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102
Ant Colony Optimization
, 2004
"... Abstract. The search process of a metaheuristic is sometimes misled. This may be caused by features of the tackled problem instance, by features of the algorithm, or by the chosen solution representation. In the field of evolutionary computation, the first case is called deception and the second cas ..."
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Cited by 591 (60 self)
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Abstract. The search process of a metaheuristic is sometimes misled. This may be caused by features of the tackled problem instance, by features of the algorithm, or by the chosen solution representation. In the field of evolutionary computation, the first case is called deception and the second case is referred to as bias. In this work we formalize the notions of deception and bias for ant colony optimization. We formally define first order deception in ant colony optimization, which corresponds to deception as being described in evolutionary computation. Furthermore, we formally define second order deception in ant colony optimization, which corresponds to the bias introduced by components of the algorithm in evolutionary computation. We show by means of an example that second order deception is a potential problem in ant colony optimization algorithms. 1
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
 ACM COMPUTING SURVEYS
, 2003
"... The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. We give a survey of the nowadays most important meta ..."
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Cited by 169 (14 self)
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The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. We give a survey of the nowadays most important metaheuristics from a conceptual point of view. We outline the different components and concepts that are used in the different metaheuristics in order to analyze their similarities and differences. Two very important concepts in metaheuristics are intensification and diversification. These are the two forces that largely determine the behaviour of a metaheuristic. They are in some way contrary but also complementary to each other. We introduce a framework, that we call the I&D frame, in order to put different intensification and diversification components into relation with each other. Outlining the advantages and disadvantages of different metaheuristic approaches we conclude by pointing out the importance of hybridization of metaheuristics as well as the integration of metaheuristics and other methods for optimization.
Deterministic JobShop Scheduling: Past, Present and Future
 European Journal of Operational Research
, 1998
"... : Due to the stubborn nature of the deterministic jobshop scheduling problem many solutions proposed are of hybrid construction cutting across the traditional disciplines. The problem has been investigated from a variety of perspectives resulting in several analytical techniques combining generic ..."
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Cited by 65 (2 self)
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: Due to the stubborn nature of the deterministic jobshop scheduling problem many solutions proposed are of hybrid construction cutting across the traditional disciplines. The problem has been investigated from a variety of perspectives resulting in several analytical techniques combining generic as well as problem specific strategies. We seek to assess a subclass of this problem in which the objective is minimising makespan, by providing an overview of the history, the techniques used and the researchers involved. The sense and direction of their work is evaluated by assessing the reported results of their techniques on the available benchmark problems. From these results the current situation and pointers for future work are provided. KEYWORDS: Scheduling Theory; JobShop; Review; Computational Study; 1. INTRODUCTION Current market trends such as consumer demand for variety, shorter product life cycles and competitive pressure to reduce costs have resulted in the need for zero i...
A Theoretical and Experimental Comparison of Constraint Propagation Techniques for Disjunctive Scheduling
, 1995
"... Disjunctive constraints are widely used to ensure that the time intervals over whichtwo activities require the same resource cannot overlap: if a resource is required bytwo activities A and B, the disjunctive constraint states that either A precedes B or B precedes A. The #propagation " of di ..."
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Cited by 60 (6 self)
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Disjunctive constraints are widely used to ensure that the time intervals over whichtwo activities require the same resource cannot overlap: if a resource is required bytwo activities A and B, the disjunctive constraint states that either A precedes B or B precedes A. The #propagation " of disjunctive constraints consists in determining cases where only one of the two orderings is feasible. It results in updating the timebounds of the two activities. The standard algorithm for propagating disjunctive constraints achieves arcBconsistency.Twotypes of methods that provide more precise timebounds are studied and compared. The #rst type of method consists in determining whether an activity A must, can, or cannot be the #rst or the last to execute among a set of activities that require the same resource. The second consists in comparing the amount of #resource energy" required over a time interval #t 1 t 2 #to the amount of energy that is available over the same interval. The main result of the study is an implementation of the #rst method in Ilog Schedule, a generic tool for constraintbased scheduling which exhibits performance in the same range of e#ciency as speci#c operations research algorithms.
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...
Heuristic Algorithms for Solving the ResourceConstrained Project Scheduling Problem: Classification and Computational Analysis
, 1998
"... Introduction The resource constrained project scheduling problem (RCPSP) can be given as follows. A single project consists of a set J = f0; 1; : : : ; n; n+1g of activities which have to be processed. Fictitious activities 0 and n + 1 correspond to the "project start" and to the "project end", res ..."
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Cited by 51 (2 self)
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Introduction The resource constrained project scheduling problem (RCPSP) can be given as follows. A single project consists of a set J = f0; 1; : : : ; n; n+1g of activities which have to be processed. Fictitious activities 0 and n + 1 correspond to the "project start" and to the "project end", respectively. The activities are interrelated by two kinds of constraints. First, precedence constraints force activity j not to be started before all its immediate predecessor activities comprised in the set P j have been finished. Second, performing the activities requires resources with limited capacities. We have K resource types, given by the set K = f1; : : : ; Kg. While being processed, activity j requires r j;k units of resource type<F
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 50 (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.
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 42 (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...
Parallel Grasp With PathRelinking For Job Shop Scheduling
 Parallel Computing
, 2002
"... In the job shop scheduling problem (JSP), a finite set of jobs is processed on a finite set of machines. Each job is required to complete a set of operations in a fixed order. Each operation is processed on a specific machine for a fixed duration. A machine can process no more than one job at a ..."
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Cited by 34 (16 self)
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In the job shop scheduling problem (JSP), a finite set of jobs is processed on a finite set of machines. Each job is required to complete a set of operations in a fixed order. Each operation is processed on a specific machine for a fixed duration. A machine can process no more than one job at a time and once a job initiates processing on a given machine it must complete processing without interruption. A schedule is an assignment of operations to time slots on the machines. The objective of the JSP is to find a schedule that minimizes the maximum completion time, or makespan, of the jobs. In this paper, we describe a parallel greedy randomized adaptive search procedure (GRASP) with pathrelinking for the JSP. A GRASP is a metaheuristic for combinatorial optimization. It usually consists of a construction procedure based on a greedy randomized algorithm and of a local search. Pathrelinking is an intensification strategy that explores trajectories that connect high quality solutions. Independent and cooperative parallelization strategies are described and implemented. Computational experience on a large set of standard test problems indicates that the parallel GRASP with pathrelinking finds goodquality approximate solutions of the job shop scheduling problem.
Search procedures and parallelism in constraint programming
 In Proc. of CP99
, 1999
"... Abstract. In this paper, we present a major improvement in the search procedures in constraint programming. First, we integrate various search procedures from AI and OR. Second, we parallelize the search on sharedmemory computers. Third, we add an objectoriented extensible control language to impl ..."
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Cited by 32 (3 self)
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Abstract. In this paper, we present a major improvement in the search procedures in constraint programming. First, we integrate various search procedures from AI and OR. Second, we parallelize the search on sharedmemory computers. Third, we add an objectoriented extensible control language to implement complex complete and incomplete search procedures. The result is a powerful set of tools which offers both brute force search using simple search procedures and parallelism, and finely tuned search procedures using that expressive control language. With this, we were able both to solve difficult and open problems using complete search procedures, and to quickly produce good results using incomplete search procedures. 1