Strategic sourcing model for green supply chain management: an insight into automobile manufacturing units in India

Predicting and assessing greenhouse gas emissions during the construction of monorail systems using artificial intelligence

Based on partial data, this paper uses BP neural network optimised by particle swarm optimisation algorithm to predict the total greenhouse gas (GHG) emissions of the line in the construction phase. The GHG emission efficiency is analysed by SBM (Slacks-Based Measure) super efficiency method. Finally, the grey relational analysis (GRA) is applied to sort the GHG emission correlation factors. Based on the existing design and quota document data of 16 stations and 16 sections of the Wuhu Monorail Line 1, we have employed a neural network optimized by particle swarm optimization algorithm to predict the total emissions of greenhouse gases during the construction phase of the entire line consisting of 25 stations and 24 sections. The GHG emissions of all stations and sections are 29,300 tons and 21,000 tons. The technical efficiency, pure technical efficiency, and scale efficiency of the stations and sections were high. As for stations, the order of influence degree is metal material consumption (0.9731) > cost (0.9486) > electric energy consumption (0.9481) > station area (0.9109) > concrete and cement consumption (0.9032) > other material consumption (0.8831) > gasoline and diesel consumption (0.7258). For the section, the order of influence degree is cost (0.9766) > concrete (0.9581) > steel reinforcement (0.9483) > other steels (0.874) > section length (0.8337) > power energy consumption (0.7169) > wood consumption (0.6684).

An integrated mathematical model based on grey optimal ranking for supplier selection considering pandemic situation

Optimal selection and allocation of suppliers are crucial decisions for an organization and it becomes more critical when the firm faces disruptive events. The recent outbreak of COVID-19 has led to massive supply disruptions in a supply chain. This paper aims to address the supplier selection and allocation problem of manufacturing firms under pandemic environment. In this study, a novel Mixed Integer Linear Programming (MILP) model integrated with grey optimal ranking of suppliers considering factors related to pandemic situation is proposed. The methodology is implemented in two subsequent stages. In the first stage, Grey Relational Analysis is adopted to determine the grey possibility scoring, and in the second stage, a supplier selection model is proposed to integrate the grey scoring to a MILP model to determine optimal allocation of suppliers. The paper presents a numerical study to demonstrate the proposed model and sensitivity analysis is conducted to deduce key managerial insights regarding the factors affecting the allocation under pandemic situation. Further, the illustration demonstrates how the proposed method integrates the expert ranking based approach and the cost minimization approach. The study is generic in nature and provides useful directions for practitioners involved in supplier selection in manufacturing organizations.