A tabu search method guided by shifting bottleneck for the job shop scheduling problem

An Advanced Tabu Search Algorithm for the Job Shop Problem

“…These are two variants of shifted bottleneck approach, namely SB-RGSL10 (called here BAL) from Balas and Vazacopolous (1998) and TSSB (called here PEZ) from Pezzella and Merelli (2000). These algorithms (together with TSAB) dominate the remaining ones, thus only they will be used for evaluations.…”

“…Generally, i -TSAB on TA11-20 7 12 24 41 52 108 2 526 3 383 TA21-30 6 15 25 47 140 278 328 34 910 4 377 TA31-40 6 9 24 66 129 275 341 14 133 5 069 TA41-50 11 27 38 99 260 536 975 11 512 10 726 SWV01-05 5 9 26 62 131 269 462 1 290 SWV06-10 5 13 22 33 67 273 514 2 917 SWV11-15 7 16 27 50 143 228 360 9 173 YN01-04 7 7 20 54 154 216 510 5 938 The time needed by i -TSAB to perform 1 milion of iterations Note. PEZ-algorithm from Pezzella and Merelli (2000), BAL-algorithm SB-RGSL10 from Balas and Vazacopoulos (1998), BAL * -found on the basis of the best solution among those provided in Balas and Vazacopoulos (1995), ACPU-B-i -TSAB on Pentium 900 MHz, PEZ on Pentium 133 MHz, BAL on Sun Sparc 330. instances TA01-50 provides ARE = 2.82%, whereas PEZ, BAL and BAL * provides only 3.68, 3.40 and 3.01%, respectively. SWV class contains 20 instances (200 ≤ o ≤ 500) organized into four groups SWV01-05, .…”

A Heuristic Algorithm for the Job Shop Scheduling Problem

“…For a feasible solution (a schedule) D H , the swap of two adjacent operations processed by the same machine on the critical path may improve the solution [17], which is usually a base to define the neighborhood for local search. For this reason, the critical path is decomposed into a series of critical blocks (B 1 , B 2 , …, B r ) [13].…”

“…Generally, Starting from an initial feasible solution, a local search method iteratively selects a proper solution from the neighborhood. As the observation of Van Laarhoven et al [17] and Nowicki et al [18], both the choice of a good-initial solution and the neighborhood structure are important aspects of algorithm's performance. This paper is a further research based on our recent work, and a heuristic algorithm that is based on a Tabu Search (TS) technology and on the improved shifting bottleneck procedure (ISB) [9] is presented.…”

A New Combined Approach for Optimizing Makespan in Job Shop Scheduling Problem

“…By contrast, approximate and heuristic methods make a tradeoff between solution quality and computational cost. These methods mainly include dispatching priority rules [5]- [7], shifting bottleneck approach [8], [9], Lagrangian relaxation [10], [11], and tabu search [12]- [14] and have made considerable achievement. In recently much attention has been devoted to metaheuristics with the emergence of new techniques from the field of artificial intelligence such as genetic algorithm (GA) [15]- [18], simulated annealing [19]- [22], ant colony optimization [23], Particle swarm optimization(PSO) [24], [25], artificial neural network [26]- [28], artificial immune system(AIS) [29].…”