Complementary slackness and the transportation problem

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SUMMARY

The discussion focuses on solving the transportation problem (T) using the northwest vertex rule and exploring the dual program (T*). The basic feasible solution derived from the cost matrix and demand/supply vectors is presented, along with the corresponding dual linear program. The user encounters difficulties applying complementary slackness conditions, as all dual variables lead to infeasible solutions when assumed to be zero. This indicates a deeper issue with the assumptions made regarding the dual variables in the context of supply and demand equality.

PREREQUISITES
  • Understanding of the transportation problem in linear programming
  • Familiarity with the northwest vertex rule for finding feasible solutions
  • Knowledge of dual linear programming concepts
  • Proficiency in applying complementary slackness conditions
NEXT STEPS
  • Study the derivation of the transportation problem's dual program (T*)
  • Learn about complementary slackness conditions in linear programming
  • Explore alternative methods for solving transportation problems, such as the MODI method
  • Investigate scenarios where supply equals demand and its implications on dual variables
USEFUL FOR

Students and professionals in operations research, linear programming enthusiasts, and anyone tackling transportation problems in optimization contexts.

catcherintherye
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hello,I have been given the transportation problem (T)

defined by the cost matrix
<br /> \left(\begin{array}{ccccccc}5&amp;3&amp;9&amp;3&amp;8&amp;2\\5&amp;6&amp;3&amp;15&amp;7&amp;16\\9&amp;20&amp;10&amp;22&amp;17&amp;25\\3&amp;7&amp;3&amp;14&amp;9&amp;14\end{array}\right)<br />


the demand vector q=(2,8,9,4,6,2)

the supply vector p=(3,13,6,9)

the problem is as follows

a) use the northwest vertex rule to find a basic feasible solution of (T)

...this i found to be

<br /> \left(\begin{array}{ccccccc}2&amp;1&amp;0&amp;0&amp;0&amp;0\\0&amp;7&amp;6&amp;0&amp;0&amp;0\\0&amp;0&amp;3&amp;3&amp;0&amp;0\0&amp;0&amp;0&amp;1&amp;6&amp;2\end{array}\right)<br />



(b) write down the linear program for the dual program (T*)

...this i found to be

(T*)

<br /> maximize \sum_{i=1}^{4}p_i y_i + \sum_{j=1}^{6}q_j z_j <br />

subject to
<br /> \\ y_i + z_j \leq c_ij, y_1,...,y_4,z_1,...z_6,<br />

(c) use the complementary slackness conditions to show that the shipping matrix

<br /> \left(\begin{array}{ccccccc}0&amp;0&amp;0&amp;1&amp;0&amp;2\\0&amp;7&amp;0&amp;0&amp;6&amp;0\\2&amp;0&amp;4&amp;0&amp;0&amp;0\\0&amp;1&amp;5&amp;3&amp;0&amp;0\end{array}\right)<br /> [\tex]<br /> <br /> solves (T)<br /> <br /> now since i have <br /> x_i_j &amp;gt; 0 <br /> <br /> for (i,j) = (1,4), (1,6), (2,2), (2,5), (3,1), (3,3), (4,2), (4,3), (4,4) complementary slackness implies that the associated dual constraints are binding and we have a system of nine equations in 10 unknowns. Now here is the rub. I would usually progress by substituting the above values (from the shipping matrix) into the primal constraints and derive that one of them is non binding, then deduce that the associated dual variable is zero by complementary slackness. However since we have here supply=demand, no such constraint will occur. I have done a similar problem where the next progression was to assume that one of the dual variables say y_1 is 0 and then the system of nine equalities from above are solvable, however if as an example i take y_1 =0, this implies that z_3 =-8 which is infeasible. In fact as it turned out assuming any dual variable y_1, ...y_4, z_1,...,z_6 to be zero will lead to another dual vaiable in the system being negative. What am I missing<img src="https://cdn.jsdelivr.net/joypixels/assets/8.0/png/unicode/64/1f615.png" class="smilie smilie--emoji" loading="lazy" width="64" height="64" alt=":confused:" title="Confused :confused:" data-smilie="5"data-shortname=":confused:" />
 
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btw, i really need a prompt answer if at all possible, this is for my referal which has to be in next week!
 

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