Multiple Objective Decision Analysis Research Articles

A Multiple Objective Decision Analysis Model for Capacity Expansion Plans Selection in Manufacturing

Capacity expansion is a critical decision with potential circumstances of risks and opportunities. This decision is further complicated given the conflicting decision measures and stakeholders’ preferences. Previous research is limited in providing a systematic framework to deal with such decisions in manufacturing environments. In this paper, decision analysis is used for the assessment of the appropriate capacity expansion plans of a mechanical contracting and fabrication company serving aluminum smelting industry. Repairing anodes used in aluminum smelting is a critical work that is outsourced to qualified companies. The increased demand for this kind of repair creates an opportunity for the contracting company to expand capacity. The expansion will qualify the company to receive more orders for anode maintenance and other mechanical services and respond more effectively to client demands, allowing for the advancement of expertise through the acquisition of large projects. However, the decision to expand may be a complex problem for a number of reasons. One of the reasons is ascribed to the appropriate form and the time required for expansion. Factors such as market rivalry, investment risk, budget constraints, bidding policies, and operational capabilities all complicate the decision making process, necessitating the use of a decision analysis model. Therefore, this article proposes the multiple objective decision analysis (MODA) approach for studying capacity expansion problem. MODA's cost-value model examines expansion plans within acceptable budgets and allows for trade-offs between value and cost, whilst the value gap analysis identifies opportunities to improve the values of the expansion plans. The sensitivity analysis demonstrates the model's robustness to changes in swing weights and value curves. The proposed model provides manufacturers with further investigation of the alternative expansion plans with insights into the associated cost and gaps for improvements.

Development and assessment of a resilient telecoms system

Emergency telecommunication infrastructure is essential for residents and emergency responders during natural disasters to coordinate life-saving and life-preserving efforts. Ensuring resilience of the emergency telecommunication infrastructure is of critical importance for regions with an increased likelihood of natural disasters. We developed an integrated modeling framework for assessing emergency telecommunication systems. The framework used performance models to assess coverage and surge capabilities for a given system architecture. The performance models assess the telecom system value using a multiple objective decision analysis value model with stake-holder and technology performance measures. After constructing a life cycle cost model for emergency telecommunication systems, we conducted an illustrative value versus cost trade-off analysis using three alternative decision frames. The decision analysis framework allows for exploration of the system design tradespace so that decision-makers can select the best emergency telecom architecture using a defensible and transparent, performance-driven methodology.

Decision-Making Framework for Evaluating Physicians’ Preference Items Using Multi-Objective Decision Analysis Principles

Physician preference items or PPIs are medical items recommended by physicians for use in medical procedures and other treatments. The recommendation of PPIs by individual physicians can cause the variety of item types that need to be managed within a health care supply chain to increase over time. To better manage the PPI selection process, healthcare organizations often select items through value analysis and discussion teams, which are highly subjective. To better control PPIs, this work uses multiple-objective decision analysis (MODA) to develop a structured quantitative framework for the PPI selection process. The established decision-making framework is based on the theory of multi-objective value analysis. It offers a structured and educated guide to decision-makers for improving value analysis outcomes, advocating sustainable healthcare management strategies. The model was tested and validated through two case studies on two different items in two hospitals in Jordan.

Redesigning the Senior Leader Engagement Program of the United States Africa Command

AFRICOM conducts hundreds of senior leader engagements (SLEs) each year throughout the African continent in order to create strategic partnerships and military relationships that preserve American interests abroad. While AFRICOM has been planning and executing these engagements since the inception of the organization in 2008, it lacks a well-defined method to systemize its SLE process. As a result, SLE development is largely ad hoc, potentially decreasing the strategic effectiveness of the engagements and increasing their cost. This paper delineates a decision-making framework to redesign and enhance AFRICOM’s SLE program. In particular, it posits a multiple objective decision analysis model that quantifies key stakeholder values and develops several alternatives for future evaluation. Of note, potential solutions imagine a more expansive system where subsets of Senior Leaders (SLs) are assigned to clusters of African countries based on the SLs’ similarity to countries within each cluster, providing a basis for relationship ownership and mutual trust.

Assessing Engineering Resilience for Systems with Multiple Performance Measures.

Recently, efforts to model and assess a system's resilience to disruptions due to environmental and adversarial threats have increased substantially. Researchers have investigated resilience in many disciplines, including sociology, psychology, computer networks, and engineering systems, to name a few. When assessing engineering system resilience, the resilience assessment typically considers a single performance measure, a disruption, a loss of performance, the time required to recover, or a combination of these elements. We define and use a resilient engineered system definition that separates system resilience into platform and mission resilience. Most complex systems have multiple performance measures; this research proposes using multiple objective decision analysis to assess system resilience for systems with multiple performance measures using two distinct methods. The first method quantifies platform resilience and includes resilience and other "ilities" directly in the value hierarchy, while the second method quantifies mission resilience and uses the "ilities" in the calculation of the expected mission performance for every performance measure in the value hierarchy. We illustrate the mission resilience method using a transportation systems-of-systems network with varying levels of resilience due to the level of connectivity and autonomy of the vehicles and platform resilience by using a notional military example. Our analysis found that it is necessary to quantify performance in context with specific mission(s) and scenario(s) under specific threat(s) and then use modeling and simulation to help determine the resilience of a system for a given set of conditions. The example demonstrates how incorporating system mission resilience can improve performance for some performance measures while negatively affecting others.

Gulf Coast Port Selection Using Multiple-Objective Decision Analysis

In today’s competitive global markets, ports play a vital role in global supply chain operations. A port selection model was developed to demonstrate the potential to aid shipping line companies with decisions to select the best ports in the U.S. Gulf Coast. Because port selection is complex, is dynamic, and includes multiple objectives, a multiple-objective decision analysis quantified the value of the various ports using industry data and an industry expert’s knowledge. In addition, a corresponding cost model was developed using available cost data for each port. Monte Carlo simulation analyzed the uncertainties in the value and cost models. For the demonstration model scenario assumptions, the results show that the Houston port would be the best alternative for shipping lines in the Gulf Coast. The value model also identified opportunities for improvement for each port compared with the best West Coast port. The demonstration models show that port data exist to support the development of a multiple-objective decision analysis model and a cost model that can provide useful insights to port selection decision makers. Furthermore, these models can be easily tailored to support port selection in other regions by tailoring the objectives and measures to the decision-maker objectives.

Engineered Resilient Systems with Value Focused Thinking

AbstractComplex DoD systems need to be resilient due to evolving adversarial and environmental threats. The DoD created a program called Engineered Resilient Systems (ERS) to increase resilience in future systems. In our ERS research, resilience has been separated into two parts, mission and platform resilience. Mission resilience is the ability of a system to repel, resist, absorb, and recover from environments and threats that occur on planned missions. Platform resilience is the ability of a system to adapt to new missions and new threats. Using capabilities based planning one can map platform and mission resilience. This paper addresses how a value‐focused thinking (VFT) multiple objective decision analysis (MODA) model is a useful tool for capabilities based assessments. If all performance objectives can be categorized as mission or platform resilience, this extra value above the threshold of the minimum performance can provide mission and platform resilience. Displaying this information in a value component chart and a floating value chart allows decision makers to consider resilience early in the acquisition decision making process.

An Energy-Efficient Skyline Query for Massively Multidimensional Sensing Data.

Cyber physical systems (CPS) sense the environment based on wireless sensor networks. The sensing data of such systems present the characteristics of massiveness and multi-dimensionality. As one of the major monitoring methods used in in safe production monitoring and disaster early-warning applications, skyline query algorithms are extensively adopted for multiple-objective decision analysis of these sensing data. With the expansion of network sizes, the amount of sensing data increases sharply. Then, how to improve the query efficiency of skyline query algorithms and reduce the transmission energy consumption become pressing and difficult to accomplish issues. Therefore, this paper proposes a new energy-efficient skyline query method for massively multidimensional sensing data. First, the method uses a node cut strategy to dynamically generate filtering tuples with little computational overhead when collecting query results instead of issuing queries with filters. It can judge the domination relationship among different nodes, remove the detected data sets of dominated nodes that are irrelevant to the query, modify the query path dynamically, and reduce the data comparison and computational overhead. The efficient dynamic filter generated by this strategy uses little non-skyline data transmission in the network, and the transmission distance is very short. Second, our method also employs the tuple-cutting strategy inside the node and generates the local cutting tuples by the sub-tree with the node itself as the root node, which will be used to cut the detected data within the nodes of the sub-tree. Therefore, it can further control the non-skyline data uploading. A large number of experimental results show that our method can quickly return an overview of the monitored area and reduce the communication overhead. Additionally, it can shorten the response time and improve the efficiency of the query.

Whole Systems Trade Analysis

AbstractGround vehicles are complex systems with many interrelated subsystems – finding the sweet‐spot among competing objectives such as performance, unit cost, operations and sustainment (O&S) costs, development risk, and growth potential is a non‐trivial task. Whole Systems Trade Analysis (WSTA) is a systems analysis and decision support methodology and tool that integrates otherwise separate subsystem models into a holistic system view mapping critical design choices to consequences relevant to stakeholders. WSTA projects are highly integrated and collaborative efforts which generate holistic systems and Multiple Objective Decision Analysis (MODA) models. The related tool captures and synthesizes subsystem and system level analyses into trade‐space visualizations designed to facilitate rapid and complete understanding of the trade‐space to stakeholders and provide drill down capability to supporting rationale. The approach has opened up trade space exploration significantly evaluating up to 1020+ potential system configurations to then return a handful of configurations which best meet design and programmatic criteria.

A matter of tradeoffs: Reintroduction as a multiple objective decision

Decision making in guidance of reintroduction efforts is made challenging by the substantial scientific uncertainty typically involved. However, a less recognized challenge is that the management objectives are often numerous and complex. Decision makers managing reintroduction efforts are often concerned with more than just how to maximize the probability of reintroduction success from a population perspective. Decision makers are also weighing other concerns such as budget limitations, public support and/or opposition, impacts on the ecosystem, and the need to consider not just a single reintroduction effort, but conservation of the entire species. Multiple objective decision analysis is a powerful tool for formal analysis of such complex decisions. We demonstrate the use of multiple objective decision analysis in the case of the Florida non-migratory whooping crane reintroduction effort. In this case, the State of Florida was considering whether to resume releases of captive-reared crane chicks into the non-migratory whooping crane population in that state. Management objectives under consideration included maximizing the probability of successful population establishment, minimizing costs, maximizing public relations benefits, maximizing the number of birds available for alternative reintroduction efforts, and maximizing learning about the demographic patterns of reintroduced whooping cranes. The State of Florida engaged in a collaborative process with their management partners, first, to evaluate and characterize important uncertainties about system behavior, and next, to formally evaluate the tradeoffs between objectives using the Simple Multi-Attribute Rating Technique (SMART). The recommendation resulting from this process, to continue releases of cranes at a moderate intensity, was adopted by the State of Florida in late 2008. Although continued releases did not receive support from the International Whooping Crane Recovery Team, this approach does provide a template for the formal, transparent consideration of multiple, potentially competing, objectives in reintroduction decision making. © 2013 The Wildlife Society.

A Multiple-Objective Decision Analysis for Terrorism Protection: Potassium Iodide Distribution in Nuclear Incidents

This paper presents a multiple-objective decision analysis approach to qualitatively and quantitatively evaluate different potassium iodide (KI) distribution plans for a hypothetical local region. We developed this approach for a U.S. National Research Council committee that was charged with figuring out the best means for protecting people against potential thyroid cancer resulting from the release of radioactive iodine from nuclear incidents occurring due to terrorism or accidents. We first identify an objectives hierarchy and then develop the single-attribute value functions and the weights for the objectives using a swing weight method. The identification of the largest value gaps between the status quo and the ideal situation helps to develop potential KI distribution plans. We then use an additive value function to assess the performance of these new alternatives, with the status quo as a benchmark, by computing their overall values. Finally, sensitivity analysis for the KI problem shows how this approach can create more key insights for the improvement of health and safety decision-making processes.

Use of Decision Analysis in the Army Base Realignment and Closure (BRAC) 2005 Military Value Analysis

In 2001, Congress enacted legislation that required a 2005 Base Realignment and Closure (BRAC) round to realign military units, remove excess facility capacity, and support defense transformation. The United States Army used multiple-objective decision analysis to determine the military value of installations and an installation portfolio model to develop the starting point to identify potential unit realignments and base closures, providing the basis for all recommendations. Ninety-five percent of the army’s recommendations were accepted by the BRAC 2005 Commission. The army expects these recommendations to create recurring savings of $1.5 billion annually after completion of BRAC implementation. This paper offers four contributions to decision analysis literature: an instructive application of multiple-objective decision analysis methods to portfolio selection, a useful method for constructing scales for interdependent attributes, a new method for assessing weights that explicitly considers importance and variation (Swing Weight Matrix), and practical advice on how to use multiple-objective decision analysis methods in a complex and controversial political environment.

A Multiple-Objective Decision Analysis of Stakeholder Values to Identify Watershed Improvement Needs

This paper describes the use of multiple-objective decision analysis to qualitatively and quantitatively assess the quality of an endangered watershed and guide future efforts to improve the quality of the watershed. The Upham Brook Watershed is an urban watershed that lies at the interface of declining inner-city Richmond, Virginia, and growth-oriented Henrico County. A section of stream within the watershed has been identified as so dangerously polluted that it threatens the health of the residents who live within the watershed boundaries. With funding provided by the National Science Foundation, the Upham Brook Watershed project committee was formed to address the quality of the Upham Brook Watershed; it consisted of experts from multiple disciplines: stream ecology, environmental policy, water policy, ground and surface water hydrology and quality, aquatic biology, political science, sociology, citizen participation, community interaction, psychology, and decision and risk analysis. Each member's values and goals were brought together using a watershed management framework to meet the overall objective of the committee: to maximize the quality of the Upham Brook Watershed. The resulting model was used to identify the largest value gaps and to identify future programs needed to improve the quality of the watershed.

Fuzzy multiple objective decision making approach to prioritize design requirements in quality function deployment

Quality function deployment (QFD) is a customer-oriented design tool for developing new or improved products to increase customer satisfaction by integrating marketing, design engineering, manufacturing, and other related functions of an organization. QFD focuses on delivering value by taking into account customer needs and then deploying this information throughout the development process. Although QFD aims to maximize customer satisfaction, technology and cost considerations limit the number and the extent of the possible design requirements that can be incorporated into a product. This paper presents a fuzzy multiple objective programming approach that incorporates imprecise and subjective information inherent in the QFD planning process to determine the level of fulfilment of design requirements. Linguistic variables are employed to represent the imprecise design information and the importance degree of each design objective. The fuzzy Delphi method is utilized to achieve the consensus of customers in determining the importance of customer needs. A pencil design example illustrates the application of the multiple objective decision analysis.

Fuzzy multiple objective programming framework to prioritize design requirements in quality function deployment

Quality Function Deployment (QFD) is a customer-oriented design tool for developmg new or improved products to increase customer satisfaction by integrating marketing, design engineering, manufacturing, and other related functions of an organization. QFD aims to maximize customer satisfaction; however, considerations such as cost budget, technical difficulty, etc. limit the number and the extent of the possible design requirements that can be incorporated into a product. This paper presents a fuzzy multiple objective programming approach that incorporates imprecise and subjective information inherent in the QFD planning process to determine the level of fulfillment of design requirements. Linguistic variables are employed to represent the imprecise design information and the importance degree of each design objective. A real-world application illustrates the proposed fuzzy multiple objective decision analysis.

Improving Resource Allocation Within the National Reconnaissance Office

Each year the Operational Support Office of the US National Reconnaissance Office searches for ways to provide better space-reconnaissance information to military and national leaders. We used future value analysis, a combination of three methods to assess future opportunities: (1) a strategic assessment of future opportunities and challenges, (2) a multiple-objective decision analysis using value-focused thinking, and (3) a portfolio analysis using optimization. We then developed a multiple-objective value model to communicate values, evaluate individual tasks, and develop higher value tasks. We used an optimization model to identify the best portfolio of tasks. The office used the models to identify the best tasks for its annual budget in 1998 and, with revisions, in the next two years.

Using Judgment in Resource Management: A Multiple Objective Analysis of a Fisheries Management Decision

This paper uses multiple objective decision analysis to conduct an ex post analysis of a specific fisheries management decision involving conflicting long-term objectives for mixed stocks. The paper illustrates the potential role of subjective judgment in fisheries and other resource management contexts and the relevance of decision analysis for in-season salmon management. The decision context is first defined in terms of objectives and alternatives, then subjective probability distributions are elicited from experts regarding uncertain biological parameters. A simulation is then used to estimate the consequences of alternative openings, given the biological uncertainties. A utility function is elicited from a fisheries manager and used to select among alternative commercial fishery openings. The results show that objectives other than those typically assumed in fisheries modeling, and subjective judgments by technical experts, can be important for in-season salmon management. The results also show that, in this application, the equivalent of nearly $8 million in potential benefits are available from delaying the opening of the commercial fishery by a single day.

Integrated economic-environmental-energy policy and conflict analysis

The conventional theory of economic policy has usually taken for granted the paradigm of rational decision-making on the basis of full information. The majority of models designed for economic policy analysis presupposed rational behavior of clearly identifiable, individual decision-makers or of one (collective) decision agency. Given a set of axioms for rational actors confronted with a complex choice situation, these models for economic policy analysis were mainly normative in nature (see for instance, Harsanyi, 1979). In many cases, this traditional view of economic policy analysis (represented inter alia by unidimensional programming or a priori known utility models) was not very realistic, as important institutional and organizational aspects of decision-making were neglected, such as multilevel and multigroup processes, vote-trading, bargaining, power conflicts, impacts of informal pressure and interest groups, and strategic behavior. These shortcomings have in recent years caused the emergence of multiple objective (or multidimensional) decision analysis (Nijkamp, 1980). Multiple objective decision models take explicitly into account the existence of goal conflicts, while institutional and procedural aspects of economic planning can be included by means of interactive decision strategies (see Rietveld, 1981, and Spronk, 1981). Furthermore, in multiple objective decision analysis, usually a more modest satisficer principle instead of a strict optimizing behavior is assumed (cf. Simon, 1958), based on a compromise between different and usually conflicting objectives. In this sense the rationality paradigm receives

Multiple-objective decision analysis applied to chemical engineering

Chemical engineers are now being faced with new decisions involving multiple (and often conflicting) objectives as a result of increases in the scale and complexity of chemical plants and a re-evaluation of their performance criteria. Reliability and environmental impacts are now considered to be as important as economic efficiency, and this must be taken into account in process design, production planning and control. This paper describes methods for solving these multiple-objective optimization problems and gives an overview of the existing software. Selected applications of multiple-objective analysis are discussed—these include the design of a twin-screw extruder, the control of a film-hardening process and a production planning problem.

A framework for systems engineering design

This paper follows on from the previous paper in the series and attends to the methodology of design as conceived within the whole-system whole-life dimensions of systems engineering. It assumes the concepts and general structure of systems engineering as outlined in the previous paper and goes on to elaborate the form and content of systems engineering from a design perspective. The paper begins by considering the systems engineering organization within the hierarchical structure for project design and management that has evolved from the one-time individual project engineer. Then the important relationships between entrepreneur/contractor, customer/owner, user are explored to illustrate why design in the system engineer's hands is dominated by overall cost-effectiveness optimized with respect to the future whole-life operations. The systems engineering design process is very similar to multiple-objective decision analysis as both designer and decision maker are faced by a similar problem: to find a way of organizing action and deploying resources so that a desirable future state is realized efficiently. The decision analytical process is structured to provide a framework for the systems design methodology itself. This follows using a top-down (hierarchical) approach in which overall system worth is determined with respect to clearly articulated objectives and evaluated with respect to a comprehensive multiattribute value criteria. Cost-effectiveness is disaggregated into its components of availability, dependability, capability and discounted whole-life cost. These in turn are related to the sub-design problems of operational performance, reliability, maintenance, logistic support, risk and impact analysis.