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35
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 176 (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.
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 54 (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...
CABINS: A Framework of Knowledge Acquisition and Iterative Revision for Schedule Improvement and Reactive Repair
 Artificial Intelligence
, 1995
"... Practical scheduling problems generally require allocation of resources in the presence of a large, diverse and typically conflicting set of constraints and optimization criteria. The illstructuredness of both the solution space and the desired objectives make scheduling problems difficult to forma ..."
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Cited by 38 (5 self)
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Practical scheduling problems generally require allocation of resources in the presence of a large, diverse and typically conflicting set of constraints and optimization criteria. The illstructuredness of both the solution space and the desired objectives make scheduling problems difficult to formalize. This paper describes a casebased learning method for acquiring contextdependent user optimization preferences and tradeoffs and using them to incrementally improve schedule quality in predictive scheduling and reactive schedule management in response to unexpected execution events. The approach, implemented in the CABINS system, uses acquired user preferences to dynamically modify search control to guide schedule improvement. During iterative repair, cases are exploited for: (1) repair action selection, (2) evaluation of intermediate repair results and (3) recovery from revision failures. The method allows the system to dynamically switch between repair heuristic actions, each of whi...
A greedy randomized adaptive search procedure for job shop scheduling
 IEEE Trans. on Power Systems
, 2001
"... Abstract. In the job shop scheduling problem (JSP), a finite set of jobs is processed on a finite set of machines. Each job is characterized by a fixed order of operations, each of which is to be processed on a specific machine for a specified duration. Each machine can process at most one job at a ..."
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Cited by 22 (3 self)
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Abstract. In the job shop scheduling problem (JSP), a finite set of jobs is processed on a finite set of machines. Each job is characterized by a fixed order of operations, each of which is to be processed on a specific machine for a specified duration. Each machine can process at most one job at a time and once a job initiates processing on a given machine it must complete processing uninterrupted. 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 greedy randomized adaptive search procedure (GRASP) for the JSP. A GRASP is a metaheuristic for combinatorial optimization. Although GRASP is a general procedure, its basic concepts are customized for the problem being solved. We describe in detail our implementation of GRASP for job shop scheduling. Further, we incorporate to the conventional GRASP two new concepts: an intensification strategy and POP (Proximate Optimality Principle) in the construction phase. These two concepts were first proposed by Fleurent & Glover (1999) in the context of the quadratic assignment problem. Computational experience on a large set of standard test problems indicates that GRASP is a competitive algorithm for finding approximate solutions of the job shop scheduling problem. 1.
Problem Difficulty for Tabu Search in JobShop Scheduling
 Artificial Intelligence
, 2002
"... Tabu search algorithms are among the most effective approaches for solving the jobshop scheduling problem (JSP). Yet, we have little understanding of why these algorithms work so well, and under what conditions. We develop a model of problem difficulty for tabu search in the JSP, borrowing from sim ..."
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Cited by 21 (8 self)
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Tabu search algorithms are among the most effective approaches for solving the jobshop scheduling problem (JSP). Yet, we have little understanding of why these algorithms work so well, and under what conditions. We develop a model of problem difficulty for tabu search in the JSP, borrowing from similar models developed for SAT and other NP  complete problems. We show that the mean distance between random local optima and the nearest optimal solution is highly correlated with the cost of locating optimal solutions to typical, random JSPs. Additionally, this model accounts for the cost of locating suboptimal solutions, and provides an explanation for differences in the relative difficulty of square versus rectangular JSPs. We also identify two important limitations of our model. First, model accuracy is inversely correlated with problem difficulty, and is exceptionally poor for rare, very highcost problem instances. Second, the model is significantly less accurate for structured, nonrandom JSPs. Our results are also likely to be useful in future research on difficulty models of local search in SAT, as local search cost in both SAT and the JSP is largely dictated by the same search space features. Similarly, our research represents the first attempt to quantitatively model the cost of tabu search for any NP complete problem, and may possibly be leveraged in an effort to understand tabu search in problems other than jobshop scheduling.
Improving Branch and Bound for Jobshop Scheduling with Constraint Propagation
, 1995
"... . Task intervals were defined in [CL94] for disjunctive scheduling so that, in a scheduling problem, one could derive much information by focusing on some key subsets of tasks. The advantage of this approach was to shorten the size of search trees for branch&bound algorithms because more propaga ..."
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Cited by 20 (4 self)
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. Task intervals were defined in [CL94] for disjunctive scheduling so that, in a scheduling problem, one could derive much information by focusing on some key subsets of tasks. The advantage of this approach was to shorten the size of search trees for branch&bound algorithms because more propagation was performed at each node. In this paper, we refine the propagation scheme and describe in detail the branch&bound algorithm with its heuristics and we compare constraint programming to integer programming. This algorithm is tested on the standard benchmarks from Muth & Thompson, Lawrence, Adams et al, Applegate & Cook and Nakano & Yamada. The achievements are the following: . Window reduction by propagation : for 23 of the 40 problems of Lawrence, the proof of optimality is found with no search, by sole propagation; for typically hard 10 × 10 problems, the search tree has less than a thousand nodes; hard problems with up to 400 tasks can be solved to optimality and among these, the open p...
A GRASP For Job Shop Scheduling
 Essays and Surveys on Metaheuristics
, 2000
"... In the job shop scheduling problem (JSP), a finite set of jobs is processed on a finite set of machines. Each job is characterized by a fixed order of operations, each of which is to be processed on a specific machine for a specified duration. Each machine can process at most one job at a time and o ..."
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Cited by 17 (7 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 characterized by a fixed order of operations, each of which is to be processed on a specific machine for a specified duration. Each machine can process at most one job at a time and once a job initiates processing on a given machine it must complete processing uninterrupted. 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 greedy randomized adaptive search procedure (GRASP) for the JSP. A GRASP is a metaheuristic for combinatorial optimization. Although GRASP is a general procedure, its basic concepts are customized for the problem being solved. We describe in detail our implementation of GRASP for job shop scheduling. Further, we incorporate to the conventional GRASP two new concepts: an ...
Deconstructing Nowicki and Smutnicki's iTSAB Tabu Search Algorithm for the JobShop Scheduling Problem
 Computers & Operations Research
, 2005
"... Over the last decade and a half, tabu search algorithms for machine scheduling have gained a nearmythical reputation by consistently equaling or establishing stateoftheart performance levels on a range of academic and realworld problems. Yet, despite these successes, remarkably little research ..."
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Cited by 8 (1 self)
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Over the last decade and a half, tabu search algorithms for machine scheduling have gained a nearmythical reputation by consistently equaling or establishing stateoftheart performance levels on a range of academic and realworld problems. Yet, despite these successes, remarkably little research has been devoted to developing an understanding of why tabu search is so e#ective on this problem class. In this paper, we report results that provide significant progress in this direction. We consider Nowicki and Smutnicki's iTSAB tabu search algorithm, which represents the current stateoftheart for the makespanminimization form of the classical jobshop scheduling problem. Via a series of controlled experiments, we identify those components of iTSAB that enable it to achieve stateoftheart performance levels. In doing so, we expose a number of misconceptions regarding the behavior and/or benefits of tabu search and other local search metaheuristics for the jobshop problem. Our results also serve to focus future research, by identifying those specific directions that are most likely to yield further improvements in performance.
Models and Techniques of Dynamic DemandResponsive Transportation Planning
 HTTP://WWW.CGI.COM/WEB2/GOVT/MODELS.HTML SAMPO (SYSTEM FOR ADVANCED MANAGEMENT OF PUBLIC TRANSPORT OPERATIONS), 1995–1997, HTTP://WWW.OKANECOM.FI/SAMPO/ ET SAMPLUS (EXTENSION DE SAMPO), 1999 SESAME CONSORTIUM, 1999, SESAME FINAL REPORT, CERTU
, 1996
"... This article provides an overview of stateoftheart technologies relevant to dynamic transportation planning problems that involve the reactive routing nnd scheduling of a fleet of vehicles in response to dynamically changing transportation demands. Specifically, we focus on a new class of compl ..."
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Cited by 5 (1 self)
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This article provides an overview of stateoftheart technologies relevant to dynamic transportation planning problems that involve the reactive routing nnd scheduling of a fleet of vehicles in response to dynamically changing transportation demands. Specifically, we focus on a new class of complex transportation planning problems, which we refer to as the "Dynamic DialARide Problem with Multiple Acceptable Destinations and/or Origins" (DDARPMADO). While this class of dynamic problems is representative of a number of practical transportation problems, it does not appear to have been the object of prior studies. This is not to say that techniques proposed for simpler routing and scheduling problems cannot be brought to bear on this problem. To the contrary, our survey shows that a number of techniques developed
Local Optimization and The JobShop Scheduling Problem
 EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
, 1994
"... We do a computational study of different local optimization methods to solve the jobshop scheduling problem. In this problem we are given a set of jobs to be scheduled on a set of machines. The objective is to schedule the jobs on the machines so that we minimize the time by which all jobs are comp ..."
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Cited by 5 (0 self)
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We do a computational study of different local optimization methods to solve the jobshop scheduling problem. In this problem we are given a set of jobs to be scheduled on a set of machines. The objective is to schedule the jobs on the machines so that we minimize the time by which all jobs are completed. This problem has been widely studied and various techniques have been used to solve them. We consider several approximation methods to solve the jobshop scheduling problem. We focus on local optimization methods, reviewing the application of the techniques known as local improvement and simulated annealing to this problem. We propose a variant of the above local optimization methods, known as largestep optimization, to solve the jobshop scheduling problem. We present computational results obtained from the application of all these methods to several instances of the problem. From the computational results we can conclude that the local improvement method is clearly inferior, even w...