Academic articles

In many industrial manufacturing companies, energy has become a major cost factor. Energy aspects are included in the decision-making system of production line planning and control to reduce manufacturing costs. For this priority, the simulation of production line processes requires not only the consideration of logistical and technical production factors but also the integration of time-dependent energy flows. A hybrid (multimethod) simulation shows the complex interactions between material flow and energy usage in production close to reality. This paper presents a simulation approach combining System Dynamics, Discrete-Event and Agent-Based Simulation for energy efficiency analysis in production, considering the energy consumption in the context of planning and scheduling operations.

Fashion is one of the world’s most important industries, driving a significant part of the global economy representing, if it was a country, the seventh-largest GDP in the world in terms of market size. Focusing on the footwear industry, assembly line balancing and sequencing represents one of the most significant challenges fashion companies have to face.

This paper presents the results of a simulation-based optimization framework implementation in the fashion industry. It also highlights the benefits of the use of simulation together with a finite capacity scheduling optimization model. Production planning and scheduling optimization include a scenario analysis that compares production KPIs (in terms of average advance, delay, and resource saturation) related to different scenarios.

This paper addresses the problem of controlling the Work-In-Process (WIP) in semiconductor manufacturing by using a global scheduling approach and manufacturing scheduling software. A WIP balancing strategy is proposed to minimize the product mix variability in terms of throughput and cycle time. This strategy is enforced using a global scheduling optimization model which is formulated as a linear programming model. The global scheduling model is coupled with a generic multi-method simulation model built with manufacturing scheduling software for evaluation purpose.

During recent years, Autonomous Vehicle Storage and Retrieval Systems (AVS/RS) have been widely applied in warehouse optimization to meet the increasing demand for rapid and flexible large-scale storage and retrieval tasks. This paper focuses on the operations control strategies with regard to the conveyor system, rack storage system, and pick-up system in order to maximize the system’s throughput capacity and minimize the storage/retrieval times of items. The study is based on a large-scale shoe manufacturer’s warehouse optimization and provides insights for system management.

Klein Mechanisch Werkplaats Eindhoven (KMWE) is a precision manufacturing company situated in the Netherlands and recently relocated to a new location known as the 'Brainport Industries Campus' (BIC). This move allowed KMWE to improve the performance of its manufacturing facility by investing in vertical automated storage and retrieval systems (AS/RSs). However, these decisions needed to be made under input uncertainties since the move to BIC and modernization of existing equipment would cause changes in operating parameters inside the facility.

In this study, the researchers show how hybrid simulation modelling was used in production planning optimization, in particular to assess the impact of input uncertainties (such as operator productivity, vertical storage height) on the throughput performance of TSC.

We discuss the application of simulation to estimate a nominal, or target, processing times for work stations on a serial assembly line. The expectation is that having different processing times per station per product will increase the throughput of the line, compared to having a constant time for all stations. A demonstration case at ABB Robotics in Sweden will be presented. This is a small part in the “Process Automation for Discrete Manufacturing Excellence” project (PADME) involving five manufacturing industry partners and four research organizations, that aim at adapting Industrie 4.0 strategies and existing state-of-the-art technologies into new configurations, serving as a framework that can be used by similar industries.

Production plans are based on fair assumptions of process performance and all operation parameters are taken as averages. There are a number of events that happen in any manufacturing setup during the course of production like periodic delivery of raw materials or changeovers on a machine. The interaction between these events is non-linear and cannot be easily visualized. As a result of which most of the production plans in any company have only a limited realization. This paper provides an example of how simulation using AnyLogic has been applied in one such plant scenario to visualize the plan outcome.

The recent circular economy movement has raised awareness and interest about untapped environmental and economic potential in the manufacturing industry. One of the crucial aspects in the implementation of circular or closedloop manufacturing approach is the design of circular products. While it is obvious that three post-use strategies, i.e., reuse, remanufacturing, and recycling, are highly relevant to achieve loop closure, it is enormously challenging to choose “the right” strategy (if at all) during the early design stage and especially at the single component level. One reason is that economic and environmental impacts of adapting these strategies are not explicit as they vary depending on the chosen business model and associated supply chains. In this scenario, decision support is essential to motivate adaptation of regenerative design strategies. The main purpose of this paper is to provide reliable decision support at the intersection of multiple lifecycle design and business models in the circular economy context to identify effects on cost.

Production planning is a complex problem that is typically decomposed into decisions carried out at different control levels. The various methods used for production planning often assume a static environment, therefore, the plans developed may not be feasible when shop floor events change dynamically. In such an operating environment, a system simulation model updated with real-time data can be used to validate a proposed plan. In this paper, we propose a framework to evaluate and validate the feasibility of high-level production plans using a simulation model at a lower level thereby providing a base for improving the upper level plan. The idea is demonstrated with an assembly plant where the aggregate plan is evaluated using discrete event simulation (DES) of shop floor operations with resources allocated according to constraints imposed by the aggregate plan. We also discuss standardized integration interfaces required between simulations and production planning tools.

Developing manufacturing simulation models usually requires experts with knowledge of multiple areas including manufacturing, modeling, and simulation software. The expertise requirements increase for virtual factory models that include representations of manufacturing at multiple resolution levels. This paper reports on an initial effort to automatically generate virtual factory models using manufacturing configuration data in standard formats as the primary input. The execution of the virtual factory generates time series data in standard formats mimicking a real factory. Steps are described for auto-generation of model components in a software environment primarily oriented for model development via a graphic user interface. Advantages and limitations of the approach and the software environment used are discussed. The paper concludes with a discussion of challenges in verification and validation of the virtual factory prototype model with its multiple hierarchical models and future directions.