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53
Nonsystematic Backtracking Search
, 1995
"... Many practical problems in Artificial Intelligence have search trees that are too large to search exhaustively in the amount of time allowed. Systematic techniques such as chronological backtracking can be applied to these problems, but the order in which they examine nodes makes them unlikely to fi ..."
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Cited by 55 (1 self)
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Many practical problems in Artificial Intelligence have search trees that are too large to search exhaustively in the amount of time allowed. Systematic techniques such as chronological backtracking can be applied to these problems, but the order in which they examine nodes makes them unlikely to find a solution in the explored fraction of the space. Nonsystematic techniques have been proposed to alleviate the problem by searching nodes in a random order. A technique known as iterative sampling follows random paths from the root of the tree to the fringe, stopping if a path ends at a goal node. Although the nonsystematic techniques do not suffer from the problem of exploring nodes in a bad order, they do reconsider nodes they have already ruled out, a problem that is serious when the density of solutions in the tree is low. Unfortunately, for many practical problems the order of examing nodes matters and the density of solutions is low. Consequently, neither chronological backtracking...
Job Shop Scheduling by Local Search
 INFORMS JOURNAL ON COMPUTING
, 1994
"... We survey solution methods for the job shop scheduling problem with an emphasis on local search. Both deterministic and randomized local search methods as well as the proposed neighborhoods are discussed. We compare the computational performance of the various methods in terms of their effectiveness ..."
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Cited by 52 (0 self)
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We survey solution methods for the job shop scheduling problem with an emphasis on local search. Both deterministic and randomized local search methods as well as the proposed neighborhoods are discussed. We compare the computational performance of the various methods in terms of their effectiveness and efficiency on a standard set of problem instances.
Stochastic Hillclimbing as a Baseline Method for Evaluating Genetic Algorithms
, 1994
"... We investigate the effectiveness of stochastic hillclimbing as a baseline for evaluating the performance of genetic algorithms (GAs) as combinatorial function optimizers. In particular, we address four problems to which GAs have been applied in the literature: the maximum cut problem, Koza's 11 ..."
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Cited by 51 (0 self)
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We investigate the effectiveness of stochastic hillclimbing as a baseline for evaluating the performance of genetic algorithms (GAs) as combinatorial function optimizers. In particular, we address four problems to which GAs have been applied in the literature: the maximum cut problem, Koza's 11multiplexer problem, MDAP (the Multiprocessor Document Allocation Problem), and the jobshop problem. We demonstrate that simple stochastic hillclimbing methods are able to achieve results comparable or superior to those obtained by the GAs designed to address these four problems. We further illustrate, in the case of the jobshop problem, how insights obtained in the formulation of a stochastic hillclimbing algorithm can lead to improvements in the encoding used by a GA. Department of Computer Science, University of California at Berkeley. Supported by a NASA Graduate Fellowship. This paper was written while the author was a visiting researcher at the Ecole Normale Sup'erieurerue d'Ulm, Group...
Applying Constraint Satisfaction Techniques to Job Shop Scheduling
, 1995
"... In this paper, we investigate the applicability of a constraint satisfaction problem solving (CSP) model, recently developed for deadline scheduling, to more commonly studied problems of schedule optimization. Our hypothesis is twofold: (1) that CSP scheduling techniques provide a basis for develop ..."
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Cited by 47 (9 self)
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In this paper, we investigate the applicability of a constraint satisfaction problem solving (CSP) model, recently developed for deadline scheduling, to more commonly studied problems of schedule optimization. Our hypothesis is twofold: (1) that CSP scheduling techniques provide a basis for developing highperformance approximate solution procedures in optimization contexts, and (2) that the representational assumptions underlying CSP models allow these procedures to naturally accommodate the idiosyncratic constraints that complicate most realworld applications. We focus specifically on the objective criterion of makespan minimization, which has received the most attention within the job shop scheduling literature. We define an extended solution procedure somewhat unconventionally by reformulating the makespan problem as one of solving a series of different but related deadline scheduling problems, and embedding a simple CSP procedure as the subproblem solver. We first present the re...
Polyhedral approaches to machine scheduling
, 1996
"... We provide a review and synthesis of polyhedral approaches to machine scheduling problems. The choice of decision variables is the prime determinant of various formulations for such problems. Constraints, such as facet inducing inequalities for corresponding polyhedra, are often needed, in addition ..."
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Cited by 37 (8 self)
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We provide a review and synthesis of polyhedral approaches to machine scheduling problems. The choice of decision variables is the prime determinant of various formulations for such problems. Constraints, such as facet inducing inequalities for corresponding polyhedra, are often needed, in addition to those just required for the validity of the initial formulation, in order to obtain useful lower bounds and structural insights. We review formulations based on time–indexed variables; on linear ordering, start time and completion time variables; on assignment and positional date variables; and on traveling salesman variables. We point out relationship between various models, and provide a number of new results, as well as simplified new proofs of known results. In particular, we emphasize the important role that supermodular polyhedra and greedy algorithms play in many formulations and we analyze the strength of the lower and upper bounds obtained from different formulations and relaxations. We discuss separation algorithms for several classes of inequalities, and their potential applicability in generating cutting planes for the practical solution of such scheduling problems. We also review some recent results on approximation algorithms based on some of these formulations.
JobShop Scheduling by Simulated Annealing Combined with Deterministic Local Search
, 1995
"... : The JobShop Scheduling Problem (JSSP) is one of the most difficult NPhard combinatorial optimization problems. This paper proposes a new method for solving JSSPs based on simulated annealing (SA), a stochastic local search, enhanced by shifting bottleneck (SB), a problem specific deterministi ..."
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Cited by 25 (6 self)
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: The JobShop Scheduling Problem (JSSP) is one of the most difficult NPhard combinatorial optimization problems. This paper proposes a new method for solving JSSPs based on simulated annealing (SA), a stochastic local search, enhanced by shifting bottleneck (SB), a problem specific deterministic local search. In our method new schedules are generated by a variant of Giffler and Thompson's active scheduler with operation permutations on the critical path. SA selects a new schedule and probabilistically accepts or rejects it. The modified SB is applied to repair the rejected schedule; the new schedule is accepted if an improvement is made. Experimental results showed the proposed method found near optimal schedules for the difficult benchmark problems and outperformed other existing local search algorithms. Key Words: Simulated annealing, shifting bottleneck, jobshop scheduling, heuristics, local search 1. Background Scheduling is allocating shared resources over time to competi...
Genetic Algorithms in Timetabling and Scheduling
, 1994
"... This thesis investigates the use of genetic algorithms (GAs) for solving a range of timetabling and scheduling problems. Such problems are very hard in general, and GAs offer a useful and successful alternative to existing techniques. A framework is presented for GAs to solve modular timetabling pro ..."
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Cited by 22 (0 self)
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This thesis investigates the use of genetic algorithms (GAs) for solving a range of timetabling and scheduling problems. Such problems are very hard in general, and GAs offer a useful and successful alternative to existing techniques. A framework is presented for GAs to solve modular timetabling problems in educational institutions. The approach involves three components: declaring problemspecific constraints, constructing a problemspecific evaluation function and using a problemindependent GA to attempt to solve the problem. Successful results are demonstrated and a general analysis of the reliability and robustness of the approach is conducted. The basic approach can readily handle a wide variety of general timetabling problem constraints, and is therefore likely to be of great practical usefulness (indeed, an earlier version is already in use). The approach relies for its success on the use of specially designed mutation operators which greatly improve upon the performance of a GA...
A Tabu Search Method Guided By Shifting Bottleneck for the Job Shop Scheduling Problem
, 2000
"... A computationally effective heuristic method for solving the minimum makespan problem of job shop scheduling is presented. The proposed local search method is based on a tabu search technique and on the shifting bottleneck procedure used to generate the initial solution and to refine the nextcurren ..."
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Cited by 14 (0 self)
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A computationally effective heuristic method for solving the minimum makespan problem of job shop scheduling is presented. The proposed local search method is based on a tabu search technique and on the shifting bottleneck procedure used to generate the initial solution and to refine the nextcurrent solutions. Computational experiments on a standard set of problem instances show that, in several cases, our approach, in a reasonable amount of computer time, yields better results than the other heuristic procedures discussed in literature.
Iterative Improvement Methods for Knowledgebased Scheduling
 AI COMMUNICATIONS
, 1995
"... For large industrial applications the constraintbased formulation of scheduling problems fits better than mathematical representations from Operational Research, because the constraint approach is more flexible and can be adapted more easily to organizational changes in the production. However, ..."
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Cited by 14 (7 self)
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For large industrial applications the constraintbased formulation of scheduling problems fits better than mathematical representations from Operational Research, because the constraint approach is more flexible and can be adapted more easily to organizational changes in the production. However, the search for a good solution for realistic applications can be very expensive and furthermore, in scheduling one is not only interested in a feasible solution but also in an optimized solution. In this paper I present iterative improvement methods that can be used to optimize a schedule that is represented by constraints. These methods start with any schedule and try to optimize it by iterative modifications. The goal of the optimization method may be a minimization of the number of constraint violations or a maximization of a function that aggregates the satisfaction degrees of all involved soft constraints. Additionally, consistency techniques for constraints can be used to che...