Operations Management Help

Operations Management is the corner stone of the realworld applications in the management science. It brings many powerful tools altogether, blending several Statistics and Optimization techniques. We offer homework assistance in several OM topics.
Commonly, the textbooks come mostly with the answers to some of the problems, but they don't show detailed, stepbystep solutions. Our goal is to provide you with clear and well explained solutions to your problems. Our turnaround time is 2448 depending on the request.
We can work with various software programs, such as POM  QM, The Management Scientist, Excel, Crystal Ball, Excel OM, LINDO, GAMS, Matlab, etc.
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We provide a quality problem solving service on the following Operations Management topics:
 Forecasting
 Moving Averages
 Exponential Smoothing
 Linear Trends
 Seasonal Components
 Statistical Process Control
 Control Charts
 Xbar, Rbar, pbar, Cbar charts
 Process Capability
 Decision Theory
 Maximax and Maximin
 Minimax Regret
 Expected Monetary Value
 Expected Value of perfect information (EVPI)
 Expected Value of sample information (EVSI)
 Decision Trees
 Utility
 Location Strategies
 Factor Rating
 Center of Gravity
 Transportation Model
 Layout Strategy
 Fixposition layout
 Processed Oriented layout
 Office layout
 Warehouse layout
 Forecasting
 Inventory Models
 ABC analysis
 Economic Order Quantity (EOQ)
 Economic Production Quantity (EPQ)
 Quantity Discount Models
 OnePeriod Model
 Just in Time systems (JIT)
 PERT
 Single Estimate
 Triple Estimate
 Cost Crashing
 Critical Paths
 GanttCharts
 Aggregate Scheduling
 Demand Chase
 Mixed Strategy
 Material Requirement Planning (MRP)
 Bills of material
 Lotforlot
 Linear Programming
 Linear Programming
 Transportation Problems
 Transshipment
 Integer Programming
 Assignment Problems
 Network Problems
 WaitingLine models
 Queues
 SingleChannel and MultiChannel models
 Simulation
 Simulations in Excel
 Monte Carlo Methods
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Sample Operations Management Problems
Question: The costs of placing an order is $150. It is estimated that 1,000 units will be used in the next 12 months. The carrying cost per unit per month is $2.50.
a. Compute the optimum order size
b. Now suppose that the company could lower the ordering cost to $50; the cost of the effort to accomplish this change is $1,000. Assume the product will be sold only for the next two years. Compute the new optimal order quantity if the lower ordering cost were implemented, and determine if the company should invest $1,000 in the orderingcost reduction program.
Solution: (a) The following is obtained:
Hence, the optimal order quantity is
\[EOQ=\sqrt{\frac{2DS}{H}}=\sqrt{\frac{2\times 1000\times 150}{2.5}}=346.41\]
The total inventory cost is TC = $866.03.
(b) IF the setup cost was reduced to $50, we would get
In this case, the optimal order quantity is EOQ = 200, and the total inventory cost is $500. The savings in cost for 2 years operation are (866.03 – 500)*2 =$732.06, which is less than $1000 (the cost of reducing the setup cost), and hence, the orderingcost reduction program should not be pursued.
Question: A logistics specialist for Wiethoff Inc. must distribute cases of parts from 3 factories to 3 assembly plants. The monthly supplies and demands, along with the percase transportation costs are:
What are the total monthly transportation costs for the optimal solution?
Solution: We have 9 variables \({{x}_{ij}}\), where \({{x}_{ij}}\) represents the amount that is sent from factoryto assembly plant. For convenience of notation, we name the factories A, B and C as 1, 2 and 3.
The problem is written as
\[\begin{align} & \text{Minimize }\sum\limits_{i,j=1}^{3}{{{C}_{ij}}{{X}_{ij}}} \\ & \text{subject to }{{X}_{11}}+{{X}_{12}}+{{X}_{13}}\le 200 \\ & \text{ }{{X}_{21}}+{{X}_{22}}+{{X}_{23}}\le 400 \\ & \text{ }{{X}_{31}}+{{X}_{32}}+{{X}_{33}}\le 200 \\ & \text{ }{{X}_{11}}+{{X}_{21}}+{{X}_{31}}=120 \\ & \text{ }{{X}_{12}}+{{X}_{22}}+{{X}_{32}}=620 \\ & \text{ }{{X}_{13}}+{{X}_{23}}+{{X}_{33}}=60 \\ & \text{ }{{X}_{ij}}\ge 0 \\ \end{align}\]
We know solve this problem using Excel’s solver. The output is shown below:
Plants 
Variables 

1 
2 
3 
1 
2 
3  
A 
5 
9 
16 
A 
0 
200 
0  
Factories 
B 
1 
2 
6 
B 
0 
400 
0  
C 
2 
8 
7 
C 
120 
20 
60  
1 
2 
3 

\({{\mathbf{c}}_{ij}}{{\mathbf{x}}_{ij}}\) 
1 
0 
1800 
0 

2 
0 
800 
0 
Objective 
3420 

3 
240 
160 
420 

Restrictions 

Supply 

Slack 1 
0 
200 

Slack 2 
0 
400 

Slack 3 
0 
200 

Demand 

Slack 4 
0 

Slack 5 
0 

Slack 6 
0 
This means that 120 cases of parts are sent from factory C to Plant 1. The total monthly transportation costs are $3,420.
Operations Management Blog
Excel OM example for Economic Order Quantity.
Check out how to deal with some waiting lines examples.
How to compute the capability index.
See this example of a PERT case study.
How to work Linear Programming problems using LINDO.
Check out these PERT case study with crashing using the Management Scientist.
Find out how to use the EOQ formulas to find the optimal inventory levels.