Interactive knowledge discovery and knowledge visualization for decision support in multi-objective optimization

Smedberg, Henrik; Bandaru, Sunith

doi:10.1016/j.ejor.2022.09.008

Cited by 33 publications

(10 citation statements)

References 38 publications

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“…This gives a DM several solutions to their problem to choose from, and determining which single solution to then implement is not trivial [14]. Algorithms to assist the DM have been developed, but lack the ability to offer the DM knowledge about which inputs are most important for the objective values [15].…”

Section: Optimization Subsystemmentioning

confidence: 99%

“…In this architecture, a customized IDSS combining elements of MCDSS and IDSS is considered where the IDSS provides methods such as clustering, partitioning, decision trees, and Flexible Pattern Mining (FPM) to use for knowledge extraction [28]. Several examples of an IDSS can be found [29,15,8]. To simplify modifications, an open-source IDSS as a base is preferred.…”

Section: Intelligent Decision-support Subsystemmentioning

confidence: 99%

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A System Architecture for Continuous Manufacturing Decision Support Using Knowledge Generated from Multi-Level Simulation-Based Optimization

Lidberg,

2024

Advances in Transdisciplinary Engineering

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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.

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Section: Optimization Subsystemmentioning

confidence: 99%

Section: Intelligent Decision-support Subsystemmentioning

confidence: 99%

A System Architecture for Continuous Manufacturing Decision Support Using Knowledge Generated from Multi-Level Simulation-Based Optimization

Lidberg,

2024

Advances in Transdisciplinary Engineering

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“…The openly available decision support tool Mimer (https://assar.his.se/ mimer/html/, accessed on 15 January 2023) was employed to generate the results. Mimer enables the interactive knowledge discovery framework for MOO proposed in [65]. The rule interactions found by using FPM regarding task allocations are presented in Table 5, where "A" and "E" refer to the related tasks for Part 1 and Part 2, respectively.…”

Section: Knowledge Discovery From Smomentioning

confidence: 99%

An Enhanced Simulation-Based Multi-Objective Optimization Approach with Knowledge Discovery for Reconfigurable Manufacturing Systems

et al. 2023

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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.

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“…They do not consider the relationship between the variable space (i.e., a parameter that can be changed) and the objective space (i.e., a parameter that should be optimized, but may not be changed directly). In [1], the first DSS to bridge variable and objective space for MOOPs was built. However, it does not address groups of decision makers.…”

Section: Introductionmentioning

confidence: 99%

Knowledge Graphs for Supporting Group Decision Making in Manufacturing Industries

Mittermeier,

Senington,

Bandaru

et al. 2024

Advances in Transdisciplinary Engineering

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Group decision making is traditionally a human-centered process, where communication, synchronization and agreement are driven by the stakeholders involved. In the area of multi-objective optimization (MOO), this becomes a challenge, because MOO usually produces a large amount of trade-off solutions that need to be analyzed and discussed by the stakeholders. Moreover, for transparent group decision making, it is important that each decision maker is able to trace the entire decision process – from associated data and models to problem formulation and solution generation, as well as to the preferences and analyses of other decision makers. A graph database is capable of capturing such diverse information in the form of a knowledge graph. It can be used to store and query all dependencies and hence can support complex decision-making tasks. Further advantages are the inherent suitability for visualization and the possibilities for pattern matching, graph analytics and, if semantically enriched, to infer new connections in the graph. In this paper, we show how such a knowledge graph can be used to support more transparent and traceable decision-making activities, particularly when multiple stakeholders with differing preferences or perspectives are involved.

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Interactive knowledge discovery and knowledge visualization for decision support in multi-objective optimization

Cited by 33 publications

References 38 publications

A System Architecture for Continuous Manufacturing Decision Support Using Knowledge Generated from Multi-Level Simulation-Based Optimization

A System Architecture for Continuous Manufacturing Decision Support Using Knowledge Generated from Multi-Level Simulation-Based Optimization

An Enhanced Simulation-Based Multi-Objective Optimization Approach with Knowledge Discovery for Reconfigurable Manufacturing Systems

Knowledge Graphs for Supporting Group Decision Making in Manufacturing Industries

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