources and importantly do not allow interactions of the user. Further, there is always resistance to utilizing mathematical programming based algorithms for practical applications. There has been therefore always a gap between theory and practice in systems analysis in water quality management. This paper presents various heuristic algorithms to bridge this gap with supporting comparisons with dynamic programming based algorithms. These heuristics make a good use of the insight gained in the system's behaviour through experience, a process akin to the one adopted by field personnel and therefore can readily be understood by a user familiar with the system. Also they allow user preferences in decision making via on-line interaction. Experience has shown that these heuristics are indeed well founded and compare very favourably with the sophisticated dynamic programming algorithms. Two examples have been included which demonstrate such a success of the heuristic algorithms.

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E. Nugraheni, Cecilia, Luciana Abednego i Maria Widyarini. "A Combination of Palmer Algorithm and Gupta Algorithm for Scheduling Problem in Apparel Industry". International Journal of Fuzzy Logic Systems 11, nr 1 (31.01.2021): 1–12. http://dx.doi.org/10.5121/ijfls.2021.11101.

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The apparel industry is a class of textile industry. Generally, the production scheduling problem in the apparel industry belongs to Flow Shop Scheduling Problems (FSSP). There are many algorithms/techniques/heuristics for solving FSSP. Two of them are the Palmer Algorithm and the Gupta Algorithm. Hyper-heuristic is a class of heuristics that enables to combine of some heuristics to produce a new heuristic. GPHH is a hyper-heuristic that is based on genetic programming that is proposed to solve FSSP [1]. This paper presents the development of a computer program that implements the GPHH. Some experiments have been conducted for measuring the performance of GPHH. From the experimental results, GPHH has shown a better performance than the Palmer Algorithm and Gupta Algorithm.

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Levy, D. N. L., i D. F. Beal. "Heuristic Programming in Artificial Intelligence". ICGA Journal 13, nr 1 (1.03.1990): 28. http://dx.doi.org/10.3233/icg-1990-13109.

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Ross, John Minor. "Puzzles as Heuristic Programming Exercises". Simulation & Gaming 21, nr 2 (czerwiec 1990): 190–97. http://dx.doi.org/10.1177/1046878190212006.

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Maher, Stephen J. "Enhancing large neighbourhood search heuristics for Benders’ decomposition". Journal of Heuristics 27, nr 4 (23.02.2021): 615–48. http://dx.doi.org/10.1007/s10732-021-09467-z.

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AbstractA general enhancement of the Benders’ decomposition (BD) algorithm can be achieved through the improved use of large neighbourhood search heuristics within mixed-integer programming solvers. While mixed-integer programming solvers are endowed with an array of large neighbourhood search heuristics, few, if any, have been designed for BD. Further, typically the use of large neighbourhood search heuristics is limited to finding solutions to the BD master problem. Given the lack of general frameworks for BD, only ad hoc approaches have been developed to enhance the ability of BD to find high quality primal feasible solutions through the use of large neighbourhood search heuristics. The general BD framework of SCIP has been extended with a trust region based heuristic and a general enhancement for large neighbourhood search heuristics. The general enhancement employs BD to solve the auxiliary problems of all large neighbourhood search heuristics to improve the quality of the identified solutions. The computational results demonstrate that the trust region heuristic and a general large neighbourhood search enhancement technique accelerate the improvement in the primal bound when applying BD.

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Soetanto, Tessa Vanina, Herry Christian Palit i Ika Munika. "STUDI PERBANDINGAN PERFORMANCE ALGORITMA HEURISTIK POUR TERHADAP MIXED INTEGER PROGRAMMING DALAM MENYELESAIKAN PENJADWALAN FLOWSHOP". Jurnal Teknik Industri 6, nr 1 (28.04.2005): 79–85. http://dx.doi.org/10.9744/jti.6.1.79-85.

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This paper presents a study about new heuristic algorithm performance compared to Mixed Integer Programming (MIP) method in solving flowshop scheduling problem to reach minimum makespan. Performance appraisal is based on Efficiency Index (EI), Relative Error (RE) and Elapsed Runtime. Abstract in Bahasa Indonesia : Makalah ini menyajikan penelitian tentang performance algoritma heuristik Pour terhadap metode Mixed Integer Programming (MIP) dalam menyelesaikan masalah penjadwalan flowshop dengan tujuan meminimalkan makespan. Penilaian performance dilakukan berdasarkan nilai Efficiency Index (EI), Relative Error (RE) dan Elapsed Runtime. Kata kunci: flowshop, makespan, algoritma heuristik Pour, Mixed Integer Programming.

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Hart, Emma, i Kevin Sim. "A Hyper-Heuristic Ensemble Method for Static Job-Shop Scheduling". Evolutionary Computation 24, nr 4 (grudzień 2016): 609–35. http://dx.doi.org/10.1162/evco_a_00183.

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We describe a new hyper-heuristic method NELLI-GP for solving job-shop scheduling problems (JSSP) that evolves an ensemble of heuristics. The ensemble adopts a divide-and-conquer approach in which each heuristic solves a unique subset of the instance set considered. NELLI-GP extends an existing ensemble method called NELLI by introducing a novel heuristic generator that evolves heuristics composed of linear sequences of dispatching rules: each rule is represented using a tree structure and is itself evolved. Following a training period, the ensemble is shown to outperform both existing dispatching rules and a standard genetic programming algorithm on a large set of new test instances. In addition, it obtains superior results on a set of 210 benchmark problems from the literature when compared to two state-of-the-art hyper-heuristic approaches. Further analysis of the relationship between heuristics in the evolved ensemble and the instances each solves provides new insights into features that might describe similar instances.

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Sinha Roy, Debdatta, Adriano Masone, Bruce Golden i Edward Wasil. "Modeling and Solving the Intersection Inspection Rural Postman Problem". INFORMS Journal on Computing 33, nr 3 (lipiec 2021): 1245–57. http://dx.doi.org/10.1287/ijoc.2020.1013.

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Local governments inspect roads to decide which segments and intersections to repair. Videos are taken using a camera mounted on a vehicle. The vehicle taking the videos proceeds straight or takes a left turn to cover an intersection fully. We introduce the intersection inspection rural postman problem (IIRPP), which is a new variant of the rural postman problem (RPP) that involves turns. We develop integer programming formulations of the IIRPP based on two different graph transformations to generate least-cost vehicle routes. One formulation is based on a new idea of transforming a graph. A second formulation is based on a graph transformation idea from the literature. Computational experiments show that the formulation involving the new graph transformation idea performs much better than the other formulation. We also develop an RPP-based heuristic and a heuristic based on a modified RPP. Heuristic solutions are improved by solving integer programming formulations on an induced subgraph. Computational experiments show that the heuristics based on the modified RPP perform much better than the RPP-based heuristics. The best-performing heuristic generates very good quality IIRPP-feasible routes on large street networks quickly. Summary of Contribution. Our paper addresses a real-world problem faced by local governments during road inspections. The real-world problem that we solve and the methodologies that we use fall at the intersection of computing and operations research. We introduce the intersection inspection rural postman problem, which is a new variant of the rural postman problem that involves turns to capture this real-world scenario. The rural postman problem is an important problem in vehicle routing. Studying new variants of this problem is key to extending the practice and theory of vehicle routing. We develop an integer programming formulation based on a new idea of transforming a graph and also develop heuristics based on the rural postman problem.

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Castro, Margarita P., Chiara Piacentini, Andre A. Cire i J. Christopher Beck. "Relaxed BDDs: An Admissible Heuristic for Delete-Free Planning Based on a Discrete Relaxation". Proceedings of the International Conference on Automated Planning and Scheduling 29 (25.05.2021): 77–85. http://dx.doi.org/10.1609/icaps.v29i1.3462.

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We investigate the use of relaxed binary decision diagrams (BDDs) as an alternative to linear programming (LP) for computing an admissible heuristic for the cost-optimal delete-free planning (DFP) problem. Our main contributions are the introduction of a novel BDD encoding, a construction algorithm for the sequential relaxation of a DFP task and a study of the effectiveness of relaxed BDD heuristics, both from a theoretical and practical perspective. We further show that relaxed BDDs can be used beyond heuristic computation to extract delete-free plans, find action landmarks, and identify redundant actions. Our empirical analysis shows that while BDD-based heuristics trail the state of the art, even small relaxed BDDs are competitive with the LP heuristic for the DFP task.

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Pesant, G., C. Quimper i A. Zanarini. "Counting-Based Search: Branching Heuristics for Constraint Satisfaction Problems". Journal of Artificial Intelligence Research 43 (26.02.2012): 173–210. http://dx.doi.org/10.1613/jair.3463.

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Designing a search heuristic for constraint programming that is reliable across problem domains has been an important research topic in recent years. This paper concentrates on one family of candidates: counting-based search. Such heuristics seek to make branching decisions that preserve most of the solutions by determining what proportion of solutions to each individual constraint agree with that decision. Whereas most generic search heuristics in constraint programming rely on local information at the level of the individual variable, our search heuristics are based on more global information at the constraint level. We design several algorithms that are used to count the number of solutions to specific families of constraints and propose some search heuristics exploiting such information. The experimental part of the paper considers eight problem domains ranging from well-established benchmark puzzles to rostering and sport scheduling. An initial empirical analysis identifies heuristic maxSD as a robust candidate among our proposals.eWe then evaluate the latter against the state of the art, including the latest generic search heuristics, restarts, and discrepancy-based tree traversals. Experimental results show that counting-based search generally outperforms other generic heuristics.

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Kapunac, Stefan. "Integer Linear Programming Models and Greedy Heuristic for the Minimum Weighted Independent Dominating Set Problem". Serdica Journal of Computing 17, nr 2 (15.04.2024): 117–36. http://dx.doi.org/10.55630/sjc.2023.17.117-136.

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This paper explores the Minimum Weighted Independent Dominating Set Problem and proposes novel approaches to tackle it. Namely, two integer linear programming formulations and a fast greedy heuristic as an alternative approach are proposed. Extensive computational experiments are conducted to evaluate the performance of these approaches on the established set of benchmark instances for the problem. The obtained results demonstrate that the introduced integer linear programming models are able to achieve optimal solutions on all instances with 100 nodes and significantly outperform existing exact methods on numerous other instances. Additionally, the greedy heuristic exhibits superior performance compared to competing greedy heuristics, particularly on random graphs. These findings suggest promising directions for future research, including the integration of these methods into hybrid algorithms or metaheuristic frameworks.

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Mallett, Ian, Sylvie Thiebaux i Felipe Trevizan. "Progression Heuristics for Planning with Probabilistic LTL Constraints". Proceedings of the AAAI Conference on Artificial Intelligence 35, nr 13 (18.05.2021): 11870–79. http://dx.doi.org/10.1609/aaai.v35i13.17410.

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Probabilistic planning subject to multi-objective probabilistic temporal logic (PLTL) constraints models the problem of computing safe and robust behaviours for agents in stochastic environments. We present novel admissible heuristics to guide the search for cost-optimal policies for these problems. These heuristics project and decompose LTL formulae obtained by progression to estimate the probability that an extension of a partial policy satisfies the constraints. Their computation with linear programming is integrated with the recent PLTL-dual heuristic search algorithm, enabling more aggressive pruning of regions violating the constraints. Our experiments show that they further widen the scalability gap between heuristic search and verification approaches to these planning problems.

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Beal, Don. "Heuristic Programming in Artificial Intelligence 3". ICGA Journal 15, nr 2 (1.06.1992): 79–80. http://dx.doi.org/10.3233/icg-1992-15206.

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Kölling, Michael, i Fraser McKay. "Heuristic Evaluation for Novice Programming Systems". ACM Transactions on Computing Education 16, nr 3 (27.06.2016): 1–30. http://dx.doi.org/10.1145/2872521.

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Ni, Zhen, Haibo He, Xiangnan Zhong i Danil V. Prokhorov. "Model-Free Dual Heuristic Dynamic Programming". IEEE Transactions on Neural Networks and Learning Systems 26, nr 8 (sierpień 2015): 1834–39. http://dx.doi.org/10.1109/tnnls.2015.2424971.

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Chang, Suk-gwon, i Dong-wan Tcha. "A heuristic for multiple choice programming". Computers & Operations Research 12, nr 1 (styczeń 1985): 25–37. http://dx.doi.org/10.1016/0305-0548(85)90004-8.

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Fontecilla, R. "A heuristic algorithm for nonlinear programming". Journal of Optimization Theory and Applications 58, nr 3 (wrzesień 1988): 431–42. http://dx.doi.org/10.1007/bf00939391.

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Venayagamoorthy, G. K., R. G. Harley i D. C. Wunsch. "Comparison of heuristic dynamic programming and dual heuristic programming adaptive critics for neurocontrol of a turbogenerator". IEEE Transactions on Neural Networks 13, nr 3 (maj 2002): 764–73. http://dx.doi.org/10.1109/tnn.2002.1000146.

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Borges, José G., Howard M. Hoganson i Dietmar W. Rose. "Combining a Decomposition Strategy with Dynamic Programming to Solve Spatially Constrained Forest Management Scheduling Problems". Forest Science 45, nr 2 (1.05.1999): 201–12. http://dx.doi.org/10.1093/forestscience/45.2.201.

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Abstract A decomposition approach to solve the forest management scheduling adjacency problem is developed for application to large forests. Overlapping subproblems amenable to exact dynamic programming solution are solved sequentially. A heuristic is used to define and link subproblems such that near-optimal solutions to the master problem are obtained. Both the contrasting size and the irregular shape of stands complicate the problem of formulating the dynamic programming network. Subproblem size and the sequencing of stands for each corresponding dynamic programming network are defined simultaneously, as model size is especially sensitive to stand sequencing. Emphasis is on efficient dynamic programming formulations to allow for large subproblems. Results from over 100 test computer runs are discussed for applications to 3 large problems. Results suggest that the strategy can consistently produce near-optimal solutions at reasonable computational cost. A procedure is developed to derive three slightly different adjacency problems so that the optimal solution can be found. Results for applications to the modified problems show that the proposed heuristic's solutions were within 0.01, 0.04, and 0.01% of the optimal solution, respectively. The proposed solution method consistently outperformed two other heuristics that were applied. For. Sci. 45(1):201-212.

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Lakshmi, D., i R. Ponnusamy. "E-mastering application utilizing pertinence heuristics evaluation for children". International Journal of Engineering & Technology 7, nr 1.9 (1.03.2018): 260. http://dx.doi.org/10.14419/ijet.v7i1.9.10009.

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Choosing ease of use assessment techniques (UEMs) to uncover convenience issues in e-learning projects is affected by time, cost, simplicity of use, and effectiveness. Heuristic assessment has turned into a generally acknowledged technique for ease of use assessment in programming improvement. This paper presents Heuristic Assessment for Tyke E-learning applications (HECE), an exhaustive arrangement of heuristics for kid e-learning alongside a definite clarification for the ease of use specialists on the most proficient method to apply them. These arrangements of heuristics depend on Nielsen's unique ten heuristics produced for programming. Nielsen heuristics are essentially nonexclusive, and won't not include ease of use credits particular to kids or e-learning. The new HECE set would conquer these weaknesses. The legitimacy and viability of these heuristics were assessed against two created e-learning programs composed by ReDSOFT for KG-2 and exceptional need understudies. The outcomes showed that HECE distinguished subjective likenesses and contrasts with client testing, and that HECE is most appropriate for assessing general and kid ease of use. Consolidated with client testing, HECE offers another track that can help with directing the kid e-learning industry to outline applications that are both instructive and pleasurable for youngsters.

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Bader-El-Den, Mohamed, Riccardo Poli i Shaheen Fatima. "Evolving timetabling heuristics using a grammar-based genetic programming hyper-heuristic framework". Memetic Computing 1, nr 3 (11.10.2009): 205–19. http://dx.doi.org/10.1007/s12293-009-0022-y.

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GAJJAR, HASMUKH K., i GAJENDRA K. ADIL. "A DYNAMIC PROGRAMMING HEURISTIC FOR RETAIL SHELF SPACE ALLOCATION PROBLEM". Asia-Pacific Journal of Operational Research 28, nr 02 (kwiecień 2011): 183–99. http://dx.doi.org/10.1142/s0217595911003120.

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Shelf space allocation to products greatly impacts the profitability in a retail store. In this paper, we consider a retail shelf-space allocation problem where retailer wishes to allocate the available spaces of different shelves to a large number of products considering direct space elasticity in the product's demand. There is a great interest to develop efficient heuristics due to NP-hard nature of this problem. We propose a dynamic programming heuristic (DPH) to obtain near optimal solution in a reasonable time to solve this problem. The empirical results found that DPH obtained near optimal solutions for randomly generated instances of problems with size (products, shelves) varying from (100, 30) to (200, 50) within a few seconds of CPU time. The performance of DPH is benchmarked against an existing local search heuristic (LSH). It was found that DPH takes substantially less CPU time and attains a solution close to that obtained by LSH. Thus, DPH has great potential to solve the problem of realistic size within reasonable time. The proposed DPH is also applied to a case of an existing retail store.

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Hanafi, Saïd, Jasmina Lazic, Nenad Mladenovic, Christophe Wilbaut i Igor Crévits. "New variable neighbourhood search based 0-1 MIP heuristics". Yugoslav Journal of Operations Research 25, nr 3 (2015): 343–60. http://dx.doi.org/10.2298/yjor140219014h.

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In recent years many so-called matheuristics have been proposed for solving Mixed Integer Programming (MIP) problems. Though most of them are very efficient, they do not all theoretically converge to an optimal solution. In this paper we suggest two matheuristics, based on the variable neighbourhood decomposition search (VNDS), and we prove their convergence. Our approach is computationally competitive with the current state-of-the-art heuristics, and on a standard benchmark of 59 0-1 MIP instances, our best heuristic achieves similar solution quality to that of a recently published VNDS heuristic for 0-1 MIPs within a shorter execution time.

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Castro, Margarita Paz, Chiara Piacentini, Andre Augusto Cire i J. Christopher Beck. "Solving Delete Free Planning with Relaxed Decision Diagram Based Heuristics". Journal of Artificial Intelligence Research 67 (19.03.2020): 607–51. http://dx.doi.org/10.1613/jair.1.11659.

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We investigate the use of relaxed decision diagrams (DDs) for computing admissible heuristics for the cost-optimal delete-free planning (DFP) problem. Our main contributions are the introduction of two novel DD encodings for a DFP task: a multivalued decision diagram that includes the sequencing aspect of the problem and a binary decision diagram representation of its sequential relaxation. We present construction algorithms for each DD that leverage these different perspectives of the DFP task and provide theoretical and empirical analyses of the associated heuristics. We further show that relaxed DDs can be used beyond heuristic computation to extract delete-free plans, find action landmarks, and identify redundant actions. Our empirical analysis shows that while DD-based heuristics trail the state of the art, even small relaxed DDs are competitive with the linear programming heuristic for the DFP task, thus, revealing novel ways of designing admissible heuristics.

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Yarmand, Hamed, i David Craft. "Effective heuristics for beam angle optimization in radiation therapy". SIMULATION 94, nr 11 (19.03.2018): 1041–49. http://dx.doi.org/10.1177/0037549718761108.

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In radiation therapy, the main challenge is to deliver the dose to the tumor while sparing healthy tissues around the tumor. One important decision to make is the beam configuration. The corresponding mathematical problem, known as beam angle optimization (BAO), is a large-scale problem. We propose three novel heuristic approaches to reduce the computation time and find high-quality treatment plans for BAO. The first heuristic is based on the fact that the beams that are geometrically close to each other (i.e., ‘adjacent’ beams) have similar impacts, and hence are less likely to be used in the optimal configuration simultaneously. Therefore, in this heuristic, referred to as ‘neighbor cuts’, their use is limited. The second heuristic is to eliminate the beams with small contribution to dose delivery in the ideal plan when all candidate beams can be used. Finally, the number of beams is reduced in the third heuristic while ensuring the quality of the plan remains within a pre-specified range. These heuristics can be applied to any formulation for BAO for various external radiation therapy techniques. We evaluate these heuristics by applying them to a mixed integer programming (MIP) formulation of BAO for a phantom liver case and a clinical liver case.

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Ni, Zhen, i Haibo He. "Heuristic dynamic programming with internal goal representation". Soft Computing 17, nr 11 (3.09.2013): 2101–8. http://dx.doi.org/10.1007/s00500-013-1112-9.

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Sen, S. K., i A. Ramful. "A direct heuristic algorithm for linear programming". Proceedings Mathematical Sciences 110, nr 1 (luty 2000): 79–101. http://dx.doi.org/10.1007/bf02829483.

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Li, Yaxian, Ozlem Ergun i George L. Nemhauser. "A dual heuristic for mixed integer programming". Operations Research Letters 43, nr 4 (lipiec 2015): 411–17. http://dx.doi.org/10.1016/j.orl.2015.05.007.

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Bastert, Oliver, Benjamin Hummel i Sven de Vries. "A Generalized Wedelin Heuristic for Integer Programming". INFORMS Journal on Computing 22, nr 1 (luty 2010): 93–107. http://dx.doi.org/10.1287/ijoc.1090.0328.

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Herzallah, Randa. "Probabilistic dual heuristic programming-based adaptive critic". International Journal of Systems Science 41, nr 2 (luty 2010): 227–39. http://dx.doi.org/10.1080/00207720903045767.

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Baessler, Felix. "A heuristic 0–1 integer programming method". Operations-Research-Spektrum 14, nr 1 (marzec 1992): 11–18. http://dx.doi.org/10.1007/bf01783498.

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Braun, Stephen, i John E. Mitchell. "A Semidefinite Programming Heuristic for Quadratic Programming Problems with Complementarity Constraints". Computational Optimization and Applications 31, nr 1 (maj 2005): 5–29. http://dx.doi.org/10.1007/s10589-005-1014-6.

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Burke, Edmund K., Matthew Hyde, Graham Kendall i John Woodward. "A Genetic Programming Hyper-Heuristic Approach for Evolving 2-D Strip Packing Heuristics". IEEE Transactions on Evolutionary Computation 14, nr 6 (grudzień 2010): 942–58. http://dx.doi.org/10.1109/tevc.2010.2041061.

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Eloundou, José, David Baudry, M’hammed Sahnoun, Abdelaziz Bensrhair, Anne Louis i Bélahcène Mazari. "Flexible Job Shop Models for Solving Scheduling and Layout Problems Using Coloured Petri Nets". Applied Mechanics and Materials 598 (lipiec 2014): 638–42. http://dx.doi.org/10.4028/www.scientific.net/amm.598.638.

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In this paper we propose models for solving both the layout manufacturing problems and the scheduling manufacturing systems. These models are based on Coloured petri Nets. The particularity of our models is the possibility to include complex programmable functions inside the petri nets models. In our case the programming language is SML/NJ. The advantage of programming language is the possibility to use Heuristics or Meta-heuristic optimization methods inside the Coloured Petri Nets (CPN) model without the necessity to relaunch the simulation of the model at each step of optimization. For describing, analysing and simulating our models we will use CPN Tools.

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Nugraha, Lanang Alun. "Optimasi Travelling Thief Problem Menggunakan Algoritma Tree Physiology Optimization Berbasis Hiper Heuristik". JATISI (Jurnal Teknik Informatika dan Sistem Informasi) 8, nr 4 (13.12.2021): 1810–20. http://dx.doi.org/10.35957/jatisi.v8i4.1167.

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Travelling thief problem (TTP) merupakan gabungan dari permasalahan travelling salesman problem dan knapsack problem. travelling thief problem sendiri merupakan permasalahan NP-Hard sehingga permasalahan sebagian besar diselesaikan menggunakan algoritma heuristic dan terus berkembang seiring berjalanya waktu. Algoritma yang digunakan pada penelitian ini adalah simple random untuk pemilihan low level heuristic (LLH) dan tree physiology optimization (TPO) untuk langkah move acceptance dengan menggunakan model Hyper-Heuristics. Pada penelitian yang telah dilakukan sebelumnya algoritma TPO mampu menghasilkan nilai yang cukup kompetitif dengan waktu komputasi yang baik, sedangkan pemodelan Hyper-Heuristics dapat menghasilkan nilai yang konsisten pada data yang beragam. Penelitian diawali dengan memodelkan algoritma TPO menjadi Hyper-Heuristics dan diuji coba dengan data dari TSPLib. Dari hasil uji coba yang dilakukan dapat dilihat bagaimana performa algoritma baru pada data yang diuji. Berdasarkan hasil yang didapat dari penelitian ini dapat disimpulkan bahwa algoritma LLH TPO dapat mengolah data TTP dengan ukuran di bawah 100 dengan cukup baik terbukti dengan hasil yang lebih baik dari metode genetic programming based hyper-heuristic (GPHS) yang telah ada sebelumnya, namun pada data di atas 100 performa LLH TPO menurun jika dibandingkan dengan metode GPHS.

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Abdul-Niby, M., M. Alameen, A. Salhieh i A. Radhi. "Improved Genetic and Simulating Annealing Algorithms to Solve the Traveling Salesman Problem Using Constraint Programming". Engineering, Technology & Applied Science Research 6, nr 2 (17.04.2016): 927–30. http://dx.doi.org/10.48084/etasr.627.

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The Traveling Salesman Problem (TSP) is an integer programming problem that falls into the category of NP-Hard problems. As the problem become larger, there is no guarantee that optimal tours will be found within reasonable computation time. Heuristics techniques, like genetic algorithm and simulating annealing, can solve TSP instances with different levels of accuracy. Choosing which algorithm to use in order to get a best solution is still considered as a hard choice. This paper suggests domain reduction as a tool to be combined with any meta-heuristic so that the obtained results will be almost the same. The hybrid approach of combining domain reduction with any meta-heuristic encountered the challenge of choosing an algorithm that matches the TSP instance in order to get the best results.

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Comploi-Taupe, Richard, Gerhard Friedrich, Konstantin Schekotihin i Antonius Weinzierl. "Domain-Specific Heuristics in Answer Set Programming: A Declarative Non-Monotonic Approach". Journal of Artificial Intelligence Research 76 (5.01.2023): 59–114. http://dx.doi.org/10.1613/jair.1.14091.

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Domain-specific heuristics are an essential technique for solving combinatorial problems efficiently. Current approaches to integrate domain-specific heuristics with Answer Set Programming (ASP) are unsatisfactory when dealing with heuristics that are specified non-monotonically on the basis of partial assignments. Such heuristics frequently occur in practice, for example, when picking an item that has not yet been placed in bin packing. Therefore, we present novel syntax and semantics for declarative specifications of domain-specific heuristics in ASP. Our approach supports heuristic statements that depend on the partial assignment maintained during solving, which has not been possible before. We provide an implementation in Alpha that makes Alpha the first lazy-grounding ASP system to support declaratively specified domain-specific heuristics. Two practical example domains are used to demonstrate the benefits of our proposal. Additionally, we use our approach to implement informed search with A*, which is tackled within ASP for the first time. A* is applied to two further search problems. The experiments confirm that combining lazy-grounding ASP solving and our novel heuristics can be vital for solving industrial-size problems.

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Aramon Bajestani, Maliheh, i J. Christopher Beck. "Scheduling an Aircraft Repair Shop". Proceedings of the International Conference on Automated Planning and Scheduling 21 (22.03.2011): 10–17. http://dx.doi.org/10.1609/icaps.v21i1.13450.

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We address a scheduling problem in the context of military aircraft maintenance where the goal is to meet the aircraft requirements for a number of missions in the presence of breakdowns. The assignment of aircraft to a mission must consider the requirements for the mission, the probability of aircraft failure, and capacity of the repair shop that maintains the aircraft. Therefore, a solution both assigns aircraft to missions and schedules the repair shop to meet the assignments. We propose a dispatching heuristic algorithm; three complete approaches based on mixed integer programming, constraint programming, and logic-based Benders decomposition; and a hybrid heuristic-complete approach. Experiments demonstrate that the logic-based Benders variation combining mixed integer programming and constraint programming outperforms the other approaches, that the dispatching heuristic can feasibly schedule the repair shop in a very short time, and that using the dispatching solution as a bound marginally improves the complete approaches.

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Trevizan, Felipe, Sylvie Thiébaux, Pedro Santana i Brian Williams. "Heuristic Search in Dual Space for Constrained Stochastic Shortest Path Problems". Proceedings of the International Conference on Automated Planning and Scheduling 26 (30.03.2016): 326–34. http://dx.doi.org/10.1609/icaps.v26i1.13768.

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We consider the problem of generating optimal stochastic policies for Constrained Stochastic Shortest Path problems, which are a natural model for planning under uncertainty for resource-bounded agents with multiple competing objectives. While unconstrained SSPs enjoy a multitude of efficient heuristic search solution methods with the ability to focus on promising areas reachable from the initial state, the state of the art for constrained SSPs revolves around linear and dynamic programming algorithms which explore the entire state space. In this paper, we present i-dual, which, to the best of our knowledge, is the first heuristic search algorithm for constrained SSPs. To concisely represent constraints and efficiently decide their violation, i-dual operates in the space of dual variables describing the policy occupation measures. It does so while retaining the ability to use standard value function heuristics computed by well-known methods. Our experiments on a suite of PPDDL problems augmented with constraints show that these features enable i-dual to achieve up to two orders of magnitude improvement in run-time and memory over linear programming algorithms.

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Mendoza, Guillermo A. "A heuristic programming approach in estimating efficient target levels in goal programming". Canadian Journal of Forest Research 16, nr 2 (1.04.1986): 363–66. http://dx.doi.org/10.1139/x86-062.

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A heuristic programming approach in estimating efficient target levels in goal programming is described. The approach is designed to improve the computational efficiency of defining the decision space of a goal programming problem. An illustrative example is used to describe the procedure. The sample problem used was adopted from a previous study.

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Hajji, Mohamed Karim, Hatem Hadda i Najoua Dridi. "Makespan Minimization for the Two-Stage Hybrid Flow Shop Problem with Dedicated Machines: A Comprehensive Study of Exact and Heuristic Approaches". Computation 11, nr 7 (10.07.2023): 137. http://dx.doi.org/10.3390/computation11070137.

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This paper presents a comprehensive approach for minimizing makespan in the challenging two-stage hybrid flowshop with dedicated machines, a problem known to be strongly NP-hard. This study proposed a constraint programming approach, a novel heuristic based on a priority rule, and Tabu search procedures to tackle this optimization problem. The constraint programming model, implemented using a commercial solver, serves as the exact resolution method, while the heuristic and Tabu search explore approximate solutions simultaneously. The motivation behind this research is the need to address the complexities of scheduling problems in the context of two-stage hybrid flowshop with dedicated machines. This problem presents significant challenges due to its NP-hard nature and the need for efficient optimization techniques. The contribution of this study lies in the development of an integrated approach that combines constraint programming, a novel heuristic, and Tabu search to provide a comprehensive and efficient solution. The proposed constraint programming model offers exact resolution capabilities, while the heuristic and Tabu search provide approximate solutions, offering a balance between accuracy and efficiency. To enhance the search process, the research introduces effective elimination rules, which reduce the search space and simplify the search effort. This approach improves the overall optimization performance and contributes to finding high-quality solutions. The results demonstrate the effectiveness of the proposed approach. The heuristic approach achieves complete success in solving all instances for specific classes, showcasing its practical applicability. Furthermore, the constraint programming model exhibits exceptional efficiency, successfully solving problems with up to n=500 jobs. This efficiency is noteworthy compared to instances solved by other exact solution approaches, indicating the scalability and effectiveness of the proposed method.

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