Reentrant Flow-shop Problem Research Articles

Makespan minimization in reentrant flow shop problem with identical jobs

Makespan minimization in reentrant flow shop problem with identical jobs

A new mathematical formulation and a hybrid evolutionary algorithm for re-entrant flow-shop problem with release date

In this paper, we address the weighted multi-objective re-entrant flow-shop scheduling problem considering release dates in order to minimize makespan, total completion time, total tardiness, maximum idle time, and number of tardy jobs. Each job is taken into account with deterministic processing times, and release dates. The flow-shop comprised of two workshops in whose jobs are entered to the main workshop and after the first part of the processing, they are transferred to the second workshop and after this stage, the jobs are returned to the main workshop for the last part of the processing. We model the problem by a new mixed integer programming based on formulating sum of idle time as a new concept. Moreover, a hybrid evolutionary algorithm is proposed based on some dispatching rules, ant colony optimization, and genetic algorithm. The performance of the proposed algorithm on some test instances is compared to the mixed integer linear programming model as well as the state-of-the-art algorithms called genetic algorithm, tabu search, bio-geography based optimization, and artificial bee colony. The computational experiments show that our proposed approach outperforms other algorithms and the results indicate efficiency and capability of the proposed algorithm in comparison with the traditional algorithms.

Multi-objective distributed reentrant permutation flow shop scheduling with sequence-dependent setup time

The distributed reentrant permutation flow shop (DRPFS) is a combination of the reentrant flow shop problem and distributed scheduling. The DRPFS is a NP-hard problem that consists of two subproblems: (1) assigning a set of jobs to a set of available factories and (2) determining the operation sequence of jobs in each factory. This paper is the first study to consider the inclusion of sequence-dependent setup time in the DRPFS. The industrial applications of flow shop indicate that the machine setup time to process a job may depend on the previously processed jobs. Particularly, in DRPFS, the effect of sequence-dependent setup time is intensified due to its reentrant characteristic. An improved version of the multi-objective adaptive large neighborhood search (MOALNS) is proposed as a solution method for the sequence-dependent DRPFS with the aim to minimize the makespan, production cost, and tardiness. The proposed algorithm enhances the standard MOALNS by embedding an improved solution acceptance and non-dominated set updating criteria to assist the algorithm in finding the near-optimal Pareto front of the factory allocation and scheduling problems. To address the multiple objectives and the issue of non-uniform setup time, a new set of destroy and repair heuristics are developed. Further, the numerical experiments demonstrate the efficiency of IMOALNS in finding high-quality solutions in a relatively short time.

Industrial scheduling with Monte Carlo tree search and machine learning

Although scheduling has been studied for decades, no general approach for solving industrial scheduling problems has yet been found. The variety of production systems often requires unique modifications of scheduling algorithms to meet industrial requirements. To address this challenge, an algorithm is proposed that combines Monte Carlo tree search and machine learning. It tries to eliminate the need for problem-specific knowledge while improving the production system’s performance. The algorithm has been investigated using a reentrant flow-shop problem. The results show that a combination of random-based search algorithms and machine learning is a promising way to handle complex industrial scheduling problems.

Minimizing total tardiness in a two-machine re-entrant flowshop with sequence-dependent setup times

We consider a two-machine re-entrant flowshop scheduling problem in which all jobs must be processed twice on each machine and there are sequence-dependent setup times on the second machine. For the problem with the objective of minimizing total tardiness, we develop dominance properties and a lower bound by extending those for a two-machine re-entrant flowshop problem (without sequence-dependent setup times) as well as heuristic algorithms, and present a branch and bound algorithm in which these dominance properties, lower bound, and heuristics are used. For evaluation of the performance of the branch and bound algorithm and heuristics, computational experiments are performed on randomly generated instances, and results are reported.

Two Proposed Algorithms for Re-Entrant Flow Shop Problem

In a re-entrant flow shop scheduling problem we proposed some algorithms to get a better TAT (turn around time) with a genetic search method. One is an operation which searches for a solution that shifts the start timing in limited areas of each lot. Another is an operation which searches for a solution that shifts left and chooses the machine which starts fastest. Some algorithms are effective on the benchmark including those proposed by Taji et al. In the first step, it is easiest to choose the probabilistic problem by local search. The second step is to search for the solution that shifts the start timing in limited areas of each lot, makes the Gantt chart, chooses the machine and gets the results. The third step is to search for the solution that again shifts left, makes the Gantt chart, chooses the machine and gets the results. The proposed algorithms are more valid than local search methods by Taji et al, such as swap, move, swap-2 neighborhood and FIFO (first in first out). The first algorithm has produced the best result in an experimental test when interval time was short. The second algorithm produced the best result of all solutions. The results have shown that the proposed algorithms are effective for interval time cut and get better TAT than previous methods.

On the connection between a cyclic job shop and a reentrant flow shop scheduling problem

Hall et al. (J. Sched. 2002; 5:307---327) investigated the cycle time minimization problem in a two-machine job shop, where each job consists of at most three operations. In this note, we reduce the problem to a two-machine reentrant flow shop problem and briefly discuss some consequences.

リエントラントフローショップ問題に対する局所探索法によるスケジューリング

Semiconductor wafer fabrications are modeled as a re-entrant flow shop problem, in which the processing order of each lot is given, but each lot may enter a production line many times. Hence, a re-entrant flow shop is a very complicated problem, which has a property similar to a job shop problem. In this paper, we consider a scheduling method for a whole production line of re-entrant flow shop. A real wafer fabrication line consists of several hundreds of processes and requires re-scheduling many often. So we propose local search methods instead of seeking exact solutions. Some numerical results indicate that the proposed methods obtain good solutions in a short time.