A Genetic Local Search Algorithm for the Parallel Machine Batch Process Scheduling Problem

Abstract

A parallel machine batch process scheduling problem (PBPSP) integrating batching decision is investigated. The problem is converted into the fixed charge transportation problem (FCTP). A genetic local search algorithm (GLSA) with intensification strategy of local search and escape strategy from local optimal solution is developed. The sorted edges attained by root-first search of spanning tree are used to encode spanning tree in the genetic local search algorithm. Efficient single point crossover operator appending edges to sub-tree is proposed. Network simplex method based local search is used to be the mutation of individual. To enhance the capacity of searching the global optimal solution, this paper presents an intensification strategy of local search that applies continuous random node local search to the current optimal solution and an escape strategy from local optimal solution based on random pivot mutation and random node local search. The results of computations demonstrate that the genetic local search algorithm is better than the permutation encoding genetic algorithm and the matrix encoding genetic algorithm on solution quality, and can find the optimal solution of all Benchmark problems. Moreover, the genetic local search algorithm is robust. Compared with the tabu heuristic search procedure, this algorithm can obtain more frequently the optimal solutions of the test problem instances.