This paper provides mathematical indications that the size of the solution space is related to the probability of developing affordable systems. In particular, the bigger the size of the solution space, the more chances to find affordable solutions. It also provides mathematical indications of why adapting priorities to needs result in higher levels of system affordability.
DOI: http://dx.doi.org/10.1016/j.procs.2014.03.067
Increasing the Probability of Developing Affordable Systems by Maximizing and Adapting the Solution Space
1. Increasing the probability of developing
affordable systems by maximizing and
adapting the solution space
Alejandro Salado
Stevens Institute of Technology
15. Proof Proposition 2
𝑝 𝑎𝑓𝑓𝑜𝑟𝑑𝑎𝑏𝑖𝑙𝑖𝑡𝑦 = 𝐾
𝑛 𝑎𝑓𝑓𝑜𝑟𝑑𝑎𝑏𝑙𝑒
𝑛 𝑢𝑛𝑖𝑣𝑒𝑟𝑠𝑒
Effectiveness
design/exploration
method
Amount
of
affordable
solutions
in the CSAmount of
solutions in the
design spcae
22. Contributions
↓ 𝐶𝑆 𝑜𝑟𝑑𝑒𝑟𝑒𝑟𝑟𝑜𝑟
→↑ 𝑝 𝑎𝑓𝑓𝑜𝑟𝑑𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑡 = 𝑡 𝑛
↑ 𝐶𝑆𝑠𝑖𝑧𝑒 →↑ 𝑝 𝑎𝑓𝑓𝑜𝑟𝑑𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑡 = 𝑡 𝑛
THEOREM 1
THEOREM 2
Effective evolutionary
priroitization?
How to max CS with
requirements?
23. Limitations
Distribution of affordable solutions is considered uniform
CS contains many more solutions than rework cycles
Learning and anchoring effects self-cancel
24. Left for the future
Investigate SENSITIVITY of ps on paff
Investigate SENSITIVITY of uniformity assumptions on paff
Investigte SENSITIVITY of number of solutions on paff
Investigate effects of LEARNING and ANCHORING
Explore effects on PROJECT data
25. TOPIC TITLE:
INCREASING THE PROBABILITY OF DEVELOPING
AFFORDABLE SYSTEMS BY MAXIMIZING AND
ADAPTING THE SOLUTION SPACE
Alejandro Salado
Stevens Institute of Technology
asaladod@stevens.edu
+49 176 321 31458