Re-entrant Flow Shop Research Articles

A Modification of Simplified Drum-Buffer-Rope for Re-entrant Flow Shop Scheduling

A Modification of Simplified Drum-Buffer-Rope for Re-entrant Flow Shop Scheduling

Maintenance Planning in Re-entrant Flow Shop (2nd Report)

This paper presents an optimization method for facility maintenance scheduling in re-entrant flow shop using Multi-start and Greedy method. The proposed approach regards maintenances as jobs within limits of starting and finishing time, so that the schedule can realize not only proper maintenance to prevent facility troubles but also high productivity with short scheduling time. A case study is presented for a semiconductor manufacturing system, in which it is difficult to find a proper plan because of complex process flow so that the jobs often re-enter into the facilities. The feasibility of the proposal is discussed comparing to Lagrangian decomposition coordination method in the first; the proposed method can realizes feasible solutions within acceptable calculation speed in real. Also, the feasibility is studied in a real semiconductor manufacturing system and confirmed the proposed method can gain its productivity.

Proactive maintenance scheduling in a re-entrant flow shop using Lagrangian decomposition coordination method

Proactive maintenance scheduling is necessary to prevent facility trouble and to ensure high productivity in semiconductor production systems. For such systems, it is usually difficult to determine a proper plan because of complex process flows, which necessitate that jobs often re-enter a facility. This paper presents a method for re-entrant production floor optimization using Lagrangian decomposition coordination. By regarding maintenance as jobs that are limited by a starting and finishing time, the proposed approach produces a schedule that can facilitate proper maintenance. Feasibility is discussed using computer simulation results: the proposed method produces feasible solutions for minimizing overall tardiness problems.

Maintenance Planning in Re-entrant Flow Shop (1st Report)

This paper presents an optimization method for maintenance scheduling in re-entrant production lines using Lagrangian decomposition coordination method. The proposed approach regards maintenances as jobs within limits of starting and finishing time, so that the schedule can realize not only proper maintenance to prevent facility troubles but also high productivity. A case study is presented for a semiconductor manufacturing system, in which it is difficult to find a proper plan because of complex process flow so that the jobs often re-enter into the facilities. The feasibility of the proposal is discussed using computer simulation results; the proposed method can realizes feasible solutions in the problems where the objective function is set to minimizing total tardiness.

Makespan Algorithms and Heuristic for Internet-Based Collaborative Manufacturing Process Using Bottleneck Approach

This paper presents makespan algorithms and scheduling heuristic for an Internet-based collaborative design and manufacturing process using bottleneck approach. The collaborative manufacturing process resembles a permutation re-entrant flow shop environment with four machines executing the process routing of M1,M2,M3,M4,M3,M4 in which the combination of the last three processes of M4,M3,M4 has high tendency of exhibiting dominant machine character-istic. It was shown that using bottleneck-based analysis, effective makespan algorithms and constructive heuristic can be developed to solve for near-optimal scheduling sequence. At strong machine dominance level and medium to large job numbers, this heuristic shows better makespan performance compared to the NEH.

551 リエントラントフローショップスケジューリング問題に関する研究 : ボトルネック機械の相対的な処理順序に着目した最適化手法の提案(GS 加工)

551 リエントラントフローショップスケジューリング問題に関する研究 : ボトルネック機械の相対的な処理順序に着目した最適化手法の提案(GS 加工)

Minimizing total tardiness on a two-machine re-entrant flowshop

We present a branch and bound algorithm for a two-machine re-entrant flowshop scheduling problem with the objective of minimizing total tardiness. In the re-entrant flowshop considered here, all jobs must be processed twice on each machine, that is, each job should be processed on machine 1, machine 2 and then machine 1 and machine 2. By regarding a job as a pair of sub-jobs, each of which represents a pass through the two machines, we develop dominance properties, a lower bound and heuristic algorithms for the problem, and use these to develop a branch and bound algorithm. For evaluation of the performance of the algorithms, computational experiments are performed on randomly generated test problems and results are reported. Results of the experiments show that the suggested branch and bound algorithm can solve problems with up to 20 sub-jobs in a reasonable amount of CPU time, and the average percentage gap of the heuristic solutions is about 13%.

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.

Multi-family scheduling in a two-machine reentrant flow shop with setups

This paper considers a two-machine multi-family scheduling problem with reentrant production flows. The problem consists of two machines, M 1 and M 2, and each job has the processing route (M 1, M 2, M 1, M 2). There are identical jobs in the same family and the jobs in the same family are processed in succession. Each machine needs a setup time before the first job in a family is processed. The objective is to minimize the maximum completion time. Examples of such a problem occur in the bridge construction, semiconductor industry and job processing on numerical controlled machines, where they usually require that the jobs are reprocessed once and there are identical jobs in the same family. This problem is shown to be NP-hard. A branch-and-bound algorithm is proposed, and computational experiments are provided.

Heuristic algorithms for two machine re-entrant flow shop

This paper focuses on a two machine re-entrant flow shop scheduling problem with the objective of minimizing makespan. In the re-entrant flow shop considered here, each job has the processing route ( M 1 , M 2 , M 1 , M 2 , …, M 1 , M 2 ). We present heuristic algorithms, some are modified from existing algorithms and some are newly developed. Extensive computational experiments are performed to evaluate the performance of the heuristics. Results of the experiments show that the performance of heuristics is significantly affected by the distribution of workloads on machines and some of them are excellent.

Study on Re-entrant Flowshop Scheduling Problem(1st report)

A new theoretical formulation for re-entrant flow shop scheduling problem is derived to plan an optimum production schedule by mixed integer programming. Flow time, which is an index of the evaluation of the planned schedule, is minimized to decrease amount of work-in-process. Cycle time, which is another important index, is determined by the total load time on a bottleneck machine to manufacture the products at the highest productivity. Both objective function and constraint conditions are described in linear expressions and a general optimization tool is adopted to solve the formulated scheduling problems. Another objective function is also introduced in order to shorten the time for the optimization. Computational scheduling results obtained by the optimization are evaluated and discussed in the present paper.

Study on Re-entrant Flowshop Scheduling Problem(2nd report)

Study on Re-entrant Flowshop Scheduling Problem(2nd report)

Minimizing makespan on a two-machine re-entrant flowshop

This paper focuses on a two-machine re-entrant flowshop scheduling problem with the objective of minimizing makespan. In the re-entrant flowshop considered here, all jobs must be processed twice on each machine, that is, each job should be processed on machine 1, machine 2 and then machine 1 and machine 2. We develop dominance properties, lower bounds and heuristic algorithms for the problem, and use these to develop a branch and bound algorithm. For evaluation of the performance of the algorithms, computational experiments are performed on randomly generated test problems. Results of the experiments show that the suggested branch and bound algorithm can solve problems with up to 200 jobs in a reasonable amount of CPU time.

Hybrid tabu search for re-entrant permutation flow-shop scheduling problem

A re-entrant flow-shop (RFS) refers to situations in which every job must be processed on machines in the order, M1, M2, … , Mm, M1, M2, … , Mm, … , and M1, M2 , … , Mm. Every job can be decomposed into several layers each of which starts on M1 and finishes on Mm. In the RFS case, if the job ordering is the same on any machine at each layer, then no passing is said to be allowed, since no job is allowed to pass any former job. The RFS scheduling problem in which no passing is allowed, is called a re-entrant permutation flow-shop (RPFS) problem. This study considers RPFS scheduling, and applies hybrid tabu search (HTS) to minimize the makespan of jobs. The hybridization method is used to improve pure tabu search (TS) performance. The HTS is compared to the optimal solutions generated using the integer programming technique, and to the near optimal solutions generated by pure TS and heuristic NEH. The experimental results show that HTS has better performance than the others tested.

1107 リエントラントフローショップスケジューリング問題に関する研究 : ディスパッチングルールによるスケジューリング(OS1-1 生産スケジューリング)

A new heuristic scheduling procedure for re-entrant flow shop scheduling problem is proposed to plan an appropriate production schedule in actual manufacturing systems. In this study, flow time, which is a index of the evaluation of the planned schedule, is optimized to decrease amount of work-in-process. Cycle time, which is another important index, is determined by the total load time on a bottleneck machine to manufacture the products in high productivity. The result indicates that the proposed dispatching procedure gives a good schedule in comparison with the optimum solution.

A scheduling algorithm for the reentrant shop: an application in semiconductor manufacture

This paper addresses a heuristic algorithm that minimizes total weighted tardiness for reentrant flow shop problems with sequence-dependent set up time. The proposed algorithm consists of three phases: prior plan phase, initial solution generation phase and solution improvement phase. In the first phase, a reentrant order with cyclic process is divided into a number of jobs. An initial solution is obtained by a RATCS (revised apparent tardiness cost with setups) rule in the second phase, and the last phase employs a RHTS (rolling horizon tabu search) to improve the solution. The experimental results show that the proposed algorithms give very efficient schedules in terms of total weighted tardiness and computational effort.

Minimizing makespan on an [formula omitted]-machine re-entrant flowshop

This paper focuses on an m -machine re-entrant flowshop scheduling problem with the objective of minimizing makespan. In the re-entrant flowshop considered here, routes of all jobs are identical as in ordinary flowshops, but the jobs must be processed multiple times on the machines. We present heuristic algorithms, which are modified from well-known existing algorithms for the general m -machine flowshop problem or newly developed in this research. For evaluation of the performance of the algorithms, computational experiments are performed on randomly generated test problems and results are reported.

A hybrid genetic algorithm for the re-entrant flow-shop scheduling problem

This study considers the production environment of the re-entrant flow-shop (RFS). In a RFS, all jobs have the same routing over the machines of the shop and the same sequence is traversed several times to complete the jobs. The aim of this study is to minimize makespan by using the genetic algorithm (GA) to move from local optimal solution to near optimal solution for RFS scheduling problems. In addition, hybrid genetic algorithms (HGA) are proposed to enhance the performance of pure GA. The HGA is compared to the optimal solutions generated by the integer programming technique, and to the near optimal solutions generated by pure GA and the non-delay schedule generation procedure. Computational experiments are performed to illustrate the effectiveness and efficiency of the proposed HGA algorithm.

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.

Integer programming models for the re-entrant shop scheduling problems

A re-entrant shop describes a manufacturing environment in which a machine can process a job more than once. A re-entrant shop can be classified into a re-entrant job-shop (RJS) and a re-entrant flow-shop (RFS). This article presents the development of the mixed binary integer programming (BIP) technique for the RJS and the RFS scheduling problems. Eight mixed BIP models are proposed for solving re-entrant shop scheduling problems to minimize makespan. The models RJS-1, RJS-2, RJS-3, and RJS-4 are for the RJS problems, while the other four models (RFS-1, RFS-2, RFS-3, and RFS-4) are for the RFS problems. Computational results show that the RJS-4 model is the best of the RJS models, and the RFS-3 model is the best of the RFS models.