Job Shop Production System Research Articles

Using a semi-automated job-shop production system model to prepare students for the challenges of Industrial Cyber-Physical Systems

Abstract Though the engineering methods, tools, and standards for Industrial Cyber-Physical Systems (ICPS) have become mature and get more defined, the challenges to apply them to develop production systems towards ICPS remain difficult to grasp for experts and practitioners and even more for students. “Digitalization and Virtualization of ICPS” (DVoICPS) is a subject relevant to different student groups in informatics, electronics, mechanical engineering, and others. To teach the idea behind DVoICPS and to sensitize students to the different challenges of the cyber and of the physical world, a semi-automated job-shop production system (model) was adopted. Three types of products were manufactured with different operations. The student tasks included evolving the legacy system toward an ICPS system with improved production performance indicators. The improvements had to be feasible for the physical model, consisting of fischertechnik© elements. Students also had to describe modules of the system as ICPS components and build an executable virtual model of the evolved system, simulating it. Additionally, the students needed to plan and describe aspects for digitalizing the system itself. This paper describes the learning concept of the teaching module, the challenges of teaching students with different disciplinary backgrounds, and the students’ deliverables and learning effects. The paper concludes with summary and proposed changes to the teaching concepts for the next round of teaching.

Programming of Job Shop Production Systems with Fuzzy Logic

This article proposes a decision-making algorithm based on the "fuzzy logic" as an optimization technique, which allows to find a good solution to the problem of determining the priority (sequencing) of service or manufacture of jobs in the programming of intermittent production systems, Job Shop. The combinatorial nature and complexity of the problem motivates the exploration of other alternatives solutions to the traditionally used. Initially, the fuzzy logic controller structure (number of input variables, rules and output) is determined in accordance with the objective functions to be optimized. Triangular membership functions are selected for the batch size, the delivery date, the processing time, the number of tools required in each operation, and the priority of the processing the jobs. The fuzzy rules base is defined, and the controller model is formulated (fuzzification, evaluation and defuzzification). The algorithm is developed in Matlab’s ®Simulink ®Fuzzy logic toolbox, achieving better results than those obtained with other methods.

Bi-objective optimization of a job shop with two types of failures for the operating machines that use automated guided vehicles

Reliability of machinery and equipment in flexible manufacturing systems are among the most important issues to reduce production costs and to increase efficiency. This paper investigates the reliability of machinery in job shop production systems, where materials, parts, and other production needs are handled by automated guided vehicles (AGV). The failures time of the parallel machines in a given shop follow either an exponential or a Weibull distribution. As there is no closed-form equation to calculate the reliability of the shop in the Weibull case, a simulation approach is taken in this paper to estimate the reliability. Then, a bi-objective nonlinear optimization model is developed for the problem under investigation to maximize shop reliability as well as to minimize production time, simultaneously. In order to assess the efficacy of the proposed model, some random instances are generated, based on which two meta-heuristic algorithms called non-dominated sorting cuckoo search (NSCS) and multi-objective teaching–learning-based optimization (MOTLBO) are designed. Finally, to evaluate and compare the effectiveness of the proposed solution algorithms, an efficient solution AHP-TOPSIS technique is utilized.

Simulation modelling and analysis of dispatching rules in an assembly job shop production system with machine breakdowns

Simulation modelling and analysis of dispatching rules in an assembly job shop production system with machine breakdowns

Simulation modelling and analysis of dispatching rules in an assembly job shop production system with machine breakdowns

This paper addresses the scheduling problem in assembly job shop systems with machine breakdowns. The present study intends to rank the dispatching rules that are applied in an assembly job shop production system. A simulation model of an assembly job shop is developed for the purpose of this ranking and eight dispatching rules from the literature are incorporated in the simulation model. The product structures considered in this study are single-level assembly structure, two-level assembly structures and three level assembly structures. The machines are subjected to non-availability due to of breakdowns. Five performance measures are considered for analysis. The performance of each dispatching rule for each performance measure is calculated individually. To identify which of the dispatching rule provides the optimum result when all the performance measures are equally important, grey relational analysis is adopted to rank the dispatching rules. The results indicate that earliest completion time rule performs better in comparison with the other rules investigated in this study for single-level structure with multiple objectives.

Dynamic due date assignment method

Purpose The purpose of this paper is to determine the interaction between dynamic due date assignment methods and scheduling decision rules in a typical dynamic job shop production system in which setup times are sequence dependent. Two due date assignment methods and six scheduling rules are considered for detailed investigation. The scheduling rules include two new rules which are modifications of the existing rules. The performance of the job shop system is evaluated using various measures related to flow time and tardiness. Design/methodology/approach A discrete-event simulation model is developed to describe the operation of the job shop. The simulation results are subjected to statistical analysis based on the method of analysis of variance. Regression-based analytical models have been developed using the simulation results. Since the due date assignment methods and the scheduling rules are qualitative in nature, they are modeled using dummy variables. The validation of the regression models involves comparing the predictions of the performance measures of the system with the results obtained through simulation. Findings The proposed scheduling rules provide better performance for the mean tardiness measure under both the due date assignment methods. The regression models yield a good prediction of the performance of the job shop. Research limitations/implications Other methods of due date assignment can also be considered. There is a need for further research to investigate the performance of due date assignment methods and scheduling rules for the experimental conditions that involve system disruptions, namely, breakdowns of machines. Practical implications The explicit consideration of sequence-dependent setup time (SDST) certainly enhances the performance of the system. With appropriate combination of due date assignment methods and scheduling rules, better performance of the system can be obtained under different shop floor conditions characterized by setup time and arrival rate of jobs. With reductions in mean flow time and mean tardiness, customers are benefitted in terms of timely delivery promises, thus leading to improved service level of the firm. Reductions in manufacturing lead time can generate numerous other benefits, including lower inventory levels, improved quality, lower costs, and lesser forecasting error. Originality/value Two modified scheduling rules for scheduling a dynamic job shop with SDST are proposed. The analysis of the dynamic due date assignment methods in a dynamic job shop with SDST is a significant contribution of the present study. The development of regression-based analytical models for a dynamic job shop operating in an SDST environment is a novelty of the present study.

Adaptive job shop control based on permanent order sequencing

Shop floors organized as job shops are indispensable for many single- and small-series productions with low repetition rates. Due to its high complexity regarding production control, job shop production systems have efficiency disadvantages compared to other forms of organization. This paper presents a new approach for a job shop control system which bases on a deviation-caused, autonomous adaption of the production schedule, aiming at low production costs.

Mixed integer lineal programming model to schedule flexible job-shop systems in make to order environments

Introduccion: Los sistemas de produccion Job Shop (JS) se caracterizan porque los trabajos a ser procesados tienen diferentes rutas de produccion. Una generalizacion de este tipo de sistemas es el Job Shop Flexible (FJS), en el cual se cuenta con mas de una maquina por estacion para realizar alguna de las operaciones.Objetivo: El objetivo de este proyecto fue proponer un modelo de programacion lineal entera mixta para programar sistemas FJS con el objetivo de minimizar el numero de trabajos tardios.Metodologia: El modelo se desarrollo utilizando un enfoque basado en variables de secuencia-posicion. Este enfoque utiliza variables binarias para decidir si una operacion determinada es asignada a una posicion en la secuencia de procesamiento de la maquina asignada. Para validar el desempeno del modelo se utilizaron datos de una pequena empresa con un sistema productivo tipo FJS que desarrolla sus operaciones en un entorno bajo pedido (Make to Order, MTO). Por esta razon los indicadores de desempeno mas importantes para la empresa son los asociados al nivel de servicio.Resultados: Los resultados muestran un desempeno razonable en terminos del objetivo que se persigue. Se encontro el programa optimo de produccion en menos de 3600 segundos en instancias de menos de 14 ordenes de produccion. En instancias mas grandes su obtuvieron soluciones factibles en el tiempo limite definido.Conclusiones: El modelo permite definir programas de produccion en sistemas en los cuales el cumplimiento de fechas de entrega es de vital importancia. Los resultados han permitido que la empresa mejore su desempeno y disminuya los costos asociados a los incumplimientos. Futuras investigaciones se pueden desarrollar para encontrar metodos de solucion mas eficientes con relacion a los tiempos computacionales para obtener soluciones de instancias de mayor tamano.

MODELLING AND SIMULATION OF PRODUCTION FLOW IN JOB-SHOP PRODUCTION SYSTEM WITH ENTERPRISE DYNAMICS SOFTWARE

MODELLING AND SIMULATION OF PRODUCTION FLOW IN JOB-SHOP PRODUCTION SYSTEM WITH ENTERPRISE DYNAMICS SOFTWARE

Investigating the effect of order release policies in scheduling a dynamic job shop production system: simulation modelling and analysis

Investigating the effect of order release policies in scheduling a dynamic job shop production system: simulation modelling and analysis

Adaptive Job-Shop Control Using Resource Accounts

The control of todays production systems has become more complex due to an increasing number of products, product variants, randomly incoming orders and non-standardized production processes. Specifically job-shop production is indispensable for single- and small-series- productions with low repetition rates. However, their productivity yields disadvantages compared with flow production. Self-regulating methods of production control like the Decentralized WIP-oriented manufacturing control (DEWIP) by Loedding [2] offer potential for compensating this disadvantages. Though, with an increasing variety in order sequence, it is getting increasingly difficult to estimate the effects of control activities on other orders in the following production flow. Thus, job-shop production with widely ramified material flows is usually organized manually. Decisions to overcome disturbances and deviations from the production plan are made locally by foremen or workers. Those decisions base on their experience and know-how, however with the same deficit regarding the estimation of the subsequent production process concerning disruptions. This paper presents a work in process (WIP) regulating method of production control which has been designed specifically for the requirements of typical job-shop productions. It combines the advantages of load balancing over the complete production, as proposed by Bechtes Load-Oriented Order Release [3] with the adaptivity of decentralized production control, like in DEWIP. By balancing current and prospective utilization via so-called resource accounts, bottlenecks are identified before they appear on the shop floor. Thus, alternative material flows bypassing the bottleneck processes can be activated adaptively as well as measures to extend the capacity can be undertaken. This paper presents the concept and its elaboration to a practical method for job-shop control. In addition, it was implemented in a simulation model in order to demonstrate the suitability and the effectiveness for production control in manual job-shop production systems.

Simulation and Comparison Analysis of Due Date Assignment Methods Using Scheduling Rules in a Job Shop Production System

Simulation and Comparison Analysis of Due Date Assignment Methods Using Scheduling Rules in a Job Shop Production System

Metodología selectiva de dinámica poblacional para optimizar un ambiente multiobjetivo de producción job shop

El presente artículo desarrolla una metodología basada en genética poblacional que permite mejorar el desempeño de dos o más variables en un sistema de producción job shop. La metodología aplica un algoritmo genético con características especiales en la selección de individuos que pasan de generación en generación. Los resultados permitieron demostrar mejores desempeños de la metodología propuesta en las variables makespan, tiempo muerto y costo de energía al ser comparada con el método FIFO. Al comparar la metodología con el método NSGA II no se obtuvieron diferencias en las variables makespan y tiempo muerto; sin embargo, se obtuvo un mejor desempeño en el costo de la energía y, principalmente, mayor eficiencia en relación al número de iteraciones realizadas para obtener el makespan óptimo.

Enhancement of Job Shop Scheduling with Time Windows Using a Wise Select Ant Colony Optimization

Provided with efficiency and effectiveness, Ant colony optimization (ACO) is a well applied heuristic in a wide variety of production scheduling environment. This study, for improving the touch panel assembly line problem, proposes a new type of state transition rule into the traditional ACO, and names the definite characteristic of ACO as “wise select ant colony optimization (WSACO).” Because numerous job shop scheduling is common in the actual production situation, this study chooses touch panel assembly line with time window problem as the subject. This study, in the job shop production system, utilizes time window and sequence dependent setup time for constraints, and solves the problem in real world. Finally, simulated data confirm the effectiveness and robustness of the proposed algorithm. The data test results demonstrate that WSACO has potential to replace ACO and become a significant heuristic algorithm for this kind of problem.

Applying Bond Graphs for Modelling the Manufacturing Dynamics

Supply chain dynamics and execution control seem not to receive as much attention as the domain of supply chain planning by means of static optimization models. The dynamics of manufacturing systems also deserves attention, since they are the fundamental pieces of a supply chain. In this paper, a dynamic model for the production control of a multi-product manufacturing system is developed using the Bond Graph modeling methodology. The proposed model is applied to depict the dynamics of a real job shop production system of propylene bags, where the control objective is to adjust the processing frequency of the machines to attend the demands of the products, while keeping the work in process at the desired levels. Exploratory simulations were carried out, and the preliminary results showed that the system could be autonomously controlled. Some advantages of the Bond Graph methodology are also highlighted.

Recent development of cellular manufacturing systems

Cellular manufacturing system has been proved a vital approach for batch and job shop production systems. Group technology has been an essential tool for developing a cellular manufacturing system. The paper aims to discuss various cell formation techniques and highlights the significant research work done in past over the years and attempts to points out the gap in research.

Schedule compliance operating curves and their application in designing the supply chain of a metal producer

Schedule compliance is one of the main logistic objectives of production systems. In order to achieve high schedule compliance, different control variables of a job shop production system can be adopted. This paper focuses on the relationship between such control variables in make-to-order processes. It provides a quantitative model to describe the effect of safety time as well as the impact of lateness distribution of the production system on schedule compliance, stock level of finished goods and delivery time. Furthermore, the applicability of this model is demonstrated by describing the results of an industry project. In this project, the model was applied to design the supply chain of a metal manufacturer to increase the capability to produce on time.

Ant colony optimization for job shop scheduling using multi-attribute dispatching rules

This paper proposes a heuristic method based on ant colony optimization to determine the suboptimal allocation of dynamic multi-attribute dispatching rules to maximize job shop system performance (four measures were analyzed: mean flow time, max flow time, mean tardiness, and max tardiness). In order to assure high adequacy of the job shop system representation, modeling is carried out using discrete-event simulation. The proposed methodology constitutes a framework of integration of simulation and heuristic optimization. Simulation is used for evaluation of the local fitness function for ants. A case study is used in this paper to illustrate how performance of a job shop production system could be affected by dynamic multi-attribute dispatching rule assignment.

Scheduling and buffer reduction algorithm for job shop production systems

Buffer management is a complex problem in the operation of production systems. Poor buffer control may cause congestion and may increase work in process and cycle times. This paper proposes a methodology which attempts to reduce the buffer requirements in job shop production systems. The first step of this approach consists of finding a near-optimal schedule. Next a number of right-shift and left-shift movements are performed within the schedule with the objective of reducing the amount of required buffer slots. The initial schedule is calculated with modified simulated annealing algorithm; the second step is essentially changing the start and/or finish operation dates without changing neither the sequence of jobs on each machine nor the value of the objective function. This last step uses machine blockings to further reduce the amount of buffers. The procedure was tested on a number of benchmark problems from the literature. The obtained results show up to 36% in buffer reduction.

A lean-based ORR system for non-repetitive manufacturing

Lean implementations are no longer limited to high-volume production and are becoming increasingly common in low-volume, high-variety non-repetitive companies. Such companies, usually with make-to-order or engineer-to-order production, have normally been modelled with a job shop production system, but many of them actually have a dominant flow in production. Moreover, one of the main characteristics of lean implementation is that it streamlines production flow, makes it unidirectional and reduces setup and lot size. Consequently, a significant number of production systems are better modelled as flow shops, rather than as job shops. This has an impact on production management approaches, and in particular on order review and release systems. In fact, ORR systems have been designed with job shops in mind, because they are the most complex systems to manage, and because they are considered the optimal system for non-repetitive production. We believe that job shop designed ORR systems are not the best ones for flow shop systems. We consequently propose a new ORR system designed for non-repetitive production in flow shops and based on lean principles. The simulation campaign run to test the new model shows that it yields lower lead times and increases output.