Showing papers on "Heuristic published in 1994"

Heuristic Motion Planning in Dynamic Environments Using Velocity Obstacles

Abstract: This paper presents heuristic methods for motion planning in dynamic environments, based on the concept of Velocity Obstacle (VO).

A genetic algorithm for multiprocessor scheduling

Abstract: The problem of multiprocessor scheduling can be stated as finding a schedule for a general task graph to be executed on a multiprocessor system so that the schedule length can be minimized. This scheduling problem is known to be NP-hard, and methods based on heuristic search have been proposed to obtain optimal and suboptimal solutions. Genetic algorithms have recently received much attention as a class of robust stochastic search algorithms for various optimization problems. In this paper, an efficient method based on genetic algorithms is developed to solve the multiprocessor scheduling problem. The representation of the search node is based on the order of the tasks being executed in each individual processor. The genetic operator proposed is based on the precedence relations between the tasks in the task graph. Simulation results comparing the proposed genetic algorithm, the list scheduling algorithm, and the optimal schedule using random task graphs, and a robot inverse dynamics computational task graph are presented. >

Scheduling flexible manufacturing systems using Petri nets and heuristic search

Abstract: Petri net modeling combined with heuristic search provides a new scheduling method for flexible manufacturing systems. The method formulates a scheduling problem with a Petri net model. Then, it generates and searches a partial reachability graph to find an optimal or near optimal feasible schedule in terms of the firing sequence of the transitions of the Petri net model. The method can handle features such as routing flexibility, shared resources, lot sizes and concurrency. By following the generated schedule, potential deadlocks in the Petri net model and the system can be avoided. Hence the analytical overhead to guarantee the liveness of the model and the system is eliminated. Some heuristic functions for efficient search are explored and the experimental results are presented. >

A hierarchical decomposition approach for transmission network expansion planning

Abstract: This paper presents a hierarchical decomposition approach for optimal transmission network expansion planning. A major difficulty in obtaining global optimal solutions for complex, real-life networks is due to the nonconvexity of the problem. Hierarchical decomposition has proved to be an efficient heuristic for coping with nonconvexity, as illustrated in the test results section of the paper. Significant reductions in investment costs have been obtained in some practical cases for which results are available in the literature. The current implementation of the hierarchical decomposition approach utilizes three different levels of network modeling: transportation models, hybrid models, and linearized power flow models. An initial solution is obtained for the simplest model (transportation model) and as one moves towards the final solution the algorithm successively switches to more accurate models. >

Heuristic and special case algorithms for dispersion problems

Abstract: The dispersion problem arises in selecting facilities to maximize some function of the distances between the facilities The problem also arises in selecting nondominated solutions for multiobjective decision making It is known to be NP-hard under two objectives: maximizing the minimum distance (MAX-MIN) between any pair of facilities and maximizing the average distance (MAX-AVG) We consider the question of obtaining near-optimal solutions for MAX-MIN, we show that if the distances do not satisfy the triangle inequality, there is no polynomial-time relative approximation algorithm unless P = NP When the distances satisfy the triangle inequality, we analyze an efficient heuristic and show that it provides a performance guarantee of two We also prove that obtaining a performance guarantee of less than two is NP-hard for MAX-AVG, we analyze an efficient heuristic and show that it provides a performance guarantee of four when the distances satisfy the triangle inequality We also present a polynomial-ti

The total tardiness problem: review and extensions

Abstract: We provide a unified framework for the total tardiness problem by surveying the related literature in the single-machine, parallel machine, flowshop and jobshop settings. We focus on critically evaluating the heuristic algorithms; we also propose new heuristics for both the single-machine and the parallel-machine tardiness problems. Finally, we identify the areas where further research is needed and we give directions for future research.

Using a semantic concordance for sense identification

Abstract: This paper proposes benchmarks for systems of automatic sense identification. A textual corpus in which open-class words had been tagged both syntactically and semantically was used to explore three statistical strategies for sense identification: a guessing heuristic, a most-frequent heuristic, and a co-occurrence heuristic. When no information about sense-frequencies was available, the guessing heuristic using the numbers of alternative senses in WordNet was correct 45% of the time. When statistics for sense-frequencies were derived from a semantic concordance, the assumption that each word is used in its most frequently occurring sense was correct 69% of the time; when that figure was calculated for polysemous words alone, it dropped to 58%. And when a co-occurrence heuristic took advantage of prior occurrences of words together in the same sentences, little improvement was observed. The semantic concordance is still too small to estimate the potential limits of a co-occurrence heuristic.

Learning Bayesian networks: the combination of knowledge and statistical data

Abstract: We describe algorithms for learning Bayesian networks from a combination of user knowledge and statistical data. The algorithms have two components: a scoring metric and a search procedure. The scoring metric takes a network structure, statistical data, and a user's prior knowledge, and returns a score proportional to the posterior probability of the network structure given the data. The search procedure generates networks for evaluation by the scoring metric. Our contributions are threefold. First, we identify two important properties of metrics, which we call score equivalence and parameter modularity. These properties have been mostly ignored, but when combined, greatly simplify the encoding of a user's prior knowledge. In particular, a user can express his knowledge—for the most part—as a single prior Bayesian network for the domain. Second, we describe greedy hill-climbing and annealing search algorithms to be used in conjunction with scoring metrics. In the special case where each node has at most one parent, we show that heuristic search can be replaced with a polynomial algorithm to identify the networks with the highest score. Third, we describe a methodology for evaluating Bayesian-network learning algorithms. We apply this approach to a comparison of our metrics and search procedures.

A No-wait Flowshop Scheduling Heuristic to Minimize Makespan

Abstract: The scheduling problem in the no-wait or constrained flowshop, with the makespan objective, is considered in this article. A simple heuristic algorithm is proposed on the basis of heuristic preference relations and job insertion. When evaluated over a large number of problems of various sizes, the solutions given by the proposed heuristic are found to be fairly accurate and much superior to those given by the two existing heuristics.

Solution reuse in dynamic constraint satisfaction problems

Abstract: Many AI problems can be modeled as constraint satisfaction problems (CSP), but many of them are actually dynamic: the set of constraints to consider evolves because of the environment, the user or other agents in the framework of a distributed system. In this context, computing a new solution from scratch after each problem change is possible, but has two important drawbacks: inefficiency and instability of the successive solutions. In this paper, we propose a method for reusing any previous solution and producing a new one by local changes on the previous one. First we give the key idea and the corresponding algorithm. Then we establish its properties: termination, correctness and completeness. We show how it can be used to produce a solution, either from an empty assignment, or from any previous assignment and how it can be improved using filtering or learning methods, such as forward-checking or nogood-recording. Experimental results related to efficiency and stability are given, with comparisons with well known algorithms such as backtrack, heuristic repair or dynamic backtracking.

Efficient network flow based min-cut balanced partitioning

Abstract: We consider the problem of bipartitioning a circuit into two balanced components that minimizes the number of crossing nets. Previously, the Kernighan and Lin type (K&L) heuristics, the simulated annealing approach, and the spectral method were given to solve the problem. However, network flow techniques were overlooked as a viable approach to min-cut balanced bipartition to due its high complexity. In this paper we propose a balanced bipartition heuristic based on repeated max-flow min-cut techniques, and give an efficient implementation that has the same asymptotic time complexity as that of one max-flow computation. We implemented our heuristic algorithm in a package called FBB. The experimental results demonstrate that FBB outperforms the K&L heuristics and the spectral method in terms of the number of crossing nets, and the efficient implementation makes it possible to partition large, circuit instances with reasonable runtime. For example, the average elapsed time for bipartitioning a circuit S35932 of almost 20K gates is less than 20 minutes.

A heuristic based fuzzy reasoning approach for distribution system service restoration

Abstract: A fuzzy reasoning approach is proposed for the service restoration of a distribution system. After the location of a fault has been identified and the faulted zone has been isolated, it is important for the operators to reach a proper service restoration plan in order to restore the electricity service outside the faulted zone. The operators tend to use their past experience and heuristic rules to devise such a restoration plan because it must satisfy a lot of practical needs and objectives. In addition, the operators' needs and heuristic rules are often expressed in imprecise linguistic terms. In this paper, fuzzy set notation is employed to deal with these imprecise linguistic variables and a set of fuzzy reasoning procedures are developed to implement the operators' heuristic rules. These procedures can be employed to solve the multiple-objective problem of service restoration described in imprecise linguistic variables. To demonstrate the effectiveness of the proposed fuzzy reasoning approach, service restoration on a distribution system within the service area of Taipei West District Office of Taiwan Power Company is examined. It is found that a proper restoration plan can be reached very efficiently by the proposed approach. >

An edge-based heuristic for Steiner routing

Abstract: A new approximation heuristic for finding a rectilinear Steiner tree of a set of nodes is presented. It starts with a rectilinear minimum spanning tree of the nodes and repeatedly connects a node to the nearest point on the rectangular layout of an edge, removing the longest edge of the loop thus formed. A simple implementation of the heuristic using conventional data structures is compared with previously existing algorithms. The performance (i.e., quality of the route produced) of our algorithm is as good as the best reported algorithm, while the running time is an order of magnitude better than that of this best algorithm. It is also shown that the asymptotic time complexity for the algorithm can be improved to O(n log n), where n is the number of points in the set. >

Capacitated lot-sizing with sequence dependent setup costs

Abstract: A new model is presented for capacitated lot-sizing with sequence dependent setup costs. The model is solved heuristically with a backward oriented method; the sequence and lot-size decisions are based on a priority rule which consists of a convex combination of setup and holding costs. A computational study is performed where the heuristic is compared with the Fleischmann approach for the discrete lot-sizing and scheduling problem with sequence dependent setup costs.

Using fuzzy sets to model the uncertainty in the fault location process of distribution networks

Abstract: In the computerized fault diagnosis of distribution networks the heuristic knowledge of the control center operators can be combined with the information obtained from the network database and SCADA system. However, the nature of the heuristic knowledge is inexact and uncertain. Also the information obtained from the remote control system contains uncertainty and may be incorrect, conflicting or inadequate. This paper proposes a method based on fuzzy set theory to deal with the uncertainty involved in the process of locating faults in distribution networks. The method is implemented in a prototype version of the distribution network operation support system. >

Alopex: A correlation-based learning algorithm for feedforward and recurrent neural networks

Abstract: We present a learning algorithm for neural networks, called Alopex. Instead of error gradient, Alopex uses local correlations between changes in individual weights and changes in the global error measure. The algorithm does not make any assumptions about transfer functions of individual neurons, and does not explicitly depend on the functional form of the error measure. Hence, it can be used in networks with arbitrary transfer functions and for minimizing a large class of error measures. The learning algorithm is the same for feedforward and recurrent networks. All the weights in a network are updated simultaneously, using only local computations. This allows complete parallelization of the algorithm. The algorithm is stochastic and it uses a “temperature” parameter in a manner similar to that in simulated annealing. A heuristic “annealing schedule” is presented that is effective in finding global minima of error surfaces. In this paper, we report extensive simulation studies illustrating these advantages and show that learning times are comparable to those for standard gradient descent methods. Feedforward networks trained with Alopex are used to solve the MONK's problems and symmetry problems. Recurrent networks trained with the same algorithm are used for solving temporal XOR problems. Scaling properties of the algorithm are demonstrated using encoder problems of different sizes and advantages of appropriate error measures are illustrated using a variety of problems.

Weak-commitment search for solving constraint satisfaction problems

Abstract: The min-conflict heuristic (Minton et al. 1992) has been introduced into backtracking algorithms and iterative improvement algorithms as a powerful heuristic for solving constraint satisfaction problems. Backtracking algorithms become inefficient when a bad partial solution is constructed, since an exhaustive search is required for revising the bad decision. On the other hand, iterative improvement algorithms do not construct a consistent partial solution and can revise a bad decision without exhaustive search. However, most of the powerful heuristics obtained through the long history of constraint satisfaction studies (e.g., forward checking (Haralick & Elliot 1980)) presuppose the existence of a consistent partial solution. Therefore, these heuristics can not be applied to iterative improvement algorithms. Furthermore, these algorithms are not theoretically complete. In this paper, a new algorithm called weak-commitment search which utilizes the min-conflict heuristic is developed. This algorithm removes the drawbacks of backtracking algorithms and iterative improvement algorithms, i.e., the algorithm can revise bad decisions without exhaustive search, the completeness of the algorithm is guaranteed, and various heuristics can be introduced since a consistent partial solution is constructed. The experimental results on various example problems show that this algorithm is 3 to 10 times more efficient than other algorithms.

Background structure in document images

Abstract: A method for analyzing the structure of the white background in document images is described, along with applications to the problem of isolating blocks of machine-printed text. The approach is based on computational-geometry algorithms for off-line enumeration of maximal white rectangles and on-line rectangle unification. These support a fast, simple, and general heuristic for geometric layout segmentation, in which white space is covered greedily by rectangles until all text blocks are isolated. Design of the heuristic can be substantially automated by an analysis of the empirical statistical distribution of properties of covering rectangles: for example, the stopping rule can be chosen by Rosenblatt’s perceptron training algorithm. Experimental trials show good behavior on the large and useful class of textual Manhattan layouts. On complex layouts from English-language technical journals of many publishers, the method finds good segmentations in a uniform and nearly parameter-free manner. On a variety of non-Latin texts, some with vertical text lines, the method finds good segmentations without prior knowledge of page and text-line orientation.

Divide and conquer in multi-agent planning

Abstract: In this paper, we suggest an approach to multi-agent planning that contains heuristic elements. Our method makes use of subgoals, and derived sub-plans, to construct a global plan. Agents solve their individual sub-plans, which are then merged into a global plan. The suggested approach may reduce overall planning time and derives a plan that approximates the optimal global plan that would have been derived by a central planner, given those original subgoals. We consider two different scenarios. The first involves a group of agents with a common goal. The second considers how agents can interleave planning and execution when planning towards a common, though dynamic, goal.

Static and Dynamic Layout Problems with Varying Areas

Abstract: This paper describes a two-step algorithm for solving the layout problem while assuming the departments can have varying areas. The first step solves a quadratic assignment problem formulation of the problem using a heuristic cutting plane routine. The second step solves a mixed-integer linear programming prob- lem to find the desired block diagram layout. The algorithm incorporates two concepts to make the solu- tions more practical. First, rearrangement costs are simultaneously considered along with flow costs in solving a dynamic layout problem involving multiple time periods. It is the only algorithm to solve a general dynamic layout problem with varying department areas. Second, regular department shapes are maintained by requiring all departments to be rectangular. Its formulation for doing this is more efficient than previous algorithms.

Heuristic Minimization of BDDs Using Don't Cares

Abstract: We present heuristic algorithms for finding a minimum BDD size cover of an incompletely specified function, assuming the variable ordering is fixed. In some algorithms based on BDDs, incompletely specified functions arise forwhich any cover of the functionwill suffice. Choosing a cover that has a small BDD representation may yield significant performance gains. We present a systematic study of this problem, establishing a unified framework for heuristic algorithms, proving optimality in some cases,and presenting experimental results.

Assembly Line Balancing Using Genetic Algorithms with Heuristic‐Generated Initial Populations and Multiple Evaluation Criteria*

Abstract: We use genetic algorithms (GA) to solve the assembly line balancing (ALB) problem. Inparticular, we show how this technique can be used to generate feasible line balances, improve upon solutions obtained by other heuristics reported in the literature, and utilizeany one or more evaluation criteria that can be expressed in functional form. The procedure is demonstrated with two examples: (1) intimating the improvement of heuristic-generated ALB solutions by including them in the GA initial population, and (2) the possibility of balancing assembly lines with multiple criteria and side constraints. These examples suggest that GA can be a powerful tool in ALB. To investigate the utility of GA on single-criterion problems, an experiment is conducted that compares both the GA approach and conventional heuristics. Results indicate that the GA solutions are significantly improved over the heuristic solutions under the conditions studied. It is also found that the presence of heuristic-generated conventional solutions in the GA initial population leads to statistically preferred results.