International Journal of Production Research, Feb 24, 2014
ABSTRACT This paper investigates the selection and assignment of workers to tasks based on indivi... more ABSTRACT This paper investigates the selection and assignment of workers to tasks based on individual learning and forgetting characteristics in order to improve system throughput. We examine the performance of five policies that rank and select workers using a learning and forgetting-based assignment to address the task allocation problem. We are interested in the interaction between the implemented policies and various factors such as the ratio of generalists to specialists in the workforce, the level of multifunctionality and the level of workforce heterogeneity. Results demonstrate that, when implementing cross-training, selecting workers based on a greedy prediction of system output is outperformed by other simpler policies based on prior expertise.
ABSTRACT This paper investigates the selection and assignment of workers to tasks based on indivi... more ABSTRACT This paper investigates the selection and assignment of workers to tasks based on individual learning and forgetting characteristics in order to improve system throughput. We examine the performance of five policies that rank and select workers using a learning and forgetting-based assignment to address the task allocation problem. We are interested in the interaction between the implemented policies and various factors such as the ratio of generalists to specialists in the workforce, the level of multifunctionality and the level of workforce heterogeneity. Results demonstrate that, when implementing cross-training, selecting workers based on a greedy prediction of system output is outperformed by other simpler policies based on prior expertise.
ABSTRACT We examine relative impacts of selection, grouping and assignment policies.•We develop a... more ABSTRACT We examine relative impacts of selection, grouping and assignment policies.•We develop an empirical model and distribution of learning and knowledge transfer.•Worker selection tended to have greater impacts than grouping or assignment.•Policies based on individual knowledge transfer parameter perform well.
International Journal of Production Research, 2014
ABSTRACT This paper investigates the selection and assignment of workers to tasks based on indivi... more ABSTRACT This paper investigates the selection and assignment of workers to tasks based on individual learning and forgetting characteristics in order to improve system throughput. We examine the performance of five policies that rank and select workers using a learning and forgetting-based assignment to address the task allocation problem. We are interested in the interaction between the implemented policies and various factors such as the ratio of generalists to specialists in the workforce, the level of multifunctionality and the level of workforce heterogeneity. Results demonstrate that, when implementing cross-training, selecting workers based on a greedy prediction of system output is outperformed by other simpler policies based on prior expertise.
International Journal of Production Economics, 2012
ABSTRACT Learning and forgetting models available in the literature are generally not intended fo... more ABSTRACT Learning and forgetting models available in the literature are generally not intended for math programming formulations, thus their incorporation within worker-task assignment problem models leads to non-linearity in the objective function, producing a Mixed Integer Nonlinear Program (MINLP) that is difficult to solve. Even though Dynamic Programming and other alternate approaches have been implemented to solve assignment problems in production systems with independent stations and independent jobs while maximizing output, such solutions are generally not optimal. In this paper, we develop results that reveal the form of the optimal solution, which allows us to solve the problem as a Mixed Integer Linear Program (MILP), rather than the more complex MINLP. We examine the effectiveness of this information to reduce the time complexity of the problem. As the presented methodology is independent of the productivity model, it has general application, irrespective of the specific learning/forgetting models employed. The approach is relevant in the context of a set of tasks with some task similarity, learning and forgetting among the workforce, and equal or unequal numbers of workers and tasks.
Based on the profit and loss account of an insurance company we derive a probabilistic model for ... more Based on the profit and loss account of an insurance company we derive a probabilistic model for the financial result of the company, thereby both assets and liabilities are marked to market. We thus focus o11 the economic value of the company. We first analyse the underwriting risk of the company. The maximization of the risk return ratio of the company is derived as optimality criterion. It is shown how the risk return ratio of heterogeneous portfolios or of catastrophe exposed portfolios can be dramatically improved through reinsurance. The improvement of the risk return ratio through portfolio diversification is also analysed. In section 3 of the paper we analyse the loss reserve risk of the company. It is shown that this risk consists of a loss reserve development risk and of a yield curve risk which stems from the discounting of the loss reserves. This latter risk can be fully hedged through asset liability matching. In section 4 we derive our general model. The portfolio of the company consists of a portfolio of insurance risks and of a portfolio of financial risks. Our model allows for a silnultaneous optimization of both portfolios of risks. A theorem is derived which gives the optimal retention policy of the company together with its optimal asset allocation. Some of the material presented in this paper is taken from Schnieper, 1997. It has been repeated here in order to make this article self contained.
International Journal of Production Research, Feb 24, 2014
ABSTRACT This paper investigates the selection and assignment of workers to tasks based on indivi... more ABSTRACT This paper investigates the selection and assignment of workers to tasks based on individual learning and forgetting characteristics in order to improve system throughput. We examine the performance of five policies that rank and select workers using a learning and forgetting-based assignment to address the task allocation problem. We are interested in the interaction between the implemented policies and various factors such as the ratio of generalists to specialists in the workforce, the level of multifunctionality and the level of workforce heterogeneity. Results demonstrate that, when implementing cross-training, selecting workers based on a greedy prediction of system output is outperformed by other simpler policies based on prior expertise.
ABSTRACT This paper investigates the selection and assignment of workers to tasks based on indivi... more ABSTRACT This paper investigates the selection and assignment of workers to tasks based on individual learning and forgetting characteristics in order to improve system throughput. We examine the performance of five policies that rank and select workers using a learning and forgetting-based assignment to address the task allocation problem. We are interested in the interaction between the implemented policies and various factors such as the ratio of generalists to specialists in the workforce, the level of multifunctionality and the level of workforce heterogeneity. Results demonstrate that, when implementing cross-training, selecting workers based on a greedy prediction of system output is outperformed by other simpler policies based on prior expertise.
ABSTRACT We examine relative impacts of selection, grouping and assignment policies.•We develop a... more ABSTRACT We examine relative impacts of selection, grouping and assignment policies.•We develop an empirical model and distribution of learning and knowledge transfer.•Worker selection tended to have greater impacts than grouping or assignment.•Policies based on individual knowledge transfer parameter perform well.
International Journal of Production Research, 2014
ABSTRACT This paper investigates the selection and assignment of workers to tasks based on indivi... more ABSTRACT This paper investigates the selection and assignment of workers to tasks based on individual learning and forgetting characteristics in order to improve system throughput. We examine the performance of five policies that rank and select workers using a learning and forgetting-based assignment to address the task allocation problem. We are interested in the interaction between the implemented policies and various factors such as the ratio of generalists to specialists in the workforce, the level of multifunctionality and the level of workforce heterogeneity. Results demonstrate that, when implementing cross-training, selecting workers based on a greedy prediction of system output is outperformed by other simpler policies based on prior expertise.
International Journal of Production Economics, 2012
ABSTRACT Learning and forgetting models available in the literature are generally not intended fo... more ABSTRACT Learning and forgetting models available in the literature are generally not intended for math programming formulations, thus their incorporation within worker-task assignment problem models leads to non-linearity in the objective function, producing a Mixed Integer Nonlinear Program (MINLP) that is difficult to solve. Even though Dynamic Programming and other alternate approaches have been implemented to solve assignment problems in production systems with independent stations and independent jobs while maximizing output, such solutions are generally not optimal. In this paper, we develop results that reveal the form of the optimal solution, which allows us to solve the problem as a Mixed Integer Linear Program (MILP), rather than the more complex MINLP. We examine the effectiveness of this information to reduce the time complexity of the problem. As the presented methodology is independent of the productivity model, it has general application, irrespective of the specific learning/forgetting models employed. The approach is relevant in the context of a set of tasks with some task similarity, learning and forgetting among the workforce, and equal or unequal numbers of workers and tasks.
Based on the profit and loss account of an insurance company we derive a probabilistic model for ... more Based on the profit and loss account of an insurance company we derive a probabilistic model for the financial result of the company, thereby both assets and liabilities are marked to market. We thus focus o11 the economic value of the company. We first analyse the underwriting risk of the company. The maximization of the risk return ratio of the company is derived as optimality criterion. It is shown how the risk return ratio of heterogeneous portfolios or of catastrophe exposed portfolios can be dramatically improved through reinsurance. The improvement of the risk return ratio through portfolio diversification is also analysed. In section 3 of the paper we analyse the loss reserve risk of the company. It is shown that this risk consists of a loss reserve development risk and of a yield curve risk which stems from the discounting of the loss reserves. This latter risk can be fully hedged through asset liability matching. In section 4 we derive our general model. The portfolio of the company consists of a portfolio of insurance risks and of a portfolio of financial risks. Our model allows for a silnultaneous optimization of both portfolios of risks. A theorem is derived which gives the optimal retention policy of the company together with its optimal asset allocation. Some of the material presented in this paper is taken from Schnieper, 1997. It has been repeated here in order to make this article self contained.
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