Difference between revisions of "ANLY482 AY2017-18T2 Group19 Methodology"

Revision as of 22:39, 14 January 2018

DATA COLLECTION

The usage of proxy browser by the users records their individual action as they search for the online course reserves they require. The usage of printed course reserves is recorded as the users borrow and return the books. Also, in-house usage of the books are recorded as the users return the books to the library counter instead of the book shelves.

These information will be provided by by the client.

DATA CLEANING AND TRANSFORMATION

The data will need to be transformed into the required formats using various techniques, such as rules and patterns technique, in order for us to perform the necessary processing later. Duplicates and irrelevant data will be removed.

EXPLORATION OF DATA

JMP, Tableau and Javascript will be used for data exploration and visualization. We set out to design a dashboard that aims to answer the following questions:

What proportion of the school is using the course reserve materials?
Are all the course reserve materials fully utilized?
When are the course reserve materials being utilized?
Are we acquiring course materials that students are not using?

To start off, we would like a graph that is capable of visualizing usage over time. Given the immense number of course reserve materials available, we settled on horizon graphs which utilizes position and color to reduce vertical space while still fulfilling functionalities exhibited by a simple line graph. A horizon graph displays metric behavior over time in relation to a baseline. Ideally, with this graph, we will be able to identify when the course reserve materials are most in used and which of the course reserve materials are most in used or not most in used.

In addition, we require a graph that allows the users to easily identify if the single measure of interest pits well against a target value, and hence, we chose to visualize with bullet graphs. Bullet graphs are able to display those information like a bar graph without compromising on the amount of space required. The following picture demonstrates how the bullet graphs can be read:

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-[[ANLY482 AY2017-18T2 Group19 Background| <font face='Century Gothic' color="#FFFFFF"><b>BACKGROUND</b></font>]]
+[[ANLY482 AY2017-18T2 Group19 Project Overview| <font face='Century Gothic' color="#FFFFFF"><b>BACKGROUND</b></font>]]
 | style="vertical-align:top;width:25%;" | <div style="padding: 3px; font-weight: bold; text-align:center; line-height: wrap_content; font-size:14px; border: 0px solid; font-family:helvetica">
@@ Line 53: / Line 53: @@
-<div style="background: #FFFFFF; padding: 15px; font-weight: bold; line-height: 0.3em; text-indent: 15px;letter-spacing:-0.03em;font-size:16px;id:UT1"><font face='Century Gothic' color=#000000 ><u>MODEL PLANNING</u></font></div>
+<div style="background: #FFFFFF; padding: 15px; font-weight: bold; line-height: 0.3em; text-indent: 15px;letter-spacing:-0.03em;font-size:16px;id:UT1"><font face='Century Gothic' color=#000000 ><u>DATA COLLECTION</u></font></div>
-<i>Problem Definition</i>
+The usage of proxy browser by the users records their individual action as they search for the online course reserves they require. The usage of printed course reserves is recorded as the users borrow and return the books. Also, in-house usage of the books are recorded as the users return the books to the library counter instead of the book shelves.
-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.
+These information will be provided by by the client.
-<i>Generation of Model Objective and Constraints </i>
+<div style="background: #FFFFFF; padding: 15px; font-weight: bold; line-height: 0.3em; text-indent: 15px;letter-spacing:-0.03em;font-size:16px;id:UT1"><font face='Century Gothic' color=#000000 ><u>DATA CLEANING AND TRANSFORMATION</u></font></div>
-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:
+The data will need to be transformed into the required formats using various techniques, such as rules and patterns technique, in order for us to perform the necessary processing later. Duplicates and irrelevant data will be removed.
-*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.
+<div style="background: #FFFFFF; padding: 15px; font-weight: bold; line-height: 0.3em; text-indent: 15px;letter-spacing:-0.03em;font-size:16px;id:UT1"><font face='Century Gothic' color=#000000 ><u>EXPLORATION OF DATA</u></font></div>
-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.
+JMP, Tableau and Javascript will be used for data exploration and visualization. We set out to design a dashboard that aims to answer the following questions:
+# What proportion of the school is using the course reserve materials?
+# Are all the course reserve materials fully utilized?
+# When are the course reserve materials being utilized?
+# Are we acquiring course materials that students are not using?
+To start off, we would like a graph that is capable of visualizing usage over time. Given the immense number of course reserve materials available, we settled on horizon graphs which utilizes position and color to reduce vertical space while still fulfilling functionalities exhibited by a simple line graph. A horizon graph displays metric behavior over time in relation to a baseline. Ideally, with this graph, we will be able to identify when the course reserve materials are most in used and which of the course reserve materials are most in used or not most in used.
-<div style="background: #FFFFFF; padding: 15px; font-weight: bold; line-height: 0.3em; text-indent: 15px;letter-spacing:-0.03em;font-size:16px;id:UT1"><font face='Century Gothic' color=#000000 ><u>MODEL BUILDING</u></font></div>
+In addition, we require a graph that allows the users to easily identify if the single measure of interest pits well against a target value, and hence, we chose to visualize with bullet graphs. Bullet graphs are able to display those information like a bar graph without compromising on the amount of space required. The following picture demonstrates how the bullet graphs can be read:
-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:
-[[Image:G19_Proposed_Dashboard.png|1000px|center]] &nbsp;
-<div style="background: #FFFFFF; padding: 15px; font-weight: bold; line-height: 0.3em; text-indent: 15px;letter-spacing:-0.03em;font-size:16px;id:UT1"><font face='Century Gothic' color=#000000 ><u>MODEL EVALUATION</u></font></div>
-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.

Difference between revisions of "ANLY482 AY2017-18T2 Group19 Methodology"

Revision as of 22:39, 14 January 2018

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