One Important Scheduling Problem Consists in Sequencing a Set of Jobs for Processing on a Single Processor or Machine. We Assume the Situation Where

This paper presents several local search meta-heuristics for the problem of scheduling a single machine to minimise total weighted tardiness. A genetic algorithm for the static single machine total weighted tardiness problem is presented, and a multi-start version named metaGA is proposed. The obtained computational results permit to conclude about their efficiency and effectiveness. The resolution of the dynamic single machine total weighted tardiness problem using a scheduling system based on Genetic Algorithms (GA) is proposed. This approach extends the resolution of static Single Machine Scheduling Problems (SMSP) to dynamic SMSP in which changes can occur continually. A new population generating mechanism for dynamic environments is proposed. This method takes into account dynamic occurrences in a system, and adapts the current modified population into a new regenerated population. Manufacturing organisations are frequently subject to several sorts of changes, which are typically treated as random occurrences, such as new job releases, machine breakdowns, job cancellation and due date and time processing changes. Due to their dynamic nature, real scheduling problems are computationally rather complex, i.e., the time required to compute an optimal solution increases exponentially with the size of the problem [Morton93]. We are mainly concerned with the study of the adequacy and efficiency of meta-heuristics for obtaining good solutions to Single Machine Scheduling Problems (SMSP) in a static and dynamic environment. When jobs have different arrival times at a single machine system, the set of jobs to be scheduled changes over time giving rise to a dynamic SMSP. Traditionally [Potts91][Morton93] [Crauwels98] [Congram98][Madureira99], local search meta-heuristics research in SMSP is mainly concerned with static problems, i.e., problems in which all jobs are known and ready to start at instant zero. The remaining sections are organised as follows. In section 2.1, some general features of single-machine scheduling problems are described, and some of their structural properties are used to design local search procedures based on alternative definitions of neighbourhoods. In section 2.2 the Weighted Tardiness problem and some approaches presented in the literature for its resolution are described. Section 2.3 defines the local search meta-heuristics methods. Section 3 describes the genetic algorithm to solve the static SMSP problem for minimising total Weighted Tardiness (WT) and also the proposed scheduling system for solving the dynamic SMSP to minimise the same measure of performance. The proposed genetic algorithm to solve the referred scheduling problem in a dynamic environment is more adapted to real world …