Job Shop Scheduling by Branch and Bound Using Genetic Programming

A classical depth-first branch and bound (BB) method is adopted to minimize the makespan of job shops based on the disjunctive graph model. The engine of the BB is Giffler-Thompson's active schedule generation method. The performance of the BB method highly depends on the selection of child nodes in earlier branching stages. To support the selection decision, several features of nodes are stored under the BB method, and the correct selection at each branching stage is informed by the mixed-integer linear programming model. The stored data of a test problem instance is analyzed by genetic programming (GP) to generate rules for selecting the correct nodes. The depth-first BB method guided by the generated rules by GP is applied for 42 benchmark instances and exhibits competitive performance when compared with the baseline rule that always selects the child node with the smallest lower bound on makespan. (C) 2019 The Authors. Published by Elsevier Ltd.