scholarly journals Interval Forecasting in Supply Chain with Small Sample

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Lifeng Wu ◽  
Yan Chen
Keyword(s):  
Supply Chain ◽  
Small Sample ◽  
Decision Makers ◽  
Small Samples ◽  
Forecasting Model ◽  
Future Trends ◽  
Human Judgment ◽  
Proposed Model ◽  
Grey Models ◽  
Short Time

To deal with the forecasting with small samples in the supply chain, three grey models with fractional order accumulation are presented. Human judgment of future trends is incorporated into the order number of accumulation. The output of the proposed model will provide decision-makers in the supply chain with more forecasting information for short time periods. The results of practical real examples demonstrate that the model provides remarkable prediction performances compared with the traditional forecasting model.

scholarly journals Optimizing a Reverse Supply Chain Network for Electronic Waste under Risk and Uncertain Factors

2021 ◽  
Vol 11 (4) ◽  
pp. 1946
Author(s):  
Linh Thi Truc Doan ◽  
Yousef Amer ◽  
Sang-Heon Lee ◽  
Phan Nguyen Ky Phuc ◽  
Tham Thi Tran
Keyword(s):  
Supply Chain ◽  
Electronic Waste ◽  
Decision Makers ◽  
Reverse Supply Chain ◽  
Satisfactory Solution ◽  
Level Of Satisfaction ◽  
Proposed Model ◽  
Objective Value ◽  
The Right ◽  
The Impact

Minimizing the impact of electronic waste (e-waste) on the environment through designing an effective reverse supply chain (RSC) is attracting the attention of both industry and academia. To obtain this goal, this study strives to develop an e-waste RSC model where the input parameters are fuzzy and risk factors are considered. The problem is then solved through crisp transformation and decision-makers are given the right to choose solutions based on their satisfaction. The result shows that the proposed model provides a practical and satisfactory solution to compromise between the level of satisfaction of constraints and the objective value. This solution includes strategic and operational decisions such as the optimal locations of facilities (i.e., disassembly, repairing, recycling facilities) and the flow quantities in the RSC.

Efficiency Evaluation of Supply Chain Network Using a Framework Based on DEA and Seller-Buyer Structure

2020 ◽  
Vol 37 (06) ◽  
pp. 2050029 ◽  
Author(s):  
Hassan Arabshahi ◽  
Hamed Fazlollahtabar ◽  
Leila Maboudi
Keyword(s):  
Supply Chain ◽  
Supplier Selection ◽  
Selection Procedure ◽  
Point Of View ◽  
Decision Makers ◽  
Efficiency Evaluation ◽  
Supply Chain Network ◽  
Stage Model ◽  
Proposed Model ◽  
Efficient Portfolios

This paper aims to develop a DEA-based framework to evaluate the efficiency of the supply chain based on the seller–buyer structure and with respect to win–win strategy. This is a bi-stage model employing the CCR model in the forms of input-oriented and output-oriented considering the intermediate measures for two different conditions under a centralized point of view. The obtained results from the extension of this model to supply chain network lead to introduce “efficient path” concept being a path including different components of the supply chain that are efficient in terms of DEA. Other kinds of proper information provided by the proposed model can help the managers and decision-makers of the supply chain field in supplier selection procedure and making efficient portfolios and collaborations across the supply chain network.

Evaluation of Supply Chain Efficiency Based on a Novel Network of Data Envelopment Analysis Model

2015 ◽  
Vol 25 (14) ◽  
pp. 1540036 ◽  
Author(s):  
Li Fang Fu ◽  
Jun Meng ◽  
Ying Liu
Keyword(s):  
Supply Chain ◽  
Data Envelopment Analysis ◽  
Decision Makers ◽  
Data Envelopment Analysis Model ◽  
Data Envelopment ◽  
Undesirable Outputs ◽  
Analysis Model ◽  
Multilayer Network ◽  
Supply Chain Efficiency ◽  
Proposed Model

Performance evaluation of supply chain (SC) is a vital topic in SC management and inherently complex problems with multilayered internal linkages and activities of multiple entities. Recently, various Network Data Envelopment Analysis (NDEA) models, which opened the “black box” of conventional DEA, were developed and applied to evaluate the complex SC with a multilayer network structure. However, most of them are input or output oriented models which cannot take into consideration the nonproportional changes of inputs and outputs simultaneously. This paper extends the Slack-based measure (SBM) model to a nonradial, nonoriented network model named as U-NSBM with the presence of undesirable outputs in the SC. A numerical example is presented to demonstrate the applicability of the model in quantifying the efficiency and ranking the supply chain performance. By comparing with the CCR and U-SBM models, it is shown that the proposed model has higher distinguishing ability and gives feasible solution in the presence of undesirable outputs. Meanwhile, it provides more insights for decision makers about the source of inefficiency as well as the guidance to improve the SC performance.

scholarly journals On the Value of Alert Systems and Gentle Rule Enforcement in Addressing Pandemics

2020 ◽  
Vol 11 ◽  
Author(s):  
Yefim Roth ◽  
Ori Plonsky ◽  
Edith Shalev ◽  
Ido Erev
Keyword(s):  
Small Sample ◽  
Decision Makers ◽  
Trust Game ◽  
Small Samples ◽  
Policy Makers ◽  
Hierarchy Model ◽  
Current Context ◽  
Cognitive Hierarchy ◽  
Reckless Behavior ◽  
Past Experiences

The COVID-19 pandemic poses a major challenge to policy makers on how to encourage compliance to social distancing and personal protection rules. This paper compares the effectiveness of two policies that aim to increase the frequency of responsible health behavior using smartphone-tracking applications. The first involves enhanced alert capabilities, which remove social externalities and protect the users from others’ reckless behavior. The second adds a rule enforcement mechanism that reduces the users’ benefit from reckless behavior. Both strategies should be effective if agents are expected-value maximizers, risk averse, and behave in accordance with cumulative prospect theory (Tversky and Kahneman, 1992) or in accordance with the Cognitive Hierarchy model (Camerer et al., 2004). A multi-player trust-game experiment was designed to compare the effectiveness of the two policies. The results reveal a substantial advantage to the enforcement application, even one with occasional misses. The enhanced-alert strategy was completely ineffective. The findings align with the small samples hypothesis, suggesting that decision makers tend to select the options that lead to the best payoff in a small sample of similar past experiences. In the current context, the tendency to rely on a small sample appears to be more consequential than other deviations from rational choice.

scholarly journals Application of multi-objective optimization based on genetic algorithm for sustainable strategic supplier selection under fuzzy environment

2017 ◽  
Vol 10 (2) ◽  
pp. 188 ◽  
Author(s):  
Muhammad Hashim ◽  
Muhammad Nazam ◽  
Liming Yao ◽  
Sajjad Ahmad Baig ◽  
Muhammad Abrar ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Supplier Selection ◽  
Decision Makers ◽  
Quality Level ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Fuzzy Environment ◽  
Proposed Model ◽  
Textile Sector

Purpose:  The incorporation of environmental objective into the conventional supplier selection practices is crucial for corporations seeking to promote green supply chain management (GSCM). Challenges and risks associated with green supplier selection have been broadly recognized by procurement and supplier management professionals. This paper aims to solve a Tetra “S” (SSSS) problem based on a fuzzy multi-objective optimization with genetic algorithm in a holistic supply chain environment. In this empirical study, a mathematical model with fuzzy coefficients is considered for sustainable strategic supplier selection (SSSS) problem and a corresponding model is developed to tackle this problem.Design/methodology/approach: Sustainable strategic supplier selection (SSSS) decisions are typically multi-objectives in nature and it is an important part of green production and supply chain management for many firms. The proposed uncertain model is transferred into deterministic model by applying the expected value mesurement (EVM) and genetic algorithm with weighted sum approach for solving the multi-objective problem. This research focus on a multi-objective optimization model for minimizing lean cost, maximizing sustainable service and greener product quality level. Finally, a mathematical case of textile sector is presented to exemplify the effectiveness of the proposed model with a sensitivity analysis.Findings: This study makes a certain contribution by introducing the Tetra ‘S’ concept in both the theoretical and practical research related to multi-objective optimization as well as in the study of sustainable strategic supplier selection (SSSS) under uncertain environment. Our results suggest that decision makers tend to select strategic supplier first then enhance the sustainability.Research limitations/implications: Although the fuzzy expected value model (EVM) with fuzzy coefficients constructed in present research should be helpful for solving real world problems. A detailed comparative analysis by using other algorithms is necessary for solving similar problems of agriculture, pharmaceutical, chemicals and services sectors in future.Practical implications: It can help the decision makers for ordering to different supplier for managing supply chain performance in efficient and effective manner. From the procurement and engineering perspectives, minimizing cost, sustaining the quality level and meeting production time line is the main consideration for selecting the supplier. Empirically, this can facilitate engineers to reduce production costs and at the same time improve the product quality.Originality/value: In this paper, we developed a novel multi-objective programming model based on genetic algorithm to select sustainable strategic supplier (SSSS) under fuzzy environment. The algorithm was tested and applied to solve a real case of textile sector in Pakistan. The experimental results and comparative sensitivity analysis illustrate the effectiveness of our proposed model.

scholarly journals An Advanced CNN-LSTM Model for Cryptocurrency Forecasting

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 287
Author(s):  
Ioannis E. Livieris ◽  
Niki Kiriakidou ◽  
Stavros Stavroyiannis ◽  
Panagiotis Pintelas
Keyword(s):  
Portfolio Management ◽  
Deep Neural Networks ◽  
Computational Cost ◽  
Forecasting Model ◽  
Time Period ◽  
Proposed Model ◽  
Multiple Input ◽  
Short Time ◽  
Fully Connected ◽  
Financial Transactions

Nowadays, cryptocurrencies are established and widely recognized as an alternative exchange currency method. They have infiltrated most financial transactions and as a result cryptocurrency trade is generally considered one of the most popular and promising types of profitable investments. Nevertheless, this constantly increasing financial market is characterized by significant volatility and strong price fluctuations over a short-time period therefore, the development of an accurate and reliable forecasting model is considered essential for portfolio management and optimization. In this research, we propose a multiple-input deep neural network model for the prediction of cryptocurrency price and movement. The proposed forecasting model utilizes as inputs different cryptocurrency data and handles them independently in order to exploit useful information from each cryptocurrency separately. An extensive empirical study was performed using three consecutive years of cryptocurrency data from three cryptocurrencies with the highest market capitalization i.e., Bitcoin (BTC), Etherium (ETH), and Ripple (XRP). The detailed experimental analysis revealed that the proposed model has the ability to efficiently exploit mixed cryptocurrency data, reduces overfitting and decreases the computational cost in comparison with traditional fully-connected deep neural networks.

scholarly journals A Small-Sample Adaptive Hybrid Model for Annual Electricity Consumption Forecasting

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Ming Meng ◽  
Yanan Fu ◽  
Huifeng Shi ◽  
Xinfang Wang
Keyword(s):  
Hybrid Model ◽  
Electricity Consumption ◽  
Small Sample ◽  
Small Samples ◽  
Proposed Model ◽  
Starting Point ◽  
Forecasting Performance ◽  
Accelerated Growth ◽  
Artificial Neural Network Ann

Annual electricity consumption forecasting is one of the important foundations of power system planning. Considering that the long-term electricity consumption curves of developing countries usually present approximately exponential growth trends and linear and accelerated growth rate trends may also appear in certain periods, this paper first proposes a small-sample adaptive hybrid model (AHM) to extrapolate the above curves. The iterative trend extrapolation equation of the proposed model can simulate the linear, exponential, and steep trends adaptively at the same time. To estimate the equation parameters using small samples, the partial least squares (PLS) and iteration starting point optimization algorithms are suggested. To evaluate forecasting performance, the artificial neural network (ANN), grey model (GM), and AHM are used to forecast electricity consumption in China from 1991 to 2014, and then the results of these models are compared. Analysis of the forecasting results shows that the AHM can overcome stochastic changes and respond quickly to changes in the main electricity consumption trend because of its specialized equation structure. Overall error analysis indicators also show that AHM often obtains more precise forecasting results than the other two models.

Multi-criteria decision model for risk prioritisation involving multiple decision-makers: an application of composition of probabilistic preferences combined with FMEA in the supply chain

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ramon Swell Gomes Rodrigues Casado ◽  
Maisa Mendonca Silva ◽  
Lucio Camara Silva
Keyword(s):  
Supply Chain ◽  
Failure Modes ◽  
Decision Makers ◽  
Raw Material ◽  
Uncertain Information ◽  
Content Type ◽  
Multiple Decision Makers ◽  
Proposed Model ◽  
Potential Risks ◽  
Practical Implications

PurposeThe paper aims to propose a multi-criteria model for risk prioritisation associated to supply chain management involving multiple decision-makers.Design/methodology/approachThe model integrates the composition of probabilistic preferences (CPP) on the failure modes analysis and its effects (FMEA) criteria. First, the authors carried out a probabilistic transformation of the numerical evaluations of the multiple decision-makers on the FMEA criteria regarding the internal risks that affect the chain of clothing pole in the Agreste region of Pernambuco. Then, the authors proposed the use of the Kendall's concordance coefficient W to aggregate these evaluations.FindingsContrary to expectations, the two main risks to be investigated as a model suggestion was related to the context of supply chain suppliers and not related to the raw material costs. Besides, a simulation with the traditional FMEA was carried out, and comparing with the model result, the simulation is worth highlighting seven consistent differences along the two rankings.Research limitations/implicationsThe focus was restricted to the use of only internal chain risks.Practical implicationsThe proposed model can contribute to the improvement of the decisions within organisations that make up the chains, thus guaranteeing a better quality in risk management.Originality/valueEstablishing a more effective representation of uncertain information related to traditional FMEA treatment involving multiple decision-makers means identifying in advance the potential risks, providing a better supply chain control.

scholarly journals On The Value of Alert Systems and Gentle Rule Enforcement in Addressing Pandemics

2020 ◽  
Author(s):  
Yefim Roth ◽  
Ori Plonsky ◽  
Edith Shalev ◽  
Ido Erev
Keyword(s):  
Small Sample ◽  
Decision Makers ◽  
Small Samples ◽  
Policy Makers ◽  
Responsible Behavior ◽  
The Social ◽  
Current Context ◽  
Cognitive Hierarchy ◽  
Reckless Behavior ◽  
Past Experiences

The recent COVID-19 pandemic poses a challenge to policy makers on how to make the population adhere to the social distancing and personal protection rules. The current research compares two ways by which tracking smartphone applications can be used to reduce the frequency of reckless behaviors that spread pandemics. The first involves the addition of alerts that increase the users’ benefit from responsible behavior. The second involves the addition of a rule enforcement mechanism that reduces the users’ benefit from reckless behavior. The effectiveness of the two additions is examined in an experimental study that focuses on an environment in which both additions are expected to be effective under the assumptions that the agents are expected-value maximizers, risk averse, behave in accordance with cumulative prospect theory (Tversky & Kahneman, 1992), or behave in accordance with the Cognitive Hierarchy model (Camerer, Ho & Chong, 2004). The results reveal a substantial advantage to the enforcement application. Indeed, the alerts addition was completely ineffective. The findings align with the small samples hypothesis, suggesting that decision makers tend to select the options that led to the best payoff in a small sample of similar past experiences. In the current context the tendency to rely on a small sample appears to be more consequential than other deviations from rational choice.

Haunted by a Doppelgänger

2014 ◽  
Vol 61 (1) ◽  
pp. 12-22 ◽  
Author(s):  
Bettina von Helversen ◽  
Stefan M. Herzog ◽  
Jörg Rieskamp
Keyword(s):  
Relevant Information ◽  
Psychological Research ◽  
Decision Makers ◽  
Human Judgment ◽  
Rule Based ◽  
New Employees ◽  
High Performing ◽  
Facial Similarity ◽  
Similarity Effect ◽  
Judgment Strategy

Judging other people is a common and important task. Every day professionals make decisions that affect the lives of other people when they diagnose medical conditions, grant parole, or hire new employees. To prevent discrimination, professional standards require that decision makers render accurate and unbiased judgments solely based on relevant information. Facial similarity to previously encountered persons can be a potential source of bias. Psychological research suggests that people only rely on similarity-based judgment strategies if the provided information does not allow them to make accurate rule-based judgments. Our study shows, however, that facial similarity to previously encountered persons influences judgment even in situations in which relevant information is available for making accurate rule-based judgments and where similarity is irrelevant for the task and relying on similarity is detrimental. In two experiments in an employment context we show that applicants who looked similar to high-performing former employees were judged as more suitable than applicants who looked similar to low-performing former employees. This similarity effect was found despite the fact that the participants used the relevant résumé information about the applicants by following a rule-based judgment strategy. These findings suggest that similarity-based and rule-based processes simultaneously underlie human judgment.

Sign in / Sign up

Export Citation Format

Share Document