Compromise Decision Support Problem Research Articles

A Decision Support Framework for Robust Multilevel Co-Design Exploration of Manufacturing Supply Networks

Abstract The design of a manufacturing supply network (MSN) requires the consideration of decisions made by different groups at multiple levels and their interactions that include potential conflicts. Decisions are typically made based on information from computational simulations that are abstractions of reality and, therefore, embody uncertainty. This necessitates focusing on design space exploration to identify robust satisficing solution sets that are relatively insensitive to uncertainty. Current frameworks that support robust satisficing design space exploration are limited by their capability to support the efficient exploration of multilevel design spaces simultaneously. In this paper, we present the Framework for Robust Multilevel Co-Design Exploration (FRoMCoDE), a decision support framework that allows designers to (i) model decision problems across multiple levels and their interactions, (ii) consider uncertainties in the decision problems, and (iii) visualize and systematically carry out simultaneous exploration of multilevel design spaces, termed co-design exploration. In FRoMCoDE, we combine the coupled-compromise Decision Support Problem construct, where a combination of the Preemptive and Archimedean formulations is used, with robust design constructs and interpretable-Self-Organizing Maps (iSOM)-based visualization to facilitate robust co-design. We use a steel MSN problem with decisions made at two levels to test the framework. Using the problem, we demonstrate FRoMCoDE's efficacy in supporting designers in (i) modeling multilevel decision problems and their interactions, considering the uncertainties, and (ii) the efficient co-design exploration of multilevel design spaces. FRoMCoDE is generic and supports designers in the robust co-design exploration of multilevel systems.

Design of Self-Organizing Systems Using Multi-Agent Reinforcement Learning and the Compromise Decision Support Problem Construct

Abstract In this paper, we address the following question: How can multi-robot self-organizing systems be designed so that they show the desired behavior and are able to perform tasks specified by the designers? Multi-robot self-organizing systems, e.g., swarm robots, have great potential for adapting when performing complex tasks in a changing environment. However, such systems are difficult to design due to the stochasticity of system performance and the non-linearity between the local actions/interaction and the desired global behavior. In order to address this, in this paper, we propose a framework for designing self-organizing systems using Multi-Agent Reinforcement Learning (MARL) and the compromise Decision-Support Problem (cDSP) construct. The proposed framework consists of two stages, namely, preliminary design followed by design improvement. In the preliminary design stage, MARL is used to help designers train the robots so that they show stable group behavior for performing the task. In the design improvement stage, the cDSP construct is used to explore the design space and identify satisfactory solutions considering several performance indicators. Surrogate models are used to map the relationship between local parameters and global performance indicators utilizing the data generated in the preliminary design. These surrogate models represent the goals of the cDSP. Our focus in this paper is to describe the framework. A multi-robot box-pushing problem is used as an example to test the framework’s efficacy. This framework is general and can be extended to design other multi-robot self-organizing systems.

An Adaptive Linear Programming Algorithm with Parameter Learning

When dealing with engineering design problems, designers often encounter nonlinear and nonconvex features, multiple objectives, coupled decision making, and various levels of fidelity of sub-systems. To realize the design with limited computational resources, problems with the features above need to be linearized and then solved using solution algorithms for linear programming. The adaptive linear programming (ALP) algorithm is an extension of the Sequential Linear Programming algorithm where a nonlinear compromise decision support problem (cDSP) is iteratively linearized, and the resulting linear programming problem is solved with satisficing solutions returned. The reduced move coefficient (RMC) is used to define how far away from the boundary the next linearization is to be performed, and currently, it is determined based on a heuristic. The choice of RMC significantly affects the efficacy of the linearization process and, hence, the rapidity of finding the solution. In this paper, we propose a rule-based parameter-learning procedure to vary the RMC at each iteration, thereby significantly increasing the speed of determining the ultimate solution. To demonstrate the efficacy of the ALP algorithm with parameter learning (ALPPL), we use an industry-inspired problem, namely, the integrated design of a hot-rolling process chain for the production of a steel rod. Using the proposed ALPPL, we can incorporate domain expertise to identify the most relevant criteria to evaluate the performance of the linearization algorithm, quantify the criteria as evaluation indices, and tune the RMC to return the solutions that fall into the most desired range of each evaluation index. Compared with the old ALP algorithm using the golden section search to update the RMC, the ALPPL improves the algorithm by identifying the RMC values with better linearization performance without adding computational complexity. The insensitive region of the RMC is better explored using the ALPPL—the ALP only explores the insensitive region twice, whereas the ALPPL explores four times throughout the iterations. With ALPPL, we have a more comprehensive definition of linearization performance—given multiple design scenarios, using evaluation indices (EIs) including the statistics of deviations, the numbers of binding (active) constraints and bounds, the numbers of accumulated linear constraints, and the number of iterations. The desired range of evaluation indices (DEI) is also learned during the iterations. The RMC value that brings the most EIs into the DEI is returned as the best RMC, which ensures a balance between the accuracy of the linearization and the robustness of the solutions. For our test problem, the hot-rolling process chain, the ALP returns the best RMC in twelve iterations considering only the deviation as the linearization performance index, whereas the ALPPL returns the best RMC in fourteen iterations considering multiple EIs. The complexity of both the ALP and the ALPPL is O(n2). The parameter-learning steps can be customized to improve the parameter determination of other algorithms.

Designing self-organizing systems using surrogate models and the compromise decision support problem construct

In this paper, we address the following question:How can self-organizing system designers steer the system towards expected global behaviors that satisfy multiple conflicting performance indicators?Self-organizing systems (SOS) have advantages in performing tasks in exploratory and hazardous domains that are not suitable for humans. The design of the SOS is however difficult because negative emergence with unwanted behaviors is likely to happen and multiple conflicting performance indicators need to be considered. To address this challenge, in this paper, we propose an SOS design method using surrogate models and the compromise Decision Support Problem (cDSP) construct. Surrogate models are used to capture the relationship between low-level rules or parameters and high-level emerging system performance. And the cDSP construct is used to explore “good enough” solutions (characterized by rule adoption rates) while managing the trade-offs among conflicting performance indicators. The efficacy of the proposed method is illustrated using a multi-agent box-pushing problem in the Webots simulation environment. It is shown in the results that our method leads to a 6.9% improvement in time efficiency, an 8.4% improvement in energy efficiency, and 26.2% in system reliability.

Realisation of responsive and sustainable reconfigurable manufacturing systems

There is a lack of a design method for the manufacturing system reconfiguration to cope with the changing demand and evolving production technologies while minimising energy consumption. The key drivers for the new industrial paradigm are flexibility and sustainable manufacturing, which have been studied independently in the prior research. The aim of this research is to study two drivers simultaneously by designing robust models and analysing manufacturing system configurations to achieve feasible solutions in any scenario that may arise due to evolving, incomplete, and unforeseen production requirements, while minimising energy usage during product manufacture. To achieve this goal, this research develops a robustly validated pre-emptive decision engineering framework (DEF) for the manufacturing system reconfiguration process to manage future uncertainty of future conditions and identifies current production vulnerabilities and alternative production portfolios. In this research, a robust RMS reconfiguration strategy is designed using a compromise decision support problem (cDSP), and decentralised decision-making designs are explored through the use of game theory. The findings provide a new production system for adaptable, responsive, and sustainable manufacturing processes in the dynamic global economy. These results can empower stakeholders to make timely design decisions that lead to significant cost savings and sustainable manufacturing.

Towards pre-emptive resilience in military supply chains: A compromise decision support model-based approach

ABSTRACT The complex and dynamic nature of military supply chains (MSC) requires constant vigilance to sense potential vulnerabilities. Several studies have employed decision support models for the optimization of their operations. These models are often limited to a best single-point solution unsuitable for complex MSC constellations. In this article, the authors present a novel approach based on decision support models to explore a range of satisficing solutions against disruptions in MSCs using a compromise Decision Support Problem (cDSP) construct and Decision Support in the Design of Engineered Systems (DSIDES). Two cases were evaluated: (1) a baseline scenario with no disruption and (2) with disruption to achieve target values of three goals: (1) minimizing lead time, (2) maximizing demand fulfilment and (3) maximizing vehicle utilization. The results obtained in Case 1 identified a more stable solution space with minimal deviations from the target value, while in Case 2 the solution space was unstable with deviations from the target values

Socio-Technical Modeling to Manage Power Distribution for Microgrid Systems With Limited Production Capacity

Abstract The quality of life (QOL) in rural communities is improved through electrification. Microgrids can provide electricity in areas where grid access to electricity is infeasible. Still, insufficient power capacity hinders the very progress that microgrids promote. Therefore, we propose a decision-making framework to manage power distribution based on its impact on the rural QOL. Parameters are examined in this paper to represent the QOL pertaining to water, safety, education, and leisure/social activities. Each parameter is evaluated based on condition, community importance, and energy dependence. A solution for power allocation is developed by executing the compromise decision support problem (cDSP) and exploring the solution space. Energy loads, such as those required for powering water pumps, streetlamps, and household devices, are prioritized in the context of the QOL. The technique also allows decision-makers to update the power distribution scheme as the dynamics between energy production and demand change over time. In this paper, we propose a framework for connecting QOL and power management. The flexibility of the approach is demonstrated using a problem with varying scenarios that may be time dependent. The work enables sustainable energy solutions that can evolve with community development.

Integrating and navigating engineering design decision-related knowledge using decision knowledge graph

Designers are usually facing a problem of finding information from a huge amount of unstructured textual documents in order to prepare for a decision to be made. The major challenge is that knowledge embedded in the textual documents are difficult to search at a semantic level and therefore not ready to support decisions in a timely manner. To address this challenge, in this paper we propose a knowledge-graph-based method for integrating and navigating decision-related knowledge in engineering design. The presented method is based on a meta-model of decision knowledge graph (mDKG) that is grounded in the compromise Decision Support Problem (cDSP) construct which is used by designers as a means to formulate design decisions linguistically and mathematically. Based on the mDKG, we propose a procedure for automatically converting word-based cDSPs to knowledge graph through natural language processing, and a procedure for rapidly and accurately navigating decision-related knowledge through divergence and convergence processes. The knowledge-graph-based method is verified using the textual data from the supply chain design domain. Results show that our method has better performance than the conventional keyword-based searching method in terms of both effectiveness and efficiency in finding the target knowledge.

A performance based method for information acquisition in engineering design under multi-parameter uncertainty

Uncertainty pertaining to multiple parameters is a critical issue in designing complex systems. Whether or not to acquire more information to reduce uncertainty, and how to acquire information are the meta-level decisions to be made. Key challenges in making such decisions are that there are multiple information sources to choose from, and the cost of information as well as its effects on the overall design utility are different. To address these challenges, a performance-based stepwise information acquisition method is proposed. In the proposed method, the utility-based compromise Decision Support Problem construct is used to formulate design decisions to maximize the overall utility. For meta-level decisions, a performance index is developed for selecting the most appropriate information in each acquisition trial. The index is an integration of the improvement potential of the overall utility, the sensitivity of each ranged parameter, and the cost of the acquired information. Advantages of this proposed method are: 1) sensitivity-efficiency ensures that acquired information is invested on the critical parameters which avoids ineffective information acquisition; 2) cost-efficiency ensures that every acquisition is cost-efficient which avoids budget overruns. The efficacy of this method is demonstrated using the design of a hot rod rolling process. It is shown in the results that the performance-based method leads to an 8–45% larger drop of improvement potential compared to the random method.

Rapid Modeling and Design Optimization of Multi-Topology Lattice Structure Based on Unit-Cell Library

Abstract Lightweight lattice structure generation and topology optimization (TO) are common design methodologies. In order to further improve potential structural stiffness of lattice structures, a method combining the multi-topology lattice structure design based on unit-cell library with topology optimization is proposed to optimize the parts. First, a parametric modeling method to rapidly generate a large number of different types of lattice cells is presented. Then, the unit-cell library and its property space are constructed by calculating the effective mechanical properties via a computational homogenization methodology. Third, the template of compromise Decision Support Problem (cDSP) is applied to generate the optimization formulation. The selective filling function of unit cells and geometric parameter computation algorithm are subsequently given to obtain the optimum lightweight lattice structure with uniformly varying densities across the design space. Lastly, for validation purposes, the effectiveness and robustness of the optimized results are analyzed through finite element analysis (FEA) simulation.

Harnessing Process Variables in Additive Manufacturing for Design Using Manufacturing Elements

Abstract Process plans in additive manufacturing (AM) have a profound impact on the performance of fabricated parts such as geometric accuracy and mechanical properties. Due to its layer-based, additive nature, AM processes can be controlled at multiple scales starting from the scan vector/pixel scale. However, most process planning methods in AM configure process settings at the part scale. This leaves large unexplored regions in the design space that may include optimal designs. To address these untapped potentials, we present a process planning strategy based on the concept of manufacturing elements (MELs) to harness process variables at low scales for design. First, we decompose a part design into multiple MELs that contain geometric and manufacturing information. Two-scale process–structure–property (PSP) relationships are then constructed for MELs and their assembly. Decision tools, including the compromise decision support problem, are employed to navigate two-scale PSP relationships for supporting designers in design exploration on process variables and optimization of process plans. The proposed strategy is illustrated with a process planning example for a lattice structure, which has multiple design goals and is to be fabricated using material extrusion.

A method for the concurrent design and analysis of networked manufacturing systems

ABSTRACTMultistage manufacturing processes (MMPs) require multiple stations and operations. Traditionally, analysis of MMPs focused on material planning and control strategies. For a given MMP, the effect of the strategy on the volume and rate of production, ability to handle product type variability, and the effects of process variability on production rate, on-process inventory, etc., have been studied individually. Such approaches, while necessary, do not address the combined effects of MMP design choices on the final product quality. In this article, a method based on the compromise decision support problem and stream of variation model is proposed to provide a way to evaluate suitable designs for the implementation of MMPs. Using the dimensional quality of the product as a measure of quality, the proposed method is illustrated using a three-stage MMP in an automobile panel stamping process while considering the conflicting requirements of diagnosability and controllability.

Multifunctional design of heterogeneous cellular structures

Mesoscale cellular structures with different desired physical properties are promising for a broad spectrum of applications. The availability of Additive Manufacturing (AM) technology has significantly relaxed the fabricating limitation of cellular structures. It enables the design of cellular structure with complex cell topologies and relative density distribution. In this paper, a multifunctional design method for heterogeneous cellular structures is proposed. To introduce this method, Function-Performance-Property-Design parameter model is proposed at first. This proposed model can help designers to analyze the complex relations between focused functions and their related design parameters. Based on the proposed F-P-P-D model, the template of compromise Decision Support Problem (DSP) is applied to generate the optimization formulation which can generally consider the performances defined for several different functions. To solve the defined optimization problem, a multifunctional design simulation infrastructure is proposed for the designed heterogeneous cellular structures. Based on this simulation infrastructure, both the value of the objective function and its gradients can be evaluated. Then, Sequential Quadratic Programming (SQP) solver can be applied to solve the defined optimization formulation. The optimal relative density of cellular structures can be achieved and converted to the heterogeneous cellular structures at the end. A case study is provided at the end of this paper. For this case study, two different cell topologies and several different combinations of optimization parameters are used. A brief discussion is made to conclude the effects of cell topologies and other optimization parameters on the optimization results. Generally, the result of this design case validates the efficiency of the proposed design method. This method provides a useful design tool for users to take advantage of heterogeneous cellular structures for multifunctional purposes.

A Goal-Oriented, Sequential, Inverse Design Method for the Horizontal Integration of a Multistage Hot Rod Rolling System

The steel manufacturing process is characterized by the requirement of expeditious development of high quality products at low cost through the effective use of available resources. Identifying solutions that meet the conflicting commercially imperative goals of such process chains is hard using traditional search techniques. The complexity in such a problem increases due to the presence of a large number of design variables, constraints and bounds, conflicting goals and the complex sequential relationships of the different stages of manufacturing. A classic example of such a manufacturing problem is the design of a rolling system for manufacturing a steel rod. This is a sequential process in which information flows from first rolling stage/pass to the last rolling pass and the decisions made at first pass influence the decisions that are made at the later passes. In this context, we define horizontal integration as the facilitation of information flow from one stage to another thereby establishing the integration of manufacturing stages to realize the end product. In this paper, we present an inverse design method based on well-established empirical models and response surface models developed through simulation experiments (finite-element based) along with the compromise decision support problem (cDSP) construct to support integrated information flow across different stages of a multistage hot rod rolling system. The method is goal-oriented because the design decisions are first made based on the end requirements identified for the process at the last rolling pass and these decisions are then passed to the preceding rolling passes following the sequential order in an inverse manner to design the entire rolling process chain to achieve the horizontal integration of stages. We illustrate the efficacy of the method by carrying out the design of a multistage rolling system. We formulate the cDSP for the second and fourth pass of a four pass rolling chain. The stages are designed by sequentially passing the design information obtained after exercising the cDSP for the last pass for different scenarios and identifying the best combination of design variables that satisfies the conflicting goals. The cDSP for the second pass helps in integrated information flow from fourth to first pass and in meeting specified goals imposed by the fourth and third passes. The end goals identified for this problem for the fourth pass are minimization of ovality (quality) of rod, maximization of throughput (productivity), and minimization of rolling load (performance and cost). The method can be instantiated for other multistage manufacturing processes such as the steel making process chain having several unit operations.

Ontology-based executable design decision template representation and reuse

AbstractIn decision-based design, the principal role of a designer is to make decisions. Decision support is crucial to augment this role. In this paper, we present an ontology that provides decision support from both the “construct” and the “information” perspectives that address the gap that existing research focus on these two perspectives separately and cannot provide effective decision support. The decision support construct in the ontology is the compromise decision support problem (cDSP) that is used to make multiobjective design decisions. The information for decision making is archived as cDSP templates and represented using frame-based ontology for facilitating reuse, consistency maintaining, and rapid execution. In order to facilitate designers’ effective reuse of the populated cDSP templates ontology instances, we identified three types of modification that can be made when design consideration evolves. In our earlier work, part of the utilization (consistency checking) of the ontology has been demonstrated through a thin-walled pressure vessel redesign example. In this paper, we comprehensively present the ontology utilization including consistency checking, trade-off analysis, and design space visualization based on the pressure vessel example.

Design Exploration to Determine Process Parameters of Ladle Refining for an Industrial Application

Due to increasing competition and the demand for light-weight steel, manufacturers strive to produce new grades of steel to meet stringent sets of property specifications. The authors are informed by engineers at a major steel-making plant that for them to determine the set-points for the ladle, tundish, and caster to make a new grade of steel it takes between 20 and 25 physical plant trials. These plant trials are expensive and very difficult to schedule. In this paper, the authors focus on explicating the method for one unit operation in the steel-making process, namely, ladle refining, which is a key operation for maintaining cleanliness and controlling composition; these have a significant bearing on the steel's mechanical properties. The challenge is to determine set points for ladle refining using computational models. In this paper, the authors present a method for visualizing and exploring the solution space using the compromise Decision Support Problem (cDSP). To illustrate the efficacy of the method, the authors determine the set points of the ladle operation in an industrial setting. The focus of the paper is to elucidate the utility of method and not on the analysis models used in the framework.

Design Exploration for Determining the Set Points of Continuous Casting Operation: An Industrial Application

To compete with other materials and/or contribute toward light-weighting of vehicles, newer grades of steel are continuously invented and experimented upon. Due to the costs and time involved in such developments, manufacture of new grades of steel at an industrial scale is difficult. We propose a method that is useful for steel manufacturers interested in producing a steel product mix with new grades of steels by predicting the required change in the operating set points of each unit operation in the manufacturing chain of products with the new grade of steel. Here, we demonstrate a method to determine the set points of one unit operation, continuous casting which is measured in terms of conflicting objectives including productivity, quality, and production costs. These parameters are sensitive to the operating set points of casting speed, superheat, mold oscillation frequency, and secondary cooling conditions. To ensure targeted performance and address the challenges of uncertainty and conflicting objectives, an integrated computational method based on surrogate models and the compromise decision support problem (cDSP) is presented. The method is used to explore the design space available for casting operations and determine operating set points to meet requirements imposed on the caster from subsequent downstream processes. This method is of value to the steel industry and enables the rapid and cost effective production of a product mix with a new grade of steel.

Design Exploration of Engineered Materials, Products, and Associated Manufacturing Processes

In the past few years, ICME-related research has been directed towards the study of multi-scale materials design. However, relatively little has been reported on model-based methods that are of relevance to industry for the realization of engineered materials, products, and associated industrial manufacturing processes. Computational models used in the realization of engineered materials and products are fraught with uncertainty, have different levels of fidelity, are incomplete and are even likely to be inaccurate. In light of this, we adopt a robust design strategy that facilitates the exploration of the solution space thereby providing decision support to a design engineer. In this paper, we describe a foundational construct embodied in our method for design exploration, namely, the compromise Decision Support Problem. We introduce a problem that we are using to establish the efficacy of our method. It involves the integrated design of steel and gears, traversing the chain of steel making, mill production, and evolution of the material during these processes, and linking this to the mechanical design and manufacture of the gear. We provide an overview of our method to determine the operating set points for the ladle, tundish and caster operations necessary to manufacture steel of a desired set of properties. Finally, we highlight the efficacy of our method.

Robust engineering: improved inductive design exploration approach to bionic system

Multidimensional space biomimetic information of space variables is acquired by evaluating bionic degree indices of them. According to bionic degree of each space variable, comprehensive hyperdimensional error margin index in variable space is defined to characterise bionic robustness of discrete points in input space based on inductive design exploration method. Feasible point checking equation is established, which considers differences between space variables, to further reduce target dimensions in optimisation and distinguish variables with different bionic degree. Compromise decision support problem method is used to identify the best solution from the subset using deviation of comprehensive hyperdimensional error margin index in each output space as objective function.

Exploring the geometry and material space in gear design

Gear design is a complex process. When new materials and manufacturing processes are introduced, traditional empirical knowledge is unavailable and considerable effort is required to find starting design concepts. This forces gear designers to go beyond the traditional standards-based design methods. The method presented here, the concept exploration method, is based on the compromise decision support problem and is demonstrated for gear design which requires the simultaneous exploration of geometry, material and manufacturing spaces to exploit synergies. The results obtained are in agreement with existing knowledge. To further develop design guidelines, ternary contour plots of goal achievement are created to show the interdependence among various design goals. These ternary charts help gear designers to visualize and understand the complexity and compromises involved and help them to make trade-offs among individual goals.