Category Archives: production planning

Constructing matrix in TI Nspire for Markov chain with Poisson distribution

In calculation for Markov chain, matrix setup can be complicated evn with the user-friendly interface on the TI Nspire. The built-in function like identity(), constructMat() etc are useful on specific cases.

For Markov chain calculation, the model setup in terms of matrix sometimes get quite complicated and demanded a few exceptions from the built-in function mentioned above that are geared towards common linear algebra.

The example below attempts to simply the construction of the Markov model by use of piece-wise functions in building matrix. The problem to solve in Markov process is on a stock resupply problem, where an imaginary shop keeps stock of 1 to 3, and replenish on each day only when all stock is exhausted. Poisson distribution is assumed on the demand side.

Firstly a function called markov_prob is created, which takes the demand as variable and return the corresponding Poisson distribution. Note that how the function handle probability for demand greater than 3. A short-hand function mkp() is created afterwards to ease reading in the matrix.

When the function is defined, the Markov model can be formulated in a new 3 x 3 matrix created using the template. It is sometimes not easy to define state transition function as in this example problem. Therefore, the shorthand function just created will help in filling in the states as matrix elements. For example, P11 means the stock next day (Sx+1) equals stock today (Sx) which is 1, and that means the demand is zero, and thus mkp(0). There are some curve ball like P12, where Sx+1 > Sx which is not possible, since that means the shop has more stock the next day for any day with non-zero stock (i.e. negative demand), and thus the probability is zero.markovstock2

After setting up the Markov model, the equilibrium distribution can be deduced. The below is one of the methods using Nelder-Mead algorithm.

Based on this model, more figures can be deduced for decision making on fine tuning stock keeping strategy.


Solving linear programming problem with Nelder-Mead method

For solving linear programming problem, the simplex method is often applied to search for solution. On the other hand, the Nelder-Mead method is mostly applied as a non-linear searching technique. It would be interesting to see how well it is applied to a linear programming problem previously solved using the Simple Method in TI-84.

The Nelder-Mead method is ran under the TI Nspire CX CAS with NM program written in the TI Basic program. The program accepts arguments including the name of the function to maximize as a string and a list of initial parameters to execute the Nelder-Mead algorithm. The function itself is declared using piece-wise function to bound the return value to the function to maximize while giving penalty to values that violate any constraints (as in the inequalities of the standard simplex method).

Previous program in the TI-84 using the simplex method obtained {200,400} as the solution. The Nelder-Mead returned a solution very close to it.


Simplex Algorithm on the Casio 9860GII

With matrix capable calculator, simplex algorithm for common maximization problem can be solved easily like in the TI-84.

The Casio 9860GII is also equipped with equivalent matrix operations to solve the same problem.





TI-84 Plus Pocket SE and the Simplex Algorithm

The TI-84+ Pocket SE is the little brother of the TI-84 Plus. They are almost identical in terms of screen resolution, processor architecture and speed, and also the OS. The Pocket version measured only 160 x 80 x 21mm in dimension and weighted at 142g, considerably more compact than the classic version.


This little critter is full of features from the 2.55 MP OS. It will easily blow away the mainstream Casio series of the same size like the fx991 and even fx5800P with TI’s built-in advanced functions like ANOVA. Nevertheless, the TI-84 Plus series is still considered a stripped down version of the TI Nspire and TI-89 Titanium, and as such personally I do not expect or intent to run on it sophisticated calculations or programs like the Nelder-Mead algorithm that fits comfortably on the Nspire or Titanium.

Having said that, many complex calculation can easily be accomplished with the rich set of advanced features available out-of-the box in the TI-84, even without programming. One such example is the linear programming method implemented in the simplex algorithm for optimization. Consider the following example: In order to maximize profit, number of products to be produced given a set of constraints can be determined by linear programming. This set of constraints can be expressed in linear programming as system of equations as

8x + 7y ≤ 4400   :Raw Material P
2x + 7y ≤ 3200   :Raw Material Q
3x + y ≤ 1400    :Raw Material R
x,y ≥ 0

In the above, two products are considered by variable x and y, representing the constraints for product A and B respectively. Each of the first three equations denote the raw material requirements to manufacture each product, for material P, Q, and R. Specifically, product A requires 8 units of raw material P, 2 units of raw material Q, and 3 units of raw material R. The total available units for these three raw materials in a production run are 4400, 3200, and 1400 units respectively. When using the Simple Algorithm to maximize the function

P = 16x + 20y

which represents the profits for product A and B are $16 and $20 each, the tableau below is set up initially with slack variables set as

  8   7 1 0 0 0 4400
  2   7 0 1 0 0 3300
  3   1 0 0 1 0 1400
-16 -20 0 0 0 1    0


Using the *row() and *row+() matrix function, the Simplex Algorithm can be implemented without even one line of code. The final answer is obtained as 11200 which is the maximum profit by producing 200 Product A and 400 Product B.