@inproceedings{ebd4c800b88c4005acf26c1d4acd9104,

title = "Efficient Models, Formulations and Algorithms for Some Variants of Fixed Interval Scheduling Problems",

abstract = "The fixed interval scheduling problem—also known as the personnel task scheduling problem—optimizes the allocation of available resources (workers, machines, or shifts) to execute a given set of jobs or tasks. We introduce a new approach to solve this problem by decomposing it into separate subproblems. We establish the mathematical basis for optimality of such a decomposition and thereafter develop several new techniques (exact and heuristic) to solve the resulting subproblems. An extensive computational analysis of the new techniques proves the efficacy of these approaches when compared to other established techniques in the literature. Specifically, a hybrid integer programming formulation presented in this paper solves several larger problem instances that were not amenable to exact techniques previously. In addition, a constructive heuristic approach (based on quantification metrics for tasks and resources) gives solutions equal to the optimal. We demonstrate that our decomposition approach is applicable for several important variants within the topic of fixed interval scheduling including tactical fixed interval scheduling problem and operational fixed interval scheduling problem.",

author = "Ramesh, {D Niraj} and M Krishnamoorthy and Ernst, {Andreas T}",

year = "2018",

doi = "10.1007/978-3-319-55914-8_4",

language = "English",

isbn = "9783319559131",

series = "Lecture Notes in Management and Industrial Engineering",

publisher = "Springer",

pages = "43--69",

editor = "Ruhul Sarker and Abbass, {Hussein A} and Simon Dunstall and Philip Kilby and Richard Davis and Leon Young",

booktitle = "Data and Decision Sciences in Action",

}