Ehl Emile Aarts

Job shop scheduling by simulated annealing

We describe an approximation algorithm for the problem of finding the minimum makespan in a job shop. The algorithm is based on simulated annealing, a generalization of the well known iterative improvement approach to combinatorial optimization problems. The generalization involves the acceptance of cost-increasing transitions with a nonzero probability to avoid getting stuck in local minima. We prove that our algorithm asymptotically converges in probability to a globally minimal solution, despite the fact that the Markov chains generated by the algorithm are generally not irreducible. Computational experiments show that our algorithm can find shorter makespans than two recent approximation approaches that are more tailored to the job shop scheduling problem. This is, however, at the cost of large running times.

Global optimization and simulated annealing

In this paper we are concerned with global optimization, which can be defined as the problem of finding points on a bounded subset of ℝn in which some real valued functionf assumes its optimal (maximal or minimal) value.We present a stochastic approach which is based on the simulated annealing algorithm. The approach closely follows the formulation of the simulated annealing algorithm as originally given for discrete optimization problems. The mathematical formulation is extended to continuous optimization problems, and we prove asymptotic convergence to the set of global optima. Furthermore, we discuss an implementation of the algorithm and compare its performance with other well-known algorithms. The performance evaluation is carried out for a standard set of test functions from the literature.

Parallel local search

We present a survey of parallel local search algorithms in which we review the concepts that can be used to incorporate parallelism into local search. For this purpose we distinguish between single-walk and multiple-walk parallel local search and between asynchronous and synchronous parallelism. Within the class of single-walk algorithms we differentiate between multiple-step and single-step parallelism. To describe parallel local search we introduce the concepts of hyper neighborhood structures and distributed neighborhood structures. Furthermore, we present templates that capture most of the parallel local search algorithms proposed in the literature. Finally, we discuss some complexity issues related to parallel local search.

A computational study of constraint satisfaction for multiple capacitated job shop scheduling

We introduce the multiple capacitated job shop scheduling problem as a generalization of the job shop scheduling problem. In this problem machines may process several operations simultaneously. We present an algorithm based on constraint satisfaction techniques to handle the problem effectively. The most important novel feature of our algorithm is the consistency checking. An empirical performance analysis is performed using a well-known set of instances of the job shop scheduling problem and a newly constructed set of instances of the multiple capacitated job shop scheduling problem. We show that our algorithm performs well for both sets of instances.

Parallel implementations of the statistical cooling algorithm

Statistical Cooling is an optimization technique based on Monte-Carlo techniques. Here we propose two parallel formulations of the statistical cooling algorithm, i.e. a systolic algorithm and a clustered algorithm. Both algorithms are based on the requirement that quasi-equilibrium is preserved throughout the optimization process. It is shown that the parallel algorithms can be executed with a polynomial-time complexity. Performance of the algorithms is discussed by means of implementations on an experimental multi-processor architecture. It is concluded that substantial reduction of computation time can be achieved by both parallel algorithms compared to the sequential algorithm.

Adaptive Persuasive Systems: A Study of Tailored Persuasive Text Messages to Reduce Snacking

This article describes the use of personalized short text messages (SMS) to reduce snacking. First, we describe the development and validation (N = 215) of a questionnaire to measure individual susceptibility to different social influence strategies. To evaluate the external validity of this Susceptibility to Persuasion Scale (STPS) we set up a two week text-messaging intervention that used text messages implementing social influence strategies as prompts to reduce snacking behavior. In this experiment (N = 73) we show that messages that are personalized (tailored) to the individual based on their scores on the STPS, lead to a higher decrease in snacking consumption than randomized messages or messages that are not tailored (contra-tailored) to the individual. We discuss the importance of this finding for the design of persuasive systems and detail how designers can use tailoring at the level of social influence strategies to increase the effects of their persuasive technologies.

A local search template

A template is presented that captures a vast majority of the local search algorithms proposed in the literature, including iterative improvement, simulated annealing, threshold accepting, tabu search and genetic algorithms. The template leads to a classification of existing local search algorithms and offers the possibility to fit in new types of local search approaches.

Periodic multiprocessor scheduling

A number of scheduling and assignment problems are presented involving the execution of periodic operations in a multiprocessor environment. We consider the computational complexity of these problems and propose approximation algorithms for operations with identical periods as well as for operations with arbitrary integer periods.

Automatic mashup generation from multiple-camera concert recordings

A large number of videos are captured and shared by the audience from musical concerts. However, such recordings are typically perceived as boring mainly because of their limited view, poor visual quality and incomplete coverage. It is our objective to enrich the viewing experience of these recordings by exploiting the abundance of content from multiple sources. In this paper, we propose a novel \Virtual Director system that automatically combines the most desirable segments from different recordings resulting in a single video stream, called mashup. We start by eliciting requirements from focus groups, interviewing professional video editors and consulting film grammar literature. We design a formal model for automatic mashup generation based on maximizing the degree of fulfillment of the requirements. Various audio-visual content analysis techniques are used to determine how well the requirements are satisfied by a recording. To validate the system, we compare our mashups with two other mashups: manually created by a professional video editor and machine generated by random segment selection. The mashups are evaluated in terms of visual quality, content diversity and pleasantness by 40 subjects. The results show that our mashups and the manual mashups are perceived as comparable, while both of them are significantly higher than the random mashups in all three terms.

Improved force-directed scheduling in high-throughput digital signal processing

This paper discusses improved force-directed scheduling and its application in the design of high-throughput DSP systems, such as real-time video VLSL circuits. We present a mathematical justification of the technique of force-directed scheduling, introduced by Paulin and Knight (1989), and we show how the algorithm can be used to find cost-effective time assignments and resource allocations, allowing trade-offs between processing units and memories. Furthermore, we present modifications that improve the effectiveness and the efficiency of the algorithm. The significance of the improvements is illustrated by an empirical performance analysis based on a number of problem instances. >