A heuristic procedure to solve the project staffing problem with discrete time/resource trade-offs and personnel scheduling constraints

Abstract

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. AbstractWhen scheduling projects under resource constraints, assumptions are typically made with respect to the resource availability and activities are planned each with its own duration and resource requirements. In resource scheduling, important assumptions are made with respect to the staffing requirements. Both problems are typically solved in a sequential manner leading to a suboptimal outcome. We integrate these two interrelated scheduling problems to determine the optimal personnel budget that minimises the overall cost. Integrating these problems increases the scheduling flexibility, which improves the overall performance. In addition, we consider some resource demand flexibility in this research as an activity can be performed in multiple modes. In this paper, we present an iterated local search procedure for the integrated multi-mode project scheduling and personnel staffing problem. Detailed computational experiments are presented to evaluate different decomposition heuristics and comparison is made with alternative optimisation techniques.

Considering Project Management Activities for Engineering Design Groups

“…In these tables, the mean deviation from optimal response to percentage (ADO) and percentage of optimum response (POF) in each example set is reported for each method. In Table 9, the performance of the proposed algorithm with Van Peteghem and Vanhoucke [119], Lova et al [86], Geiger [57], and Van Den Eeckhout et al [118] genetic algorithms, Ranjbar et al [96], Roghanian et al [100], and Chakrabortty et al [29] sparse search, and Józefowska et al [74], Xu et al [125], and Altintas and Azizoglu [5] simulated annealing are compared based on the 10,000 generated results. That is, for each set of problems, the operation is stopped after producing 10,000 answers and the best answer is obtained until that moment is reported as the answer to the problem.…”

A graph-based formulation for the shift rostering problem

“…Fügener, Pahr, & Brunner, 2018;Vermuyten, Rosa, Marques, Beliën, & Barbosa-Póvoa, 2018 ), project management (e.g. Maenhout and Vanhoucke (2016) ; Van Den Eeckhout, Maenhout, and Vanhoucke (2019) ), airline sector (e.g. Bruecker et al (2018) ; Doi et al (2018) ), call-centers (e.g.…”

“…Recent metaheuristic approaches include iterated local search (e.g. Van Den Eeckhout et al, 2019 ) and variable neighborhood search (e.g. Smet, Ernst, & Berghe, 2016b;Vermuyten et al, 2018;Zheng, Liu, & Gong, 2017 ).…”

Designing a resource-constrained project scheduling model considering multiple routes for flexible project activities: meta-heuristic algorithms

“…Fernandes et al [33] regarded a path-relinking (PR) algorithm for MRCPSP to minimize makespan of the project. Van Den Eeckhout et al [34] integrated multi-mode RCPSP and resource scheduling in order to introduce some flexibilities in the scheduling process to determine the optimal personnel budget that minimized the overall cost.…”