Job Shop Scheduling by Local Search

We survey solution methods for the job shop scheduling problem with an emphasis on local search. Both deterministic and randomized local search methods as well as the proposed neighborhoods are discussed. We compare the computational performance of the various methods in terms of their eeectiveness and eeciency on a standard set of problem instances. In the job shop scheduling problem we are given a set of jobs and a set of machines. Each machine can handle at most one job at a time. Each job consists of a chain of operations, each of which needs to be processed during an uninterrupted time period of a given length on a given machine. The purpose is to nd a schedule, that is, an allocation of the operations to time intervals on the machines, that has minimum length. The problem is diicult to solve to optimality. For example, a relatively small instance with 10 jobs, 10 machines and 100 operations due to Fisher and Thompson 29] remained unsolved until 1986. Many solution methods have been proposed, ranging from simple and fast dispatching rules to sophisticated branch-and-bound algorithms. We survey algorithms for the job shop scheduling problem with an emphasis on local search. During the last decade many diierent types of local search algorithms for job shop scheduling have been developed, and some of them have proved to be very eeective. The paper is structured as follows. In Section 1 several models for the job shop scheduling problem are presented. In Section 2 the complexity of the problem and methods for its solution are reviewed. Section 3 introduces local search and Section 4 discusses 1