A shifting bottleneck heuristic for minimizing the total weighted tardiness in a job shop

Abstract

We present a shifting bottleneck heuristic for minimizing the total weighted tardiness in a job shop. The method decomposes the job shop into a number of single‐machine subproblems that are solved one after another. Each machine is scheduled according to the solution of its corresponding subproblem. The order in which the single machine subproblems are solved has a significant impact on the quality of the overall solution and on the time required to obtain this solution. We therefore test a number of different orders for solving the subproblems. Computational results on 66 instances with ten jobs and ten machines show that our heuristic yields solutions that are close to optimal, and it clearly outperforms a well‐known dispatching rule enhanced with backtracking mechanisms. © 1999 John Wiley & Sons, Inc. Naval Research Logistics 46: 1–17, 1999

A genetic local search algorithm for minimizing total weighted tardiness in the job-shop scheduling problem

“…If a time limit is fixed then the computational effort has to be balanced between the number of generations and the number of iterations of the ILS. So if a good solution has to be found within a few seconds, it is recommended to vary n sd in the range [10,20]. On the other hand, if the stopping criterion is a maximum number of generations (> 100) then large values of n sd ∈ [50, 80] are recommended.…”

“…In order to make an accurate comparison of our GLS with the approaches of [10,12], the maximum computation time is set to 18 s. Indeed, in [10] the maximum run times for the shifting bottleneck on a Pentium 90 MHz were set to 600 s. The large step random walk algorithm of [12] is tested on a Pentium 233 MHz with a maximum run time of 200 s. Tables 8-10 present the results of the comparison. For each value of f, column Opt gives the (optimal) value obtained by the branch and bound of [15], column SB gives the value obtained by the shifting bottleneck of [10], column LSRW shows the average value and the number of times the large step random walk heuristic finds the optimal solutions over 5 runs.…”

“…For each value of f, column Opt gives the (optimal) value obtained by the branch and bound of [15], column SB gives the value obtained by the shifting bottleneck of [10], column LSRW shows the average value and the number of times the large step random walk heuristic finds the optimal solutions over 5 runs. Our results of 10 independent runs are given in column GLS in which in addition to the average and best values, the number of times the (optimal) solutions are obtained or improved, are presented.…”

“…Some dispatching rules are developed in [9], and a shifting bottleneck procedure is proposed in [10] where single machine problems are solved using dispatching rules. A taboo search heuristic that makes use of intensification and diversification strategies is proposed in [11] for the case of unit weights.…”

Optimal Planning of a Multi-Station System with Sojourn Time Constraints

“…According to Yang, Yan, and Sethi (1999), algorithms for deterministic models can serve as a base of methods for stochastic flow-line models. This is also true for discrete systems such as the stochastic flow shop and job shop, for which Singer (2000) derives a scheduling method based on the deterministic shifting bottleneck heuristic by Pinedo and Singer (1999). Yang, Yan, and Sethi (1999) study a deterministic system where multiple products visit the stations in a serial manner, minimizing the inventory and backlog cost.…”