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# Benihana Optimisation

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## Georgi Stoev

on 5 March 2015

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#### Transcript of Benihana Optimisation

Different variables effect others
Optimal solutions found for each one in isolation did not result in the highest net profit when combining as one challenge
Simulation only requires optimizing for nightly net profit, however there are other factors that need to be taken into consideration such as Benihana's business model or brand goals
Net Profit vs. # of Customer Served
Operations is a key factor in attaining optimal net profit, however, it is not the only factor that must be considered when developing the right strategy for the business
Conclusions
Group B
Regina Berengolts, Christophe Duong,
Yasith Fernando, Mustafa Ibrahim,
Andres Jaramillo, Georgi Stoev

Simulation Objectives
Taking into account :
Batching
Bar size
Hours of operation
Challenge 1
Batching vs. Non-Batching
Challenge 2
Optimizing Bar Seats / Restaurant Tables
Keep batching
Maximizing bar seats to 79
Setting table number to 11
Challenge 3
Dining Time Length
Pre-Peak
45 min dining time
Peak
45 min dining time
Post-Peak
75 min dining time
Default vs. Chosen Configuration
Customers Served
Default Config.
(60min, 60min, 60min)
Chosen Config.
(45min, 45min, 75min)
Obtained Results
Challenge 4
Happy Hour
Open at 5.00pm
Challenge 5
Different Batching Types
Pre-Peak
Table of 8
Peak
Table of 8
Post-Peak
Table of 4
Challenge 6
Obtained Results
Process for Finding the Optimal Model
Conclusion
Customers Lost
23
Process for Finding the Optimal Model
A system of linear inequalities defines a polytope as a feasible region. The simplex algorithm begins at a starting vertex and moves along the edges of the polytope until it reaches the vertex of the optimum solution.
Polyhedron of simplex algorithm in 3D
http://en.wikipedia.org/wiki/Simplex_algorithm