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This title presents a large variety of models and algorithms dedicated to the resource-constrained project scheduling problem (RCPSP), which aims at scheduling at minimal duration a set of activities subject to precedence constraints and limited resource availabilities. In the first part, the standard variant of RCPSP is presented and analyzed as a combinatorial optimization problem. Constraint programming and integer linear programming formulations are given. Relaxations based on these formulations and also on related scheduling problems are presented. Exact methods and heuristics are surveyed. Computational experiments, aiming at providing an empirical insight on the difficulty of the problem, are provided. The second part of the book focuses on several other variants of the RCPSP and on their solution methods. Each variant takes account of real-life characteristics which are not considered in the standard version, such as possible interruptions of activities, production and consumption of resources, cost-based approaches and uncertainty considerations. The last part presents industrial case studies where the RCPSP plays a central part. Applications are presented in various domains such as assembly shop and rolling ingots production scheduling, project management in information technology companies and instruction scheduling for VLIW processor architectures.
The book is devoted to structural issues, algorithms, and applications of resource allocation problems in project management. Special emphasis is given to a unifying framework within which a large variety of project scheduling problems can be treated. Those problems involve general temporal constraints among project activities, different types of scarce resources, and a broad class of regular and nonregular objective functions ranging from time-based and financial to resource levelling functions. The diversity of the models proposed allows for covering many features arising in scheduling applications beyond the field of project management such as short-term production planning in the manufacturing or process industries.
Industrial, financial, commercial or any kinds of project have at least one common feature: the better organized they are, the higher the profit or the lower the cost. Project management is the principle of planning different projects and keeping them on track within time, cost and resource constraints. The need for effective project management is ever-increasing. The complexity of the environment we live in requires more sophisticated methods than it did just a couple of decades ago. Project managers might face insurmountable obstacles in their work if they do not adapt themselves to the changing circumstances. On the other hand, better knowledge of project management can result in better plans, schedules and, last but not least, more contracts and more profit. This knowledge can help individuals and firms to stay alive in this competitive market and, in the global sense, utilize the finite resources of our planet in a more efficient way.
Discrete Optimization I
Combinatorial (or discrete) optimization is one of the most active fields in the interface of operations research, computer science, and applied math ematics. Combinatorial optimization problems arise in various applications, including communications network design, VLSI design, machine vision, air line crew scheduling, corporate planning, computer-aided design and man ufacturing, database query design, cellular telephone frequency assignment, constraint directed reasoning, and computational biology. Furthermore, combinatorial optimization problems occur in many diverse areas such as linear and integer programming, graph theory, artificial intelligence, and number theory. All these problems, when formulated mathematically as the minimization or maximization of a certain function defined on some domain, have a commonality of discreteness. Historically, combinatorial optimization starts with linear programming. Linear programming has an entire range of important applications including production planning and distribution, personnel assignment, finance, alloca tion of economic resources, circuit simulation, and control systems. Leonid Kantorovich and Tjalling Koopmans received the Nobel Prize (1975) for their work on the optimal allocation of resources. Two important discover ies, the ellipsoid method (1979) and interior point approaches (1984) both provide polynomial time algorithms for linear programming. These algo rithms have had a profound effect in combinatorial optimization. Many polynomial-time solvable combinatorial optimization problems are special cases of linear programming (e.g. matching and maximum flow). In addi tion, linear programming relaxations are often the basis for many approxi mation algorithms for solving NP-hard problems (e.g. dual heuristics)."
The topic of this book is known as dynamic scheduling, and is used to refer to three dimensions of project management and scheduling: the construction of a baseline schedule and the analysis of a project schedule’s risk as preparation of the project control phase during project progress. This dynamic scheduling point of view implicitly assumes that the usability of a project’s baseline schedule is rather limited and only acts as a point of reference in the project life cycle. Consequently, a project schedule should especially be considered as nothing more than a predictive model that can be used for resource efficiency calculations, time and cost risk analyses, project tracking and performance measurement, and so on. In this book, the three dimensions of dynamic scheduling are highlighted in detail and are based on and inspired by a combination of academic research studies at Ghent University (www.ugent.be), in-company trainings at Vlerick Leuven Gent Management School (www.vlerick.com) and consultancy projects at OR-AS (www.or-as.be). First, the construction of a project baseline schedule is a central theme throughout the various chapters of the book, and is discussed from a complexity point of view with and without the presence of project resources. Second, the creation of an awareness of the weak parts in a baseline schedule is discussed at the end of the two baseline scheduling parts as schedule risk analysis techniques that can be applied on top of the baseline schedule. Third, the baseline schedule and its risk analyses can be used as guidelines during the project control step where actual deviations can be corrected within the margins of the project’s time and cost reserves.
The author have used numerical examples as the means for presentation of the underlying ideas of different operations research techniques.Accordingly,a large number of comprehensive solved examples,taken from a variety of fields,have been added in every chapter and they are followed by a set of unsolved problems with answers(and hints wherever required)through which readers can test their understanding of the subject matter.The book,in its present form,contains around 650,examples,1,280 illustrative diagrams.
Integer Prograw~ing is one of the most fascinating and difficult areas in the field of Mathematical Optimization. Due to this fact notable research contributions to Integer Programming have been made in very different branches of mathematics and its applications. Since these publications are scattered over many journals, proceedings volumes, monographs, and working papers, a comprehensive bibliography of all these sources is a helpful tool even for specialists in this field. I initiated this compilation of literature in 1970 at the Institut fur ~konometrie und Operations Research, University of Bonn. Since then many collaborators have contributed to and worked on it. Among them Dipl.-Math. Claus Kastning has done the bulk of the work. With great perseverance and diligence he has gathered all the material and checked it with the original sources. The main aim was to incorporate rare and not easily accessible sources like Russian journals, preprints or unpublished papers. Without the invaluable and dedicated engagement of Claus Kastning the bibliography would never have reached this final version. For this reason he must be considered its responsible editor. As with any other collection this literature list has a subjective viewpoint and may be in some sense incomplete. We have however tried to be as complete as possible. The bibliography contains 4704 different publications by 6767 authors which were classified by 11839 descriptor entries.
In the early 1980s, a trend towards formal undeIStanding and knowledge-based assistance for the development and maintenance of database-intensive information systems became apparent. The group of John Mylopoulos at the UniveISity of Toronto and their European collaboratoIS moved from semantic models of information systems design (Taxis project) towards earlier stages of the software lifecycle. Joachim Schmidt's group at the University of Hamburg completed their early work on the design and implementation of database programming languages (Pascal/R) and began to consider tools for the development of large database program packages. The Belgian company BIM developed a fast commercial Prolog which turned out to be useful as an implementation language for object oriented knowledge representation schemes and as a prototyping tool for formal design models. Case studies by Vasant Dhar and Matthias Jarke in New York pointed out the need for formally representing process knowledge, and a number of projects in the US and Europe began to consider computer assistance (CASE) as a viable approach to support software engineering. In 1985, the time appeared ripe for an attempt at integrating these experiences in a comprehensive CASE framework relating all phases of an information systems lifecycle. The Commission of the European Communities decided in early 1986 to fund this joint effort by six European software houses and research institutions in the Software Technology section of the ESPRIT I program. The project was given the number 892 and the title DAIDA - Development Assistance for Intelligent Database Applications.