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This paper presents an algorithm that can elicitate (infer) all or any combination of elimination and choice expressing reality (ELECTRE) Tri-B parameters. For example, a decision maker can maintain the values for indifference, preference and veto thresholds, and the study’s algorithm can find the criteria weights, reference profiles and the lambda cutting level. The study’s approach is inspired by a machine learning ensemble technique, the random forest, and for that, the authors named the study’s approach as ELECTRE tree algorithm.

First, the authors generate a set of ELECTRE Tri-B models, where each model solves a random sample of criteria and alternates. Each sample is made with replacement, having at least two criteria and between 10% and 25% of alternates. Each model has its parameters optimized by a genetic algorithm (GA) that can use an ordered cluster or an assignment example as a reference to the optimization. Finally, after the optimization phase, two procedures can be performed; the first one will merge all models, finding in this way the elicitated parameters and in the second procedure, each alternate is classified (voted) by each separated model, and the majority vote decides the final class.

The authors have noted that concerning the voting procedure, nonlinear decision boundaries are generated and they can be suitable in analyzing problems of the same nature. In contrast, the merged model generates linear decision boundaries.

The elicitation of ELECTRE Tri-B parameters is made by an ensemble technique that is composed of a set of multicriteria models that are engaged in generating robust solutions.

The purpose of this study is to systematically review the existing literature on the blood supply chain (BSC) from a network design perspective and highlight the research gaps in this area. Moreover, it also aims to pinpoint new research opportunities based on the recent innovative technologies for the BSC network design.

The study gives a comprehensive systematic review of the BSC network design studies until October 2021. This review was carried out in accordance with preferred reporting items for systematic reviews and meta-analyses (PRISMA). In the literature review, a total of 87 studies were analyzed under six main categories as model structure, application model, solution approach, problem type, the parties of the supply chain and innovative technologies.

The results of the study present the researchers’ tendencies and preferences when designing their BSC network models.

The study presents a guide for researchers and practitioners on BSC from the point of view of network design and encourages adopting innovative technologies in their BSC network designs.

The study provides a comprehensive systematic review of related studies from the BSC network design perspective and explores research gaps in the collection and distribution processes. Furthermore, it addresses innovative research opportunities by using innovative technologies in the area of BSC network design.

The purpose of this paper is to apply a recent kind of neural networks in a reliability optimization problem for a series system with multiple‐choice constraints incorporated at each subsystem, to maximize the system reliability subject to the system budget and weight. The problem is formulated as a non‐linear binary integer programming problem and characterized as an NP‐hard problem.

The design of neural network to solve this problem efficiently is based on a quantized Hopfield network (QHN). It has been found that this network allows one to obtain optimal design solutions very frequently and much more quickly than other Hopfield networks.

For systems more complex than series systems considered in this paper, the proposed approach needs to be adapted. The QHN‐based solution approach can be applied in many industrial systems where reliability is considered as an important design measure, e.g. in manufacturing systems, telecommunication systems and power systems.

The paper develops a new efficient method for reliability optimization. The most interesting characteristic of this method is related to its high‐speed computation, since the practical importance lies in the short computation time needed to obtain an optimal or nearly optimal solution for large industrial problems.

The purpose of this paper is to capture the dynamic variations in sales of a product based upon the dynamic estimation of the time series data and propose a model that imitates the price discounting and promotion strategy for a product category in a retail organization. A modest attempt has been made in the study to capture the relationship between the sales promotion, price discount and the batch procurement strategy of a particular product category to maximize sales volume and profitability.

Time series data relating to sales have been used to model the sales estimates using moving average and proportional and derivative control; thereafter a sales forecast is generated to estimate the sales of a particular product category. This provides valuable inputs for taking lot sizing decisions regarding procurement of the products that considerably impact the sales promotion and intelligent pricing decisions. A conceptual framework is developed for modeling the dynamic price discounting strategy in retail using fuzzy logic.

The model captures the lag effect of sales promotion and price discounting strategy; other strategies have been formulated based upon the sales forecast that was done for taking the lot sizing decisions regarding procurement of products in the selected category. This has helped minimize the inventory cost thereby keeping the profitability of the retail organization intact.

There is no appropriate empirical data to verify the models. In light of the research approach (modeling based upon historical time series data of a particular product category) that was undertaken, there is a possibility that the research results may be valid for the product category that was selected. Therefore, the researchers are advised to test the proposed propositions further for other product categories.

The study provides valuable insight on how to use the real-time sales data for designing a dynamic automated model for product sales promotion and price discounting strategy using fuzzy logic for a retail organization.

This paper aims to prioritize the most efficient Six Sigma project that can generate the greatest benefit to the organization, according to the relative performance among a set of homogenous projects (in here, DMUs). The selection of a Six Sigma project is a multiple-criteria decision-making problem, which is difficult in practice because the projects are not yet complete and the values of evaluation indicators are often interval or imprecise data. Managers stress the need for developing an effective performance evaluation methodology for selecting a Six Sigma project.

This study proposes a modified model considering interval or imprecise data based on common weight data envelopment analysis (DEA) approach to solve problems on project selection.

By comparing its findings with an example from a previous study, the new model obtained realistic and fair evaluation results and significantly reduced the difficulties and the time spent during calculation. Moreover, not only the best project is identified, but also the exact indicator information is obtained.

This study solves the problem of selecting the most efficient Six Sigma project in the preference of interval or imprecise data. Many studies have shown how a Six Sigma project is chosen, but only a few have integrated interval data into the selection process.

The purpose of this paper is to capture the dynamic variations in sales of a product based upon the dynamic estimation of the time series data and propose a model that imitates the price discounting and promotion strategy for a product category in a retail organization.

Time series data relating to sales has been used to model the sales estimates using moving average and proportional and derivative control; thereafter a sales forecast is generated to estimate the sales of a particular product category. This provides valuable inputs for taking lot sizing decisions regarding procurement of the products and selection of suppliers. A hybrid model has been proposed and explained with a hypothetical case, which considerably impacts the sales promotion and intelligent pricing decisions.

A conceptual framework is developed for modeling the dynamic price discounting strategy in retail using fuzzy logic. The model imitates sales promotion and price discounting strategy. This has helped minimize the inventory cost thereby keeping the profitability of the retail organization intact.

There is no appropriate empirical data to verify the models. In light of the research approach (modeling based upon historical time series data of a particular product category) that was undertaken, there is a possibility that the research results may be valid for the product category that was selected. Therefore, the researchers are advised to test the proposed propositions further for other product categories.

The study provides valuable insight on how to use the real-time sales data for designing a dynamic automated model for product sales promotion and price discounting strategy using fuzzy logic for a retail organization.

Nowadays, green supply chain (SC) management acts as an important strategic issue for the manufacturers. The effective SC design requires the development of analytical models and design tools. Because of the key role of steel in the infrastructure of industries, this metal is called the development metal. Despite the importance of this industry and its economic and environmental impacts through its SC, the SC structure of this industry has been less studied at the macro level. Therefore, the purpose of this paper is twofold: first, to design the structure of a steel industry SC at three levels; and second, to find the most effective and efficient carbon dioxide emitted industry among the supplier industries of the steel industry SC in China as a case study.

In this paper, due to the relationships among different industries, DEMATEL as a multi-criteria decision-making method has been applied.

A SC structure for the steel industry has been designed at three levels. The results indicated that the industries that had the highest relationship with the steel industry are mine industry, electricity, water, and gas industry, and optical and electrical equipment industry, which were recognized as the first-level suppliers for the steel industry. On the other hand, considering the relationship among the embodied carbon dioxide emissions of various industries in China as a case study, it can be said that among the steel suppliers, the most important polluting industries, respectively, are mining industry, electricity, water, and gas industry, optical and electrical equipment industry, machinery industry, chemicals and chemical products industry and coke, refined petroleum and nuclear fuel industry.

The developed SC can help in providing the steel industries’ managers a basic model for their supplier selection problem at the macro level. This paper can also help the industrial managers to understand the causal relationships among the suppliers of their industries. Finally, this paper can help government and industries managers to discover the most polluted industrial suppliers in the steel industry.

The novelty of this study belongs to the usage of DEMATEL method based on the input-output table to discover the relationships among the industries as well as identifying the main raw material suppliers of the steel industry at three levels. Furthermore, this research discovers the relationships among the embedded carbon dioxide emission of various industries in steel SC to determine the most important polluting industries in steel SC.

The purpose of this paper deals with a capacitated multi-item dynamic lot-sizing problem with the simultaneous sequence-dependent setup scheduling of the parallel resource under complex uncertainty.

An improved chance-constrained method is developed, in which confidence level of uncertain parameters is used to process uncertainty, and based on this, the reliability of the constraints is measured. Then, this study proposes a robust reconstruction method to transform the chance-constrained model into a deterministic model that is easy to solve, in which the robust transformation methods are used to deal with constraints with uncertainty on the right/left. Then, experimental studies using a real-world production data set provided by a gearbox synchronizer factory of an automobile supplier is carried out.

This study has demonstrated the merits of the proposed approach where the inventory of products tends to increase with the increase of confidence level. Due to a larger confidence level may result in a more strict constraint, which means that the decision-maker becomes more conservative, and thus tends to satisfy more external demands at the cost of an increase of production and stocks.

Joint decisions of production lot-sizing and scheduling widely applied in industries can effectively avert the infeasibility of lot-size decisions, caused by capacity of lot-sing alone decision and complex uncertainty such as product demand and production cost. is also challenging.

This study provides more choices for the decision-makers and can also help production planners find bottleneck resources in the production system, thus developing a more feasible and reasonable production plan in a complex uncertain environment.

The purpose of this paper is to present an efficient method for the numerical treatment of robust optimization problems with absolute reliability constraints.

Optimization with anti-optimization based on response surface techniques; polynomial chaos for approximation of the stochastic objective function.

The number of function calls is comparable to that of the corresponding deterministic problem. Thus, the method is well suited for complex technical systems. The performance of the method is demonstrated on an optimal design problem for turbochargers.

The highlights of this paper are: algorithms for robust and deterministic problems show comparable complexity; no derivatives required; good convergence properties because of special set up of optimization problem; application in complex industrial examples.

The purpose of this paper is to explore selection of the best ERP suppliers in the clothing industry by using analytic hierarchy process (AHP).

AHP is used in order to achieve the paper's purpose; selection criteria are determined by managers and experts.

Three different enterprise resource planning (ERP) suppliers are investigated and best alternative is selected by using AHP. After the best alternative is selected, cost benefit analysis is calculated in order to define decisive result. All calculations are verified by performing the consistency test.

Selection criteria and their evaluations can be changed depending on size of the clothing manufacturer and product type.

The results of the study will be helpful to clothing manufacturers which plan to implement an ERP system in their organizations. Furthermore, they can use AHP in other decision problems as well.