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Bahram Alidaee

Other affiliations: West Texas A&M University
Bio: Bahram Alidaee is an academic researcher from University of Mississippi. The author has contributed to research in topics: Tabu search & Metaheuristic. The author has an hindex of 26, co-authored 80 publications receiving 2555 citations. Previous affiliations of Bahram Alidaee include West Texas A&M University.


Papers
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Journal ArticleDOI
TL;DR: This paper reviews the rapidly growing literature on single machine scheduling models with time dependent processing times and attention is focused on linear, piecewise linear and non-linear processing time functions for jobs.
Abstract: In classical scheduling theory job processing times are constant However, there are many situations where processing time of a job depends on the starting time of the job in the queue This paper reviews the rapidly growing literature on single machine scheduling models with time dependent processing times Attention is focused on linear, piecewise linear and non-linear processing time functions for jobs We survey known results and introduce new solvable cases Finally, we identify the areas and give directions where further research is needed

471 citations

Journal ArticleDOI
TL;DR: This paper describes the development and use of adaptive memory tabu search procedures to solve binary quadratic programs, and demonstrates that the approach is significantly more efficient and yields better solutions than the best heuristic method reported to date.
Abstract: Recent studies have demonstrated the effectiveness of applying adaptive memory tabu search procedures to combinatorial optimization problems. In this paper we describe the development and use of such an approach to solve binary quadratic programs. Computational experience is reported, showing that the approach optimally solves the most difficult problems reported in the literature. For challenging problems of limited size, which are capable of being approached by exact procedures, we find optimal solutions considerably faster than the best reported exact method. Moreover, we demonstrate that our approach is significantly more efficient and yields better solutions than the best heuristic method reported to date. Finally, we give outcomes for larger problems that are considerably more challenging than any currently reported in the literature.

206 citations

Journal ArticleDOI
TL;DR: Tabu search (TS) as discussed by the authors was used as an alternative to backpropagation for neural network optimization in the context of forecasting out-of-sample data, and the results showed that TS derived solutions that were significantly superior to those of backpropaggregation solutions for in-sample, interpolation, and extrapolation test data for all seven test functions.

147 citations

Journal ArticleDOI
TL;DR: This note considers a problem of scheduling n single-operation jobs on m non-identical machines where the sequencing of the jobs and their processing times are decision variables and reduces each problem to a transportation problem that can be solved by a polynomial time algorithm.

108 citations

Journal ArticleDOI
TL;DR: How a particular unified modeling framework, coupled with latest advances in heuristic search methods, makes it possible to solve problems from a wide range of important model classes.
Abstract: Combinatorial optimization problems are often too complex to be solved within reasonable time limits by exact methods, in spite of the theoretical guarantee that such methods will ultimately obtain an optimal solution. Instead, heuristic methods, which do not offer a convergence guarantee, but which have greater flexibility to take advantage of special properties of the search space, are commonly a preferred alternative. The standard procedure is to craft a heuristic method to suit the particular characteristics of the problem at hand, exploiting to the extent possible the structure available. Such tailored methods, however, typically have limited usefulness in other problems domains.

103 citations


Cited by
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Book
31 Jul 1997
TL;DR: This book explores the meta-heuristics approach called tabu search, which is dramatically changing the authors' ability to solve a host of problems that stretch over the realms of resource planning, telecommunications, VLSI design, financial analysis, scheduling, spaceplanning, energy distribution, molecular engineering, logistics, pattern classification, flexible manufacturing, waste management,mineral exploration, biomedical analysis, environmental conservation and scores of other problems.
Abstract: From the Publisher: This book explores the meta-heuristics approach called tabu search, which is dramatically changing our ability to solve a hostof problems that stretch over the realms of resource planning,telecommunications, VLSI design, financial analysis, scheduling, spaceplanning, energy distribution, molecular engineering, logistics,pattern classification, flexible manufacturing, waste management,mineral exploration, biomedical analysis, environmental conservationand scores of other problems. The major ideas of tabu search arepresented with examples that show their relevance to multipleapplications. Numerous illustrations and diagrams are used to clarifyprinciples that deserve emphasis, and that have not always been wellunderstood or applied. The book's goal is to provide ''hands-on' knowledge and insight alike, rather than to focus exclusively eitheron computational recipes or on abstract themes. This book is designedto be useful and accessible to researchers and practitioners inmanagement science, industrial engineering, economics, and computerscience. It can appropriately be used as a textbook in a masterscourse or in a doctoral seminar. Because of its emphasis on presentingideas through illustrations and diagrams, and on identifyingassociated practical applications, it can also be used as asupplementary text in upper division undergraduate courses. Finally, there are many more applications of tabu search than canpossibly be covered in a single book, and new ones are emerging everyday. The book's goal is to provide a grounding in the essential ideasof tabu search that will allow readers to create successfulapplications of their own. Along with the essentialideas,understanding of advanced issues is provided, enabling researchers togo beyond today's developments and create the methods of tomorrow.

6,373 citations

Book
01 Jan 1988
TL;DR: In this paper, the evolution of the Toyota production system is discussed, starting from need, further development, Genealogy of the production system, and the true intention of the Ford system.
Abstract: * Starting from Need* Evolution of the Toyota Production System* Further Development* Genealogy of the Toyota Production System* The True Intention of the Ford System* Surviving the Low-Growth Period

1,793 citations

Journal ArticleDOI
TL;DR: The hierarchical Bayesian approach is considerably more robust than either of the other approaches in the presence of outliers and is expected to prove useful for a wide range of group studies, not only in the context of DCM, but also for other modelling endeavours, e.g. comparing different source reconstruction methods for EEG/MEG.

1,353 citations

Journal ArticleDOI
TL;DR: This survey examines the state of the art of a variety of problems related to pseudo-Boolean optimization, i.e. to the optimization of set functions represented by closed algebraic expressions.

903 citations

Journal ArticleDOI
TL;DR: It is shown in this paper that even with the introduction of learning to job processing times two important types of single-machine problems remain polynomially solvable.

678 citations