Solving multi-objective scheduling problems : An integrated systems approach

In the past, numerous approaches have been formulated either for approximating Pareto-optimal alternatives or supporting the decision making process with an interactive multi criteria decision aiding methodology. The article on the other hand presents an integrated system for the resolution of problems under multiple objectives, combining both aspects. A method base of metaheuristics is made available for the identification of optimal alternatives of machine scheduling problems, and the selection of a most preferred solution is supported in an interactive decision making procedure. As the system is aimed at end users, a graphical interface allows the easy adaptation of metaheuristic techniques. Contrary to existing software class libraries, the system therefore enables users with little or no knowledge in the mentioned areas to successfully solve scheduling problems and customize and test metaheuristics. After successfully competing in the finals in Ronneby (Sweden), the software has been awarded the European Academic Software Award 2002IFIP International Conference on Artificial Intelligence in Theory and Practice - Planning and SchedulingRed de Universidades con Carreras en Informática (RedUNCI

Solving multi-objective scheduling problems : An integrated systems approach

In the past, numerous approaches have been formulated either for approximating Pareto-optimal alternatives or supporting the decision making process with an interactive multi criteria decision aiding methodology. The article on the other hand presents an integrated system for the resolution of problems under multiple objectives, combining both aspects. A method base of metaheuristics is made available for the identification of optimal alternatives of machine scheduling problems, and the selection of a most preferred solution is supported in an interactive decision making procedure. As the system is aimed at end users, a graphical interface allows the easy adaptation of metaheuristic techniques. Contrary to existing software class libraries, the system therefore enables users with little or no knowledge in the mentioned areas to successfully solve scheduling problems and customize and test metaheuristics. After successfully competing in the finals in Ronneby (Sweden), the software has been awarded the European Academic Software Award 2002IFIP International Conference on Artificial Intelligence in Theory and Practice - Planning and SchedulingRed de Universidades con Carreras en Informática (RedUNCI

Survey on Combinatorial Register Allocation and Instruction Scheduling

Register allocation (mapping variables to processor registers or memory) and instruction scheduling (reordering instructions to increase instruction-level parallelism) are essential tasks for generating efficient assembly code in a compiler. In the last three decades, combinatorial optimization has emerged as an alternative to traditional, heuristic algorithms for these two tasks. Combinatorial optimization approaches can deliver optimal solutions according to a model, can precisely capture trade-offs between conflicting decisions, and are more flexible at the expense of increased compilation time. This paper provides an exhaustive literature review and a classification of combinatorial optimization approaches to register allocation and instruction scheduling, with a focus on the techniques that are most applied in this context: integer programming, constraint programming, partitioned Boolean quadratic programming, and enumeration. Researchers in compilers and combinatorial optimization can benefit from identifying developments, trends, and challenges in the area; compiler practitioners may discern opportunities and grasp the potential benefit of applying combinatorial optimization

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

The Project Scheduling Problem with Non-Deterministic Activities Duration: A Literature Review

Purpose: The goal of this article is to provide an extensive literature review of the models and solution procedures proposed by many researchers interested on the Project Scheduling Problem with nondeterministic activities duration. Design/methodology/approach: This paper presents an exhaustive literature review, identifying the existing models where the activities duration were taken as uncertain or random parameters. In order to get published articles since 1996, was employed the Scopus database. The articles were selected on the basis of reviews of abstracts, methodologies, and conclusions. The results were classified according to following characteristics: year of publication, mathematical representation of the activities duration, solution techniques applied, and type of problem solved. Findings: Genetic Algorithms (GA) was pointed out as the main solution technique employed by researchers, and the Resource-Constrained Project Scheduling Problem (RCPSP) as the most studied type of problem. On the other hand, the application of new solution techniques, and the possibility of incorporating traditional methods into new PSP variants was presented as research trends. Originality/value: This literature review contents not only a descriptive analysis of the published articles but also a statistical information section in order to examine the state of the research activity carried out in relation to the Project Scheduling Problem with non-deterministic activities duration.Peer Reviewe

A simheuristic algorithm for solving an integrated resource allocation and scheduling problem

Modern companies have to face challenging configuration issues in their manufacturing chains. One of these challenges is related to the integrated allocation and scheduling of resources such as machines, workers, energy, etc. These integrated optimization problems are difficult to solve, but they can be even more challenging when real-life uncertainty is considered. In this paper, we study an integrated allocation and scheduling optimization problem with stochastic processing times. A simheuristic algorithm is proposed in order to effectively solve this integrated and stochastic problem. Our approach relies on the hybridization of simulation with a metaheuristic to deal with the stochastic version of the allocation-scheduling problem. A series of numerical experiments contribute to illustrate the efficiency of our methodology as well as their potential applications in real-life enterprise settings