Solving the open shop scheduling problem

Abstract

SUMMARYOnly few exact solution methods are available for the open shop scheduling problem. We describe a branch-and-bound algorithm for solving this problem which performs better than other existing algorithms. The key to the e ciency of our algorithm lies in the following approach: instead of analysing and improving the search strategies for ÿnding solutions, we focus on constraint propagation based methods for reducing the search space. Extensive computational experiments on several sets of well-known benchmark problem instances are reported. For the ÿrst time, many problem instances are solved to optimality in a short amount of computation time.

An Optimal Constraint Programming Approach to the Open-Shop Problem

“…To address this issue, shaving and intelligent backtracking techniques have been widely studied (Guéret et al, 2000;Dorndorf et al, 2001;Laborie, 2005). Shaving tries to assign a value to a variable and applies a consistency filtering algorithm.…”

“…time intervals in which no operation can start or end in an optimal solution (Guéret and Prins, 1998). Dorndorf et al (2001) improved the Brucker algorithm by using consistency techniques. Instead of analyzing and improving the search strategies, they focused on constraint propagation techniques for reducing the search space.…”

Makespan Optimization in Job Shop Scheduling Problem using Differential Genetic Algorithm

“…In a job shop problem, there are some precedence constraints for each job, according to which the jobs are completed [5]. In an open shop problem, the operations of all the jobs may be executed in any order [6]. This study is focused on Job Shop Scheduling Problem (JSSP), under the category of NP-Hard problems, in which the solution space increases exponentially as the number of jobs increase, and thus, the number of operations also increase [1].…”

Integrating the sequence dependent setup time open shop problem and preventive maintenance policies

“…For exact methods, Brucker et al provided a method based on branch and bound for the general m-machine OSSP (Brucker et al, 1997). Dorndorf et al developed a branch and bound method to tackle OSSP in which the search space is reduced using constraint propagation methods (Dorndorf et al, 2001). For constructive methods, Blum and Sampels (2004) provided an ant colony optimization algorithm, which uses a strong neighborhood search for constructing solutions and a local search to improve the quality of solutions, for OSSP.…”