Rostering from staffing levels: a branch-and-price approach

Many rostering methods first create shifts from some given staffing levels, and after that create rosters from the set of created shifts. Although such a method has some nice properties, it also has some bad ones. In this paper we outline a method that creates rosters directly from staffing levels. We use a Branch-and-Price (B\&P) method to solve this rostering problem and compare it to an ILP formulation of the subclass of rostering problems studied in this paper. The two methods perform almost equally well. Branch-and-Price, though, turns out to be a far more flexible approach to solve rostering problems. It is not too hard to extend the Branch-and-Price model with extra rostering constraints. However, for ILP this is much harder, if not impossible. Next to this, the Branch-and-Price method is more open to improvements and hence, combined with the larger flexibility, we consider it better suited to create rosters directly from staffing levels in practice

Self-rostering applied to case studies - An ILP method to construct a feasible schedule

We discuss the self-rostering problem, a concept receiving more and more attention from both theory and practice. We outline our methodology and discuss its application to a number of practical case studies

Optimal staffing under an annualized hours regime using Cross-Entropy optimization

This paper discusses staffing under annualized hours. Staffing is the selection of the most cost-efficient workforce to cover workforce demand. Annualized hours measure working time per year instead of per week, relaxing the restriction for employees to work the same number of hours every week. To solve the underlying combinatorial optimization problem this paper develops a Cross-Entropy optimization implementation that includes a penalty function and a repair function to guarantee feasible solutions. Our experimental results show Cross-Entropy optimization is efficient across a broad range of instances, where real-life sized instances are solved in seconds, which significantly outperforms an MILP formulation solved with CPLEX. In addition, the solution quality of Cross-Entropy closely approaches the optimal solutions obtained by CPLEX. Our Cross-Entropy implementation offers an outstanding method for real-time decision making, for example in response to unexpected staff illnesses, and scenario analysis

Shift rostering using decomposition: assign weekend shifts first

This paper introduces a shift rostering problem that surprisingly has not been studied in literature: the weekend shift rostering problem. It is motivated by our experience that employees’ shift preferences predominantly focus on the weekends, since many social activities happen during weekends. The Weekend Rostering Problem (WRP) addresses the rostering of weekend shifts, for which we design a problem specific heuristic. We consider the WRP as the first phase of the shift rostering problem. To complete the shift roster, the second phase assigns the weekday shifts using an existing algorithm. We discuss effects of this two-phase approach both on the weekend shift roster and on the roster as a whole. We demonstrate that our first-phase heuristic is effective both on generated instances and real-life instances. For situations where the weekend shift roster is one of the key determinants of the quality of the complete roster, our two-phase approach shows to be effective when incorporated in a commercially implemented algorithm

TN03: Broadcast Scan - Disruptive Technology Search for Space Applications

This Technical Note (TN) documents on Work Package 4000, which is the Broadcast Scan. The aim of this work package is the creation of a search strategy, identify spin-in markets, scan for potential DSTs and perform a pre-selection for further evaluation. It fits within the overall research as the search strategy development part, highlighted in the overall structure of the research, depicted in Figure 1. In this figure, the second chapter covers the Search Method and Techniques Review part in which various technology search methods are reviewed and evaluated. The third chapter focusses on DST Market Identification, in which several external markets are investigated which are of high interest to the space sector. The fourth chapter involves the creation of a DST Search Strategy in which a strategy is devised to identify potential DSTs and store them into a custom designed database. The fifth chapter documents upon the pre-selection of the identified technologies, these criteria are based on the AHP method and the STEP-Criteria explained in TN02

Cost-efficient staffing under annualized hours

We study how flexibility in workforce capacity can be used to efficiently match capacity and demand. Flexibility in workforce capacity is introduced by the annualized hours regime. Annualized hours allow organizations to measure working time per year, instead of per month or per week. An additional source of flexibility is hiring employees with different contract types, like full-time, part-time, and min-max, and by hiring subcontractors. We propose a mathematical programming formulation that incorporates annualized hours and shows to be very flexible with regard to modeling various contract types. The objective of our model is to minimize salary cost, thereby covering workforce demand, and using annualized hours. Our model is able to address various business questions regarding tactical workforce planning problems, e.g., with regard to annualized hours, subcontracting, and vacation planning. In a case study for a Dutch hospital two of these business questions are addressed, and we demonstrate that applying annualized hours potentially saves up to 5.2% in personnel wages annually

An assessment of a days off decomposition approach to personnel scheduling

This paper studies a two-phase decomposition approach to solve the personnel scheduling problem. The first phase creates a days off schedule, indicating working days and days off for each employee. The second phase assigns shifts to the working days in the days off schedule. This decomposition is motivated by the fact that personnel scheduling constraints are often divided in two categories: one specifies constraints on working days and days off, while the other specifies constraints on shift assignments. To assess the consequences of the decomposition approach, we apply it to public benchmark instances, and compare this to solving the personnel scheduling problem directly. In all steps we use mathematical programming. We also study the extension that includes night shifts in thefirst phase of the decomposition. We present a detailed results analysis, and analyze the effect of various instance parameters on the decompositions' results. In general, we observe that the decompositions significantly reduce the computation time, and that they produce good solutions for most instances