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Variable and value ordering heuristics for the job shop scheduling constraint satisfaction problem
 Artificial Intelligence
, 1996
"... Practical Constraint Satisfaction Problems (CSPs) such as design of integrated circuits or scheduling generally entail large search spaces with hundreds or even thousands of variables, each with hundreds or thousands of possible values. Often, only a very tiny fraction of all these possible assignme ..."
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Cited by 59 (2 self)
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Practical Constraint Satisfaction Problems (CSPs) such as design of integrated circuits or scheduling generally entail large search spaces with hundreds or even thousands of variables, each with hundreds or thousands of possible values. Often, only a very tiny fraction of all these possible assignments participates in a satisfactory solution. This article discusses techniques that aim at reducing the effective size of the search space to be explored in order to find a satisfactory solution by judiciously selecting the order in which variables are instantiated and the sequence in which possible values are tried for each variable. In the CSP literature, these techniques are commonly referred to as variable and value ordering heuristics. Our investigation is conducted in the job shop scheduling domain. We show that, in contrast with problems studied earlier in the CSP literature, generic variable and value heuristics do not perform well in this domain. This is attributed to the difficulty of these heuristics to properly account for the tightness of constraints and/or the connectivity of the constraint graphs induced by job shop scheduling CSPs. A new probabilistic framework is introduced that better captures these key aspects of the job shop scheduling search space. Empirical results show that variable and value ordering heuristics
Exploiting Problem Structure for Distributed Constraint Optimization
 In Proceedings of the First International Conference on MultiAgent Systems
, 1995
"... Distributed constraint optimization imposes considerable complexity in agents' coordinated search for an optimal solution. However, in many application domains, problems often exhibit special structures that can be exploited to facilitate more efficient problem solving. One of the most recurren ..."
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Cited by 46 (2 self)
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Distributed constraint optimization imposes considerable complexity in agents' coordinated search for an optimal solution. However, in many application domains, problems often exhibit special structures that can be exploited to facilitate more efficient problem solving. One of the most recurrent structures involves disparity among subproblems. We present a coordination mechanism, Anchor&Ascend, for distributed constraint optimization that takes advantage of disparity among subproblems to efficiently guide distributed local search for global optimality. The coordination mechanism assigns different overlapping subproblems to agents who must interact and iteratively converge on a solution. In particular, an anchor agent who conducts local best first search to optimize its subsolution interacts with the rest of the agents who perform distributed constraint satisfaction to enforce problem constraints and constraints imposed by the anchor agent. We focus our study on the wellknown NPcomple...
A Genetic Algorithm Approach to Dynamic Job Shop Scheduling
 Problems”, Proceedings of the Seventh International Conference on Genetic Algorithms
, 1997
"... This paper describes a genetic algorithm approach to the dynamic job shop scheduling problem with jobs arriving continually. Both deterministic and stochastic models of the dynamic problem were investigated. The objective functions examined were weighted flow time, maximum tardiness, weighted tardin ..."
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Cited by 38 (0 self)
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This paper describes a genetic algorithm approach to the dynamic job shop scheduling problem with jobs arriving continually. Both deterministic and stochastic models of the dynamic problem were investigated. The objective functions examined were weighted flow time, maximum tardiness, weighted tardiness, weighted lateness, weighted number of tardy jobs, and weighted earliness plus weighted tardiness. In the stochastic model, we further tested the approach under various manufacturing environments with respect to the machine workload, imbalance of machine workload, and due date tightness. The results indicate that the approach performs well and is robust with regard to the objective function and the manufacturing environment in comparison with priority rule approaches. 1
Applications of Distributed Artificial Intelligence in Industry
, 1994
"... In many industrial applications, large centralized software systems are not as effective as distributed networks of relatively simpler computerized agents. For example, to compete effectively in today's markets, manufacturers must be able to design, implement, reconfigure, resize, and maintain ..."
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Cited by 37 (1 self)
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In many industrial applications, large centralized software systems are not as effective as distributed networks of relatively simpler computerized agents. For example, to compete effectively in today's markets, manufacturers must be able to design, implement, reconfigure, resize, and maintain manufacturing facilities rapidly and inexpensively. Because modern manufacturing depends heavily on computer systems, these same requirements apply to manufacturing control software, and are more easily satisfied by small modules than by large monolithic systems. This paper reviews industrial needs for Distributed Artificial Intelligence (DAI), giving special attention to systems for manufacturing scheduling and control. It describes a taxonomy of such systems, gives case studies of several advanced research applications and actual industrial installations, and identifies steps that need to be taken to deploy these technologies more broadly.
Maximizing Flexibility: A Retraction Heuristic for Oversubscribed Scheduling Problems
, 2003
"... In this paper we consider the solution of scheduling problems that are inherently oversubscribed. ..."
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Cited by 36 (10 self)
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In this paper we consider the solution of scheduling problems that are inherently oversubscribed.
Multiagent Coordination in Tightly Coupled Task Scheduling
, 1996
"... We consider an environment where agents' tasks are tightly coupled and require realtime scheduling and execution. In order to complete their tasks, agents need to coordinate their actions both constantly and extensively. We present an approach that consists of a standard operating procedure an ..."
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Cited by 35 (1 self)
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We consider an environment where agents' tasks are tightly coupled and require realtime scheduling and execution. In order to complete their tasks, agents need to coordinate their actions both constantly and extensively. We present an approach that consists of a standard operating procedure and a lookahead coordination. The standard operating procedure regulates task coupling and minimizes communication. The lookahead coordination increases agents' global visibility and provides indicative information for decision adjustment. The goal of our approach is to prune decision myopia while maintaining system responsiveness in realtime, dynamic environments. Experimental results in job shop scheduling problems show that (1) the lookahead coordination significantly enhances the performance of the standard operating procedure in solution quality, (2) the approach is capable of producing solutions of very high quality in a realtime environment. Introduction Most research on multiagent sy...
Current trends in deterministic scheduling
 ANNALS OF OPERATIONS RESEARCH
, 1997
"... Scheduling is concerned with allocating limited resources to tasks to optimize certain objective functions. Due to the popularity of the Total Quality Management concept, ontime delivery of jobs has become one of the crucial factors for customer satisfaction. Scheduling plays an important role in ac ..."
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Cited by 35 (1 self)
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Scheduling is concerned with allocating limited resources to tasks to optimize certain objective functions. Due to the popularity of the Total Quality Management concept, ontime delivery of jobs has become one of the crucial factors for customer satisfaction. Scheduling plays an important role in achieving this goal. Recent developments in scheduling theory have focused on extending the models to include more practical constraints. Furthermore, due to the complexity studies conducted during the last two decades, it is now widely understood that most practical problems are NPhard. This is one of the reasons why local search methods have been studied so extensively during the last decade. In this paper, we review briefly some of the recent extensions of scheduling theory, the recent developments in local search techniques and the new developments of scheduling in practice. Particularly, we survey two recent extensions of theory: scheduling with a 1jobonrmachine pattern and machine scheduling with availability constraints. We also review several local search techniques, including simulated annealing, tabu search, genetic algorithms and constraint guided heuristic search. Finally, we study the robotic cell scheduling problem, the automated guided vehicles scheduling problem, and the hoist scheduling problem.
A StateOfTheArt Review Of JobShop Scheduling Techniques
, 1998
"... A great deal of research has been focused on solving the jobshop problem (P J ), over the last forty years, resulting in a wide variety of approaches. Recently, much effort has been concentrated on hybrid methods to solve P J as a single technique cannot solve this stubborn problem. As a result muc ..."
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Cited by 32 (0 self)
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A great deal of research has been focused on solving the jobshop problem (P J ), over the last forty years, resulting in a wide variety of approaches. Recently, much effort has been concentrated on hybrid methods to solve P J as a single technique cannot solve this stubborn problem. As a result much effort has recently been concentrated on techniques that combine myopic problem specific methods and a metastrategy which guides the search out of local optima. These approaches currently provide the best results. Such hybrid techniques are known as iterated local search algorithms or metaheuristics. In this paper we seek to assess the work done in the jobshop domain by providing a review of many of the techniques used. The impact of the major contributions is indicated by applying these techniques to a set of standard benchmark problems. It is established that methods such as Tabu Search, Genetic Algorithms, Simulated Annealing should be considered complementary rather than competitive...
The max karmed bandit: A new model of exploration applied to search heuristic selection
 In AAAI
, 2005
"... The multiarmed bandit is often used as an analogy for the tradeoff between exploration and exploitation in search problems. The classic problem involves allocating trials to the arms of a multiarmed slot machine to maximize the expected sum of rewards. We pose a new variation of the multiarmed bandi ..."
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Cited by 31 (3 self)
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The multiarmed bandit is often used as an analogy for the tradeoff between exploration and exploitation in search problems. The classic problem involves allocating trials to the arms of a multiarmed slot machine to maximize the expected sum of rewards. We pose a new variation of the multiarmed bandit—the Max KArmed Bandit—in which trials must be allocated among the arms to maximize the expected best single sample reward of the series of trials. Motivation for the Max KArmed Bandit is the allocation of restarts among a set of multistart stochastic search algorithms. We present an analysis of this Max KArmed Bandit showing under certain assumptions that the optimal strategy allocates trials to the observed best arm at a rate increasing double exponentially relative to the other arms. This motivates an exploration strategy that follows a Boltzmann distribution with an exponentially decaying temperature parameter. We compare this exploration policy to policies that allocate trials to the observed best arm at rates faster (and slower) than double exponentially. The results confirm, for two scheduling domains, that the double exponential increase in the rate of allocations to the observed best heuristic outperforms the other approaches.
Utility Accrual RealTime Scheduling: Models and Algorithms
 PH.D. DISSERTATION, VIRGINIA TECH
, 2004
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