Flow Shop Production Lines Research Articles

Event-Triggered Hybrid Energy-aware Scheduling in Manufacturing Systems

Incorporating renewable energy sources (RESs) into manufacturing systems has been an active research area in order to address many challenges originating from the unpredictable nature of RESs such as photovoltaics. In the energy-aware scheduling for manufacturing systems, the traditional off-line scheduling techniques cannot always work well due to their lack of robustness with respect to uncertainties coming from imprecise models or unexpected situations. On the other hand, on-line scheduling or rescheduling, which can improve the robustness by using the model and the latest measurements simultaneously, suffer from a high computational cost. This work proposes a hybrid scheduling framework, which combines the advantages of both off-line scheduling and on-line scheduling, to provide a balanced solution between robustness and computational cost. A novel concept of partially-dispatchable state is introduced. It can be treated as a constant in scheduling when the model works well. When the model does not work well, it is triggered as the variable to tune to improve the performance. Such an event-triggered structure can reduce the number of rescheduling and computational costs while achieving a reasonable performance and enhancing system robustness. Moreover, the choice of partially-dispatchable state also provides an extra design freedom in achieving green manufacturing. Simulation examples on a manufacturing system, which consists of a 100-kW solar photovoltaic system, a 10-machine flow shop production line, a 50-kWh energy storage system, a 100-kW gas turbine, and the grid for power supply, demonstrate the validity and applicability of this event-triggered hybrid scheduling (ETHS) framework.

A parameter tuned hybrid algorithm for solving flow shop scheduling problems with parallel assembly stages

In this paper, we study the scheduling problem for a customized production system consisting of a flow shop production line with a parallel assembly stage that produces various products in two stages. In the first stage of the production line, parts are produced using a flow shop production line, and in the second stage, products are assembled on one of the parallel assembly lines. The objective is to minimize the time required to complete all goods (makespan) using efficient scheduling. A mathematical model is developed; however, the model is NP-hard and cannot be solved in a reasonable amount of time. To solve this NP-hard problem, we propose two well-known metaheuristics and a hybrid algorithm. To calibrate and improve the performance of our algorithms, we employ the Taguchi method. We evaluate the performance of our hybrid algorithm with the two well-known methods of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) and demonstrate that our hybrid algorithm outperforms both the GA and PSO approaches in terms of efficiency.

The application of LPT, CEGA, and PSO method on flow shop scheduling with parallel machine

The production system in a fabrication company often face some problems, such as the inability to deliver orders to the customer at the due time. This situation could drive the company to bear the cost of penalties. This problem can be overcome if the production schedule arranged properly. One of the indicator optimal scheduling is minimize of makespan. This research investigates a company who dealing with fabrication structure and make to order production system with flow shop production lines and parallel machine arrangements. In order to solve the scheduling problem, several methods are applied consist of the heuristic approach of the Longest Processing Time (LPT) method and the metaheuristic approach using the Cross Entropy method combined with Genetic Algorithm to Cross-Entropy Genetic Algorithm (CEGA) and Particle Swarm Optimization (PSO). The algorithm was run in MATLAB software and the results show that proposed method using the Particle Swarm Optimization (PSO) and Cross Entropy Genetic Algorithm (CEGA) obtained makespan value of 8,394 minutes. The makespan for existing scheduling condition is 9,117 minutes. The proposed methods are shown a better results.

Adaptive Preventive Maintenance for Flow Shop Scheduling With Resumable Processing

In this article, we focus on a joint scheduling problem that considers the corrective maintenance (CM) due to unexpected breakdowns and the scheduled preventive maintenance (PM) in a generic $M$ -machine flow shop. The objective is to find the optimal job sequence and PM schedule such that the total of the tardiness cost, PM cost, and CM cost is minimized. Currently, most existing studies on the PM schedules are based on a fixed PM interval, which is rigid and may lead to poor performance, as the fixed strategy fails to effectively balance the trade-offs between the production scheduling and maintenance. To address this critical research issue, our novel idea is to dynamically update the PM interval based on the real-time machine age, such that the maintenance activity coordinates with the job scheduling to the maximum extent, which results in an overall cost saving. Specifically, a correction factor is introduced to dynamically update the PM interval and to help evaluate whether it is worthwhile to process the job first at the risk of the CM before performing the PM action. To demonstrate the effectiveness of the adaptive strategy, simulations and a case study on mining operations are conducted to show that the adaptive strategy outperforms the existing methods with a less total cost. Note to Practitioners —This article is motivated by the critical problem of balancing the trade-offs between production scheduling and maintenance in a flow shop production line, where jobs are processed on the machines in the same route. On the one hand, production scheduling aims to meet the customer demands on time. On the other hand, the maintenance actions help restore the machines’ reliability by reducing the machine failure rate. Most existing approaches to scheduling the PM are based on the fixed PM interval, which is rigid and may lead to poor performance. Although the nonperiodic PM interval strategy has been proposed in the literature for production scheduling, it still does not consider the adaptive PM strategy in the context of flow shop scheduling with multiple machines. To fill this literature gap, in this article, we first model a generic $M$ -machine flow shop considering both the CM and PM. Then, we suggest a new adaptive and easy-to-implement approach to dynamically update the PM interval and improve the decision-making on the PM schedules. Compared with the conventional maintenance policy, the adaptive one greatly reduces the total cost in real-time scheduling.

Evaluation of SME (Small Medium Enterprise) production system with discrete system simulation method

Like manufacturing companies that have flowshop production lines, SME facing the amount of flow time and makespan. Unfulfilled production targets, buildup on some machines that result in other machines being idle, increasing waiting times across multiple machines, and poor performance of workers are a series of problems facing SME. This article uses a discrete system simulation method to analyze and evaluate SME production lines to improve performance. Simulation is an appropriate tool used when experiments are needed in order to find the best response from system components. From the results of modeling and simulation done found the root of the problem is due to accumulation that occurs in one machine and the lack of utility of the operator in producing bags. So do the experimental design with 3 scenarios on the system that have been modeled and obtained some solutions that can be offered to solve the problem. Furthermore, after the alternative selection is obtained the best scenario based on the alternative selection test is the scenario3 by adding 2 new machines and 1 new operator on the production line obtained a significant output increase compared to other scenarios of approximately 30%. While the best scenario based on the minimum cost is the scenario2 by adding 2 new operators obtained output that is not much different than the scenario3. However, it has not been able to solve a series of problems facing SME. Therefore, the results of this study to solve a series of problems faced by SME is to add facilities in the production line of 2 new machines and 1 operator (scenario3).

Research on a Main-Branch Hybrid Flow Shop Scheduling Problem in Production Manufacturing System

A main-branch hybrid Flow shop scheduling problem in production manufacturing system is studied. Under the premise of JIT, targeting of smallest cost, a Flow-Shop production line scheduling model is built in cycle time of buffer. Two stages Quantum Genetic Algorithm (QGA) is proposed. By the results of numerical example, the effective and advantageous of QGA was shown.

Permutation flow shop scheduling with order acceptance and weighted tardiness

In this paper we study the permutation flow shop scheduling problem with order acceptance and weighted tardiness (PFSS-OAWT) faced by firms that have a number of candidate orders to be selected and scheduled on a flow shop production line. The objective is to maximize the total net profit with weighted tardiness penalties. We formulate the PFSS-OAWT problem as an integer programming (IP) model. A heuristic algorithm named Simulated Annealing Based on Partial Optimization (SABPO) is developed for solving the IP model and obtaining near-optimal solutions. Computational studies are carried out on solving 160 problem instances with different scales (small, medium, large, and very large). The experimental results show that the SABPO algorithm exhibits good optimality for small-sized problems and robustness for medium/large-sized problems compared with benchmarks.

Aggregate production planning in the automotive industry with special consideration of workforce flexibility

We present a new mixed integer linear programming approach for the problem of aggregate production planning of flowshop production lines in the automotive industry. Our model integrates production capacity planning and workforce flexibility planning. In contrast to traditional approaches, it considers discrete capacity adaptations which originate from technical characteristics of assembly lines as well as from work regulations and shift planning. In particular, our approach takes change costs into account and explicitly represents a working time account via a linear approximation. A solution framework containing different primal heuristics and preprocessing techniques is embedded into a decision support system. Finally, we present an illustrative case study and computational results on problem instances of practically relevant complexity.

Performance evaluation of hybrid-CLP vs. GA: non-permutation flowshop with constrained resequencing buffers

This paper is located in the area of mixed model non-permutation flowshop production lines where jobs of more than one model are being processed on the same production line in an arbitrary sequence. Nevertheless, the majority of publications in this area are limited to solutions which determine the job sequence before the jobs enters the line and maintains it without interchanging jobs until the end of the production line, which is known as permutation flowshop. The present work considers a non-permutation flowshop. Resequencing is permitted where stations have access to intermediate or centralised resequencing buffers. The access to the buffers is restricted by the number of available buffer places and the physical size of the products. The primary objective is the minimisation of the make span, but also setup-cost and setup-time is contemplated. A hybrid approach, using constraint logic programming (CLP), is presented and compared to a genetic algorithm (GA). These solution methods are conceptually different and recommendations for their applicability are presented.

Simulation-based approach to design a multi-stage flow-shop in steel works

This paper describes a new simulation-based approach for redesign of a multi-stage flow-shop production line. A performance measure which represents an 'actual' production rate of a machine is defined in order to identify bottlenecks. A simulation procedure based on the proposed measure is developed in order to determine how best to alleviate the bottlenecks. This new approach is applied for redesigning an actual large-scale multi-stage flow-shop production line in the authors' steelworks and they can determine the proper specifications of this line which can achieve an increase of productivity with a minimal investment. This approach is practical and powerful for redesign of a multi-stage flow-shop production line. >