A Bottleneck Detection Algorithm for Complex Product Assembly Line Based on Maximum Operation Capacity

Algorithm 1

Solving the optimal value.

Input( ); % Parameter assignment

gen = 0; % Given initial value

Chrom = cribp; % Initial population structure

ObjV = fit( ); % The objective function is calculated

keep the best( ); % retaining the best chromosome

While

  FitnV = ranking( ); % Distribution of fitness

  SelCh = select( ); % Selection

  SelCh = recombine( ); % Restructuring

  SelCh = mut( ); % Variation

  ObjVsel = fit( ); % The objective function is calculated

  keep the best( ); % retaining the best chromosome

  [Chrom ObjV] = reins (Chrom, SelCh, 1, 1ObjV, ObjVsel); % Insert new offspring population

  keen = gen + 1; % Counter plus 1

  End;

Output( ); %