: The paper describes an algorithm for the generation of a general class of precedence- and resource-constrained scheduling problems. Easy and hard instances for the single- and multi-mode resource-constrained project scheduling problem are benchmarked by using the state of the art (branch-and-bound-) procedures. The strong impact of the chosen parametric characterization of the problems is shown via an in-depth computational study. The results provided, demonstrate that the classical benchmark instances used by several researchers over decades belong to the subset of the very easy ones. In addition it is shown that hard instances, being far more smaller in size than presumed in the literature, may not be solved to optimality even within a huge amount of computational time. Keywords: Project scheduling, precedence- and resource-constraints, nonpreemptive case, singlemode, multiple-modes, project generator, branch-and-bound methods, easy and hard instances. 1 Introduction From the begi...

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In this paper we present an exact solution procedure of the branch-and-bound type for solving the muti-mode resource-constrained project scheduling problem. The simplicity of the enumeration scheme enables a compact representation of the current state of the search process. This representation can be employed to formulate and prove search tree reduction schemes which highly increase the performance of the algorithm. Among the benefits of the approach are ease of description, ease of implementation, ease of generalization, and, additionally, superior performance of the exact approach as well as reasonable heuristic capabilities of the truncated method. The procedure has been coded in C and implemented on a personal computer as well as on a workstation. In the second part of the paper we present the results of our experimental investigations. The experimental results obtained by using the standard project generator ProGen demonstrate a superior performance of the exact and heur...

Heuristic optimization algorithms seek good feasible solutions to optimization problems in circumstances where the complexities of the problem or the limited time available for solution do not allow exact solution. Although worst case and probabilistic analysis of algorithms have produced insight on some classic models, most of the heuristics developed for large optimization problem must be evaluated empirically—by applying procedures to a collection of specific instances and comparing the observed solution quality and computational burden. This paper focuses on the methodological issues that must be confronted by researchers undertaking such experimental evaluations of heuristics, including experimental design, sources of test instances, measures of algorithmic performance, analysis of results, and presentation in papers and talks. The questions are difficult, and there are no clear right answers. We seek only to highlight the main issues, present alternative ways of addressing them under different circumstances, and caution about pitfalls to avoid. Key Words: Heuristic optimization, computational experiments 1.

Carrying out empirical studies is widely held to be of importance. A view less widely held is that experiments should be replicated externally to verify and validate the original results. This paper serves two main functions. First, the need for external replications is established. The role of replication in experimental software engineering is discussed. Without the confirming power of external replications, results in experimental software engineering should only be provisionally accepted, if at all. An extension to the framework for experimentation in software engineering by Basili et al [5] is proposed to differentiate between the various kinds of internal and external replication and their powers of confirmation and to allow a better appreciation of the context of a piece of empirical work. Second, this paper presents a concrete example of an external replication of an experiment which tested the benefits to maintenance of using modular code against non-modular (monolithic) code....

Results are reported of testing a number of existing state of the art solvers for global constrained optimization and constraint satisfaction on a set of over 1000 test problems in up to 1000 variables.

Most scheduling problems are notoriously intractable, so the majority of algorithms for them are heuristic in nature. Priority rule-based methods still constitute the most important class of these heuristics. Of these, in turn, parameterized biased random sampling methods have attracted particular interest, due to the fact that they outperform all other priority rule-based methods known.

this report was partially supported by Grant CCR-90-24549 from the National Science Foundation. This is a report to the National Science Foundation and other agencies; it is not a report by or of the National Science Foundation or any other agency. Participants at the Workshop on Research Directions in Integrating Numerical Analysis, Symbolic Computing, Computational Geometry, and Artificial Intelligence for Computational Science Conference Organizers

In this paper we review the current state of the problem solving environment (PSE) field and make projections for the future. First we describe the computing context, the definition of a PSE and the goals of a PSE. The state-of-the-art is summarized along with sources (books, bibliographics, web sites) of more detailed information. The principal components and paradigms for building PSEs are presented. The discussion of the future is given in three parts: future trends, scenarios for 2010/2025, and research

ABSTRACT. We investigate a real-world large scale vehicle dispatching problem with strict real-time requirements, posed by our cooperation partner, the German Automobile Association. We present computational experience on real-world data with a dynamic column generation method employing a portfolio of acceleration techniques. Our computer program ZIBDIP yields solutions on heavy-load real-world instances (215 service requests, 95 service units) in less than a minute that are no worse than 1 % from optimum on state-of-the-art personal computers.