The Simulation of Implications of Sensor Technology on the New Product Development to Solve Lot-Sizing Problems with Fuzzy Approach

Algorithm 1

DATA:

a = 14;

b = 0.04;

m =30000000;! The total size of the potential market;

Elas = 2;! Elasticity;

H = 0.7;! Holding costs;

S = 21000;! Setup cost;

I = 18000000;! New product introduction costs;

Alpha = 0.7;

Lambda =100;! Coef in cap-doglass function for variable cost;

Gamma = 1; ! Elasticity parameter in cap-doglass function for variable cost;

pp = 5;

ENDDATA.

SETS:

Time/1...pp/: D, P, Inv, X, F, Delta, V;

ENDSETS.

CALC:

@for (Time (t): F(t) = (1 - @exp (-bt))/(1 + (a @exp (-bt))));

ENDCALC.

Max = (@sum (Time (t): ((P(t)- V(t))D(t)) - (S Delta(t)) - (H Inv (t))) - I)/period;

@for (Time (t)| t #GT#1: Inv(t) = Inv(t-1) + X(t)- D(t));

@for (Time (t) | t #EQ#1: Inv(t) = 0);

@for (Time (j: @)sum(Time (t) | t #LE# j: X(t) - D(t)) > = 0);

@for (Time (t): m Delta(t) - X(t) > = 0);

!@for (Time (t) | t #GT#1: D(t) = (0.9 + (0.1 alpha)) m (F(t) - F(t-1)) @pow((P(t)/P(1, -))elas));

@for (Time (t) | t #GT#1: D(t) = (1.1 - (0.1 alpha)) m (F(t) - F(t-1)) @pow((P(t)/P(1, -))elas));

@for (Time (t) | t #EQ#1: P(t) = (V(t) elas)/(elas -1));

@for (Time (t) | t #GT#1: P(t) = (((V(t) + (Ht)) elas)/(elas -1)));

@for (Time (t): V(t) = lambda @pow (X(t),-gamma));

@for (Time (t: @)bin(Delta(t)));

@for (Time (t: @)Gin(X(t)));

Period < = pp;

Period > = 1;

@Gin (period);

Algorithm 1