Meta-heuristics for manufacturing scheduling and logistics problems

The Special Issue of International Journal of Production Economics focuses on the new and significant results on developing new heuristic approaches or combining meta-heuristics with other problem-solving paradigms to find an effective solution for manufacturing scheduling and logistic problems. Wang and Chen (2013) proposed an evolutionary algorithm. The authors formulated the problem into a mixed binary integer programming model in order to minimize the number of vehicles and minimize the total traveling distance. Pang(2013) proposed a genetic algorithm-based heuristic for two machine no-wait flow-shop scheduling problems with class setup times that minimizes maximum lateness. Chou (2013) developed a particle swarm optimization (PSO) with cocktail decoding method for hybrid flow-shop scheduling problems with multiprocessor tasks. Hamtaetal (2013) developed a hybrid PSO algorithm for a multi-objective assembly line balancing problem with flexible operation times, sequence-dependent setup times and learning effect.