Multiple Objective Decision 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.

A hybrid multi-attribute decision-making and data envelopment analysis model with heterogeneous attributes: The case of sustainable development goals

This study presents an integrated multi-attribute decision-making (MADM) and data envelopment analysis (DEA) framework for solving problems with heterogeneous attributes. We classify the heterogeneous attributes into desirable and undesirable classes and provide a model for aggregating the attributes’ weights and the alternatives’ scores. The proposed model is initially designed as a Multiple Objective Decision Making (MODM) problem with a Data Envelopment Analysis (DEA) policy and then reformulated as a linear programming model tackled through a goal programming approach. We apply the proposed model to a set of European countries based on their fulfillment of the 17 Sustainable Development Goals (SDGs) defined by the United Nations. We show the proposed approach minimizes computational efforts and complexities and maximizes the participation and satisfaction of decision-makers. We compare the rankings derived from our model with those obtained from standard MADM techniques such as Euclid and TOPSIS. We illustrate how the different normalization methods are applied to condition the discrimination power of the models and analyze the reversals triggered by TOPSIS relative to the other techniques. We conclude by noting that our model does not rely on the weights defined by the experts to determine the ranking, which constitutes a significant advantage over the standard MADM techniques in strategic evaluation environments.

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.

On the evaluation of hierarchical forecasts

The aim of this paper is to provide a thinking road-map and a practical guide to researchers and practitioners working on hierarchical forecasting problems. Evaluating the performance of hierarchical forecasts comes with new challenges stemming from both the structure of the hierarchy and the application context. We discuss several relevant dimensions for researchers and analysts: the scale and units of the time series, the issue of intermittency, the forecast horizon, the importance of multiple evaluation windows and the multiple objective decision context. We conclude with a series of practical recommendations.

Fuzzy Linear programming approach in Water Pollution Control

Purpose of the study: The purpose of the study is to calculate the water pollution level of a source. Because water pollution is a very big problem in front of us. Mining and the related activities are responsible for large scale water pollution.
 Methodology: The problem of water pollution control (WPC) may be treated as a multiple objective decision making problem. This paper deals with use of fuzzy linear programming in water pollution control problem. Min operator has been used to construct the overall achievement function. A semi hypothetical case has been studied with regard to mining vis-a-vis water pollution.
 Main Findings: After calculation and using all the methods the main finding is that total WPC cost is 54411.12 units.
 Applications of this study: We can use this method of water purification to purify the big source of water supply. After using this method the cost of water purification can be calculated and minimized.
 Novelty/Originality of this study: This calculation is based on the secondary data source and this can help the system to use the proper method and calculation of expense in purifying water.

Interactive portfolio optimization model based on rough fundamental analysis and rational fuzzy constraints

Research on mean–variance portfolio optimization (MVPO) has made significant progress in the application of advanced solution algorithms or multiple criteria decision-making methods to achieve superior outcomes. However, there is little specific guidance on how to approach individually preferred optimization bounded by rationality. Therefore, the main goal of this study is to extend MVPO research by incorporating an investor’s preferences and rational expectations during optimizations. Using a set of equities from the Taiwan stock market, this study proposes an interactive MVPO model guided by personal risk-return preferences and bounded by rational fuzzy constraints. Furthermore, this study adopts a rule-based bipolar model to prioritize candidate equities and support the interactive optimization process. This hybrid model is an early attempt to investigate bounded rationality in MVPO, which integrates multiple rule-based decision-making with multiple objective decision-making. The obtained portfolio outperformed two benchmark indexes in 2020, suggesting the feasibility of this approach.

An integrated multiple objective decision making approach for exploring the competitiveness of pharmaceutical multinational enterprises

An integrated multiple objective decision making approach for exploring the competitiveness of pharmaceutical multinational enterprises

Training Algorithm Based on the Levenberg–Marquardt Method for Training Decision Neural Network to Solve Multiple Objective Decision-Making Problems

Training Algorithm Based on the Levenberg–Marquardt Method for Training Decision Neural Network to Solve Multiple Objective Decision-Making Problems

Training Algorithm Based on the Levenberg–Marquardt Method for Training Decision Neural Network to Solve Multiple Objective Decision-Making Problems

Training Algorithm Based on the Levenberg–Marquardt Method for Training Decision Neural Network to Solve Multiple Objective Decision-Making Problems

An integrated entropy-based multi-attribute decision-making model for phase change material selection and passive thermal management

The growing environmental concerns related to fossil-based power usage have led to a global transition towards renewable and conservative energy usage practices. In the context of human thermal comfort within enclosures, Phase Change Materials (PCMs) are deployed as effective passive conditioning media to curtail active air-conditioning demands. The successful functioning of PCM is strongly dependent upon a match between its thermophysical properties and the working environment’s thermal attributes. This creates a multifaceted selection scenario, given the availability of PCMs with a wide range of thermophysical properties. In this study, we investigate the case of selecting suitable PCM for passive thermal management in an automobile cabin. Various decision strategies and analytical methods are implemented to evaluate the efficacy of PCM selection using Multiple Objective Decision-Making (MODM) and Multiple Attribute Decision-Making (MADM) approaches. This study integrates different approaches and provides a comparative analysis of the selection processes. MODM is used for objectively evaluating the attributes (or properties of the PCMs) with Ashby’s approach. The Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are used along with the entropy-based weights. The selection algorithms suggest the suitability of Gallium and a few other PCMs for the desired application. This study could be a precursor to experimental and computational fluid dynamics (CFD)-based investigations for problems where the test set of PCMs could be narrowed down in the initial stage to save subsequent analysis time and money.

Mechanistic invasive species management models and their application in conservation

AbstractManagement strategies to address the challenges associated with invasive species are critical for effective conservation. An increasing variety of mathematical models offer insight into invasive populations, and can help managers identify cost effective prevention, control, and eradication actions. Despite this, as model complexity grows, so does the inaccessibility of these tools to conservation practitioners making decisions about management. Here, we seek to narrow the science‐practice gap by reviewing invasive species management models (ISMMs). We define ISMMs as mechanistic models used to explore invasive species management strategies, and include reaction‐advection–diffusion models, integrodifference equations, gravity models, particle transport models, nonspatial and spatial discrete‐time population growth models, cellular automata, and individual‐based models. For each approach, we describe the model framework and its implementation, discuss strengths and weaknesses, and give examples of conservation applications. We conclude by discussing how ISMMs can be used in concert with adaptive management to address scientific uncertainties impeding action and with multiple objective decision processes to evaluate tradeoffs among management objectives. We undertook this review to support more effective decision‐making involving invasive species by providing conservation practitioners with the information they need to identify tools most useful for their applications.

Analysis of Elastic Deflection of Single Point Cutting Tool and Optimization of Cutting Speed in Turning Operation by Using MADM Method

In the present paper, an analysis was carried out to optimize the cutting parameter i.e. speed in turning operation. The turning was done on a cylindrical rod with diameter and length of 35 mm and 300 mm respectively. The actual length of cutting was 100 mm. Five different cutting operations were taken place with five different cutting speeds i.e. 45, 105, 160, 270, 630 (in rpm) respectively. The surface roughness of the job and forces involved during cutting were measured through profilo-meter and dynamometer respectively. Also, the tool life was calculated by using Taylor’s tool life equation in each cutting condition. The elastic deflection of the tool in each cutting condition was simulated by using FUSION360 software. The optimum cutting speed was found out by applying multiple objective decision making (MADM) method.

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.

Implementasi Metode ANP-PROMETHEE Untuk Pemilihan Supplier (Studi Kasus PT. Lamongan Marine Industry)

The selection of suppliers is essential to minimize the cost of procuring raw materials for production. This research is important to do in order to increase company profits and ensure the smooth production process. This study aims to select suppliers of shipbuilding raw materials of PT. LMI based on the best ranking order using the ANP-PROMETHEE method implemented in a decision support system. The variables used are 16 sub criteria and 6 alternatives. The ANP-PROMETHEE method is used because of its advantages in handling multi-criteria assessment based on subjective assumptions from decision makers. Stages of the study began with the ANP method to get the importance of weight then proceed to the calculation with the PROMETHEE method to get an alternative ranking. The results of this study is an application as a Decision Support System (DSS) that can help make supplier selection based on predetermined criteria. From the results of the study it was concluded that the ANP-PROMETHEE method can be implemented and is the right solution for supplier selection, ANP handles the problem of multiple objective decision making so that objectivity is maintained, while PROMETHEE handles the multi attribute decision making problem. Other conclusions are obtained that the PROMETHEE calculation has a more significant influence in determining the final outcome of an alternative ranking than the ANP. An input of ANP value is needed with a long number interval so that the results of the calculation of the Consistency Ratio (CR) approach the CR threshold (0.1) so that the ANP has a significant influence in determining the final outcome of an alternative ranking. The CR number that was tested was 0.0629 and proved unable to change the alternative ranking final results, when the CR number that was tested was 0.0993, it was proven to be able to change the alternative ranking final results.

A bi-objective robust possibilistic programming model for blood supply chain design in the mass casualty event response phase: A M/M/1/K queuing model with real world application

This paper presents a bi-objective blood supply chain design model for disaster relief. The model aims to simultaneously minimise the total costs of the supply chain, the trade-off between the total expected waiting times of patients in hospitals and the average hospital idle-time probability, unsatisfied demands and the average delivery time from mobile blood facilities to healthcare centres as the first, second, third and fourth objective functions. Since the critical parameters are tainted with great degree of epistemic uncertainty, basic chance constraint programming (BCCP) and robust fuzzy chance constraint programming (RFCCP) are utilised to deal with the uncertain nature of the supply chain. In order to solve the proposed model, two different multiple objective decision making approaches are used. The applicability of the proposed model for earthquake response phase is demonstrated via a real case study in a region of Iran. Useful managerial insights are also provided through conducting some sensitivity analyses.

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.

Topology design of remote patient monitoring system concerning qualitative and quantitative issues

Piecewise linear functions have been used to model and solve non-linear problems in science, social science, and technology fields since the 1950′s. Multi-choice goal programming (MCGP) has been widely used to solve multiple objective decision-making (MODM) problems in which each goal mapping with multiple aspiration levels is allowed, expanding the original feasible region to obtain better solutions in the MODM problems. This paper integrates the efficient S-shaped penalty method, arbitrary piecewise linear utility functions, trapezoidal utility functions, and MCGP to solve a topology design problem in a remote patient monitoring system (RPMS) by providing universal senior citizen coverage in which quantitative (e.g., patient satisfaction-related cost) and qualitative (e.g., satisfaction) issues are considered simultaneously. In addition, some novel utility functions such as the force utility function, indicator utility function, and arbitrary utility function are proposed to improve the usefulness of MCGP in the field of management science. These proposed utility functions can be easily used to model qualitative issues in real-world problems. A topology design problem for an RPMS is demonstrated to justify the feasibility, usefulness, and compatibility of the proposed methods. Further, sensitivity analysis and managerial implications are provided using an RPMS in Taiwan.

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.

A structured method for smart city project selection

The concept of a smart city, based on advanced information and communications technology (ICT), emerged to mitigate the impact of rapid urbanization and was considered feasible. However, the selection of technology and policy for providing better services to citizens and ensuring sustainable development is a multiple-objective decision process that is usually performed by experts in relevant domains. The major goal of this study is to propose a structural method for policy selection, which consists of three phases. In the first phase, the modified Delphi method is used to determine the elements of the decision by surveying panel members for their opinions. In the second phase, an analytic hierarchy process is used to ascertain the priority of each alternative according to the goal of the decision. In the third phase, zero-one goal programming models are developed to select a feasible portfolio based on the political goal and the annual budget. We conducted an empirical study to demonstrate and validate that the proposed model can induce the municipality to consider citizens’ requirements, identify the strengths and weaknesses of proposed policies, and select a feasible project portfolio in response to public expectations. In addition, the study found that a feasible portfolio, including consideration of citizens, business, and the environment, enables the public perceptions of government performance within the resource constraints of the organization.

The roles of aspirations, coefficients and utility functions in multiple objective decision making

Decisions using multiple aspiration levels, coefficients, and utility functions to maximize profit/minimize cost are commonly seen in many companies. Understanding the roles of these factors may improve decision making effectiveness. However, the roles of these techniques are rarely discussed in conjunction with solving real problems. This paper addresses the roles of aspirations levels, coefficients attached to the objective/constraints and utility functions for representing the decision makers’ preference. Accordingly, a decision maker can easily use these techniques to solve their management/decision problems. This study creates several practical models to improve their utilization in the decision and management fields. In addition, new utility functions are also proposed as a qualitative tool to enrich the usefulness of the proposed models and increase the usage of MODM methods in solving business decision problems. In short, both qualitative and quantitative problems are considered at the same time in the proposed model to deal with more real-world situations. Therefore, the usefulness of the proposed methods in the MODM field is improved.