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

A greedy heuristic approach for the project scheduling with labour allocation problem

Responding to the growing need of generating a robust project scheduling, in this article we present a greedy algorithm to generate the project baseline schedule. The robustness achieved by integrating two dimensions of the human resources flexibilities. The first is the operators’ polyvalence, i.e. each operator has one or more secondary skill(s) beside his principal one, his mastering level being characterized by a factor we call “efficiency”. The second refers to the working time modulation, i.e. the workers have a flexible time-table that may vary on a daily or weekly basis respecting annualized working strategy. Moreover, the activity processing time is a non-increasing function of the number of workforce allocated to create it, also of their heterogynous working efficiencies. This modelling approach has led to a nonlinear optimization model with mixed variables. We present: the problem under study, the greedy algorithm used to solve it, and then results in comparison with those of the genetic algorithms

Using a MILP model to establish a framework for an annualised hours agreement

Production flexibility is essential for industrial companies that have to deal with seasonal demand. Human resources are one of the main sources of flexibility. Annualising working hours (i.e., the possibility of irregularly distributing the total number of working hours over the course of a year) is a tool that provides flexibility to organizations; it enables a firm to adapt production capacity to fluctuations in demand. However, it can imply a worsening of the staff’s working conditions. To take the human aspect into account, the planning and scheduling of working time should comply with constraints derived from the law or from a collective bargaining agreement. Furthermore, new and more difficult working-time planning and scheduling problems are arising. This paper proposes a mixed-integer linear program model to solve the problem of planning the production and the working hours of a human team that operates in a multi-product process. Solving the model for different settings provides the essential quantitative information to negotiate the best conditions of the annualised hours system (the elements to establish the trade-off between weekly flexibility and economic or working-time reduction compensation can be obtained). The results achieved in a computational experiment were very satisfactory.Peer Reviewe

Decision-based genetic algorithms for solving multi-period project scheduling with dynamically experienced workforce

The importance of the flexibility of resources increased rapidly with the turbulent changes in the industrial context, to meet the customers’ requirements. Among all resources, the most important and considered as the hardest to manage are human resources, in reasons of availability and/or conventions. In this article, we present an approach to solve project scheduling with multi-period human resources allocation taking into account two flexibility levers. The first is the annual hours and working time regulation, and the second is the actors’ multi-skills. The productivity of each operator was considered as dynamic, developing or degrading depending on the prior allocation decisions. The solving approach mainly uses decision-based genetic algorithms, in which, chromosomes don’t represent directly the problem solution; they simply present three decisions: tasks’ priorities for execution, actors’ priorities for carrying out these tasks, and finally the priority of working time strategy that can be considered during the specified working period. Also the principle of critical skill was taken into account. Based on these decisions and during a serial scheduling generating scheme, one can in a sequential manner introduce the project scheduling and the corresponding workforce allocations

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

Flexible resources allocation techniques: characteristics and modelling

At the interface between engineering, economics, social sciences and humanities, industrial engineering aims to provide answers to various sectors of business problems. One of these problems is the adjustment between the workload needed by the work to be realised and the availability of the company resources. The objective of this work is to help to find a methodology for the allocation of flexible human resources in industrial activities planning and scheduling. This model takes into account two levers of flexibility, one related to the working time modulation, and the other to the varieties of tasks that can be performed by a given resource (multi–skilled actor). On the one hand, multi–skilled actors will help to guide the various choices of the allocation to appreciate the impact of these choices on the tasks durations. On the other hand, the working time modulation that allows actors to have a work planning varying according to the workload which the company has to face

Planning production and working time within an annualised hours scheme framework

Production flexibility is essential for industrial companies that have to deal with seasonal demand. Human resources are one of the main sources of flexibility. Annualising working hours (i.e., the possibility of irregularly distributing the total number of working hours over the course of a year) is a tool that provides organisations with flexibility; it enables a firm to adapt production capacity to fluctuations in demand. However, it can involve a worsening of the staff’s working conditions. To take this into account, the planning and scheduling of working time should comply with constraints derived from the law or from a collective bargaining agreement. Thus, new and more difficult working-time and production planning and scheduling problems are arising. This paper proposes two mixed-integer linear program models for solving the problem of planning the production, the working hours and the holiday weeks of the members of a human team operating in a multi-product process in which products are perishable, demand can be deferred and temporary workers are hired to stand in for employees. The results of a computational experiment are presented.Peer Reviewe

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