Evaluation of energy production technologies from biomass using analytical hierarchy process: The case of Iran

Item classification is an issue among inventory managers who want to achieve key-point management of items with different emphases, thus prompting managers and researchers to pursue efficient and effective classification algorithms. In processing multiple-criteria inventory classifications, back-propagation neural network (BPNN) shows its superiority in balancing items’ multiple competing attributes. However, because the training processes and the final results of BPNN rely on the initial connection weights and thresholds, finding reasonable values of the two parameters is a challenge. This paper introduces biogeography-based optimization (BBO) and an adaptive mutation particle swarm optimizer (AMPSO) to BPNN to optimize the training parameters, i.e., the global initial connection weights and thresholds, of BPNN. On the basis of this, two hybrid classification algorithms—BBO-BPNN and AMPSO-BPNN—are presented. Real-life data from three cases are adopted to verify the effectiveness and feasibility of the two proposed hybrid algorithms. Experimental results demonstrate that BBO-BPNN and AMPSO-BPNN show higher classification accuracy than other hybrid models, i.e., BPNN, PSO-BPNN, DE-BPNN, and GA-BPNN, in the multiple-criteria inventory classification problem.

BBO-BPNN and AMPSO-BPNN for multiple-criteria inventory classification

Corn cultivation location selection for bioethanol production: An application of BWM and extended PROMETHEE II

Appropriate implementation and organization of logistics activities greatly contributes to the creation of a better business environment in companies. This is reflected in increased business efficiency, cost rationalization, increased productivity and better overall quality. In order for a company to achieve sustainability of its business and its competitiveness, the link between the marketing logistics system and other logistics subsystems is particularly evident. Thereby, it is necessary to lead proactive management with a focus on key resources. In this paper, two novel integrated models in fuzzy form have been created. The first model includes the integration of the fuzzy Full Consistency Method (fuzzy FUCOM) and the fuzzy Evaluation based on Distance from Average Solution (EDAS) method for sorting 78 products regarding the following four criteria: quantity, unit price, annual procurement costs and demand. The second model involves the integration of the fuzzy FUCOM method and ABC analysis for the purpose of inventory sorting considering different significance of criteria. A range of values has been formed for each product category within the fuzzy FUCOM and fuzzy EDAS models, on the basis of which their sorting has been performed. The advantages and verification of the developed integrated fuzzy models have been performed through comparison with former traditional approaches. It has been determined based on an extensive sensitivity analysis that the developed models have better performance compared to the existing ones.

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A novel fuzzy mcdm model for inventory management in order to increase business efficiency

Generally, the Particle Swarm Optimization (PSO) algorithm has two memory dimensions: cognitive and social. In this study, a new dimension called family memory has been introduced to enhance PSO-based clustering's performance wherein not only the data were clustered but also the particles. Earlier studies on PSO-based clustering had used uniform distribution to create the particles' coordinates. In contrast, a multi-normal distribution-based swarm initialization was proposed in this study wherein K-means clustering outcomes were used as an input, which ensured the relationship within a particle's coordinates. The performance improvement through each modification to the Conventional PSO (CPSO) algorithm was demonstrated, and the statistical tests proved the same. Based on the two proposed changes, a modified PSO (MPSO) algorithm was developed. To extend the validation, both the proposed changes were incorporated into four different PSO variants, and the performance improvements were tested. Further, the MPSO algorithm was compared with the four different clustering algorithms (three algorithms are evolutionary type, and one is a non-evolutionary type) present in the recent literature. Statistical tests for the same proved the MPSO’s significance. As an application, the MPSO algorithm was applied to Multi-criteria Inventory classification (MCIC) data. Its classification was compared with the nineteen recent inventory models’ classification using the total relevant cost, fill rate, total safety stock cost, and the validity indices. Besides, a new index has been developed to identify the most similar models to MPSO.

PSO based data clustering with a different perception

So far, many methods have been proposed to classify items based on ABC analysis, but the results of these methods have had relatively low compliance with the principles of ABC. More precisely, collective value and sometimes the number of items belonging to each category in the methods provided do not meet the basic requirements of ABC called Pareto’s principle. In this study, a number of hybrid methodologies including Shannon’s entropy, TOPSIS (the technique for order preference by similarity to ideal solution) and goal programming are respectively used for determining the weight of criteria which are effective in the inventory items classification, calculations of each item value and its classification based on Pareto’s principle. To this end, the value of each item as well as classification of inventory items is calculated based on Pareto’s principle. The performance of the proposed method is evaluated through (1) statistical analysis, (2) checking the percentage of similarity with other methods and (3) comparison with another method in terms of the number and value allocated to each class. The results confirm the capability of the listed method.

ABC classification according to Pareto’s principle: a hybrid methodology

In planting rice, a significant amount of irrigation water is required to prepare the farmlands and do transplanting and this is directly related to the number of machines and workers available; that is, the more the length of plowing and transplanting process due to the lack of required machinery and labor, the more the water volume consumed. Therefore, in such conditions, it is important to have an appropriate planning for the optimized allocation of machinery and labor for the agricultural lands. Determining the optimal times of opening and closing valves based on the factors directly influencing the volume of the water in paddy fields is also of great importance. To meet the above conditions and by using linear and non-linear programming, two different models are developed in this article, with the purpose of reducing the time period of plowing and transplanting operations and also lowering the water consumption, respectively. Comparing the outputs of these two models with the real irrigation situation demonstrates the efficiency of the proposed models.

Reza Salehpour

Papers

ABC classification according to Pareto’s principle: a hybrid methodology

The optimization of the paddy field irrigation scheduling using mathematical programming