Calibrating cross-training to meet demand mix variation and employee absence

We address the problem of determining the cross-training that a work team needs in order to cope with demand mix variation and absences. We consider the case in which all workers can be trained on all tasks, the workforce is a resource that determines the capacity and a complete forecasting of demand is not available. The demand mix variation that the organization wants to be able to cope with is fixed by establishing a maximum time to devote to each product. We contend that this approach is straightforward, has managerial practicality and can be applied to a broad range of practical scenarios. It is required that the demand mix variation be met, even if there are a certain level of absences. To numerically solve the mathematical problem, a constraint-based selection procedure is developed, which we term CODEMI. We provide illustrated examples demonstrating solution quality for the approximation, and we report on an illustrative set of computational cases. (C) 2015 Elsevier B.V. and Association of European Operational Research Societies (EURO) within the International Federation of Operational Research Societies (IFORS). All rights reserved.Peer ReviewedPostprint (published version

Integrated planning of spare parts and service engineers with partial backlogging

In this paper, we consider the integrated planning of resources in a service maintenance logistics system in which spare parts supply and service engineers deployment are considered simultaneously. The objective is to determine close-to-optimal stock levels as well as the number of service engineers that minimize the total average costs under a maximum total average waiting time constraint. When a failure occurs, a spare part and a service engineer are requested for the repair call. In case of a stock-out at spare parts inventory, the repair call will be satisfied entirely via an emergency channel with a fast replenishment time but at a high cost. However, if the requested spare part is in stock, the backlogging policy is followed for engineers. We model the problem as a queueing network. An exact method and two approximations for the evaluation of a given policy are presented. We exploit evaluation methods in a greedy heuristic procedure to optimize this integrated planning. In a numerical study, we show that for problems with more than five types of spare parts it is preferable to use approximate evaluations as they become significantly faster than exact evaluation. Moreover, approximation errors decrease as problems get larger. Furthermore, we test how the greedy optimization heuristic performs compared to other discrete search algorithms in terms of total costs and computation times. Finally, in a rather large case study, we show that we may incur up to 27% cost savings when using the integrated planning as compared to a separated optimization. , The Author(s).This publication was made possible by the NPRP award [NPRP 7-308-2-128] from the Qatar National Research Fund (a member of The Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

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