Process Optimization & Real Time Analytics - Case Study Example (Part 2) – YOU CANalytics-

 

case study on linear programming

Transportation Problem: A Special Case for Linear Programming Problems J. Reeb and S. Leavengood EM • June Linear programming, or LP, is a method of allocating resources mal solution. In this publication, we discuss a special case of LP, the transportation problem. Section Finally, we put all of these concepts together in an extensive case study in Section What is a linear program? We can reduce the structure that characterizes linear programming problems (perhaps after several manipulations) into the following form: 3. Here is a small Warehouse case study of Cequent a US base company, watch this video for a more clear understanding. Linear programming is also used in organized retail for shelf space optimization. Since the number of products in the market have increased in leaps and bounds, it is important to understand what does the customer want.


Linear Programming - Case Study


In this case study example, you are helping a company reduce their process turn-around-time through advanced analytics and data science.

As you will notice later, on some metaphysical level, the solution to this problem is about solving a jigsaw puzzle of yin yang to produce harmony, case study on linear programming. For over years jigsaw puzzles are among the most popular past times for youngsters and adults alike. Solving a jigsaw puzzle requires the player to connect the right pieces together to complete the picture.

The objective of the solution for this case study example is to solve a jigsaw puzzle between callers and agents. The purpose of this solution is case study on linear programming patch the callers to the right agents so that the overall call duration can be minimized. The underlining assumption is that certain agents are better suited to solve a certain kind of problems. Real Time Analytics — Automated Intelligence to connect callers with agents to minimize call duration. This will be quite an elaborate solution with the following steps.

I will primarily focus on step 3 of real-time analytics and optimization through integer programming in this case study example. I will briefly discuss steps 1, and 2 in the next few segments. We have discussed both unsupervised and supervised learning algorithms in a great detail in two different case study examples on YOU CANalytics.

Please read those case study on linear programming studies to gain an intuitive and practical understanding of these learning algorithms. Unsupervised learning — Case Study Example. The first step for this solution is to segment the customer base into groups based on the similarity between them.

The customer base is segmented based the following broad classes of variables. Cluster analysis of customer base is a highly creative exercise which requires a good understanding of customer demographics and sociology, case study on linear programming. Some of the common unsupervised learning methods are K-mean clustering, hierarchy clustering, and self-organizing maps SOM. Unsupervised learning and cluster analysis is discussed in greater details in an earlier case study example on YOU CANalytics.

I recommend you read the telecom case study to learn more about unsupervised learning. Supervised Learning — Case Study Example. I recommend you read the telecom case study to learn more about supervised learning.

Steps 1, and 2 are iterative in nature to arrive at optimal segments of callers and agents. Moreover, there will be as many propensity or estimation models for step 2 as the number of caller profiles in step 1. This optimization process is part of real-time analytics. Think of this step as a telephone operator connecting the callers to agents in real time with the objective of fast query resolution time.

To get a grip of this method we will use a simpler case study on linear programming of connecting 4 waiting-callers with 4 available agents as displayed in the adjacent schematic. Objective or Goalin this case, is to minimize the average call time between callers and agents.

In the previous 2 steps, we have derived the average time each agent takes to resolve the queries for the caller profiles.

This table displayed the average time in seconds. This is roughly 9 minutes 45 seconds average call time. Even the most likely average call time for this matrix is close to 7 minutes.

The idea is to identify connections to achieve minimum average call. There is a total of 16 possibilities of connections i. Here, 0 means no connection and 1 means connection. This is, of course, a special case where the number of callers is equal to the number of agents.

Moreover, other business rules can also be added to the list of constraints such as preferential treatment for a certain set of customers etc. This makes the average call time equal to 4 minutes and 51 seconds. Jigsaw puzzles have so many similarities with life. Our every act case study on linear programming life is to create connections with the next step.

We get a good education in the hope of a good job. A good job is to provide for our loved ones, case study on linear programming. Dear M, case study on linear programming. Roopam Upadhyay… after greeting recently I study your case study example on the call centre, and I want to implement it in matlab, really I want more clarification on how this example works, my question:- 1. Do you have real data set to apply on this example??

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case study on linear programming

 

Feb 17,  · Application of linear programming — a case study Mi Yin Wu Graduate of Town and Country Planning Department, Sydney University, Sydney, Cited by: 5. Transportation Problem: A Special Case for Linear Programming Problems J. Reeb and S. Leavengood EM • June Linear programming, or LP, is a method of allocating resources mal solution. In this publication, we discuss a special case of LP, the transportation problem. Section Finally, we put all of these concepts together in an extensive case study in Section What is a linear program? We can reduce the structure that characterizes linear programming problems (perhaps after several manipulations) into the following form: 3.