Amos H. C. Ng

Wood supply chains are complex, involving many stakeholders, intricate processes, and logistical challenges to ensure the timely and accurate delivery of wood products to customers. Weather-related variations in forest road accessibility further complicate operations. This paper explores the challenges faced by forest managers in targeting many delivery requirements—four or more. To address this, simulation-based optimization, using NSGA-III, a many-objective optimization algorithm, is proposed to simultaneously optimize often conflicting objectives primarily by minimizing delivery lead time, delivery deviations in backlogs, and delivery variation. NSGA-III enables the exploration of a diverse set of Pareto-optimal solutions that show trade-offs across a flexible set of four, or more, delivery objectives. A Discrete Event Simulation model is integrated to evaluate objectives in a complex wood supply chain. The implementation of NSGA-III within the framework allows forestry decision-makers to navigate between different harvest schedules and evaluate how they target a set of preference-based delivery objectives. The simulation can also provide detailed insights into how a specific harvest schedule affects the supply chain when post-processing possible solutions, facilitating decision making. This study shows that NSGA-III could substitute NSGA-II to optimize the wood supply chain for more than three objective functions.

Virtual manufacturing, simulation, and optimization provide a wealth of knowledge about the possibilities of future production systems to support decision makers. However, this knowledge usually remains with a handful of domain experts, is hard to share even within the same team and cannot be easily used to support further optimization activities. At the same time, simulations can benefit from the incorporation of linked data from real factories once a process is running, to improve the accuracy of the simulation and hence the accuracy of optimization results. Graph databases provide a possible approach to storing and managing this form of interrelated heterogeneous data, with powerful querying capabilities that can identify important or interesting patterns that might otherwise remain hidden. This paper provides a structured literature review of the current directions of research within this broad but interconnected set of topics, with a special focus on how graphs can support finding knowledge within Virtual Factories, used by larger teams for industrial planning and optimization. Papers have been selected from a wide number of research databases and reviewed from the perspectives of the industries they focus on, the technologies they focus on and the approach they have taken. We conclude that current research focuses on one or two aspects of this problem but does not address all at once, despite the potential benefits of the combination.

Practitioners of multi-objective optimization currently lack open tools that provide decision support through knowledge discovery. There exist many software platforms for multi-objective optimization, but they often fall short of implementing methods for rigorous post-optimality analysis and knowledge discovery from the generated solutions. This paper presents Mimer, a multi-criteria decision support tool for solution exploration, preference elicitation, knowledge discovery, and knowledge visualization. Mimer is openly available as a web-based tool and uses state-of-the-art web-technologies based on WebAssembly to perform heavy computations on the client-side. Its features include multiple linked visualizations and input methods that enable the decision maker to interact with the solutions, knowledge discovery through interactive data mining and graph-based knowledge visualization. It also includes a complete Python programming interface for advanced data manipulation tasks that may be too specific for the graphical interface. Mimer is evaluated through a user study in which the participants are asked to perform representative tasks simulating practical analysis and decision making. The participants also complete a questionnaire about their experience and the features available in Mimer. The survey indicates that participants find Mimer useful for decision support. The participants also offered suggestions for enhancing some features and implementing new features to extend the capabilities of the tool.

Manufacturing is becoming increasingly complex as product life cycles shorten, and new disruptive technologies are introduced. The increased complexity in the manufacturing footprint also complicates industrial decision-making. Proposed improvements to alleviate bottlenecks do not guarantee effective problem resolution. Instead, improvement efforts can become misguided, targeting a bottleneck that affects a single production line rather than the entire site. An effective method for identifying production issues and predicting system performance is discrete-event simulation. When coupled with multi-objective optimization and multi-level modeling, production performance issues can be identified at both the site and workstation levels. However, optimization studies yield vast amounts of data, which can be challenging to extract useful knowledge from. To address this, we employ data-mining methods to assist decision-makers in extracting valuable insights from optimization data. This study presents an architecture for a decision support system that utilizes simulation-based optimization to continuously aid in industrial decision-making. Through a novel model generation method, simulation models are automatically generated and updated using logged data from the manufacturing shop floor and product lifecycle management systems. To reduce the computational complexity of the optimization, model simplification, varying replication numbers, surrogate modeling, and parallel computing in the cloud are also employed within this architecture. The results are presented to a decision-maker in an intelligent decision-support system, allowing for timely and relevant industrial decisions.

In today’s uncertain and competitive market, where manufacturing enterprises are subjected to increasingly shortened product lifecycles and frequent volume changes, reconfigurable manufacturing system (RMS) applications play significant roles in the success of the manufacturing industry. Despite the advantages offered by RMSs, achieving high efficiency constitutes a challenging task for stakeholders and decision makers when they face the trade-off decisions inherent in these complex systems. This study addresses work task and resource allocations to workstations together with buffer capacity allocation in an RMS. The aim is to simultaneously maximize throughput and to minimize total buffer capacity under fluctuating production volumes and capacity changes while considering the stochastic behavior of the system. An enhanced simulation-based multi-objective optimization (SMO) approach with customized simulation and optimization components is proposed to address the abovementioned challenges. Apart from presenting the optimal solutions subject to volume and capacity changes, the proposed approach supports decision makers with knowledge discovery to further understand RMS design. In particular, this study presents a customized SMO approach combined with a novel flexible pattern mining method for optimizing an RMS and conducts post-optimal analyses. To this extent, this study demonstrates the benefits of applying SMO and knowledge discovery methods for fast decision support and production planning of an RMS.