Alkis Vazacopoulos

Heuristic methods and applications: A categorized survey

An extensive search of journal publications on heuristic methods and applications produced 442 articles published in 37 journals during the last sixteen years. A scheme is employed to categorize each article according to 12 classes of heuristic approaches and 144 areas of applications (the latter taken from the OR/MS subject classification codes). An analysis of these data reveals some interesting historical patterns and directions for future work. This categorized survey should be helpful to students, teachers, researchers and practitioners interested in heuristics.

Job shop scheduling with setup times, deadlines and precedence constraints

In the last 15 years several procedures have been developed that can find solutions of acceptable quality in reasonable computing time to Job Shop Scheduling problems in environments that do not involve sequence-dependent setup times of the machines. The presence of the latter, however, changes the picture dramatically. In this paper we adapt one of the best known heuristics, the Shifting Bottleneck Procedure, to the case when sequence dependent setup times play an important role. This is done by treating the single machine scheduling problems that arise in the process as Traveling Salesman Problems with time windows, and solving the latter by an efficient dynamic programming algorithm. The model treated here also incorporates precedence constraints, release times and deadlines. Computational experience on a vast array of instances, mainly from the semiconductor industry, shows our procedure to advance substantially the state of the art.

Data Mining in Biomedicine

This volume presents an extensive collection of chapters covering various aspects of the exciting and important research area of data mining techniques in biomedicine. The topics include: - new approaches for the analysis of biomedical data, - applications of data mining techniques to real-life problems in medical practice, - comprehensive reviews of recent trends in the field. The book addresses the problems arising in fundamental areas of biomedical research, such as genomics, proteomics, protein characterization, and neuroscience. This volume would be of interest to scientists and practitioners working in the field of biomedicine, as well as related areas of engineering, mathematics, and computer science. It can also be helpful to graduate students and young researchers looking for new exciting directions in their work. Since each chapter can be read independently, readers interested in specific problems and applications may find the material of certain chapters useful.

Solving Hard Mixed-Integer Programming Problems with Xpress-MP: A MIPLIB 2003 Case Study

Despite the fact that no polynomial-time algorithm is known for solving mixed-integer programming (MIP) problems, there has been remarkable success in recent years in solving a wide range of difficult MIPs. In this paper, we take a look at some of the hardest problems in the MIPLIB 2003 test set and show how Xpress-MP can be used to solve some of the problems that were previously thought to be intractable.

Solving job shop scheduling problems utilizing the properties of backbone and big valley

In this paper, a new metaheuristic for the job shop scheduling problem is proposed. Our approach uses the backbone and “big valley” properties of the job shop scheduling problem. The results of the computational experiments have demonstrated the high efficiency of our approach. New upper bounds have been obtained for many problems.

Modeling & Solving Stochastic Programming Problems in Supply Chain Management Using Xpress-SP

Supply chains continually face the challenge of efficient decision-making in a complex environment coupled with uncertainty. While plenty of forecasting and analytical tools are available in the market to evaluate and enhance Supply Chain performance, the current functionalities are not sufficient to address issues related to efficient decision making under uncertainty. In this paper we discuss expanding the modeling paradigm to incorporate uncertain events naturally and concisely in a stochastic programming framework, and demonstrate how Xpress-SP—a, stochastic programming suite—can be used for modeling, solving and analyzing problems occurring in supply chain management.

Solving Sports Scheduling and Timetabling Problems with Constraint Programming

In the last fifteen years, many successful industrial applications have been implemented using constraint programming tools. Many of those applications include specific constraints, which are difficult to model using the existing modeling and optimization tools. Industrial applications that are particularly suitable for constraint programming include production planning, scheduling and resource allocation.

Generalized capital investment planning of oil-refineries using MILP and sequence-dependent setups

Abstract Due to quantity times quality nonlinear terms inherent in the oil-refining industry, performing industrial-sized capital investment planning (CIP) in this field is traditionally done using linear (LP) or nonlinear (NLP) models whereby a gamut of scenarios are generated and manually searched to make expand and/or install decisions. Though mixed-integer nonlinear (MINLP) solvers have made significant advancements, they are often slow for large industrial applications in optimization; hence, we propose a more tractable approach to solve the CIP problem using a mixed-integer linear programming (MILP) model and input–output (Leontief) models, where the nonlinearities are approximated to linearized operations, activities, or modes in large-scaled flowsheet problems. To model the different types of CIPs known as revamping, retrofitting, and repairing, we unify the modeling by combining planning balances with scheduling concepts of sequence-dependent changeovers to represent the construction, commission, and correction stages explicitly in similar applications such as process design synthesis, asset allocation and utilization, and turnaround and inspection scheduling. Two motivating examples illustrate the modeling, and a retrofit example and an oil-refinery investment planning problem are also highlighted.

Optimization Techniques in Medicine

We give a brief overview of a rapidly emerging interdisciplinary research area of optimization techniques in medicine. Applying optimization approaches proved to be successful in various medical applications. We identify the main research directions and describe several important problems arising in this area, including disease diagnosis, risk prediction, treatment planning, etc.

Network-based Models and Algorithms in Data Mining and Knowledge Discovery

In this chapter, we have addressed several issues regarding the use of network-based mathematical programming techniques for solving various problems arising in the broad area of data mining. We have pointed out that applying these approaches often proved to be effective in many applications, including biomedicine, finance, telecommunications, etc. In particular, if a real-world massive dataset can be appropriately represented as a network structure, its analysis using standard graph-theoretical techniques often yields important practical results.