Abstract: The annualisation of working hours (i.e., the irregular distribution of the total number of working hours over the course of a year) makes it possible to adapt production capacity to fluctuations in demand. The required capacity, which is an essential data for the optimal planning of working time, usually depends on several complex factors. Often, it is impossible to reliably predict the required capacity or it is unrealistic to adjust it to a probability distribution. In some cases, it is possible to determine a set of required-capacity scenarios, each with a related probability. This paper presents a multistage stochastic optimisation model that provides a robust solution (i.e., feasible for any possible scenario) and minimises the expected total capacity shortage. (C) 2007 Elsevier B.V. All rights reserved.

Abstract: 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. (C) 2007 Elsevier B.V. All rights reserved.

Abstract: Annualizing working hours (AH) is a means of achieving flexibility in the use of human resources to cope with the seasonal nature of demand. Some existing planning procedures are able to minimize costs through the use of overtime and temporary workers. However, due to the great difficulty in solving the problem, it is normally assumed both that holiday weeks are fixed beforehand and that workers from different categories who are able to perform a specific type of task have the same efficiency. Often the reality is different, and thus there is a gap between academic and real problems. In the present paper, those constraints are relaxed and a much more general and true-to-life problem is solved in an exact and very efficient way.

Abstract: A variable neighbourhood search algorithm that employs new neighbourhoods is proposed for solving a task allocation problem whose main characteristics are: ( i) each task requires a certain amount of resources and each processor has a capacity constraint which limits the total resource of the tasks that are assigned to it; (ii) the cost of solution includes fixed costs when using processors, task assignment costs, and communication costs between tasks assigned to different processors. A computational study shows that the algorithm performs well in terms of time and solution quality relative to other local search procedures that have been proposed.

Abstract: Annualising working hours (i.e., the possibility of irregularly distributing the total number of working hours over a year) permits companies to adapt capacity to fluctuations in demand, thus reducing overtime, temporary workers and inventory costs. Since annual hours can lead to a worsening of the staffs working conditions, many laws and collective bargaining agreements contain constraints that affect the distribution of working time. This paper proposes a MILP model to solve an annualised working hours planning problem in which workers are considered to be cross-trained, and in which the number of weekly working hours must belong to a previously agreed finite set. A computational experiment demonstrates the effectiveness of the model. (c) 2005 Elsevier B.V. All rights reserved.

Abstract: 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 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.

Abstract: 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 staffs 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. (c) 2005 Elsevier B.V. All rights reserved.

Abstract: The need to adjust productive capacity to seasonal demand and the increasing flexibility in the distribution of annual working hours has given rise to new problems with respect to the organisation of staff working time. This paper presents a problem of planning annual working hours, in which the number of weekly working hours for any worker must belong to a previously agreed finite set and holiday weeks are the same for all the staff members. The problem is modelled and solved as a mixed integer linear program.

Abstract: Annualising working hours (AH) is a means to achieve flexibility in the use of human resources in order to face the seasonal nature of the demand. MILP models are proposed to solve the problem of planning the staffs working hours with an annual horizon. The computational experience with the models leads to the conclusion that MILP is an appropriate way to deal with the problem in many real cases.