In the event-based modeling formalism a system is modeled by defining the changes that occur at event times and the system dynamics can be described using an ‘Event Graph’. An event graph model is a network of event nodes describing the events that take place in the system and the relationships among these events. In the paper we extend the Event Graph framework by proposing an event-graph modeling formalism suitable to represent discrete event simulation models developed through the object-oriented approach, named Object-Oriented Event Graph (OOEG). The importance of Object–Oriented simulation is recently growing under the Industry 4.0 paradigm, in which optimization via simulation, real-time simulation, automatic decisions systems based on simulation, on line scenario analysis play a relevant role. The OOEG representation provides the same compactness of the EG representation. The advantage of the OOEG representation is that allows supporting a modelling methodology where systems are described by linking components analogously as the system components are linked, and the dynamic of the systems can be visualized in terms of interactions between objects, which have their physical correspondence in the real world.
ALS (Assembly Line Simulator), is a Java based simulator for mixed model assembly lines. ALS is the result of the ALS Research Project, and has been developed with the scope to be usable by researchers and practitioners. With respect to the preceding version of ALS (version 2.2), version 2.3 provides the following features: - Outputs related to the detailed timeline of each WS have been added for the straight line version (library and application versions) - The Graphical User Interface has been updated
Line balancing and job rotation have been recently found to be effective methods to reduce workers' ergonomic risks since the design/planning phase of assembly lines, and at the same time to limit performances deterioration and/or costs increments. As far as we know, there are no papers in the literature that deals with the problem of assigning rest times to workers in assembly lines with the aim to reduce their ergonomic workload. In the paper the problem of assigning rest times to workers in assembly lines is considered (Rest Time Assignment Problem). We show how rest times assignment affects ergonomic risk of workers in mixed-model assembly lines when this is evaluated through the Occupational Repetitive Action index (OCRA), and suggest and approach to simultaneously find solutions for the Assembly Line Balancing Problem and the Rest Time Assignment Problem.
In the paper we proposed and tested on a real industrial case, related to a company in the segment of Agricultural Equipment, an approach to design asynchronous assembly lines in compliance with ergonomic legislation. We considered the OCRA index as method for ergonomic risk assessment, as it is the preferred method indicated in international norms for detailed risk assessment related to handling of low loads at high frequency. A genetic algorithm approach able to integrate the ergonomic risks evaluation and balancing/sequencing is proposed. The approach allow designing line configurations taking into account many characteristics of the complex scenario of real industrial cases: mixed models assembly lines, stochastic task times, precedence constraints among tasks, equipment and line feeding duplication costs associated to parallel workstations. Thanks to the integration of a discrete event simulator, it is also possible to consider the effect of blocking and starvation phenomena on the effective cycle time and on worker’s ergonomic load. The respect of ergonomic norms is often view by companies as an onerous obligation, being often associated to the increase of required manpower. Results show that, using the proposed approach, extra costs due to the compliance with ergonomic legislation can be very limited. This should encourage companies to adopt design methodologies able at the same time to comply with ergonomic norms and to defend their profitability.
In the paper a genetic algorithm approach is proposed to balance asynchronous mixed-model U-shaped lines with stochastic task times. U-shaped lines have become popular in recent years for their ability to outperform straight assembly lines in terms of line efficiency. The great majority of studies in the literature deal with paced synchronous U-shaped lines. Asynchronous lines can be more efficient than synchronous lines, but are more difficult to study, due to blocking and starvation phenomena caused by the variability of completion times: this makes it difficult to calculate the effective throughput. This variability, that in straight lines comes from the stochastic nature of task times and from the changing of models entering the line, is even higher in U-shaped lines, where an operator can work at two different models in the same cycle at the two sides of the line. For this reason, the genetic algorithm proposed is coupled to a parametric simulator for the evaluation of the objective function, which contains the simulated throughput. Two alternative chromosomal representations are tested on an ample set of instances from the literature. The best solutions are also compared with the best solutions known in the literature, on the same instances, for straight lines with buffers and parallel workstations. From the comparison it turns out that U-shaped lines are generally more efficient with respect to straight lines with buffers. This is because crossover work centers naturally act similarly to unitary buffers, providing two places in which two loads can be placed simultaneously. The superiority of U-shaped lines holds true as long as it is possible to take full advantage of the employment of crossover work centers. For particular types of instances, depending on the distribution of task times, this possibility decreases, so that straight lines with parallel workstations and buffers are preferable.
ALS (Assembly Line Simulator), is a Java based simulator for mixed model assembly lines. ALS is the result of the ALS Research Project (http://impianti.dii.unipg.it/tiacci/English/research/ALS.html), and has been developed with the scope to be usable by researchers and practitioners. With respect to the preceding version of ALS (version 2.1), version 2.2 provides the following features: - Bug correction for random sequence of models entering U-shaped lines - The possibility to indicate different coefficients of variation for each task (library version) - Javadoc documentation (library version) available - The possibility to copy and paste from/to task times and line configuration tables from/to Excel spreadsheets (application version)
In the paper a genetic algorithm approach to form potential Collaborative Networked Organizations (CNOs) is presented. When analyzing a set of companies that are potential partners of a CNO, it is possible to collect specific data from each company through which evaluate, once aggregated, for which Strategic Objective (SO) the potential aggregation is most suited. At this purpose a metric, consisting in a set of performance parameters related to different SO types, has been created. Given a large number of companies, through a genetic algorithm approach is then possible to set a specific objective function related to a particular SO (eg. maximize potential creation of new Business Opportunities), and to find the cluster (or clusters) of companies that maximizes the objective function.
In this paper an event and object oriented simulator for assembly lines is presented. The tool, developed in Java, is capable to simulate mixed model assembly lines, with stochastic task times, parallel stations, fixed scheduling sequences, and buffers within workstations. The simulator is a flexible supporting tool in finding solution of the mixed model assembly line balancing problem (and the optimal sequencing and buffer allocation problems associated to it). It is capable to immediately calculate the throughput of a complex line, by simply receiving as inputs three arrays representing: the task times durations, the line configuration (number of workcentres, of buffers within them, of parallel workstations in each workcentre, of tasks assigned to each workcentre), and the sequence of models entering the line. Its fastness and flexibility allow its utilization in those algorithms and procedures where the evaluation of a fitness function (which includes the throughput as performance indicator) has to be performed several times. It allows overcoming the limit of using others measures of throughput, presented in literature, that are poorly correlated to its real value when the complexity of the line increases. The simulator is an expandable tool; in its current version provides the possibility to simulate both straight and U-shaped lines, and to generate both random and fixed sequences of models entering the line.
ALS (Assembly Line Simulator), is a Java based simulator for mixed model assembly lines. ALS is the result of the ALS Research Project (http://impianti.dii.unipg.it/tiacci/English/research/ALS.html), and has been developed with the scope to be usable by researchers and practitioners. With respect to the preceding version of ALS (version 1.0), version 1.1 provides the following features: - Inputs related to simulation length: now it is possible to indicate or the length in time units, or the number of loads that have to be completed; - Outputs related to buffers utilization statistics (time persistent average value; maximum value)
To evaluate performances of U-shaped un-paced mixed model assembly line may be complicated. This complication is a result of blockage and starvation caused by the arrival of different models to the line, having different assembly time requirements at each station. Considering the throughput as the main operational design objective, the effects of these phenomena on line throughput are very difficult to evaluate. Unfortunately, its evaluation is fundamental in almost all procedures and algorithms developed to solve U-MALBP (U-shaped Mixed-model Assembly Line Balancing Problem), since the estimation of objective functions that includes performance indicators is often required. The only practical method to accurately evaluate throughput is a simulation study, which is very time consuming and hard to perform. For this reason, instead, various performance measures, not simulation based, have been presented in literature in order to evaluate and compare design alternatives. Unfortunately, these performance measures are often poorly correlated to the real value of the throughput when the complexity of the line increases. The aim of the Assembly Line Simulator Project is to overcome the limit of using performance measures instead of simulated throughput, by developing a parametric simulator for mixed model lines that can quickly simulate different line configurations and can be used iteratively in algorithms and procedures. In the paper, the last version of the simulator, designed for U-shaped lines, is presented and tested.
ALS (Assembly Line Simulator), is a Java based simulator for mixed model assembly lines. ALS is the result of the ALS Research Project (http://impianti.dii.unipg.it/tiacci/English/research/ALS.html), and has been developed with the scope to be usable by researchers and practitioners. With respect to the preceding version of ALS (version 2.0), version 2.1 provides the following features: - The possibility to generate a random input sequence of models, respecting a specified demand proportion of models.
In the paper, an innovative approach to deal with the Mixed Model Assembly Line Balancing Problem (MALBP) with stochastic task times and parallel workstations is presented. At the current stage of research, advances in solving realistic and complex assembly line balancing problem, as the one analyzed, are often limited by the poor capability to effectively evaluate the line throughput. Although algorithms are potentially able to consider many features of realistic problems and to effectively explore the solution space, a lack of precision in their objective function evaluation (which usually includes a performance parameter, as the throughput) limits in fact their capability to find good solutions. Traditionally, algorithms use indirect measures of throughput (such as workload smoothness), that are easy to calculate, but whose correlation with the throughput is often poor, especially when the complexity of the problem increases. Algorithms are thus substantially driven towards wrong objectives. The aim of this paper is to show how a decisive step forward can be done in this filed by coupling the most recent advances of simulation techniques with a genetic algorithm approach. A parametric simulator, developed under the event/object oriented paradigm, has been embedded in a genetic algorithm for the evaluation of the objective function, which contains the simulated throughput. The results of an ample simulation study, in which the proposed approach has been compared with other two traditional approaches from the literature, demonstrate that significant improvements are obtainable.
The buffer allocation problem (BAP) and the assembly line balancing problem (ALBP) are amongst the most studied problems in the literature on production systems. However they have been so far approached separately, although they are closely interrelated. This paper for the first time considers these two problems simultaneously. An innovative approach, consisting in coupling the most recent advances of simulation techniques with a genetic algorithm approach, is presented to solve a very complex problem: the Mixed Model Assembly Line Balancing Problem (MALBP) with stochastic task times, parallel workstations, and buffers between workstations. An opportune chromosomal representation allows the solutions space to be explored very efficiently, varying simultaneously task assignments and buffer capacities among workstations. A parametric simulator has been used to calculate the objective function of each individual, evaluating at the same time the effect of task assignment and buffer allocation decisions on the line throughput. The results of extensive experimentation demonstrate that using buffers can improve line efficiency. Even when considering a cost per unit buffer space, it is often possible to find solutions that provide higher throughput than for the case without buffers, and at the same time have a lower design cost.
This file contains the best solutions related to Experiment 2 found by the Genetic Algorithm presented in the paper "Coupling a genetic algorithm approach and a discrete event simulator to design mixed-model un-paced assembly lines with parallel workstations and stochastic task times"
This file contains the best solutions related to Experiment 2 found by the GA and GA+BUF algorithms presented in the paper by the title "Simultaneous balancing and buffer allocation decisions for the design of mixed-model assembly lines with parallel workstations and stochastic task times"
This file contains the best solutions related to Experiment 1 found by the GA and GA+BUF algorithms presented in the paper by the title "Simultaneous balancing and buffer allocation decisions for the design of mixed-model assembly lines with parallel workstations and stochastic task times"