Characteristics and classification of the annualised workings hours planning problems

Annualising working hours (i.e., the possibility of irregularly distributing throughout the year the total amount of the employees working hours) is one of the means to face the seasonal nature of the demand. Annualising working hours provides much flexibility to the production system (which is one of the main principles of lean production), so the company is allowed to plan the staff working time more efficiently. However, doing irregular working weeks implies a worsening of the working conditions that could be compensated by reducing the workers annual working time. On the other hand, conditions are provided in order to avoid an excessive workers’ overburden in long periods of strong demand. This paper introduces annualised hours (AH) as a mean to achieve production flexibility and proposes a classification scheme of the annualised working hours planning problems that arise in services and in manufacturing, as well as an approach for solving them by using mixed and integer linear programming and assessing the benefits of introducing AH.Peer Reviewe

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

Annualised Hours: A Real Flexibility Tool

Annualising working hours (AH) is a mean to face the seasonal nature of the demand. AH provides much flexibility to the production system, so the company is allowed to plan the staff working time more efficiently. However, the introduction of AH entails new optimisation problems to solve, as is the working time planning problem. These problems should be solved in an efficient way so AH could be a real and operative flexibility tool. Mixed and integer linear programming has been shown as a very effective approach in the resolution of some real and artificial generated AH problems.Peer Reviewe

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

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

Annualised hours (possibility of irregularly distributing working hours over a year) permit companies to adapt capacity to demand, thus reducing overtime, temporary workers and inventory costs. To avoid a significant worsening of the working conditions, many laws and agreements constraint the distribution of working time. One way to constrain solutions is by specifying a finite set of weekly working hours and bounding the annual number of weeks of each type. Even though its impact on the solution, that set is usually agreed without considering all data available (demand, cost, etc.). In this paper two MILP models are used to determine in on step the most appropriate set of weekly working hours, the annual number of weeks of each type and the annual working time planning

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

Analysis and implementation of volume flexibility in manufacturing plans

Manufacturing flexibility - the ability to change or respond quickly has been heralded as a major competitive weapon for manufacturing organisations operating in turbulent markets and markets characterised by fierce competition and rapid developments in technology. It is also important for the achievement of new management paradigms such as time-based competition, lean production, business process re-engineering and mass customisation. However, many issues on the concept of manufacturing flexibility such as, the clarification of why flexibility is needed, when it is needed, and how it can be implemented in manufacturing organisations have not been sufficiently addressed and resolved in the literature. This research project has been carried out to resolve some of these issues by focusing on one aspect of manufacturing flexibility - volume flexibility. The research design, which was developed to address the research issues, comprised the use of both quantitative and qualitative research methods. The quantitative research method is an exploratory mail survey of UK manufacturing plants in all the major industrial classifications. The survey was used to obtain broad patterns and evidence concerning the conditions that drive manufacturing plants to require volume flexibility and also to identify the mechanisms which manufacturing plants employ to achieve volume flexibility. The qualitative research method is an explanatory case-based research. Manufacturing plants in each sector that responded to the survey and provided rich and contrasting information about the issues being investigated were selected for the case study research. The case study research was used to confirm the survey results (triangulation) and more importantly to explain the trends and patterns observed in the survey analysis. The research concluded that high variability in demand levels is a major driver of volume flexibility and that it is generic in nature. Other drivers of volume flexibility were also identified. However, the applicability of these drivers to manufacturing plants was found to be independent of the sector to which the plants belong but on other specific characteristics of the plants. Mechanisms being employed to achieve volume flexibility in UK manufacturing plants were identified and referred to as enablers of volume flexibility. These enablers are not sector dependent but they do depend on specific market conditions, and their perceived costs and benefits. Substitute and complementary enablers were identified. Substitute enablers can be used to replace other enablers to achieve volume flexibility and complementary enablers aid other enablers in achieving volume flexibility. The research project also identified strategies, which can be employed by manufacturing plants to implement the enablers in achieving volume flexibility

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

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

Using MILP to plan holidays and working hours under an annualised hours agreement

Annualising work hours (AH) is a means of achievement flexibility in the use of human resources to face the seasonal nature of demand. In Corominas et al. (1) two MILP models are used to solve the problem of planning staff working hours with annual horizon. The costs due to overtime and to the employment of temporary workers are minimised, and the distribution of working time over the course of the year for each worker and the distribution of working time provided by temporary workers are regularised. In the aforementioned paper, the following is assumed: (i) the holiday weeks are fixed a priori and (ii) the workers are from different categories who are able to perform specific type of task have se same efficiency; moreover, the values of the binary variables (and others) in the second model are fixed to those in the first model (thus, in the second model these will intervene as constants and not as variables, resulting in an LP model). In the present paper, these assumptions are relaxed and a more general problem is solved. The computational experiment leads to the conclusion that MILP is a technique suited to dealing with the problem