Stochastic Decision Model Research Articles

A stochastic fuzzy multi-criteria decision-making model for optimal selection of clean technologies

Abstract Selection of clean technology options requires systematic evaluation based on multiple criteria which are often conflicting. The optimal choice should consider not just technical performance but also the economic, environmental and social aspects of technologies. Furthermore, the interdependencies of these aspects should also be considered. The decision-maker often needs to make explicit trade-offs while ranking the alternatives. In addition, data gaps and imprecise information that are typical when dealing with emerging technologies make conventional methods ineffective. This work thus proposes a Stochastic Fuzzy Analytic Hierarchical Network Process decision model to address the complexity and uncertainty involved in the clean technology selection process. This method first decomposes the problem into a hierarchical network structure, and then derives the probability distribution of the priority weights needed for ranking. The capabilities of the methodology are demonstrated with three case studies, involving the comparison of different carbon nanotube synthesis methods, nutrient removal treatment technology options for municipal wastewater, and low-carbon electricity sources in the Philippines.

Stochastic decision model of consumer product preference on Ecommerce

Stochastic decision model of consumer product preference on Ecommerce

Decision support to product configuration considering component replenishment uncertainty: A stochastic programming approach

Product configuration is to make decisions on component selections and combination to constitute a customized product under mass customization production. However, the uncertainties (such as component supplies) in product configuration setting are not considered in the existing product configurators. To handle the uncertainty in component replenishment lead-time, a new stochastic decision model is proposed in this paper using two-stage stochastic programming approach. Further, a pre-procuring strategy for component supply is employed to reduce total configuration costs and shorten the delivery date of customized products. The stochastic decision model for product configuration is solved by using Lagrangian relaxation algorithm. The effectiveness of the stochastic decision model is demonstrated through case studies from both computer configuration and ranger drilling machine configuration. Computational comparisons with a commercial solver (CPLEX) indicate that the proposed stochastic decision model provides competitive solution results.

Human-Robot Mutual Adaptation in Shared Autonomy.

Shared autonomy integrates user input with robot autonomy in order to control a robot and help the user to complete a task. Our work aims to improve the performance of such a human-robot team: the robot tries to guide the human towards an effective strategy, sometimes against the human's own preference, while still retaining his trust. We achieve this through a principled human-robot mutual adaptation formalism. We integrate a bounded-memory adaptation model of the human into a partially observable stochastic decision model, which enables the robot to adapt to an adaptable human. When the human is adaptable, the robot guides the human towards a good strategy, maybe unknown to the human in advance. When the human is stubborn and not adaptable, the robot complies with the human's preference in order to retain their trust. In the shared autonomy setting, unlike many other common human-robot collaboration settings, only the robot actions can change the physical state of the world, and the human and robot goals are not fully observable. We address these challenges and show in a human subject experiment that the proposed mutual adaptation formalism improves human-robot team performance, while retaining a high level of user trust in the robot, compared to the common approach of having the robot strictly following participants' preference.

Development of Stochastic Decision Model for Estimation of Optimal In-depth Inspection Period of Harbor Structures

ê²½ì‚¬ì œ 피복재와 같은 항만 구조물의 ìœ ì§€ê´€ë¦¬ 계획에서 중요한 ìµœì  ì •ë°€ì ê²€ì‹œê¸°ë¥¼ 쉽게 ê²°ì •í• ìˆ˜ 있는 RRP(Renewal Reward Process)기반 ê¸°ëŒ€í• ì¸ë¹„ìš©ëª¨í˜•ì¸ ì¶”ê³„í•™ì  ê²°ì •ëª¨í˜•ì„ 개발하였다. PIM(Periodic Inspection and Maintenance)ê³¼ CBIM(Condition-Based Inspection and Maintenance) ì •ì± ì„ 동시에 ì ìš©í•˜ì—¬ ì´ì „ 모형들의 한계성을 ê·¹ë³µí• ìˆ˜ 있는 ìˆ˜í•™ì  모형을 수립하였다. 또한 모형에 연속복리계수를 ë„ìž í•˜ì—¬ ì ê²€ 및 보수보강과 ê´€ë ¨ëœ 비용들의 시간에 따른 가치변화를 ê³ ë ¤í•˜ì˜€ë‹¤. ë¨¼ì € 파괴율 함수가 ì¼ì •í•œ 조건에서 해석해를 ìœ ë„í•˜ê³ , 분포 함수에 따른 영향 등 ë‹¤ê°ì  민감도 분석을 수행하여 본 연구에서 ìœ ë„ëœ 해석해가 기존에 ì œì‹œëœ 해석해를 포함하며 ì ìš©ì„±ì´ 더 우수함을 확인 í• ìˆ˜ 있었다. ì¶”ê³„í•™ì  í™•ë¥ ê³¼ì •ì„ 이용하는 경우에도 본 연구에서 수립된 모형은 ê²½ì‚¬ì œ 피복재와 같은 구조물의 ì¶”ê³„í•™ì  ëˆ„ì í”¼í•´ë„ì˜ ë¹„ì„ í˜•ì„±ì„ 올바로 í•´ì„í• ìˆ˜ 있다. 특히 MCS(Monte-Carlo Simulation) 기반 표본경로기법을 사용하여 모형의 피해강도함수의 계수들을 ë¹„êµì  쉽게 ì‚°ì •í• ìˆ˜ 있었다. 마지막으로 본 연구에서 개발된 ì¶”ê³„í•™ì  ê²°ì • 모형을 ê²½ì‚¬ì œ 피복재에 만족스럽게 ì ìš©í•˜ì˜€ë‹¤. ëˆ„ì í”¼í•´ì˜ 거동 특성, 사용한계의 수준 ê·¸ë¦¬ê³ êµ¬ì¡°ë¬¼ì˜ 중요도에 따라 단위시간당 기대 총 비용이 최소가 되는 ê²½ì‚¬ì œì˜ 피복재의 ìµœì  ì •ë°€ì ê²€ ì‹œì ì„ ë¹„êµì  쉽게 ê²°ì •í• ìˆ˜ 있었다. 핵심용어: 항만 구조물, ìµœì  ì •ë°€ì ê²€ì‹œê¸°, ì¶”ê³„í•™ì  ê²°ì •ëª¨í˜•, 피해강도함수, 표본경로기법

State of Art, Channel Allocation of Cellular Network for Adavanced Metering Infrastrcture Based on Markov Chain

Increasing demand of cellular communication service and limit frequency spectrum that cellular network has, is a problem for to channel allocation. This paper provides a way to reuse the frequency spectrum in cellular network while this hasn't be used pated, for that exist much of the radio spectrum being inefficiently used in cellular network to occupy the Advanced Measurement Infrastructure (AMI) of this way achieving also optimize the infrastructure of communication getting in a spectrum two types of data to be handled by spectrum agents in charge of sharing among different networks. All this is achieved by using the cognitive network, which is able to dynamically adapt to the demand achieving meet the needs of the user according to the policies defined by the operator. The goal is to prove that it is possible to use the white spaces spectrum network in a manner reliably and securely by using a stochastic decision model and limiting its time of use.

A stochastic decision model for strategic supplier relationship portfolio management

Management of a portfolio of strategic supplier relationships is complicated by their dynamism, uncertainty, information vagueness, significant profit impact, long–term orientation, substantial switching cost, and the interdependency among alternatives. To address this challenge, this paper first suggests a three–layer framework for strategic supplier relationship portfolio management. It then proposes a fuzzy binomial tree approximation–based stochastic model to analyse relationship dynamics and the value of a supplier relationship portfolio, taking into account both randomness and fuzziness uncertainty. Furthermore, it develops a decision model for supplier relationship portfolio configuration and adaptive development planning. Numerical examples are provided and some managerial implications are discussed.

Multi-period investment decision problem based on time consistent generalized convex risk measure and extremum scenarios

Purpose – To help investors find an investment policy with strong competitiveness, the purpose of this paper is to construct a multi-period investment decision model with practicality and superior performance. Design/methodology/approach – The paper uses a suitable multi-period risk measure to construct a multi-period portfolio selection model, where target returns at intermediate periods and market frictions are taken into account simultaneously. An efficient scenario tree generation approach is proposed in order to transform the complex multi-period portfolio selection problem into a tractable one. Findings – Numerical results show the new scenario tree generation algorithms are stable and can further reduce the tree size. With the scenario tree generated by the new scenario tree generation approach, the optimal investment strategy obtained under the multi-period investment decision model has more superior performance and robustness than the corresponding optimal investment strategy obtained under the single period investment model or the multi-period investment model only paying attention to the terminal cash flow. Research limitations/implications – The new risk measure and multi-period investment decision models can stimulate readers to find even better models and to efficiently solve realistic multi-period portfolio selection problems. Practical implications – The empirical results show the superior performance and robustness of optimal investment strategy obtained with the new models. What's more important, the empirical analyses tell readers how different market frictions affect the performance of optimal portfolios, which can guide them to efficiently solve real multi-period investment decision problems in practice. Originality/value – The paper first derives the concrete structure of the time consistent generalized convex multi-period risk measure, then constructs a multi-period portfolio selection model based on the new multi-period risk measure, and proposes a new extremum scenario tree generation algorithm. The authors construct a realistic multi-period investment decision model. Furthermore, using the proposed scenario tree generation algorithm, the authors transform the established stochastic investment decision model into a deterministic optimization problem, which can provide optimal investment decisions with robustness and superior performance.

A pragmatic stochastic decision model for supporting goods trans-shipments in a supply chain environment

This paper develops a set of decision rules to assist wholesalers to decide whether it is more cost effective to trans-ship urgent outstanding retailer orders from other wholesalers, very fast but at a higher purchase cost, or to order from their suppliers. By considering the uncertainty in demand, it models the total cost encountered by wholesalers, including purchasing, backordering and holding costs in the inventory replenishment process. Unlike previous models that are complex, highly mathematical and difficult to apply, this model provides a pragmatic and less complex method adoptable by ordinary logistics managers and requires input data that are accessible from the previous transaction records of an organization. The application of the proposed decision rules are illustrated considering different scenarios of wholesaler–supplier combinations.

Towards Human–Robot Teams: Model-Based Analysis of Human Decision Making in Two-Alternative Choice Tasks With Social Feedback

With a principled methodology for systematic design of human-robot decision-making teams as a motivating goal, we seek an analytic, model-based description of the influence of team and network design parameters on decision-making performance. Given that there are few reliably predictive models of human decision making, we consider the relatively well-understood two-alternative choice tasks from cognitive psychology, where individuals make sequential decisions with limited information, and we study a stochastic decision-making model, which has been successfully fitted to human behavioral and neural data for a range of such tasks. We use an extension of the model, fitted to experimental data from groups of humans performing the same task simultaneously and receiving feedback on the choices of others in the group. First, we show how the task and model can be regarded as a Markov process. Then, we derive analytically the steady-state probability distributions for decisions and performance as a function of model and design parameters such as the strength and path of the social feedback. Finally, we discuss application to human-robot team and network design and next steps with a multirobot testbed.

A Two-State Eukaryotic Cell Migration Model

Cells are dynamic and motile objects and the way they make decisions about migratory directions has been an area of intensive research. To explain the intricate effects of genetic mutations and environmental changes on cell migration, we have devised a discrete stochastic trajectory decision model from simple mechanochemical principles. In the current model, the cells have been treated as mobile machines with two states of migration: persistent and random. The state transitions are assumed to obey Markovian dynamics. We used molecular diffusion and polymer analogies to estimate likelihoods of the trajectories. The model has been tested on human fibrosarcoma and fibroblast wild type cells, various knockdowns, and cells performing chemotaxis and galvanotaxis. The results demonstrate the adequacy of the model in fitting the experimental cell speed distribution, velocity autocorrelation, mean squared displacement and angles between consecutive velocity steps. The model has the potential to better explain more complex trajectory dynamics of cells including cancer invasion, wound healing, and cellular development.

A two‐stage procurement model for humanitarian relief supply chains

PurposeThe purpose of this paper is to discuss and to help address the need for quantitative models to support and improve procurement in the context of humanitarian relief efforts.Design/methodology/approachThis research presents a two‐stage stochastic decision model with recourse for procurement in humanitarian relief supply chains, and compares its effectiveness on an illustrative example with respect to a standard solution approach.FindingsResults show the ability of the new model to capture and model both the procurement process and the uncertainty inherent in a disaster relief situation, in support of more efficient and effective procurement plans.Research limitations/implicationsThe research focus is on sudden onset disasters and it does not differentiate between local and international suppliers. A number of extensions of the base model could be implemented, however, so as to address the specific needs of a given organization and their procurement process.Practical implicationsDespite the prevalence of procurement expenditures in humanitarian efforts, procurement in humanitarian contexts is a topic that previously has only been discussed in a qualitative manner in the literature. This work provides practitioners with a new approach to quantitatively assess and improve their procurement decision processes.Originality/valueThis study adds to the existing literature by demonstrating the applicability and effectiveness of an analytic modeling technique based on uncertainty, such as stochastic programming with recourse, in the context of humanitarian relief procurement activities.

Closed-Form Approximations of First-Passage Distributions for a Stochastic Decision-Making Model.

In free response choice tasks, decision making is often modeled as a first-passage problem for a stochastic differential equation. In particular, drift-diffusion processes with constant or time-varying drift rates and noise can reproduce behavioral data (accuracy and response-time distributions) and neuronal firing rates. However, no exact solutions are known for the first-passage problem with time-varying data. Recognizing the importance of simple closed-form expressions for modeling and inference, we show that an interrogation or cued-response protocol, appropriately interpreted, can yield approximate first-passage (response time) distributions for a specific class of time-varying processes used to model evidence accumulation. We test these against exact expressions for the constant drift case and compare them with data from a class of sigmoidal functions. We find that both the direct interrogation approximation and an error-minimizing interrogation approximation can capture a variety of distribution shapes and mode numbers but that the direct approximation, in particular, is systematically biased away from the correct free response distribution.

Multi-Level Coordination and Decision-Making in Service Operations

Decisions in service operations are complex since they involve various interdependent decision-makers (agents) at different hierarchical levels, ranging from customer representatives over account managers to top executives. We formulate a three-level maintenance service problem as a stochastic decision-making model, with an account manager, supervisor, and worker at each of the three levels. The proposed incentive mechanism aligns the interests of lower level agents with the goals of the top agent. The agents' strategic interactions are analyzed game-theoretically and results show that a Pareto-efficient Nash equilibrium can be attained given certain organizational properties. Furthermore, we show that local information can be sufficient for organization-wide optimal decisions. In a final step, the three-level model is generalized for multi-level, i.e., multi-organizational-scale, systems. [Service Science, ISSN 2164-3962 (print), ISSN 2164-3970 (online), was published by Services Science Global (SSG) from 2009 to 2011 as issues under ISBN 978-1-4276-2090-3.]

Some comments on the experimental measurement of utility

Experiments designed to measure people's utility functions usually involve choices to be made by subjects among different wagers. As a rule, complex statistical methods are used to decide whether a pattern of choices is consistent with a particular utility function and an associated stochastic decision model. Here the author takes issue with the methods used by two investigators to interpret the results of their experiments.

A study on optimal scheduling for software projects

AbstractSoftware projects often suffer from unexpected rework and delays. Therefore, project scheduling remains a difficult task for the managers. In this article, we compute optimal scheduling strategies for a set of sample software projects and simulate their behavior. The computations are based on a stochastic Markov decision model for software projects, which focuses on capturing the feedback between concurrent development activities. Since the underlying process model is stochastic, the strategies are stochastically optimal, that is, they minimize the expected project duration. The ultimate goal of this research is to develop guidelines for managers to schedule their software projects under uncertainty in the best possible way.The sample projects are similar, but differ in certain characteristics of the project or product, such as the strength of the coupling between the components or the degree of specialization of the teams on the tasks. By using a set of related projects, we can study how the project characteristics influence the optimal scheduling decisions in a project. After computing the optimal scheduling policies for the sample projects, we use extensive discrete‐event simulations to study the behavior of the optimal policy for each given setting and compare the performance of the optimal policy against the possible list policies. List policies are a simple and commonly used class of scheduling policies. For our sample projects, the simulations show that the best list policy in general is not optimal. The higher the degree of specialization of the teams, the larger the performance gap is. On the other hand, the stronger the coupling between the components, the smaller is the improvement that the optimal policy achieves over the best list policy. Copyright © 2006 John Wiley & Sons, Ltd.

A Decision Model for Software Maintenance

In this paper we address the problem of increasing software maintenance costs in a custom software development environment, and develop a stochastic decision model for the maintenance of information systems. Based on this modeling framework, we derive an optimal decision rule for software systems maintenance, and present sensitivity analysis of the optimal policy. We illustrate an application of this model to a large telecommunications switching software system, and present sensitivity analysis of the optimal state for major upgrade derived from our model. Our modeling framework also allows for computing the expected time to perform major upgrade to software systems.

A labeling method for dynamic driver-task assignment with uncertain task durations

The problem of assigning drivers to cover tasks with service time windows and uncertain task durations is formulated as a dynamic stochastic decision model. We develop an adaptive labeling solution procedure that can incorporate various practical constraints and work rules. Experiments are conducted to evaluate the procedure's performance and compare the stochastic and deterministic formulations.

An inhibition-based stochastic countable-time decision model.

A new stochastic model to account for reaction-time fluctuation in prolonged work tasks is presented. Transition probabilities from work periods to distraction periods and vice versa are dependent on inhibition, which increases during work and decreases during distractions. The model presented here differs from all other inhibition-based models in that transitions can take place only at certain random points in time, and is referred to as a countable-time decision model. It is argued that the proposed model is a more plausible alternative to other existing inhibition-based models, while at the same time being highly flexible in that it is able to approximate other models arbitrarily well. This model is compared to an existing inhibition-based continuous-time decision model and the probability distribution functions for work and distraction periods are derived.

Stochastic Learning of Strategic Equilibria for Auctions

This article presents a new application of stochastic adaptive learning algorithms to the computation of strategic equilibria in auctions. The proposed approach addresses the problems of tracking a moving target and balancing exploration (of action space) versus exploitation (of better modeled regions of action space). Neural networks are used to represent a stochastic decision model for each bidder. Experiments confirm the correctness and usefulness of the approach.