Comparison of a Fuzzy Genetic and Simulated Annealing Algorithm Approach for Project Time-Cost Tradeoff

Algorithm 2

/* Algorithm for solving SA-fuzzy optimization model */

(01) set 𝑁_𝑐 = number constraints

(02) set count temperature  𝑡=0

(03) set initial temperature 𝑇 > 0

(04) set the threshold α > 0

(05) select an initial state  𝑥

(06) for  𝑖=1  to  𝑁_𝑐

(07)  µ(𝑖):= membership value of the constraint and coefficient of objective function 𝑅_𝑖(𝑥)

(08)  min1:= aggregation(µ(1), …, µ(𝑁_𝑐))

(09) repeat

(10) for  𝑖=1  to  𝑁_𝑐

(11)    µ(i):= membership value of the constraint and coefficient of objective function 𝑅_𝑖(𝑦)

(12) min2:= aggregation(µ(1), …, µ(𝑁_𝑐))

(13) calculate Δ = min2 − min1

(14) if Δ > 0 then

(15)    𝑋=𝑌;

(16)   min1 = min2;

(17) else

(18)    Create random number 𝑝 = rand(0, 1)

(19)    if exp(Δ/𝑇)>𝑝

(20)   𝑋=𝑌;

(21)   min1 = min2;

(22)    else

(23)    𝑌=𝑋;

(24)    min2 = min1;

(25)  𝑡=𝑡+1

(26) until  𝑡 > 𝑇