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

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

Enabling flexibility through strategic management of complex engineering systems

”Flexibility is a highly desired attribute of many systems operating in changing or uncertain conditions. It is a common theme in complex systems to identify where flexibility is generated within a system and how to model the processes needed to maintain and sustain flexibility. The key research question that is addressed is: how do we create a new definition of workforce flexibility within a human-technology-artificial intelligence environment? Workforce flexibility is the management of organizational labor capacities and capabilities in operational environments using a broad and diffuse set of tools and approaches to mitigate system imbalances caused by uncertainties or changes. We establish a baseline reference for managers to use in choosing flexibility methods for specific applications and we determine the scope and effectiveness of these traditional flexibility methods. The unique contributions of this research are: a) a new definition of workforce flexibility for a human-technology work environment versus traditional definitions; b) using a system of systems (SoS) approach to create and sustain that flexibility; and c) applying a coordinating strategy for optimal workforce flexibility within the human- technology framework. This dissertation research fills the gap of how we can model flexibility using SoS engineering to show where flexibility emerges and what strategies a manager can use to manage flexibility within this technology construct”--Abstract, page iii

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

Lomien kustannustehokas suunnittelu vaihtelevan kysynnän ja vaihtelevan työvoiman määrän tilanteessa

Vacation planning can be a complicated process as multiple law and contract based rules must be respected, while at the same time the wishes of employees must be taken into account. The problem is especially difficult in transit industry, where demand and available manpower can vary and the products of transit industry have no shelf life. Also, temporary workers cannot be recruited as long training is needed. In this thesis, a constraint programming formulation for solving vacation planning problems is developed. Constraint programming allows modeling each vacation as a single interval variable. This makes the approach more effective than modeling the problem as MILP, which would require a large amount of additional constraints and variables to model the problem, especially the consecutiveness of vacations. The objective of vacation planning is to find a solution, which has as large as possible minimum reserve of employees after all vacations are assigned. An additional objective of minimizing maximum reserve is introduced to even out the distribution of reserve. The problem is solved to optimality with a commercial optimization solver with running times varying from a few seconds to three minutes. The results of two real world cases of a transportation company show that the model provides improvement in solution quality and the planning time needed is reduced considerably. The issue of planning vacations has received little attention in literature. In many cases the vacations are planned by mutual agreement or a named employee assigns vacations by hand. This can result in a lot of manual labor after which the solution quality might still be poor. This thesis presents the first constraint programming based approach for planning employees’ vacations. It allows the modeling of multiple constraints that are used to improve solution quality, and takes into account the preferences of the employees, the planning personnel and the company.Lomien suunnittelu voi olla hankala prosessi, koska lain ja työehtosopimuksen asettamia rajoitteita pitää kunnioittaa ja samalla työntekijöiden toiveet pitää ottaa huomioon. Ongelma on erityisen hankala kuljetusalalla, koska kysyntä ja työvoiman määrä voivat vaihdella, ja kuljetuksia ei voi laittaa varastoon. Lisäksi väliaikaisia työntekijöitä ei voida palkata vaadittavan pitkän koulutuksen vuoksi. Tässä työssä kehitetään rajoiteohjelmointimalli (engl. constraint programming), jota käytetään lomien suunnitteluongelman ratkaisemiseen. Rajoiteohjelmointi mahdollistaa yksittäisen loman mallintamisen yhtenä intervallimuuttujana. Tämä tekee lähestymistavasta paljon tehokkaamman kuin ongelman mallintaminen MILP-tehtävänä, mikä vaatii monia lisärajoitteita ja –muuttujia, erityisesti lomien yhdenjaksoisuuden mallintamiseksi. Lomien suunnittelussa on tavoitteena tuottaa ratkaisu, jossa on mahdollisimman suuri minimityöntekijäreservi lomien kiinnittämisen jälkeen. Lisätavoitteena otetaan käyttöön suurimman reservin minimointi, mikä tasoittaa reservin ajallista jakautumista. Ongelma ratkaistaan optimiin kaupallisella optimointiohjelmistolla ja ratkaisuajat vaihtelevat muutamista sekunneista kolmeen minuuttiin. Kaksi oikeaan dataan perustuvaa esimerkkitapausta näyttävät, että kehitetty malli parantaa tulosten laatua ja vähentää huomattavasti lomien suunnitteluun tarvittavia työtunteja. Lomien suunnittelu on saanut vain vähän huomiota kirjallisuudessa. Monissa tapauksissa lomat suunnitellaan yhteisellä sopimisella tai yksi työntekijä suunnittelee käsin kaikkien lomien ajankohdat. Tämä voi vaatia paljon manuaalista työtä ja silti tulosten laatu voi olla huono. Tässä tutkielmassa esitetään ensimmäinen rajoiteohjelmointiin perustuva lähestymistapa työntekijöiden lomien suunnitteluun, mikä mahdollistaa useiden ratkaisujen laatua parantavien rajoitteiden mallintamisen, ottaen huomioon työntekijöiden, henkilöstösuunnittelijoiden ja työnantajan preferenssit

Determining the most appropriate set of weekly working hours for planning annualised working time

Annualised hours—the irregular distribution of working hours over a year—allow companies to adapt capacity to demand, thus reducing overtime, the number of temporary workers and inventory costs. To avoid a significant deterioration in working conditions, many laws and agreements constrain the distribution of working time. One way of doing this is by specifying a finite set of weekly working hours and bounding the annual number of weeks of each type. Although this set has a great impact on the solution, it is usually agreed without taking all the available data (demand, costs, etc.) into consideration. This paper proposes a method for selecting the most appropriate set of weekly working hours and establishing an annual plan or working time for each worker as a way of optimising service level. To this end, two mathematical programming models are proposed. By means of a computational experiment, it is shown that one of the models can be solved in short computing times and can thus be used as a decision-making tool.Peer Reviewe

Strategic planning of a heterogenous workforce

Dieser Artikel ist eine konsequente Weiterentwicklung des strategischen Personalplanungsmodells. Dabei wird die Dynamik der Unternehmensum- welt parametrisiert und in das Planungsmodell mit aufgenommen. Die Per- sonalplanung startet bei der Optimierung mit dem vorhandenen Personal des Unternehmens, kann somit von einer Planungsperiode zur nächsten am Status Quo des Unternehmens ansetzen. Die individuellen Unterschiede einzelner Personen werden für weite Be- reiche der praktischen Anwendung modelliert. So werden nicht nur die Ko- sten der Personalakquise oder der Abfindung explizit behandelt, auch die individuellen körperlichen und geistigen Fähigkeiten, sowie Aus- und Wei- terbildungskosten eines jeden Arbeiters werden berücksichtigt. Modellierung, Implementierung sowie Anwendung wurden durch den Autoren Martin Mundschenk ausgearbeitet

Manpower Planning with Annualized Hours Flexibility: a Fuzzy Mathematical Programming Approach

We have considered the problem of Annualized Hours (AH) in workforce management. AH is a method of distributing working hours with respect to the demand over a year. In this paper the basic manpower planning problem with AH flexibility is formulated as a fuzzy mathematical programming problem with flexible constraints. Three models of AH planning problem under conditions of fuzzy uncertainty are presented using different aggregation operators. These fuzzy models softens the rigidity of deterministic model by relaxing some of the constraints using flexible programming. Finally, an illustration is given with a computational experiment performed on realistic scale case problem of an automobile company to demonstrate and analyze the effectiveness of the fuzzy approach over deterministic model

Planning holidays and working time under annualised hours

Annualising working hours (AH) is a mean of achieving flexibility in the use of human resources to face the seasonal nature of demand. Some of the existing planning procedures are able to minimise cost due to overtime and temporary workers but, due to the difficulty of solving the problem, it is normally assumed both that the holidays week are fixed beforehand and that workers from different categories who are able to perform specific type of task have the same efficiency. In the present paper, those assumptions are relaxed and a more general problem is solved. The computational experiment leads to the conclusion that MILP is a technique suitable to dealing with the problem