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weather the storm and to urge them to remain resolute because better days were coming In an internal Pepper Pod worthy of one of FDR s fireside chats, Charles wrote: We are passing through the most unusual Depression in the history of our country It is further emphasized by the fact that it closely follows our greatest peak of prosperity General conditions have been, and still are, serious Nearly everyone has a feeling of concern Every person, every group, every class of society is affected We, as individuals and as a group, can keep ourselves in a happier and healthier state of mind by adjusting ourselves to conditions as they are Self-pity, pride, and vanity will not lighten our burden More than ever, we need to be hopeful, energetic, and ambitious for, after all, present conditions are going to improve It is my impression that we will just how soon, I cannot say, but we will enter into one of the longest eras of prosperity that we have ever enjoyed So be courageous and be patient Be your best! Do your best! Pass this spirit on to your customers Give them cheer; encourage them They like it; we all do! A smile is like a ray of sunshine on a dark day It is even more helpful than the medicine you sell, and what is more, its benefits are contagious And whatever you do, do not acknowledge defeat, for remember: as surely as the sun rises BETTER DAYS ARE COMING 5 It helped Walgreen that his case was much easier to make than FDR s In 1930, the first full year of the Depression, Walgreens sales actually jumped $5 million to a record $52 million Though the chain s profits had been cut in half from the previous year s high of $4 million, during the Depression, any profit was a good profit In another internal edition of the Pepper Pod, Walgreen stated his belief that the chain s remarkable resilience was not because of one dominating idea but because of the disciplined application of 10 simple.

i[p(i, 0) + p(i, 1)]

The latter result uses Little s formula L = W Continuous-time Markov chains with rewards In many applications a reward structure is imposed on the continuous-time Markov chain model Let us assume the following reward structure A reward at a rate of r(j ) per time unit is earned whenever the process is in state j , while a lump reward of Fjk is earned each time the process jumps from state j to state k (= j ) In addition to Assumption 421 involving the regeneration state r, we make the following assumption Assumption 422 (a) The total reward earned between two visits of the process {X(t)} to state r has a nite expectation and |r(j )| pj +

P {a customer joins the boat in (t, t + = ( t) e (T t) + o( t),

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Thus, by Theorem 131, the number of passengers joining a given tour is Poisson T distributed with mean 0 (t) dt = ( / )(1 e T )

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Another illustration of the usefulness of the non-stationary Poisson process is provided by the following example Example 132 Replacement with minimal repair A machine has a stochastic lifetime with a continuous distribution The machine is replaced by a new one at xed times T , 2T , , whereas a minimal repair is done at each failure occurring between two planned replacements A minimal repair returns the machine into the condition it was in just before the failure It is assumed that each minimal repair takes a negligible time What is the probability distribution of the total number of minimal repairs between two planned replacements Let F (x) and f (x) denote the probability distribution function and the probability density of the lifetime of the machine Also, let r(t) = f (t)/[1 F (t)] denote the failure rate function of the machine It is assumed that f (x) is continuous Then the answer to the above question is P {there are k minimal repairs between two planned replacements} = e M(T )

[M(T )]k , k!

k = 0, 1, ,

.

where M(T ) = 0 r(t) dt This result follows directly from Theorem 131 by noting that the process counting the number of minimal repairs between two planned replacements satis es the properties (a), (b), (c) and (d) of De nition 131 Use the fact that the probability of a failure of the machine in a small time interval (t, t + t] is equal to r(t) t + o( t), as shown in Appendix B

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