Environmental multi objective uncertain transport trail model using variant of predator prey evolutionary strategy. (29th December 2014)

• Record Type: Journal Article
• Authors: Chand, Padmabati; Mohanty, J.R.
• Is Part Of: International journal of applied decision sciences. Volume 8:Number 1(2015)
• Page Start: 21
• Page End: 51
• Publication Date: 2014-12-29
• Subjects: environmental pollution -- multi–objective modelling -- transport trail model -- index–based crossover -- insert random mutation -- IRMT -- fuel consumption -- greenhouse gases -- GHG emissions -- vehicle routing problem -- VRP -- predator prey evolutionary strategy -- PPES -- uncertain tours -- uncertain travel time -- uncertain customers -- uncertain demand -- sustainable development -- sustainability -- air pollution; Decision making -- Periodicals; Management science -- Periodicals; 658.403005
• Languages: English
• ISSNs: 1755-8077

Abstract

The increasingly conspicuous problems of energy crisis and environmental pollution are paid attention to by governments and people all over the world since they seriously affect the sustainable development of the human society. As the automotive industry is developing and the quantity of vehicles is dramatically increasing, the emission gas is discharging more and more pollutant into the environment. Correlation between uncertain parameters is possibly a major stumbling block in many application areas. Assigning and scheduling vehicle routes in an uncertain environment are a crucial management problem. The assumption that in a real life environment everything goes according to a priori determined static schedule. The paper considers a version of vehicle routing problem which not only optimise travel time, distance and number of vehicles but also reduces fuel consumption and green house gas emission. In this paper, a variant of predator prey evolutionary strategy (variant–I) is proposed. We modified existing cross over and mutation technique and named as index–based crossover technique and insert random mutation technique. The performance of the algorithm is discussed under a variety of problem settings and parameters value by the numerical experiments and sensitivity analysis. A comparative study of proposed and classical predator prey evolutionary strategy algorithm is specified in this paper.