ANLY482 AY2017-18T2 Group19 Methodology
Problem Definition
Firstly, the problem will be defined. Our client has previously worked on this problem and investigated a multi-period Home Health Care Delivery Problem (HHCDP) under stochastic service and travel times. HHCDP can be classified as a workforce scheduling and routing problem, and essentially an extension of an Orienteering Problem (OP) which involves coming up with an optimal organization of tasks for each worker. This delegation of tasks dictates the deployment of particular personnels to specific locations at specific timings.
Generation of Model Objective and Constraints
Secondly, a model will then be constructed based on the problem description previously defined. In tackling this problem, we will have to define the decision variables, objective function and constraints. There have been previous attempts at solving this problem, or variants of this problem, as listed below:
- Mota et al. solved a Team Orienteering Problem with Time Windows (TOPTW) aimed to maximize throughput while being constrained by only being able to arrive at a particular node within the starting and ending time windows established.
- Rasmussen et al. solved it as a Vehicle Routing Problem with Time Windows (VRPTW) which aims to maximize the demand that is satisfied while being constrained by the resource’s capacity and the visiting time windows.
- Yuan and Fugenschuh looked to minimizing total cost and total working time, whilst ensuring that it does not compromise on service quality.
Taking these previous works into account, we hence propose our own model. Ultimately, our team aims to provide a model that would be practical and beneficial for a typical firm operating in the healthcare industry. In such a service-oriented industry, it is tacit knowledge that customer satisfaction is indispensable. In addition, while having to operate in a country constantly facing the problem of labor crunch, it is essential that each resource obtained be utilized efficiently. We reflect these concerns in our model’s objective function, which is to maximise both patients’ satisfaction and the utilization of resources available in our model. If the patient has been assigned a nurse, their satisfaction will be a factor of a multitude of elements including their preference on the nurse assigned to attend to their needs and the appointment time slot assigned. The utilization of nurses will be measured based on the average labor utilization formula (labor content/(labor content + direct idle time)). Taking into account the fact that overworking nurses and thus achieving high labor utilization rates would be at the expense of patients’ satisfaction, we will cap their utilization rates to 85%. Our model will attempt to illustrate real world constraints including time windows, transportation modality, start-end locations, and skills and qualifications of the staff deployed. These constraints would ensure that the model emulate situations most befitting and applicable to the real world, thereby establishing its relevance.
The third step involves solving the model and finding possible solutions. In terms of technologies, we will be utilizing JMP and SAS to deal with the input data, IBM CPLEX and Python to build the optimization model and final visualization. In our final visualization, we aim to build a dashboard that displays the nurse utilization and information on the route churned out by the algorithm. We propose building our dashboard in the following format:
Lastly, post-solution analysis will then be conducted. Here, a list of performance measures will be developed in order to determine the value of the system generated. While maximizing patients’ satisfaction levels, it is also essential to the firm that they ensure maximum utilization and efficiency of their available resources.
