A hybrid dual-population genetic algorithm for the single machine maximum lateness problem

We consider the problem of scheduling a number of jobs, each job having a release time, a processing time and a due date, on a single machine with the objective of minimizing the maximum lateness. We developed a hybrid dual-population genetic algorithm and compared its performance with alternative methods on a new diverse data set. Extensions from a single to a dual population by taking problem specific characteristics into account can be seen as a stimulator to add diversity in the search process, which has a positive influence on the important balance between intensification and diversification. Based on a comprehensive literature study on genetic algorithms in single machine scheduling, a fair comparison of genetic operators was made

Reframing talent identification as a status-organising process:Examining talent hierarchies through data mining

We examine how peers form talent appraisals of team members, reframing talent identification as a status-organising social process. Using decision trees, we modelled configurations of characteristics and behaviours that predicted dominant versus parallel routes to achieving the status of most talented team member. Across 44 multidisciplinary teams, talent status was most often granted to peers perceived as having both leadership and analytic talent; a STEM degree served a dominant signalling function. Where previous studies assumed that degree operates as a specific status characteristic, we show that a STEM degree operates as a diffuse status characteristic, which predicts status in general. We thus discovered that status hierarchies in teams are also based on the type of talent—and not just the level of talent—members are perceived to possess. In so doing, we offer a proof of concept of what we call ‘talent hierarchies’ in teams, for future research to build on

Hybrid (meta-)heuristic optimization for single machine, parallel machine and job shop scheduling problems

In this doctoral thesis, we study a widely investigated branch of the operational research domain, i.e. the scheduling of production systems. Scheduling, in general, can be seen as the allocation of limited resources to tasks in order to optimize a certain objective function. Machine scheduling, in particular, refers to problems in a manufacturing environment where jobs have to be scheduled for processing on one or more machines to optimize one or more objectives. Within the machine scheduling field there is a large variety of problem types, based on the characteristics of the jobs, the restrictions of the process and the objectives to be optimized. In this dissertation, several machine scheduling problems, ranging from the single machine environment to the multi-stage job shop environment, with varying job characteristics, process restrictions and objective functions, are scrutinized. For these problems, various algorithmic optimization approaches as well as two practical case studies are discussed. Our study starts with the analysis of a number of single machine scheduling problems for which a number of meta-heuristic solution procedures are developed. We then make the extension to the parallel machine scheduling environment and present a novel hybrid solution approach. Our study is further extended to the multi-stage job shop environment with the development of priority rules as well as two meta-heuristic solution procedures. Finally, in order to bridge the gap between theory and practice, we discuss two real-life case studies in a manufacturing and a service industry environment. In a first case study, we develop a hybrid job shop scheduling procedure that serves as a simulation tool for a Belgian manufacturing company in order to improve its current scheduling approach. Next, a link to the health care environment is made by means of a case study performed at two Belgian hospitals. This study aims at investigating the usefulness of existing machine scheduling principles and procedures in a dynamic and uncertain patient scheduling environment and at formulating critical and well-defined guidelines to improve the current way of patient planning

A hybrid heuristic for the machine scheduling problem with parallel machines

We consider the unrelated parallel machine scheduling problem with a makespan objective. We discuss two heuristic approaches and the hybridization of these heuristics with a truncated branch-and-bound procedure. We compare the performances of these heuristics on standard data available in literature and examine the influence of the different heuristic parameters. The computational experiments reveal that the hybrid heuristics are able to compete with the best known results from the literature

A hybrid genetic algorithm for the single machine maximum lateness problem with release times and family setups

We consider the problem of scheduling a number of jobs, each job having a release time, a processing time, a due date and a family setup time, on a single machine with the objective of minimizing the maximum lateness. We develop a hybrid genetic algorithm and validate its performance on a newly developed diverse data set. We perform an extensive study of local search algorithms, based on the trade-off between intensification and diversification strategies, taking the characteristics of the problem into account. We combine different local search neighborhood structures in an intelligent manner to further improve the solution quality. We use the hybrid genetic algorithm to perform a comprehensive analysis of the influence of the different problem parameters on the average maximum lateness value and the performance of the algorithm(s)

Genetic algorithms for single machine scheduling problems: a trade-off between intensifications and diversification

This article reviews the literature on attitudes toward change. This narrative review of 58 journal articles published between 1993 and 2007 indicates that there is a need for a more complete typology of attitudes toward change that also fully captures the core essence of this concept. By means of content analysis we first examined the conceptual overlap between the eight attitude-related constructs included in this review and the working definition of attitudes toward change. Second, the concept “attitudes toward change” was described along four major theoretical lenses: (a) nature of change, (b) level of change, (c) positive—negative view on change, and (d) research perspective. This conceptual review not only summarizes the current state of research but also offers a more complete typology of attitudes toward change, and highlights directions for possible future inquiry