Decision Problem Research Articles

Offshoring Location Decision in Fuzzy Environment

Offshoring location selection is a crucial decision for firms in terms of competitiveness, flexibility, productivity, and profitability. Determining an efficient and appropriate location for offshoring has been a substantial multicriteria decision-making (MCDM) problem. Considering that the outcome of an MCDM method alone can be misleading, a novel hybrid approach is presented in this study. Thus, five MCDM methods are utilized to solve the problem, and the results of four MCDM methods are integrated to assure an optimal offshoring location. A Fuzzy-AHP (analytical hierarchy process) integrated with the technique for order preference by similarity to ideal solution (TOPSIS), additive ratio assessment (ARAS), elimination et choix traduisant la realité (ELECTRE), and weighted sum method (WSM) methodology is proposed for the appraisal and selection of the optimal offshoring location. In this context, fifteen alternative locations are determined based on the attractiveness of the locations in terms of offshoring. Fuzzy-AHP is implemented to analyze the problem's structure and find the weights of the quantitative and qualitative criteria. Consistency tests are implemented to assess the quality of inputs of an expert. Then, TOPSIS, WSM, ARAS, and ELECTRE are used to evaluate and rank the candidate locations and present a comparative analysis. By considering fifteen countries and using real data, offshoring location selection is conducted through the proposed methodology. Moreover, sensitivity analysis is made to diminish the subjectivity and assess the robustness of the techniques. The results demonstrated that giving more weights to the labor characteristics and proximity to market criteria might improve the quality of the best offshoring country index.

Synchronous Dynamical Systems on Directed Acyclic Graphs: Complexity and Algorithms

Discrete dynamical systems serve as useful formal models to study diffusion phenomena in social networks. Several recent articles have studied the algorithmic and complexity aspects of some decision problems on synchronous Boolean networks, which are discrete dynamical systems whose underlying graphs are directed, and may contain directed cycles. Such problems can be regarded as reachability problems in the phase space of the corresponding dynamical system. Previous work has shown that some of these decision problems become efficiently solvable for systems on directed acyclic graphs (DAGs). Motivated by this line of work, we investigate a number of decision problems for dynamical systems whose underlying graphs are DAGs. We show that computational intractability (i.e., PSPACE -completeness) results for reachability problems hold even for dynamical systems on DAGs. We also identify some restricted versions of dynamical systems on DAGs for which reachability problem can be solved efficiently. In addition, we show that a decision problem (namely, Convergence), which is efficiently solvable for dynamical systems on DAGs, becomes PSPACE -complete for Quasi-DAGs (i.e., graphs that become DAGs by the removal of a single edge). In the process of establishing the above results, we also develop several structural properties of the phase spaces of dynamical systems on DAGs.

Decision Model of Taxi Drivers at High-Speed Railway Stations Based on Profit Priority

ABSTRACT After dropping off passengers at the high-speed railway (HSR) station, taxi drivers face a crucial decision: whether to stay at the station or return empty. To solve the decision problem, this paper establishes a weighting model utilizing the Analytic Hierarchy Process (AHP) combined with the entropy weight method. This model determines the significance of various factors influencing drivers’ decisions. Subsequently, a decision model for taxi drivers at the HSR station is established by comparing the profitability of staying at the station versus leaving. Furthermore, the sensitivity analysis method is employed to quantitatively assess the model's dependency on influencing factors to confirm the model's effectiveness. Utilizing GPS trajectory data from Lanzhou taxis, this paper validates the decision-making model. The findings suggest that in the afternoon and evening, drivers are more likely to wait in the parking lot for passengers, whereas in the morning, they are more inclined to return empty.

On the computational complexity of the strong geodetic recognition problem

A vertex set $S$ of a graph $G$ is a strong geodetic set if it is possible to choose exactly one shortest path for each pair of vertices in $S$, and cover all the remaining vertices of the graph with these paths. In the \textsc{Strong Geodetic} problem (SG) a graph~$G$ and a positive integer~$k$ are given as input and one has to decide whether~$G$ has a strong geodetic set of cardinality at most~$k$. This problem is known to be \NP-complete for general graphs. In this work we introduce the \textsc{Strong Geodetic Recognition} problem (SGR), which consists in determining whether a given vertex set~$S \subseteq V(G)$ is strong geodetic. We demonstrate that this version is \NP-complete. We investigate and compare the computational complexity of both decision problems restricted to some graph classes, deriving polynomial-time algorithms, \NP-completeness proofs, and initial parameterized complexity results, including an answer to an open question in the literature for the complexity of SG for chordal graphs.

An integrated group decision-making framework for assessing S3PRLPs based on MULTIMOORA-WASPAS with q-rung orthopair fuzzy information

Sustainable third-party reverse logistics has gradually risen to prominence as a component of contemporary commercial development as a result of the acceleration of global economic integration and the prominent growth of information technology in the logistics industry. In the procedure of sustainable third-party reverse logistics providers (S3PRLPs) selection, indeterminacy and conflict information bring great challenges to decision experts. In view of the significant superiority of q-rung orthopair fuzzy (q-ROF) set in expressing uncertain and vague assessment information, this essay designs a comprehensive assessment framework through merging the best and worst method (BWM), Multiplicative Multi-objective Optimization by Ratio Analysis with Full Multiplicative Form (MULTIMOORA) and weighted aggregated sum product assessment (WASPAS) method to address the S3PRLPs selection issue with entirely unknown weight information under q-ROF setting. Firstly, we present a novel score function for comparing q-ROF numbers after analyzing the inadequacies of previous works. Secondly, the q-ROF Frank interactive weighted average (q-ROFFIWA) and q-ROF Frank interactive weighted geometric (q-ROFFIWG) operators are advanced based on the constructed operations to take into consideration the interactive impact of information fusion procedure. Thirdly, the q-ROF-MULTIMOORA-WASPAS decision framework is built based on novel score function and the developed operators, in which the synthetic weights of the criterion are determined by the modified BWM and entropy weight method to reflect both the subjectivity of the decision expert and the objectivity of the decision information. Ultimately, an empirical example was used to evaluate S3PRLPs to demonstrate the applicability and feasibility of the developed methodology, and a comparative analysis was conducted with other existing methods to highlight its advantages in dealing with complex decision problems. The discussion from the research indicates that the developed methodology can be used to evaluate S3PRLPs and further improve the quality of logistics services for organizations.

A Confidence and Conflict-Based Consensus Reaching Process for Large-Scale Group Decision-Making Problems With Intuitionistic Fuzzy Representations

A Confidence and Conflict-Based Consensus Reaching Process for Large-Scale Group Decision-Making Problems With Intuitionistic Fuzzy Representations

Stackelberg game based optimal water allocation from the perspective of energy-water nexus: A case study of Minjiang River, China

Energy and water are interdependent. Water is essential for energy production, and energy is indispensable for water processes. This paper develops a Stackelberg game based bi-level multi-objective model for optimal water allocation under uncertainties from an energy-water nexus perspective, which considers energy-related water consumption and water-related energy production. It handles the hierarchical decision-making problem between the river basin authority and the subareas, as well as the tradeoffs between social equality and ecological damage. The upper level of the model makes schemes on water allocation for multiple subareas, and the lower level decides on water allocation for different types of power generation. To generate reasonable and practical strategies for water allocation, ɛ-constraint method and KKT conditions are used. A case study of Minjiang River, China is given to demonstrate the applicability and practicality. Scenarios analysis is also conducted to demonstrate the effectiveness of the model and the effects of the control parameter changes. The results indicate that the river basin authority would allocate larger water resources to the subareas with higher energy demand. It is concluded that the proposed allocation method is advantageous to the subareas with higher water utilization efficiency. The proposed bi-level model demonstrates its reasonableness and superiority to the single-level model. Policy implications are finally provided to support the sustainable development of energy-water nexus.

Evaluating the deep learning software tools for large-scale enterprises using a novel TODIFFA-MCDM framework

Deep learning (DL) is one of the most promising technological developments emerging in the fourth industrial revolution era for businesses to improve processes, increase efficiency, and reduce errors. Accordingly, hierarchical learning software selection is one of the most critical decision-making problems in integrating neural network applications into business models. However, selecting appropriate reinforcement learning software for integrating deep learning applications into enterprises’ business models takes much work for decision-makers. There are several reasons for this: first, practitioners’ limited knowledge and experience of DL makes it difficult for decision-makers to adapt this technology into their enterprises’ business model and significantly increases complex uncertainties. Secondly, according to the authors’ knowledge, no study in the literature addresses deep structured learning solutions with the help of MCDM approaches. Consequently, making inferences concerning criteria that should be considered in an evaluation process is impossible by considering the studies in the relevant literature. Considering these gaps, this study presents a novel decision-making approach developed by the authors. It involves the combination of two new decision-making approaches, MAXC (MAXimum of Criterion) and TODIFFA (the total differential of alternative), which were developed to solve current decision-making problems. When the most important advantages of this model are considered, it associates objective and subjective approaches and eliminates some critical limitations of these methodologies. Besides, it has an easily followable algorithm without the need for advanced mathematical knowledge for practitioners and provides highly stable and reliable results in solving complex decision-making problems. Another novelty of the study is that the criteria are determined with a long-term negotiation process that is part of comprehensive fieldwork with specialists. When the conclusions obtained using this model are briefly reviewed, the C2 “Data Availability and Quality” criterion is the most influential in selecting deep learning software. The C7 “Time Constraints” criterion follows the most influential factor. Remarkably, prior research has overlooked the correlation between the performance of Deep Learning (DL) platforms and the quality and accessibility of data. The findings of this study underscore the necessity for DL platform developers to devise solutions to enable DL platforms to operate effectively, notwithstanding the availability of clean, high-quality, and adequate data. Finally, the robustness check carried out to test the validity of the proposed model confirms the accuracy and robustness of the results obtained by implementing the suggested model.

Improvement of 5G Core Network Performance using Network Slicing and Deep Reinforcement Learning

Users have increasingly been having more use cases for the network while expecting the best Quality of Service (QoS) and Quality of Experience (QoE). The Fifth Generation of mobile telecommunications technology (5G) network had promised to satisfy most of the expectations and network slicing had been introduced in 5G to be able to satisfy various use cases. However, creating slices in a real-life environment with just the resources required while having optimized QoS has been a challenge. This has necessitated more intelligence to be required in the network and machine learning (ML) has been used recently to add the intelligence and ensure zero-touch automation. This research addresses the open question of creating slices to satisfy various use cases based on their QoS requirements, managing, and orchestrating them optimally with minimal resources while allowing the isolation of services by introducing a Deep reinforcement Machine Learning (DRL) algorithm. This research first evaluates the previous work done in improving QoS in the 5G core. 5G architecture is simulated by following the ETSI NFV MANO (European Telecommunications Standards Institute for Network Function Virtualization Management and Orchestration) framework and uses Open5G in 5G core, UERANISM for RAN, Openstack for Virtual Infrastructure Manager (VIM), and Tacker for Virtual Network Function Management and orchestration (VNFMO). The research simulates network slicing at the User Plane Function (UPF) level and evaluates how it has improved QoS. The network slicing function is automated by following ETSI Closed Loop Architecture and using Deep Reinforcement Learning (DRL) by modeling the problem as a Markov Decision Problem (MDP). Throughput is the Reward for the actions of the DRL agent. Comparison is done on the impact of slicing on throughput and compares models that have not been sliced, the ones that have been sliced and combined to work together, and models with slices that have been assigned more bandwidth. Sliced networks have better throughput than the ones not sliced. If more slices are load-balanced the throughput is increased. Deep Reinforcement Learning has managed to achieve the dynamic assigning of slices to compensate for declining throughput.

A dynamic model for performance evaluations: an integrated approach based on P-AHP and aggregation operators

PurposeMulti-criteria methods represent an adequate tool for solving complex decision problems that provide real support to the decision maker in the choice process. This paper analyzes a decision problem that recurs over time using one of the newer methods as the Parsimonious AHP.Design/methodology/approachIn this paper we integrated the P-AHP with: (1) the weighted average which takes into account the objectivity of the data; (2) ordered weighted average (OWA) aggregation operators that address the subjective nature of the data; (3) the Choquet integral and (4) the Sugeno integral which also considers the uncertain nature of the final ranking as it is defined on a fuzzy measure.FindingsThe present paper proves that variations in the final ranking, due to the different mathematical properties of the selected aggregators, are fundamental to select the best alternative without neglecting any characteristic of the input data. In fact, it is discussed and underlined how and why the best alternative is one that never excels but has very good positions with respect to all aggregation operator rankings.Originality/valueThe aim and innovation presented in this work is the use of the Parsimonious AHP (P-AHP) method in a dynamic way with the use of different aggregation techniques.

Determining Risk Management Priorities and Suggesting Strategies in the Management of Family Health Centres

Aim: The purpose of this study is to identify the risks faced by decision-makers in the management of Family Health Center (FHC) and to propose strategies according to their relative importance. Materials and Methods: Eight different risks were identified as a result of the literature review. In order to evaluate the identified risks, four different physicians with at least ten years of managerial experience in FHCs and two health managers with at least ten years of experience in their field were included in the study. SWARA (Stepwise Weight Assessment Ratio Analysis) method was used to evaluate and analyze the opinions. SWARA method is a weight calculation method based on expert opinions developed for the finalization of decision problems. Results: According to the results of the analysis, experts ranked financial risks (%27), clinical and patient safety risks (%21), and operational risks (%15) as the most important risks, respectively. Hazard risks (%4) were assessed as the least risky in relative terms. For FHCs to carry out their activities effectively, financial risks should be minimized first. Conclusions: Additional measures should be taken by the public for the use and rental of FHCs. Diversification of other sources of income can be ensured by expanding the health services provided beyond the resource transfer linked to the public budget. Regional investment and incentives can be developed by municipalities. Strengthening data protection policies to assess the compliance of medical practices with legal standards and to ensure the security of personal patient data can also reduce legal risks. In FHCs, general safety issues, especially patient safety, are also priority issues that should be investigated and legislation should be developed. In addition, management courses/training can be added to the family medicine specialty curriculum to improve the managerial skills of physicians.

The grey decision model and its application based on generalized greyness of interval grey number

PurposeIn order to solve the decision-making problem that the attributive weight and attributive value are both interval grey numbers, this paper tries to construct a multi-attribute grey decision-making model based on generalized greyness of interval grey number.Design/methodology/approachFirstly, according to the nature of the generalized gresness of interval grey number, the generalized weighted greyness distance between interval grey numbers is given, and the transformation relationship between greyness distance and real number distance is analyzed. Then according to the objective function that the square sum of generalized weighted greyness distances from the decision scheme to the best scheme and the worst scheme is the minimum, a multi-attribute grey decision-making model is constructed, and the simplified form of the model is given. Finally, the grey decision-making model proposed in this paper is applied to the evaluation of technological innovation capability of 6 provinces in China to verify the effectiveness of the model.FindingsThe results show that the grey decision-making model proposed in this paper has a strict mathematical foundation, clear physical meaning, simple calculation and easy programming. The application example shows that the grey decision model in this paper is feasible and effective. The research results not only enrich the grey system theory, but also provide a new way for the decision-making problem that the attributive weights and attributive values are interval grey numbers.Practical implicationsThe decision-making model proposed in this paper does not need to seek the optimal solution of the attributive weight and the attributive value, and can save the decision-making labor and capital investment. The model in this paper is also suitable for the decision-making problem that deals with the coexistence of interval grey numbers and real numbers.Originality/valueThe paper succeeds in realizing the multi-attribute grey decision-making model based on generalized gresness and its simplified forms, which provide a new method for grey decision analysis.

Reframing wildlife disease management problems with decision analysis.

Contemporary wildlife disease management is complex because managers need to respond to a wide range of stakeholders, multiple uncertainties, and difficult trade-offs that characterize the interconnected challenges of today. Despite general acknowledgment of these complexities, managing wildlife disease tends to be framed as a scientific problem, in which the major challenge is lack of knowledge. The complex and multifactorial process of decision-making is collapsed into a scientific endeavor to reduce uncertainty. As a result, contemporary decision-making may be oversimplified, rely on simple heuristics, and fail to account for the broader legal, social, and economic context in which the decisions are made. Concurrently, scientific research on wildlife disease may be distant from this decision context, resulting in information that may not be directly relevant to the pertinent management questions. We propose reframing wildlife disease management challenges as decision problems and addressing them with decision analytical tools to divide the complex problems into more cognitively manageable elements. In particular, structured decision-making has the potential to improve the quality, rigor, and transparency of decisions about wildlife disease in a variety of systems. Examples of management of severe acute respiratory syndrome coronavirus 2, white-nose syndrome, avian influenza, and chytridiomycosis illustrate the most common impediments to decision-making, including competing objectives, risks, prediction uncertainty, and limited resources.

Personnel Selection Based on the LBWA, TOPSIS and GRA Methods: A Case Study on Foreign Trade Company

Recruitment and personnel selection are affected by significant factors. Thus, personnel selection is one of the main decision-making problems for a company’s long-term survival. The objective of this study is to identify the most suitable candidate for the export department of a company operating in Mersin, using the Level Based Weight Assessment (LBWA)-based Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) and Grey Relational Analysis (GRA) methods. The criteria were determined based on the literature review and experts’ opinions. The weight of criteria was calculated by the LBWA method, and the alternatives (candidates) were ranked using the TOPSIS and GRA methods. The LBWA results showed that fluency in a foreign language and team player were the most and least important criteria, respectively. The results from both methods (TOPSIS and GRA) suggested different candidates for the relevant positions. Furthermore, sensitivity analyses were conducted to assess the validity and robustness of the results. In conclusion, the findings of this study provide valuable insights to decision-makers involved in the personnel selection process.

Twin-system recurrent reinforcement learning for optimizing portfolio strategy

Portfolio management is important for sequential investment decisions in response to fluctuating financial markets. As portfolio management can be formulated as a sequential decision-making problem, it has been addressed using reinforcement learning in recent years. However, reinforcement learning methods face challenges in addressing portfolio management problems considering practical constraints. To overcome the limitations, this study proposes a twin-system approach, establishing a tractable twin that mirrors the original problem but with more manageable constraints and system dynamics. Once an optimized portfolio strategy is achieved within the tractable twin, the proposed mapping function translates it back to the original problem, ensuring the retention of optimized performance. Unlike the previous study, the proposed recurrent reinforcement learning method optimizes the portfolio strategy for a single agent managing all candidate assets. This method allows for comprehensive investment decisions by incorporating the features of candidate assets, leading to a more globally optimized portfolio strategy. Experimental studies demonstrate that the proposed method consistently outperforms benchmark strategies on the US sector and foreign exchange portfolios.

Optimized Decision-Making for Multi-Market Green Power Transactions of Electricity Retailers under Demand-Side Response: The Chinese Market Case Study

With the energy structure transition and the development of the green power market, the role of electricity retailers in multi-market green power trading has become more and more important. Particularly in China, where aggressive green energy policies and rapid market transformations provide a distinct context for such studies, the challenges are pronounced. Under demand-side response, electricity retailers face the uncertainty of users’ electricity consumption and incentives, which complicates decision-making processes. The purpose of this paper is to explore the optimization decision-making problem of multi-market green power trading for electricity retailers under demand-side response, with a special focus on the Chinese market due to its leadership in implementing substantial green energy initiatives and its potential to set precedents for global practices. We first construct a two-party benefit optimization model, which comprehensively considers the profit objectives for electricity retailers and utility maximization for users. Then, the model is solved by the Lagrange multiplier method and distributed subgradient algorithm to obtain the optimal solution. Finally, the effectiveness of the incentive optimization strategy under the multi-market to promote green power consumption and improve the profit of electricity retailers is verified by arithmetic simulation. The results of this study show that the incentive optimization strategy under multi-market, particularly within the Chinese context, is expected to provide a reference for electricity retailers to develop more flexible and effective trading strategies in the green power market and to contribute to the process of promoting green power consumption globally.

Multi-agent reinforcement learning clustering algorithm based on silhouette coefficient

As an important branch of emerging artificial intelligence algorithms, multi-agent reinforcement learning (MARL) has shown strong performance in collaborative environments. It can utilize multiple agents to find the optimal set of strategies for solving sequential decision problem through trial-and-error. One of the main challenges facing multi-agent system is the non-stationarity problem, which brings poor convergence and seriously affects its performance. Clustering is a commonly used unsupervised analytical method in machine learning, which aims to group samples with similar internal properties into the same cluster. In this paper, we propose a MARL clustering algorithm based on silhouette coefficient (SC-MARLC), and use the trial-and-error strategy to find the best cluster groups. In SC-MARLC, we establish a mapping relationship between multi-agent and samples, construct a novel clustering model based on MARL, and design a good clustering subset structure based on the sample silhouette coefficient. The designed structure is helpful for multi-agent system to solve the non-stationary problem. Finally, we compare the performance of SC-MARLC with 11 existing clustering algorithms on fifteen public datasets. The results show that the new clustering algorithm performs best on ten datasets.

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.

On Computing the \(k\) -Shortcut Fréchet Distance

The Fréchet distance is a popular measure of dissimilarity for polygonal curves. It is defined as a min-max formulation that considers all orientation-preserving bijective mappings between the two curves. Because of its susceptibility to noise, Driemel and Har-Peled introduced the shortcut Fréchet distance in 2012, where one is allowed to take shortcuts along one of the curves, similar to the edit distance for sequences. We analyse the parameterized version of this problem, where the number of shortcuts is bounded by a parameter \(k\) . The corresponding decision problem can be stated as follows: Given two polygonal curves \(T\) and \(B\) of at most \(n\) vertices, a parameter \(k\) and a distance threshold \(\delta\) , is it possible to introduce \(k\) shortcuts along \(B\) such that the Fréchet distance of the resulting curve and the curve \(T\) is at most \(\delta\) ? We study this problem for polygonal curves in the plane. We provide a complexity analysis for this problem with the following results: (i) there exists a decision algorithm with running time in \(\mathcal{O}(kn^{2k+2}\log n)\) ; (ii) assuming the exponential-time-hypothesis (ETH), there exists no algorithm with running time bounded by \(n^{o(k)}\) . In contrast, we also show that efficient approximate decider algorithms are possible, even when \(k\) is large. We present a \((3+\varepsilon)\) -approximate decider algorithm with running time in \(\mathcal{O}(kn^{2}\log^{2}n)\) for fixed \(\varepsilon\) . In addition, we can show that, if \(k\) is a constant and the two curves are \(c\) -packed for some constant \(c\) , then the approximate decider algorithm runs in near-linear time.

A Comprehensive Guide to the COPRAS method for Multi-Criteria Decision Making

MCDM has been utilized as a proficient decision-making technique for numerous decades. Complex Proportional Assessment (COPRAS) method, a prominent technique in multi-criteria decision-making (MCDM) which offers a systematic and effective framework for evaluating alternatives and making informed choices. The versatility of COPRAS is demonstrated via case studies across various domains, such as engineering, business, and environmental management, showcasing its adaptability and robustness in providing solutions to diverse decision-making scenarios. There is a lack of a comprehensive guide and a reviewing of application, strengths, and limitation for this method in the literature. Therefore, this study aims to offer an in-depth understanding of the COPRAS approach, including its applications, advantages, and disadvantages. Additionally, it provides detailed guidance on how to utilize the COPRAS methodology for decision-making and real-life problems.