A Hybrid RF-GA Approach to Bottleneck Machine Diagnosis and Suggestion in Parallel Machine Job-Shop Scheduling

In this paper, a hybrid Random Forest-Genetic Algorithm approach to detect and solve bottleneck machine problems in parallel machine Job-shop scheduling is developed with the aim of minimizing the makespan and the additional cost. The drawbacks of the existing methods for diagnosing bottlenecks is that they either do not consider the severity of the bottleneck or they do not consider the existence of multiple bottlenecks. In the existing models for solving bottlenecks, the cost is not considered as an objective function and only shifting of bottlenecks is utilized to solve the bottleneck machine problem. This approach is not feasible if the maximum capacity of the workshop has been reached. In this paper, a Random Forest classification model is utilized to diagnose bottleneck machine with different severity where the severity of the machines on the shop floor can either be none, low, medium, or high. Due to the lack of historical data, the Random Forest algorithm is trained using bottleneck data generated by simulating several identical parallel machine Job-shop scheduling problems. The trained Random Forest algorithm is then used in conjunction with Genetic Algorithm for finding the optimal actions to be taken for the most severe bottlenecks machines in order to reduce the makespan and the additional cost by optimizing the number of additional parallel machines to be utilized and overtime hours for the most severe bottleneck machines. The two objectives, makespan and additional cost, are combined into a single objective value by the use of weight values. These weight values depend on severity of the most severe bottleneck machine. If the bottleneck severity is “high” then makespan has a higher weight value than cost, if the severity is “medium” then both cost and makespan are weighed equally, and if the severity is “low” then cost has a higher weight value than makespan. In order to show the validity of the proposed approach it is used for diagnosing and solving the bottleneck problems in three different identical parallel machine Job-shop scheduling case studies 1. 3 jobs with 6 machines 2. 5 jobs with 9 machines and 3. 5 jobs with 12 machines. By utilizing the proposed approach the makespan and cost were reduced by 19.0%, 24.5% and 25.4% in case studies 1, 2, and 3 respectively. The results show that the trained Random Forest algorithm was able to correctly diagnose the bottleneck machines and their severity and Genetic algorithm was able to find the optimal number of additional hours and additional machines for the most severe bottleneck machines on the shop floor.

Identification approach for bottleneck clusters in a job shop based on theory of constraints and sensitivity analysis

Manufacturing systems are constrained by one or more bottlenecks. Reducing bottlenecks improves the entire system. Finding bottlenecks, however, is a difficult task. In this study, a new bottleneck detection method based on theory of constrains and sensitivity analysis is presented to overcome the disadvantages of existing bottleneck identification methods for a job shop. First, a bottleneck index matrix is obtained by examining the sensitivity of system production performance to the capacity of each machine. Technique for order preference by similarity to ideal solution is then employed to calculate the comprehensive bottleneck index of each machine. Based on the calculation result, bottleneck machine clusters under different hierarchies are obtained through hierarchical cluster analysis. The designed identification approach, as a prior-to-run method, can identify bottleneck machine clusters under different hierarchies before the overall system circulation, thereby providing good guidance for subsequent production optimization. Finally, a set of job-shop scheduling problem benchmarks with different scales is selected for comparison between the proposed approach and existing approaches, such as, the shifting bottleneck detection method, the bottleneck detection method based on orthogonal experiment, and the bottleneck cluster identification method. By comparison, the proposed approach is proven to be credible and superior.

Bottleneck-based opportunistic maintenance model for series production systems

Purpose – The purpose of this paper is to develop a bottleneck-based opportunistic maintenance (OM) model for the series production systems with the integration of the imperfect effect into maintenance activities. Design/methodology/approach – On the analysis of availability and maintenance cost, preventive maintenance (PM) models subjected to imperfect maintenance for different equipment types are built. And then, a cost-saving function of OM is established to find out an optimal OM strategy, depending on whether the front-bottleneck machines adopt OM strategy or not. A numerical example is given to show how the proposed bottleneck-based OM model proceeded. Findings – The simulation results indicate that the proposed model is better than the methods to maintain the machines separately and the policy to maintain all machines when bottleneck machine reaches its reliability threshold. Furthermore, the relationship between OM strategy and corresponding parameters is identified through sensitivity analysis. Practical implications – In practical situations, the bottleneck machine always determines the throughput of the whole series production system. Whenever a PM activity is carried out on the bottleneck machine, there will be an opportunity to maintenance other machines. Under such circumstances, findings of this paper can be utilized for the determination of optimal OM policy with the objective of minimizing total maintenance cost of the system. Originality/value – This paper presents a bottleneck-based OM optimization model with the integration of the imperfect effect as a new method to schedule maintenance activities for a series production system with buffers. Furthermore, to the best of the knowledge, this paper presents the first attempt to considering the bottleneck constraint on system capacity and diverse types of machines as a means to minimize the maintenance cost and ensure the system throughput.

Batch Splitting Activity in Scheduling of Virtual Cell with Capacity Constraints Based on Bi-Level Mathematical Model

Although there has been some researches about virtual cell manufacturing system, the existing literature lack of discussion about the scheduling model that considering with bottleneck machines in the virtual cells. In view of this deficiency and the new characteristics of the batch splitting problem, this paper considered the batch splitting (or lot splitting) problem in scheduling of virtual manufacturing cells with bottlenecks and multiple machine types, and each of which has several identical machines. In consideration of the hierarchical decision structure of the problem, we developed a bi-level multi-objective mathematical model. Scheduling results and batch splitting strategies of both bottleneck and non-bottleneck machines are given in separate decision levels and additional scheduling objectives are improved in the second model level, while maintaining the maximum use of the bottleneck machine ability. In order to demonstrate how this approach works, application example was shown in this paper.

SCHEDULING PROPORTIONALLY DETERIORATING JOBS IN TWO-MACHINE OPEN SHOP WITH A NON-BOTTLENECK MACHINE

We address the problem of scheduling proportionally deteriorating jobs in two-machine open shop in which one of the machines is non-bottleneck. The objective is to minimize the makespan. We show that the decision version of the problem is [Formula: see text]-complete in the ordinary sense, and present for it a fully polynomial-time approximation scheme.