Cindy Kuijpers - Academia.edu (original) (raw)

Papers by Cindy Kuijpers

This report is the result of a study of literature carried out by the authors. It is a review of ... more This report is the result of a study of literature carried out by the authors. It is a review of earlier studies by other researchers' published articles. The authors have not added any new information to them. Rather, they have tried to produce a review of the dierent attempts made to solve the Travelling Salesman Problem (TSP) with evolutionary algorithms. Because of time and space limitations, the authors restricted themselves to three branches of evolutionary algorithms: genetic algorithms, evolution strategies and evolutionary programming. The dierent operators which might be used in these evolutionary algorithms make up the main subject of this report. Other subjects, e.g. selection and stop criterions of the algorithms, are not concerned, and the performance of algorithms is dealt with only briey. Keywords: Evolutionary algorithms

Web-Based Green Products Life Cycle Management Systems

Successful supply chain collaboration is one of the principal means of achieving competitive adva... more Successful supply chain collaboration is one of the principal means of achieving competitive advantage. New concepts such as vendor managed inventory, efficient consumer response, and factory gate pricing, among others, have been developed to optimize supply chains. The dual focus of supply chain collaboration has traditionally been customer service and cost. Sustainability is now also a primary focus. In this chapter, we study how trust impacts sustainability. Trust is often seen as a key moderator in supply chain performance. Yet, little is known about the role it plays in achieving sustainable supply chains. The ongoing debate about the greenhouse effect highlights the relevance of this topic. We look at trust and sustainability in supply chains using an advanced management game played by master students. We present the empirical data collected and then develop tentative propositions. We conclude with a discussion of the potential impact of the results for business and make sugge...

In this thesis, we consider a cyclic scheduling problem. It is very difficult to give a clear def... more In this thesis, we consider a cyclic scheduling problem. It is very difficult to give a clear definition of what a cyclic scheduling problem is, since in the literature, sometimes very different scheduling problems are called cyclic. In general, one can say that a scheduling problem is cyclic if it displays some kind of repetitive behavior. That it why cyclic scheduling problems are also often referred to as periodic scheduling problems. Frequently, problems of the following form are studied (see, e.g., Liu & Layland [1973], Labetoulle [1974], Leung & Merrill [1980], Lawler & Martel [1981], Bertossi & Bonuccelli [1985]). Given is a set of jobs J, where each job j ∈ J is characterized by a quadruple (r j , p j , d j , π j). Job j initially makes a request for processing at time r j , and thereafter at the times r j + kπ j (k ∈ N). Each processing of task j takes p j time units, and the deadline for this processing occurs d j time units after its request. A schedule is called feasible if no deadline is ever missed. The objective is to find a feasible schedule on either a given, or a minimal, number of machines. In some studies, the values of r j (j ∈ J) are given; in others, they are part of the decision process. Other cyclic scheduling problems concern problems of the following type. Given a set of machines, a set of jobs J, the processing times of the jobs and some constraints (e.g., on the order in which the jobs can be processed by the machines.) The target is to find a schedule that can be repeated every certain period of time and with which the processed number of jobs per unit of time is maximized. See for problems of this type, e.g.

Statistics and Computing, 1997

In this paper we consider the optimal decomposition of Bayesian networks. More concretely, we exa... more In this paper we consider the optimal decomposition of Bayesian networks. More concretely, we examine empirically the applicability of genetic algorithms to the problem of the triangulation of moral graphs. This problem constitutes the only difficult step in the evidence propagation algorithm of Lauritzen and Spiegelhalter (1988) and is known to be NP-hard (Wen, 1991). We carry out experiments with

In this paper we present a ne_(l n [!ii ' with respect to Bayesian networks con- ogy for ind... more In this paper we present a ne_(l n [!ii ' with respect to Bayesian networks con- ogy for inducing Bayesian network structures frop3 titute the roblem of the evidence propagation and a database of cases. The methodology is based oap&lll searching for the best ordering of the system vari- the problem of the model search. The problem of shies by means of genetic algorithl{. Since his th_vidence propagation consists of once the vMproblem of finding an optimal ordea. teeuarue}rables are known, the assignment of resembles the traveling salesman p'FolUleh)ve use .... IW. ....... probablhles to the values of the rest of the van genetic operators that were developed for the latter - problem. The quality of a variable ordering is eval- ables. Cooper [4] demonstrated that this problem Mated with the algorithm K2. We present empirical results that were obtained with a simulation of the ALARM network.

In this paper we present a new methodology for inducing Bayesian network structures from a databa... more In this paper we present a new methodology for inducing Bayesian network structures from a database of cases. The methodology is based on searching for the best ordering of the system variables by means of genetic algorithms. Since this problem of finding an optimal ordering of variables resembles the traveling salesman problem, we use genetic operators that were developed for the latter problem. The quality of a variable ordering is evaluated with the structure-learning algorithm K2. We present empirical results that were obtained with a simulation of the ALARM network.

22-2 1. irudia. Algoritmo Genetiko Sinplearen sasikodea.

Statistica Neerlandica, 1997

We describe algorithms for the determination of fast movement strategies for a step-and-scan wafe... more We describe algorithms for the determination of fast movement strategies for a step-and-scan wafer stepper, a device that is used for the photolithographic processing of integrated circuits. The proposed solution strategy consists of two parts. First, we determine the maximum number of congruent rectangular chips that can be packed on a wafer, subject to the restriction that the chips are placed in a rectangular grid. Second, we find fast movement strategies for scanning all chips of a given packing, given the mechanical restrictions of the wafer stepper. The corresponding combinatorial optimization problem is formulated as a generalized asymmetric traveling salesman problem. We show how feasible scan strategies are determined, and how these strategies are improved by local search techniques, such as iterative improvement based on 2-and 3-exchanges, and simulated annealing based on 2-exchanges.

International Journal of Information Technology

This paper is the result of a literature study carried out by the authors. It is a review of the ... more This paper is the result of a literature study carried out by the authors. It is a review of the dierentattempts made to solve the Travelling Salesman Problem with Genetic Algorithms. We present crossoverand mutation operators, developed to tackle the Travelling Salesman Problem with Genetic Algorithmswith dierent representations such as: binary representation, path representation, adjacency representation,ordinal representation and matrix representation.

Wiley StatsRef: Statistics Reference Online, 2014

Lecture Notes in Statistics, 1996

This paper demonstrates how genetic algorithms can be used to discover the structureof a Bayesian... more This paper demonstrates how genetic algorithms can be used to discover the structureof a Bayesian network from a given database with cases. The results presented, were obtained byapplying four different types of genetic algorithms -- SSGA (Steady State Genetic Algorithm), GAe(Genetic Algorithm elistist of degree ), hSSGA (hybrid Steady State Genetic Algorithm) and thehGAe (hybrid Genetic Algorithm elitist of degree

Encyclopedia of Statistical Sciences, 2004

Artificial Intelligence Review - AIR, 1999

IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 1996

Abstract| In this paper we present a new methodology for inducing Bayesian network structures fro... more Abstract| In this paper we present a new methodology for inducing Bayesian network structures from a database of cases. The methodology is based on searching for the best ordering of the system variables by means of genetic algorithms. Since this problem of nding an optimal ordering of variables resembles the traveling salesman problem, we use genetic operators that were developed for the latter problem. The quality of a variable ordering is evaluated with the algorithm K2. We present empirical results that were obtained with a simulation of the ALARM network.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996

We present a new approach to structure learning in the field of Bayesian networks: We tackle the ... more We present a new approach to structure learning in the field of Bayesian networks: We tackle the problem of the search for the best Bayesian network structure, given a database of cases, using the genetic algorithm philosophy for searching among alternative structures. We start by assuming an ordering between the nodes of the network structures. This assumption is necessary to guarantee that the networks that are created by the genetic algorithms are legal Bayesian network structures. Next, we release the ordering assumption by using a "repair operator" which converts illegal structures into legal ones. We present empirical results and analyze them statistically. The best results are obtained with an elitist genetic algorithm that contains a local optimizer.

Statistics and Computing, 1997

In this paper we consider the optimal decomposition of Bayesian networks. More concretely, we exa... more In this paper we consider the optimal decomposition of Bayesian networks. More concretely, we examine empirically the applicability of genetic algorithms to the problem of the triangulation of moral graphs. This problem constitutes the only di cult step in the evidence propagation ...

This report is the result of a study of literature carried out by the authors. It is a review of ... more This report is the result of a study of literature carried out by the authors. It is a review of earlier studies by other researchers' published articles. The authors have not added any new information to them. Rather, they have tried to produce a review of the dierent attempts made to solve the Travelling Salesman Problem (TSP) with evolutionary algorithms. Because of time and space limitations, the authors restricted themselves to three branches of evolutionary algorithms: genetic algorithms, evolution strategies and evolutionary programming. The dierent operators which might be used in these evolutionary algorithms make up the main subject of this report. Other subjects, e.g. selection and stop criterions of the algorithms, are not concerned, and the performance of algorithms is dealt with only briey. Keywords: Evolutionary algorithms

Web-Based Green Products Life Cycle Management Systems

Successful supply chain collaboration is one of the principal means of achieving competitive adva... more Successful supply chain collaboration is one of the principal means of achieving competitive advantage. New concepts such as vendor managed inventory, efficient consumer response, and factory gate pricing, among others, have been developed to optimize supply chains. The dual focus of supply chain collaboration has traditionally been customer service and cost. Sustainability is now also a primary focus. In this chapter, we study how trust impacts sustainability. Trust is often seen as a key moderator in supply chain performance. Yet, little is known about the role it plays in achieving sustainable supply chains. The ongoing debate about the greenhouse effect highlights the relevance of this topic. We look at trust and sustainability in supply chains using an advanced management game played by master students. We present the empirical data collected and then develop tentative propositions. We conclude with a discussion of the potential impact of the results for business and make sugge...

In this thesis, we consider a cyclic scheduling problem. It is very difficult to give a clear def... more In this thesis, we consider a cyclic scheduling problem. It is very difficult to give a clear definition of what a cyclic scheduling problem is, since in the literature, sometimes very different scheduling problems are called cyclic. In general, one can say that a scheduling problem is cyclic if it displays some kind of repetitive behavior. That it why cyclic scheduling problems are also often referred to as periodic scheduling problems. Frequently, problems of the following form are studied (see, e.g., Liu & Layland [1973], Labetoulle [1974], Leung & Merrill [1980], Lawler & Martel [1981], Bertossi & Bonuccelli [1985]). Given is a set of jobs J, where each job j ∈ J is characterized by a quadruple (r j , p j , d j , π j). Job j initially makes a request for processing at time r j , and thereafter at the times r j + kπ j (k ∈ N). Each processing of task j takes p j time units, and the deadline for this processing occurs d j time units after its request. A schedule is called feasible if no deadline is ever missed. The objective is to find a feasible schedule on either a given, or a minimal, number of machines. In some studies, the values of r j (j ∈ J) are given; in others, they are part of the decision process. Other cyclic scheduling problems concern problems of the following type. Given a set of machines, a set of jobs J, the processing times of the jobs and some constraints (e.g., on the order in which the jobs can be processed by the machines.) The target is to find a schedule that can be repeated every certain period of time and with which the processed number of jobs per unit of time is maximized. See for problems of this type, e.g.

Statistics and Computing, 1997

In this paper we consider the optimal decomposition of Bayesian networks. More concretely, we exa... more In this paper we consider the optimal decomposition of Bayesian networks. More concretely, we examine empirically the applicability of genetic algorithms to the problem of the triangulation of moral graphs. This problem constitutes the only difficult step in the evidence propagation algorithm of Lauritzen and Spiegelhalter (1988) and is known to be NP-hard (Wen, 1991). We carry out experiments with

In this paper we present a ne_(l n [!ii ' with respect to Bayesian networks con- ogy for ind... more In this paper we present a ne_(l n [!ii ' with respect to Bayesian networks con- ogy for inducing Bayesian network structures frop3 titute the roblem of the evidence propagation and a database of cases. The methodology is based oap&lll searching for the best ordering of the system vari- the problem of the model search. The problem of shies by means of genetic algorithl{. Since his th_vidence propagation consists of once the vMproblem of finding an optimal ordea. teeuarue}rables are known, the assignment of resembles the traveling salesman p'FolUleh)ve use .... IW. ....... probablhles to the values of the rest of the van genetic operators that were developed for the latter - problem. The quality of a variable ordering is eval- ables. Cooper [4] demonstrated that this problem Mated with the algorithm K2. We present empirical results that were obtained with a simulation of the ALARM network.

In this paper we present a new methodology for inducing Bayesian network structures from a databa... more In this paper we present a new methodology for inducing Bayesian network structures from a database of cases. The methodology is based on searching for the best ordering of the system variables by means of genetic algorithms. Since this problem of finding an optimal ordering of variables resembles the traveling salesman problem, we use genetic operators that were developed for the latter problem. The quality of a variable ordering is evaluated with the structure-learning algorithm K2. We present empirical results that were obtained with a simulation of the ALARM network.

22-2 1. irudia. Algoritmo Genetiko Sinplearen sasikodea.

Statistica Neerlandica, 1997

We describe algorithms for the determination of fast movement strategies for a step-and-scan wafe... more We describe algorithms for the determination of fast movement strategies for a step-and-scan wafer stepper, a device that is used for the photolithographic processing of integrated circuits. The proposed solution strategy consists of two parts. First, we determine the maximum number of congruent rectangular chips that can be packed on a wafer, subject to the restriction that the chips are placed in a rectangular grid. Second, we find fast movement strategies for scanning all chips of a given packing, given the mechanical restrictions of the wafer stepper. The corresponding combinatorial optimization problem is formulated as a generalized asymmetric traveling salesman problem. We show how feasible scan strategies are determined, and how these strategies are improved by local search techniques, such as iterative improvement based on 2-and 3-exchanges, and simulated annealing based on 2-exchanges.

International Journal of Information Technology

This paper is the result of a literature study carried out by the authors. It is a review of the ... more This paper is the result of a literature study carried out by the authors. It is a review of the dierentattempts made to solve the Travelling Salesman Problem with Genetic Algorithms. We present crossoverand mutation operators, developed to tackle the Travelling Salesman Problem with Genetic Algorithmswith dierent representations such as: binary representation, path representation, adjacency representation,ordinal representation and matrix representation.

Wiley StatsRef: Statistics Reference Online, 2014

Lecture Notes in Statistics, 1996

This paper demonstrates how genetic algorithms can be used to discover the structureof a Bayesian... more This paper demonstrates how genetic algorithms can be used to discover the structureof a Bayesian network from a given database with cases. The results presented, were obtained byapplying four different types of genetic algorithms -- SSGA (Steady State Genetic Algorithm), GAe(Genetic Algorithm elistist of degree ), hSSGA (hybrid Steady State Genetic Algorithm) and thehGAe (hybrid Genetic Algorithm elitist of degree

Encyclopedia of Statistical Sciences, 2004

Artificial Intelligence Review - AIR, 1999

IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 1996

Abstract| In this paper we present a new methodology for inducing Bayesian network structures fro... more Abstract| In this paper we present a new methodology for inducing Bayesian network structures from a database of cases. The methodology is based on searching for the best ordering of the system variables by means of genetic algorithms. Since this problem of nding an optimal ordering of variables resembles the traveling salesman problem, we use genetic operators that were developed for the latter problem. The quality of a variable ordering is evaluated with the algorithm K2. We present empirical results that were obtained with a simulation of the ALARM network.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996

We present a new approach to structure learning in the field of Bayesian networks: We tackle the ... more We present a new approach to structure learning in the field of Bayesian networks: We tackle the problem of the search for the best Bayesian network structure, given a database of cases, using the genetic algorithm philosophy for searching among alternative structures. We start by assuming an ordering between the nodes of the network structures. This assumption is necessary to guarantee that the networks that are created by the genetic algorithms are legal Bayesian network structures. Next, we release the ordering assumption by using a "repair operator" which converts illegal structures into legal ones. We present empirical results and analyze them statistically. The best results are obtained with an elitist genetic algorithm that contains a local optimizer.

Statistics and Computing, 1997

In this paper we consider the optimal decomposition of Bayesian networks. More concretely, we exa... more In this paper we consider the optimal decomposition of Bayesian networks. More concretely, we examine empirically the applicability of genetic algorithms to the problem of the triangulation of moral graphs. This problem constitutes the only di cult step in the evidence propagation ...