Multiskilling with closed chains in a service industry: A robust optimization approach
Multiskilling with closed chains in a service industry: A robust optimization approach
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DOI：
10.1016/j.ijpe.2016.06.013
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发表时间：
2016-09
影响因子：
12
通讯作者：
César Augusto Henao;J. Ferrer;J. Muñoz;Jorge R. Vera
中科院分区：
文献类型：
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作者：
César Augusto Henao;J. Ferrer;J. Muñoz;Jorge R. Vera
多变性和季节性是许多服务行业的固有特征。这就导致了员工供需不匹配的问题。在这项工作中，我们提出了一个混合整数线性规划模型和建设性的启发式来解决这个问题。所提出的方法适用的概念，多技能，一个有吸引力的灵活性，提高服务水平和降低成本的公司的员工短缺和过剩引起的员工需求的变化。该模型构建了一组员工的多技能特征。最初确定性，然后修改模型，将一个强大的优化方法，其中明确包括需求的不确定性。蒙特卡洛模拟进行评估模型的鲁棒性解决方案的不同层次的需求变化和决策者的风险厌恶。该方法创建了不同长度的闭链多杀结构，每个可变性水平都具有出色的成本效益。最后，定义了一些用户指南，用于选择适当的风险规避水平并生成技能培训计划，该计划将确保多技能的大部分总潜在利益实际上是以远低于最保守者所需的投资（即，最坏情况）鲁棒解决方案。
Variability and seasonality is an inherent characteristic in many service industries. This leads to the problem of mismatch between employee supply and demand. In this work we present a mixed integer linear programming model and a constructive heuristic to address this problem. The proposed methodology applies the concept of multiskilling, an attractive source of flexibility for improving service levels and reducing the costs to firms of staff shortages and surpluses induced by employee demand variability. The model structures the multiskilling characteristics of a set of employees. Initially deterministic, the model is then modified to incorporate a robust optimization approach in which demand uncertainty is explicitly included. A Monte Carlo simulation is conducted to evaluate the model's robust solutions for different levels of demand variability and decision-maker risk aversion. The methodology creates closed-chain multiskilling structures of different lengths with excellent cost-effective performance for each variability level. Finally, some user guidelines are defined for choosing the appropriate risk aversion level and generating a skills training plan that will ensure most of the total potential benefits of multiskilling are actually obtained with an investment much less than what would be required by the most conservative (i.e., worst case) robust solution.