A two-stage stochastic program for scheduling and allocating cross-trained workers

A two-stage stochastic program is developed for scheduling and allocating cross-trained workers in a multi-department service environment with random demands. The first stage corresponds to scheduling days-off over a time horizon such as a week or month. The second stage is the recourse action that deals with allocating available workers at the beginning of a day to accommodate realized demands. After the general two-stage model is formulated, a special case is introduced for computational testing. The testing helps quantify the value of cross-training as a function of problem characteristics. Results show that cross-training can be more valuable than perfect information, especially when demand uncertainty is high

The assignment problem with dependent costs.

Assigning workers, each with their own skill set, to tasks which demand different skills in an efficient manner is a challenging problem that often requires workers to receive additional training. The training of workers is very costly with Training Magazine’s Annual Industry Report stating 58.5 billion dollars were spent in 2007 on employee training in the United States. Therefore assigning workers to tasks in such a way as to minimize the overall training costs is an important problem in many organizations. In this research, the assignment problem with dependent cost is considered, i.e. the training cost associated with assigning a worker to a particular task depends on the training the worker receives for their other assigned tasks. Once a worker is trained in a skill that training will available for any additional tasks that may be assigned. The problem is formulated mathematically as an integer linear program. Based on past research, high quality solutions to large-size problems are difficult to obtain. This research develops and upper bound approach and three heuristic solution methodologies. The basic idea of the heuristics is to form groups of tasks which require similar skills, then assign a worker to the task group. The Shortest Augmenting Path (SAP) algorithm of Jonker and Volgenant is known to quickly find the optimal assignment of N workers to N tasks. This SAP algorithm will be used in this research after grouping the tasks into N groups which can then be assigned to the N workers. The task grouping heuristic methods developed in this research were tested for several randomly generated large-sized data sets. Results showed an average 7.34% improvement compared to previous solution methods. Additionally to consider workers’ preferences, a multiple-objective model is presented for the skills management problem to maximize workers’ preferences and aggregate training while minimizing training cost. The model is demonstrated for randomly generated data sets

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

Coordinated Production and Delivery Operations With Parallel Machines and Multiple Vehicles

This paper investigated a coordinated optimization problem of production and delivery operations with parallel machines and multiple vehicles so that a more cost-effective and sustainable supply chain performance can be achieved. We propose an effective hybrid metaheuristic solution framework to deal with this problem, by which the investigated problem is decomposed into 3 sub-problems namely, vehicle assignment, parallel machine scheduling and traveling salesman sub-problem. This framework is established for handling the 3 sub-problems in a coordinated manner so as to simplify the optimization process and to reduce the computational complexity. To evaluate the effectiveness of the methodology, this paper integrates a genetic algorithm, the longest processing time heuristic and a tabu search under this framework to solve the investigated problem. Extensive numerical experiments have been conducted and experimental results show that the proposed solution framework can handle the investigated problem efficiently and effectively

A survey on constructing rosters for air traffic controllers

In this survey the state-of-the-art technology and the literature to date are discussed. In particular, we will discuss the gap in the literature concerning rostering staff to tasks by qualifications, with the inclusion of restrictions on a measure of task familiarity, which is a unique consequence of the structure of ATC operations

PhD Thesis Proposal: Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Resource optimization in health care, manufacturing, and military operations requires the careful choreography of people and equipment to effectively fulfill the responsibilities of the profession. However, resource optimization is a computationally challenging problem, and poorly utilizing resources can have drastic consequences. Within these professions, there are human domain experts who are able to learn from experience to develop strategies, heuristics, and rules-of-thumb to effectively utilize the resources at their disposal. Manually codifying these heuristics within a computational tool is a laborious process and leaves much to be desired. Even with a codified set of heuristics, it is not clear how to best insert an autonomous decision-support system into the human decision-making process. The aim of this thesis is to develop an autonomous computational method for learning domain-expert heuristics from demonstration that can support the human decision-making process. We propose a new framework, called apprenticeship scheduling, which learns and embeds these heuristics within a scalable resource optimization algorithm for real-time decision-support. Our initial investigation, comprised of developing scalable methods for scheduling and studying shared control in human-machine collaborative resource optimization, inspires the development of our apprenticeship scheduling approach. We present a promising, initial prototype for learning heuristics from demonstration and outline a plan for our continuing work