Flow Shop Production Research Articles

Scheduling of Flow Shop Production Systems with Particle Swarm Optimization

El control de motores de DC con métodos de conmutación es un dispositivo interesante con muchas aplicaciones prácticas. Un convertidor reductor-elevador DC-DC es uno de los métodos utilizados para operar el motor DC. Aunque el convertidor reductor-elevador de CC para aplicaciones de motores de CC se ha estudiado ampliamente, tiene algunas desventajas. En este estudio, la estructura del inversor multinivel que es diferente de los métodos de conducción del motor de CC se utiliza para impulsar el motor de DC. No habrá elementos pasivos en el circuito propuesto aunque los convertidores dc-dc tienen elementos pasivos. Por lotanto, mientras se eliminarán las deficiencias para las cargas que necesitan alta corriente, se pueden evitar las desventajas de la conmutación. La corriente, el par y el voltaje en la carga también serán creados por el Método de Amplitud de Ancho de Pulso (PWAM). A diferencia de otros métodos de modulación de ancho de pulso (PWM); el par, la corriente y el voltaje del motor se pueden controlar con PWAM en los ejes horizontal y vertical. En la etapa de diseño; La estructura y operación del interruptor inversor se dan después del método de PWM producido introducido. Las ecuaciones de los voltajes que ocurrirán en las cargas se crean de acuerdo con la estructura de trabajo de los interruptores. También se da la estructura del motor de DC. En la etapa desimulación; Se realizan modos continuos y discontinuos del circuito propuesto. Luego, el par del motor de DC, la corriente de armadura y la velocidad se prueban de acuerdo con la conmutación a diferentes frecuencias y diferentes índices de modulación.

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.

Sustainable scheduling: Development and application of an integrated method combining NEH heuristic and life cycle assessment

ABSTRACT This paper has developed a model to perform more sustainable scheduling in manufacturing operations based on the combination of makespan, profit margin, and environmental impact indicators. A case study was designed to run the developed model in the production of automotive identification labels. The proposed model combined the NEH heuristics for a flow-shop production scheduling problem followed by the cradle-to-gate Life Cycle Assessment of the labels, in order to find scheduling solutions for minimising major environmental impacts and aggregate more value to the production flow. The main results showed that the proposed model was able to increase the company’s schedule performance. For the scenario analysis, two-hybrid heuristics were compared: NEHS-1 and NEHS-2, and both showed better schedule results than the classical NEH. However, the best results were presented by the NEHS-2, and it should be further improved with local search heuristics, in future research.

Real-time admission control in a queue-time loop production system

The typical flow shop production system with queue time constraints consists of a queue-time loop with multiple stages and a limit on queue time between two consecutive stages. This paper proposes a mixed integer linear programming (MILP) model to address the dynamic environment such as job arrivals and machine failures of the production system. We determine the admission control decision at each stage in a queue-time loop and then reschedule the production after the real-time status of job arrivals and machine availability are updated. The combinatorial Benders’ cuts (CBC) is used to solve the MILP model which decomposes the variables into integer and continuous parts. In order to reduce the model’s size, the phase-step method (PS) is proposed and then is combined with the CBC, called CBC-PS method. Further, the production schedule of a multistage queue-time loop is generated. We find that the CBC-PS method can reduce up to 39.1% of the number of scrap jobs compared to first-in-first-out (FIFO), threshold dispatching (TH), and reaction chains (RC) heuristics.

The impact of local search strategies on chaotic hybrid firefly particle swarm optimization algorithm in flow-shop scheduling

A flow-shop production environment is a widely used production setting in the industry, especially preferred when the variety of products and processes is limited. The constant development of technology and the increasing competition in the market compel researchers to seek ways to increase efficiency. Therefore, scheduling remains an important area of study for researchers. When the size of the problem increases, particularly in real-life contexts, it becomes almost impossible to work out a feasible solution to scheduling problems through current computer technologies. In such cases, solutions are often developed using heuristic or meta-heuristic methods. However, the performance of these methods may vary according to the problem or the components of the suggested method of solution.This study aimed to investigate the effect of local search strategies on the solution performance of hybrid firefly and particle swarm optimization algorithm, developed using chaotic local search. We used four different solution methods: the triple local search strategy consisting of insertion, swap and inversion, which were used frequently in earlier studies, and the individual use of each of these three strategies. In this study, the average relative difference values calculated using insertion, swap, inversion and triple strategy were found to be 3.03, 1.27, 2.54 and 1.20, respectively. As a result of the analyses carried out to compare the results of the solutions obtained in the flow-shop scheduling problems of Taillard, it was found that better results were obtained using the triple strategy. These findings can be a point of reference for future research.

Simulation-based optimization of the polca ordering system

Given an increase in consumer demand for product quality, companies need to continually improve their means of production. The use of computational resources assists companies to choose an ordering system that best suits their reality. In this sense, the present study aims to analyze and compare the performance of the Paired-cell Overlapping Loops of Cards with Authorization (POLCA) system, according to pre-established parameters in a real automobile company case, which has a flow-shop production environment. In order to do this, the research has a hypothetical-deductive scientific explanation. Also, the quantitative approach, and the experimental research procedure were employed due to the use of simulation and optimization. The computer simulation was performed using ProModel®. The initial model was optimized, and the results of the two elaborated scenarios were compared. It was verified that the optimized scenario showed improvement in the average total output of the system. The simulation of the optimized model presented an increase in production of approximately 95.29% when compared to the initial scenario. Nevertheless, trade-offs were verified. It is noticeable in the scenario analyzed that to increase the production of axles, the use of intermediate stocks must be increased. Finally, the present research contributes to the academic community since it proposed the study of an ordering system that has a limited number of studies, mainly in Brazil. It also contributes to the business community by encouraging the use of simulation in companies so that a better performance analysis of ordering systems can be performed prior to actual deployment.

Flow shop optimization of hybrid make-to-order and make-to-stock in precast concrete component production

As the world environment is faced with the severe challenge of global warming, prefabricated construction is the path for the construction industry towards green production. Precast component manufacturers produce standardized and non-standardized components simultaneously. Non-standard components are produced make-to-order (MTO). Standard components are produced make-to-stock (MTS). In this study, the flow shop precast component production scheduling problem under a hybrid MTO-MTS environment is studied. A hybrid MTO-MTS precast component flow shop production model is developed. The object is to minimize total tardiness and earliness of delivery. An iterative job insertion algorithm based on whale optimization is developed. The proposed method involves two stages: scheduling and insertion. The first is to optimize the scheduling of customized MTO components. Then the standard MTS job is inserted. Through the computational evaluation, the performance and effect of the proposed approach are tested. It can conclude that the proposed algorithm contributes to the effective and efficient optimization of Hybrid MTO-MTS precast component production.

Optimized flowshop scheduling for precast production considering process connection and blocking

Precast production scheduling is a key issue in prefabricated construction. Process connection and blocking are two characteristics of the flowshop production of precast components; however, they have received little attention in research on production scheduling, which may prevent actual production from following optimized scheduling schemes. In this study, an optimization model for the production scheduling of precast components was established considering process connection and blocking for minimizing the total penalty cost of earliness and tardiness. A solution for the model was designed and applied to a practical case for validation. The results indicate that the improved scheduling scheme can reduce the penalty cost and facilitate on-time delivery of precast components under two scenarios: tardiness partially allowed and no tardiness allowed. The proposed model and method can help precast plants to improve efficiency and reduce cost in addition to enhancing the practicality of precast production scheduling schemes.

Assembly line for the production of light gauge steel frame modular housing

Abstract: For manufactured homes, the Light Gauge Steel Frame system does not have an in-depth study on the relationship of its assembly techniques and the behavior of the production system based on assembly lines. Thus, this article presents the development of a flow shop production system using assembly lines for the production of Light Gauge Steel Frame modular houses. The main result was that the system was able to deliver a house in a little more than eight working hours, using four-hour cycles. For this, the article made use of experimental research procedure with the use of computational simulation. The abstracted model of the designed system was simulated, and its behavior analyzed concerning the hypothesis of improvement proposed in four different scenarios. The results can be considered relevant because it fills gaps in the body of knowledge in question with the use of the Light Gauge Steel Frame as a technology for the production of manufactured houses from assembly lines, also providing parameters of performance and design for the implementation of the proposed solution in the real estate market.

Modified multi-objective simulated annealing algorithm for scheduling a flow shop production system with setup times

Modified multi-objective simulated annealing algorithm for scheduling a flow shop production system with setup times

Minimization of total tardiness in no-wait flowshop production systems with preventive maintenance

Efficient business organizations must balance quality, cost, and time constraints in competitive environments. Reflecting the complexity of this task, we consider manufacturing systems including several stages of production chains requiring time measurement. When production scheduling is not prioritized in such enterprises, several negative effects may occur. A corporation may suffer financial penalties as well as negative brand exposure, and thus may find its credibility challenged. Therefore, in this study, we propose constructive methods to minimize a total tardiness criterion, considering preventative maintenance constraints to reflect the reality of industrial practice, focusing on a no-wait flowshop environment in which jobs are successively processed without operational interruptions. In addition to proposing constructive methods to solve the no-wait flowshop production scheduling problem, a metaheuristic is presented as an approach to improve results obtained by constructive methods. Computational experiments were designed and performed to compare several production scheduling algorithms. Among various constructive heuristics considered, an algorithm called HENLL using an insertion logic showed the best performance. The proposed metaheuristic is based on the iterated greedy (IG) search method, and the results obtained demonstrated significant improvement compared to the heuristics alone. It is expected that this study may be used by production planning and control (PPC) professionals to apply the proposed method to schedule production more efficiently. We show that the proposed method successfully presented a better solution in relation to total tardiness, considering the above mentioned environment.

A Deep Reinforcement Learning approach for the throughput control of a Flow-Shop production system

Abstract This paper proposes a new method for controlling a flow shop in terms of throughput and Work In Process (WIP). In order to achieve a throughput target, a Deep Q-Network (DQN) is used to define the constant WIP quantity in the system. The main contribution of this paper is the novel approach used to formulate the state, action space, and reward function. An extensive pre-experimental campaign is conducted to determine the best network structure and appropriate hyperparameter values. Finally, the system’s performance is compared to the known results of an analytical model from the literature (Practical Worst Case, PWC).

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.

Scheduling of Flow Shop Production Systems with Particle Swarm Optimization

This article proposes a decision-making algorithm as an optimization technique based on a particle swarm (particle swarm optimization-PSO), which allowsfinding a good solution to the problem of determining the priority (sequencing) of service or manufacturing of jobs scheduling of Flow Shop production systems. The combinatorial nature and complexity of the problem motivates the exploration of other alternative solutions to those traditionally used. Previously defined the objective functions to optimize and the size of the swarm, the position and velocity of the particles are initialized. The objective function is then calculated and the best individual and global positions in the swarm are determined. Finally, speed and position are updated, repeating this procedure according to the number of iterations proposed. The algorithm is developed in Microsoft® Excel® and ®Matlab,achieving better results than those obtained with other methods, such as the service factor, which increases by 19.9% for one machine and 20% for two machines.

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.

Model for Integrating Production Scheduling and Maintenance Planning of Flow Shop Production System

In this paper, a model for integrating production scheduling and maintenance planning is proposed for flow shop production system. The suggested model in this paper is based on the optimal jobs sequence for jobs that will be processed in multiple machines connected in series. The objective of this study is to find the optimal sequence for jobs while reducing the total production and maintenance costs. The model works by generating an initial solution using longest processing time (LPT) dispatching rule. Then, tabu search algorithm is established to obtain the optimal sequence for jobs. Computational experiments are performed on problems with five serially machines which are assigned to process eight diverse jobs from the same product family. The result is compared with the genetic algorithm optimization technique under individual PM scheme for obtaining superior solutions that has been proved in the literature to be one of the best approach. The computational results show that the recommended approach is qualified over the simulation based genetic algorithm optimization technique.

Optimal Sizing of Renewable Microgrid for Flow Shop Systems under Island Operations

This paper addresses a critical question pertaining to manufacturing sustainability: is it economically viable to implement an island microgrid to power a flow shop system under power demand and supply uncertainty? Though many studies on microgrid sizing are available, the majority assume the microgrid is interconnected with main grid. This paper aims to size wind turbine, photovoltaic and battery storage to energize a multi-stage flow shop system in island mode. A mixed-integer, non-linear programming model is formulated to optimize the renewable portfolio and capacity with the goal of minimizing the levelized cost of energy. The island microgrid is tested in three locations with diverse climate profiles. The results show that net zero energy flow shop production is economically feasible in the areas where the average wind speed exceed 8 m/s at 80-meter tower height, or the battery cost drops below $100,000/MWh. Sensitivity analyses are further carried out with respect to installation cost, demand response program, production scalability, and weather seasonality.

Development of a Heuristic for No-Wait Flowshop Production Process to Minimize Makespan

Development of a Heuristic for No-Wait Flowshop Production Process to Minimize Makespan

A decentralized coordination algorithm for multi-objective linear programming with block angular structure

ABSTRACT This article considers linear multi-objective programming problems with block angular structure, which are analogous to multi-disciplinary optimization environments where disciplines must collaborate to achieve a common overall goal. In this decentralized environment, a mechanism to guide locally optimized decision makers’ solutions to a Pareto-optimal solution without sharing the entire local information is developed. The mechanism is based on an augmented Lagrangian approach to generate a solution and is separated into two phases: phase I determines an ideal point for each of the single objectives and phase II searches for a compromise solution starting from a single ideal point. Theoretical results show that the algorithm converges and the solution generated is Pareto optimal. The algorithm’s effectiveness is demonstrated via an illustrative example and a real-world bi-objective re-entrant flow-shop production planning problem. The real-world experimental results showed that the decentralized method had an average 50% better performance compared to other centralized methods.

Trade-off balancing between maximum and total completion times for no-wait flow shop production

We propose a trade-off balancing (TOB) heuristic in a no-wait flow shop to minimise the weighted sum of maximum completion time () and total completion time (TCT) based on machine idle times. We introduce a factorisation scheme to construct the initial sequence based on current and future idle times at the operational level. In addition, we propose a novel estimation method to establish the mathematical relationship between the objectives min() and min(TCT) at the production line level. To evaluate the performance of the TOB heuristic, computational experiments are conducted on the classic Taillard's benchmark and one-year historical data from University of Kentucky HealthCare (UKHC). The computational results show that minimisations of and TCT yield inconsistent scheduling sequences, and these two sequences are relatively uncorrelated. We also show that our TOB heuristic performs better than the best existing heuristics with the same computational complexity and generates stable performances in balancing trade-offs.