Green optimisation for LRP problem using a genetic algorithm and a dynamic island model

Abstract

It has grown quite conspicuous that no company is immune to the increase in fuel prices and energy sources used for air conditioning, refrigeration and heating, as well as traffic congestion and the degradation of road infrastructures. It is for this reason that companies are increasingly concerned about energy and environmental issues and are, therefore, more aware of the need to revise their logistics for the purpose of reducing costs and increasing competitiveness. In order to minimise the energy costs associated with transportation, it is sensical to consider a two-echelon location routing problem (2E-LRP) where two distribution levels are composed of three disjoint sets of nodes corresponding to the depots, the distribution centres and the customers, respectively. For this, we propose a mathematical model, a genetic algorithm, and a dynamic island model to optimise the assignment and the routing of freight. Eventual results show a minimisation of energy cost and CO2 rate.

Logistics Distribution Location Algorithm Based on Improved Imperial Competition Algorithm

“…Constraint (6) indicates that vehicle s is assigned to distribution center j. Constraint (7) means that vehicles must leave the distribution center. Constraint (8) means that if the distribution center is not opened, no vehicles will leave. Constraint (9) means that the vehicle enters from a point but exits from that point; that is, the path is guaranteed to be a loop.…”

“…e green LRP problem is a combination of the classical location routing problem and the pollution routing problem. is method can minimize energy costs and carbon dioxide emissions [8]. Wang et al used an ecological package to optimize the location of green logistics.…”

A Novel Multi-Objective Model for the Cold Chain Logistics Considering Multiple Effects

“…As an efficient micro view, CMEM has been widely used in the GLRP, which is easily applicable and is capable to accurately estimate fuel consumption and carbon emission. Only Benotmane et al [27] used a macro version to calculate fuel consumption. (3) For handling the fuel consumption and carbon emission, several methods have been used as (a) a part of logistics costs, namely penalty function, (b) the main objective like Pitakaso et al [28], (c) constraints like Qazvini et al [29], and (d) an objective in the multi-objective model.…”

“…Constraint (26) makes sure that the vehicle must reach each client before its closing time. Constraint (27) is the limitation on that each vehicle must return to its departed depot before its closing time window.…”