A Pseudo-Parallel Genetic Algorithm Integrating Simulated Annealing for Stochastic Location-Inventory-Routing Problem with Consideration of Returns in E-Commerce

Pseudocode 1

Pseudocode of the proposed PPGASA.

Procedure: PPGASA for LIRP

Input: coordinates of nodes, demands and returns of retailers, MC parameters, vehicle capacity, PPGASA parameters

Output: the best solution (include routes, MCs locations, order times and order size)

Begin

Take pop

 for i = 1 to pop_size

 Calculate individual fitness value  

 end

n = 1

k = 1

T = 100

while n ≤ 300

  select operator

  if random ≤   then

  crossover operator

 end

 if random ≤   then

 mutation operator

 end

newpop

   for m = 1 to newpop_size

    if objvalue(m) < objvalue1(m)

     Sel(m,:) = Z(m,:)

    else

     Sel(m,:) = Cos(m,:)

    end

   end

  pops1 = zhuanh(Sel)

 [objvalue1 = calobjvalue(pops1, d)

  for i = 1: popsize

   if objvalue(i) > objvalue1(i)

    objvalue(i) = objvalue1(i)

    Z(i,:) = Sel(i,:)

   else

    p = rand

    if p ≤ exp((objvalue1(i) − objvalue(i))/T)

     objvalue(i) = objvalue1(i)

    Z(i,:) = Sel(i,:)

    end

   end

  end

  gen = gen + 1

  pops2 = zhuanh(Z)

 [objvalue2] = calobjvalue(pops2, d)

 [hh, kk] = min(objvalue2)

  GY(gen, 1) = hh

   GX(gen,:) = pops2(kk,:)

   end

   T = α * T

 end

 output the best solution

 end