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CONCLUSIONWhen implementing the Simulated Annealing Algorithm, itis important to focus on a two key factors that may beoverlooked: 1) How will next states be generated? Will theybe completely random or will they follow some thread ofsimilarity from one successor to another? The more randomthe successor, a cooling function with higher temperaturesmight produce better results. 2) Most importantly:Understand the curvature of the state-space. With the wayelevation is defined, what shape does it take? If there seemsto be only one extremum – one can always reach higherelevation from any state – the use of the stochastic aspect ofSimulated Annealing should be limited, if any. Just becausethe Simulated Annealing Algorithm is more complex thanthe Hill-Climbing algorithm it doesn’t necessarily mean it isalways the best choice.ACKNOWLEDGMENTSThe work described in this paper was conducted as part of aFall 2012 Artificial Intelligence course, taught in theComputer Science department of the University ofMassachusetts Lowell by Prof. Fred Martin. We would alsolike to thank Anthony Vardaro for helping us understandclasses and reading in files and Rich Lee for ponderingcooling functions with us.REFERENCES1. Jin, Q., & Li-xin, M. (2009, May). Combined simulatedannealing algorithm for logistics network designproblem. In Intelligent Systems and Applications, 2009.ISA 2009. International Workshop on (pp. 1-4). IEEE.2. Qing-kui, C., Xue-kun, D., & Xian-xin, Z. (2009, May).A Simulated Annealing Methodology to Estate LogisticWarehouse Location Selection and Distribution ofCustomers' Requirement. In Intelligent Systems andApplications, 2009. ISA 2009. International Workshopon (pp. 1-4). IEEE.3. Gao, M., & Tian, J. (2009, May). Modeling andForecasting of Urban Logistics Demand Based onImproved Simulated Annealing Neural Network.In Intelligent Systems, 2009. GCIS'09. WRI GlobalCongress on (Vol. 4, pp. 116-119). IEEE.