Management Decision Problems Research Articles

The CO<SUB align="right">2 management decision problem in tactical sales planning of light commercial vehicle manufacturers in Europe

The CO<SUB align="right">2 management decision problem in tactical sales planning of light commercial vehicle manufacturers in Europe

The CO2 management decision problem in tactical sales planning of light commercial vehicle manufacturers in Europe

The CO2 management decision problem in tactical sales planning of light commercial vehicle manufacturers in Europe

Customer segmentation, pricing, and lead time decisions: A stochastic-user-equilibrium perspective

Multiple price/lead time options are often used as a competitive tool in supply chain operations when customers have heterogeneous price and lead time preferences. We study a two-echelon supply chain network consisting of manufacturers and retailers facing customers that differ in their price- and time-sensitivity. We examine how many price/lead time options should be provided by manufacturers and retailers under decentralized and centralized supply chain management with time-cost tradeoffs. We adopt a stochastic-user-equilibrium (SUE) approach in a supply chain network by incorporating discrete choice theory and using a multinomial logit-based variational inequality to express equilibrium conditions. This is one of the first papers to study time-cost tradeoffs in a supply chain network by introducing an SUE approach. One critical part of our analysis is the establishment of concavity of profit functions, which allows for analytical derivation of the equilibrium strategies. Another critical part of our analysis is the development of SUE conditions in decentralized and centralized supply chain networks. We demonstrate that the variance of heterogeneous customers’ time-sensitivity distribution plays a crucial role in customer segmentations in a time-cost tradeoff supply chain. We find that under SUE conditions, there exists a unique equilibrium in the decentralized and centralized supply chain networks, respectively. We conduct comparative statics analyze to demonstrate the effect of SUE and UE conditions and the influence of decentralized versus centralized supply chain management paradigms. We show how the SUE approach can be applied to supply chain network management with time-cost tradeoffs. Our approach can be extended to other tradeoff decision problems in supply chain network management, especially those in which customers are heterogeneous.

Variable precision multi-granulation composite rough sets with multi-decision and their applications to medical diagnosis

Rough set theory has become an effective tool to address uncertain decision-making problems. Nevertheless, existing rough set-based decision-making methods cannot effectively deal with the management decision-making problems with heterogeneity, multi-decision, and diversified attributes simultaneously. Given this, the purpose of this paper is to explore an effective classification solution for solving decision-making problems with the above-described characteristics from the perspective of granular computing. First, we present the notion of heterogeneous diversified attribute multi-decision systems. Because attribute semantics is crucial to multi-attribute decision analysis, we then divide an attribute set into nine subsets according to attribute semantics. Further, a composite binary relation that fits the realistic attribute features is constructed. Subsequently, considering different decision scenarios, we establish four kinds of variable precision multi-decision multi-granulation rough set models. Meanwhile, the related properties and relationships of these models are explored. We find that the established models are extensions of the existing classical rough sets. In the end, we apply the established models to an illustrative example of medical diagnosis. The results of the case and comparative analyses demonstrate the models’ feasibility, effectiveness, and superiority. As a result, our work provides a kind of new decision thinking with reasonable semantic analysis for solving complex management decision-making problems in reality.

Novel distance and entropy definitions for linear Diophantine fuzzy sets and an extension of TOPSIS (LDF‐TOPSIS)

AbstractThe literature of multiple attribute decision making (MADM) is fruitful since there are various and successful applications of different fuzzy set extensions such as intuitionistic, Pythagorean and q‐Rung orthopair fuzzy sets (IFS, PFS and q‐ROFS). Besides their powerful aspects, some definitional limitations are known. In order to eradicate these boundaries regarding the definitions of membership and non‐membership degrees, linear Diophantine fuzzy set (LDFS) concept has been recently emerged. By considering two parameters, LDFS extends the representation area of the previous fuzzy set definitions and provides more extensive human judgement coverage field. In this study, the first distance and entropy measures in the literature have been developed for LDFSs. Their axiomatic definitions are given, and the proofs are shown. Also, thanks to our extensive literature review, we became aware that there is no MADM extension dedicatedly proposed for LDFS. So, the first MADM method extension for LDFS environment has also been developed in this study. A very well‐known MADM approach, TOPSIS, has been extended into LDFS environment for the first time in the literature. The applicability is shown in a healthcare management decision problem and the validity is checked and approved by comparing the alternative rankings LDF‐TOPSIS and the aggregation operators that were obtained from the literature produced.

A sequential decision and data analytics framework for maximizing value and reliability of CO2 storage monitoring

Carbon capture and sequestration (carbon capture and storage or CCS) represents a unique potential strategy that can minimize CO2 emissions in the atmosphere, and it creates a pathway toward a neutral carbon balance, which cannot be solely achieved by combining energy efficiency and other forms of low carbon energy. To contribute to the decision-making process and ensure that CCS is successful and safe, an adequate monitoring program must be implemented to prevent storage reservoir leakage and contamination of drinking water in groundwater aquifers. In this paper, we propose an approach to perform value of information (VOI) analyses to address sequential decision problems in reservoir management in the context of monitoring the geological storage of CO2 operations. These sequential decision problems are often solved and modeled by approximate dynamic programming (ADP), which is a powerful technique for handling complex large-scale problems and finding a near-optimal solution for intractable sequential decision-making. In this study, we tested machine learning techniques that fall within ADP to estimate the VOI and determine the optimal time to stop CO2 injections into the reservoir based on information from seismic surveys. This ADP approach accounts for both the effect of the information obtained before a decision and the effect of the information that might be obtained to support future decisions while significantly improving the timing, value of the decision, and uncertainty of the CO2 plume behavior, thereby significantly increasing economic performance. The Utsira saline aquifer west of Norway was used to exemplify ADP’s ability to improve decision support regarding CO2 storage projects.

Application of machine learning to assess the value of information in polymer flooding

In this work, we provide a more consistent alternative for performing value of information (VOI) analyses to address sequential decision problems in reservoir management and generate insights on the process of reservoir decision-making. These sequential decision problems are often solved and modeled as stochastic dynamic programs, but once the state space becomes large and complex, traditional techniques, such as policy iteration and backward induction, quickly become computationally demanding and intractable. To resolve these issues and utilize fewer computational resources, we instead make use of a viable alternative called approximate dynamic programming (ADP), which is a powerful solution technique that can handle complex, large-scale problems and discover a near-optimal solution for intractable sequential decision making. We compare and test the performance of several machine learning techniques that lie within the domain of ADP to determine the optimal time for beginning a polymer flooding process within a reservoir development plan. The approximate dynamic approach utilized here takes into account both the effect of the information obtained before a decision is made and the effect of the information that might be obtained to support future decisions while significantly improving both the timing and the value of the decision, thereby leading to a significant increase in economic performance.

Integrated Laycan and Berth Allocation and time-invariant Quay Crane Assignment Problem in tidal ports with multiple quays

Efficient management of port resources plays a crucial role in reducing vessel stay times and avoiding the payment of demurrage charges. In this paper, we focus on the integrated Laycan and Berth Allocation and Quay Crane Assignment Problem, which considers three main decision problems in port management in an integrated way: the Laycan Allocation Problem, the dynamic continuous Berth Allocation Problem and the time-invariant Quay Crane Assignment Problem. In a second part, the integrated problem is extended to the Specific Quay Crane Assignment, which includes the assignment of a set of specific quay cranes to each vessel, considering the productivity of quay cranes and their maximum outreach. The proposed integer programming models are original in several ways. First, the formulation of the models uses predicates which ensure flexibility in the implementation, and significantly improve the computational performance. The numerical study shows that the problems of practical size can be solved to optimality in a reasonable time using commercial software. Second, since the studied problems have different decision levels, a change of decision time-interval is incorporated inside the planning horizon for seamless decision-making. Third, to ensure that this integrated problem is as close as possible to reality, we consider both physical characteristics of the ports rarely studied together (tidal ports with multiple quays and different water depths) and contractual clauses (non-working periods and Charter Party clauses). The output of the models is an efficient schedule for berthing chartered vessels with an efficient quay crane assignment, and laycans to new vessels to charter.

A Fuzzy Multi-objective Mathematical Programming Model for Project Management Decisions Considering Quality and Contractual Reward and Penalty Costs in a Project Network

Project management is a process that schemes and controls the project life cycle via the easiest and the best way to achieve project goals. Project managers always aim to simultaneously handle conflicting goals in the organization. In this paper, a new mathematical model is proposed that simultaneously minimizes total cost and completion time and maximizes the quality in the project management decision problem. Contractual penalty cost and contractual reward cost with a new method are the other consideration in the proposed model. In the projects, the relation between time and direct cost is a nonlinear function. Hence, a linearization technique is presented with attention to variable change and piecewise linearization, in which nonlinear function is converted to the linear programming model. On the other hand, in real conditions according to uncertainty in environmental situations and incomplete information, there can be ambiguity in parameters and variables of the problem. The uncertainty of the parameters and variables is expressed with fuzzy sets theory and fuzzy mathematical programming. The other aim of this paper is to introduce a modified version of fully fuzzy multi-objective linear programming for the problem. For analyzing a fully fuzzy time–cost–quality project management model, a practical example of the literature is provided. By examining the results of the model with conflicting objectives, two scenarios are presented to explore the interactions of conflicting objectives on the project, and the results are reported.

Optimization-Based Consensus Model to Solve Multi-Criteria Large Group Decision Making Problems

In this contribution, a consensus model is proposed to acquire a unified and converging solution of multi-criteria large group decision making problems. Unlike the iterative process and feedback mechanism based models, the suggested approach features the optimization theory to establish the consensus in one go only among the efficient experts. The time salvation characteristic of the model makes it expedient for the emergency planning and management decision problems. The algorithm is validated using the hurricane evacuation notification time problem of United States.

Redesigning a transportation network: the case of a pharmaceutical supply chain

Designing transportation networks - as one of the most important decision problems in supply chain management - has become the focus of research attention in recent years. This paper introduces a framework which deals with the design of a transportation network for pharmaceutical supply chains. It consists of three steps: 1) identifying the configuration of the current distribution network; 2) designing an optimal distribution network while determining the appropriate location-allocation decisions; 3) choosing the most appropriate transportation network through the application of the appropriate multi-criteria decision analysis method (MCDA). The proposed framework is illustrated in redesigning a transportation network for a pharmaceutical supply chain.

Redesigning a transportation network: the case of a pharmaceutical supply chain

Designing transportation networks - as one of the most important decision problems in supply chain management - has become the focus of research attention in recent years. This paper introduces a framework which deals with the design of a transportation network for pharmaceutical supply chains. It consists of three steps: 1) identifying the configuration of the current distribution network; 2) designing an optimal distribution network while determining the appropriate location-allocation decisions; 3) choosing the most appropriate transportation network through the application of the appropriate multi-criteria decision analysis method (MCDA). The proposed framework is illustrated in redesigning a transportation network for a pharmaceutical supply chain.

NEURO-FUZZY MULTICRITERIA ASSESSMENT MODEL

Context. The research of the actual problem of development of models and methods of multicriteria evaluation using neurofuzzy technologies is carried out. The purpose of this work is to develop a model for obtaining an aggregate evaluation of the significance of the object of study, which on the one hand uses different characteristics of the object, evaluated by quantitative indicators and on the basis of different models of representation of knowledge about the object, and on the other uses experience, knowledge and the expertise of experts in the relevant subject area. Objective. The object of the study is the process of modeling the experience, knowledge and competence of experts to quantify the object of study on the basis of neuro-fuzzy networks. The subject of the study is a neuro-fuzzy model of quantifying an object of study for decision making in expert data. Method. For the first time, a five-layer neuro-fuzzy model has been developed to derive quantitative and linguistic assessments of the object of the study using the expertise, expertise and expertise of the subject area. For the first time, it is proposed to use quan- titative estimates of the object of study (aggregated estimates using multicriteria models) and linguistic expert reasoning on a neurofuzzy network. For the first time, a model has been tested and verified for an example of assessing the risk of financing a startup project in the business expansion phase, and is also offered as a training for the neuro-fuzzy synaptic weight interval network. Comparison of the results of the study on different approaches to determining synaptic weights and real data with error detection. Results. The result of the study is a neural-fuzzy model for evaluating an object by many criteria. The developed model allows to combine quantitative characteristics of an object with expert opinions in the form of qualitative estimates. The rationality of the evaluation proves the advantages of the developed models. Conclusions. Sharing the apparatus of fuzzy sets and neural networks theory is a convenient simulation tool for multicriteria selection problems. As a rule, important information for management decision support systems comes from two sources: 1) obtaining object estimates by certain quantitative indicators, which creates inaccuracy; 2) from expert people who describe their subject matter knowledge, which creates subjectivity and uncertainty. Therefore, maintaining expert judgment and inaccurate data requires the ability to work with them. The paper deals with the scientific and applied problem of developing a model for obtaining an aggregate estimation of an object based on a neural-fuzzy network and can be applied in solving management decision-making problems in socio-economic systems.

Universal rapid method of assessment and management decision-making of situational problems in the enterprise

Situational approach in management does not lose its relevance for a hundred years. During this time he constantly developed, being a part of various concepts, methodologies, methods, techniques and other forms of theoretical and practical implementation of scientific thought in different areas of human activity. The results of the analysis of scientific sources in the article over the past eight years confirmed the presence of the situational approach unlimited development potential. As one of the ways to develop the situational approach, the authors propose a new method of evaluation and management decision-making problems of a situational nature in the enterprise. In contrast to the existing methods, the proposed method is characterized by a high level of efficiency and versatility. The methodology provides decisions to the problems of situational nature of the three categories of complexity. For each category there are special forms in the form of different hierarchies. To reduce the number of possible errors and improve the efficiency of the methodology implementation, a special algorithm, as well as the necessary instructions for users, have been developed and presented. In addition, various information technologies are provided as tools for the implementation of the methodology. The method of analysis of T. Saati hierarchies was used as the core of the new method. The method of decision-making by Benjamin Franklin was adopted as a complement of the method. The final part of the article presents the results of testing the proposed method. In the majority of users of a technique really noted its universal and express properties. Thus, according to the results of testing at the enterprises, the technique is recognized as effective and viable.

Considering bipolarity of attributes with regards to objectives in decisions evaluation

Many decision problems in engineering, economics, and management consist of selecting and/or ranking alternatives that are characterized by multiple attributes in order to satisfy multiple and possible antagonist objectives. Besides this double multiplicity aspect, decision making mainly in engineering economics (decision problems where potential solutions of a problem must be economically viable along with some technical aspects) generally necessitate to cope with many stakeholders opinion regarding the importance to assign to each attribute and/or each objective as well as uncertainty that can impact any of these components (attributes values, objectives measurement and stakeholders statements). Decision processes in this context go through some steps such as 1) definition of the overall decision goal (find the best sustainable site to build a waste management facility for instance), this goal can further be split into many objectives to satisfy; 2) identification of potential alternatives; 3) elicitation and evaluation of attributes that characterize alternatives; 4) and finally evaluate alternatives to recommend the “good enough” alternative to implement. The aim of the research undertaken in this paper is to address the question raised in the third and fourth points by exploiting the bipolar nature of attributes with regards to objective to elicit and assess attributes and to build a final recommendation procedure. Indeed, we will highlight the bipolar nature of attributes with regards to pursued objectives (given an objective, there will be attributes that act in the sense of realization of this objective, that we refer to as supporting attributes and those working against the achievement of this objective, referred to as rejecting attributes) to derive a framework for elicitation and assessment of attributes values. Finally, relying on the bipolar nature of obtained attributes, satisficing game approach is used as a tool to support the evaluation and final recommendation process. The approach is applied to a real world problem which aim is to select the best sustainable site to build a waste management facility.

On the communication of statistical information about uncertainty in flood risk management

Uncertainty analysis is not typically performed in hydrological and hydraulic modelling. This is problematic because this may lead to inefficient decision making in water management. We therefore explored the role of statistical knowledge on uncertainty in decision-making processes in long term flood risk management within the context of regional water boards in the Netherlands. Research questions were: (1) in which parts of flood risk management statistical information about uncertainty is presented to professionals of district water boards, and in which forms?; (2) how is this information interpreted and used by these professionals, and how does this influence decision-making processes in district water boards?; and (3) how can communication about statistically quantified uncertainty be improved? To answer these questions we conducted interviews and surveys among professionals and board members of Dutch district water boards. Results suggest that statistical information on uncertainty is hard to interpret by professionals. The amount of statistical information on uncertainty strongly reduces during the decision making process, during which the information transforms from quantitative to qualitative. As a result the statistical information on uncertainty is not utilized to solve flood risk management decision problems. These decision problems are not formulated within statistical frameworks for decision making, and statistical information on uncertainty is not collected and presented with the purpose to be input of such frameworks. Practical recommendations for long term flood risk management are discussed.

Adaptive Distributionally Robust Optimization

We develop a modular and tractable framework for solving an adaptive distributionally robust linear optimization problem, where we minimize the worst-case expected cost over an ambiguity set of probability distributions. The adaptive distributionally robust optimization framework caters for dynamic decision making, where decisions adapt to the uncertain outcomes as they unfold in stages. For tractability considerations, we focus on a class of second-order conic (SOC) representable ambiguity set, though our results can easily be extended to more general conic representations. We show that the adaptive distributionally robust linear optimization problem can be formulated as a classical robust optimization problem. To obtain a tractable formulation, we approximate the adaptive distributionally robust optimization problem using linear decision rule (LDR) techniques. More interestingly, by incorporating the primary and auxiliary random variables of the lifted ambiguity set in the LDR approximation, we can significantly improve the solutions, and for a class of adaptive distributionally robust optimization problems, exact solutions can also be obtained. Using the new LDR approximation, we can transform the distributionally adaptive robust optimization problem to a classical robust optimization problem with an SOC representable uncertainty set. Finally, to demonstrate the potential for solving management decision problems, we develop an algebraic modeling package and illustrate how it can be used to facilitate modeling and obtain high-quality solutions for medical appointment scheduling and inventory management problems. The electronic companion is available at https://doi.org/10.1287/mnsc.2017.2952 . This paper was accepted by Noah Gans, optimization.

Markovian strategies for piecewise deterministic differential games with continuous and impulse controls

This paper is concerned with the Markovian feedback strategies of piecewise deterministic differential games and their applications to business and management decision-making problems that involve multiple agents and continuous and impulse controls. For a class of piecewise deterministic differential games in finite or infinite horizons we formulate conditions for the value functions in the form of quasi-variational inequalities, prove a verification theorem, and derive a criterion for the Markovian regime change in certain case. These results are applied to a technology adoption problem that involves multiple companies engaged in extraction of an exhaustible resource with different technologies. Using the model proposed by Long et al in [16], we show the existence of a pure Markovian strategy and develop an algorithm for computing the solutions.

Embracing data uncertainty in water decision-making: an application to evaluate water supply and sewerage in Spain

Abstract Analyses of complex water management decision-making problems, involving tradeoffs amongst multiple criteria, are often undertaken using multi-criteria decision analysis (MCDA) techniques. Various forms of uncertainty may arise in the application of MCDA methods, including imprecision, inaccuracy or ill determination of data. The ELECTRE family methods deal with imperfect knowledge of data by incorporating ‘pseudo-criteria’, with discrimination thresholds, to interpret the outranking relation as a fuzzy relation. However, the task of selecting thresholds for each criterion can be difficult and ambiguous for decision-makers. In this paper, we propose a confidence-interval-based approach which aims to reduce the subjective input required by decision-makers. The proposed approach involves defining the uncertainty in the input values using confidence intervals and expressing thresholds as a function of the interval estimates. The usefulness of the approach is illustrated by applying it to evaluate the water supply and sewerage services in Spain. Results show that the confidence interval approach may be interesting in some cases (e.g. when dealing with statistical data from surveys or measuring equipment), but should never replace the preferences or judgments of the actors involved in the decision process.

Emergency decision-making combining cumulative prospect theory and group decision-making

With respect to the characteristic of risk and the potential evolvement of scenarios in emergency management analysis, this study proposes an emergency decision-making method with interval probability based on cumulative prospect theory and group decision-making. Under emergency risk environment, there is a tremendous need to consider decision-maker’s psychological behavior which affects the decision results. In addition, an emergency decision generally involves joint participation among departments, which inevitably brings about group decision-making. Therefore, aiming at decision problems in emergency management, this paper provides an algorithm of emergency group decision-making considering psychological behaviors. For illustration and verification, a numerical example and two comparisons are presented to demonstrate the effectiveness of proposed method. The contribution of this study is characterized by three aspects. First, cumulative prospect theory is introduced to quantify the impact of psychological behaviors. Second, group decision-making is considered as a think tank, which makes the decision more persuasive than single-person methods. Third, this study proposes a novel intelligent optimization algorithm, plant growth simulation algorithm, to integrate the different individual evaluations.