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Annals of Operations Research

, Volume 273, Issue 1–2, pp 311–375 | Cite as

Consideration of triple bottom line objectives for sustainability in the optimization of vehicle routing and loading operations: a systematic literature review

  • Carlos A. Vega-Mejía
  • Jairo R. Montoya-TorresEmail author
  • Sardar M. N. Islam
S.I.: OR in Transportation

Abstract

The current global interest in improving the use of ever-scarcer natural resources calls for the re-alignment of supply chain operations to include not only economic factors, but environmental and social factors as well. Two of the most important supply chain activities that logistics managers have to deal with are the planning and improvement of the packing and distribution of products. Although the optimization of these two activities has been thoroughly studied by means of Vehicle Routing Problems and Packing Problems, their analysis is often done separately and, in most cases, they consider only the economic decisions. Independent optimization of these two operations may overlook the structural dependencies between them, resulting in impractical solutions; while the consideration of only the economic criteria can overlook the environmental and social impacts of distribution activities, in the scope of sustainable supply chains. With the objective of improving distribution logistics, the aim of this review is to provide an overview of recent optimization developments for integrating packing and routing problems, in order to propose a simple classification scheme for re-aligning the optimization criteria and operational constraints, taking into consideration the issues of sustainability.

Keywords

Vehicle Routing Problem Packing Problem Loading constraints Triple bottom line Sustainable Supply Chain Management Systematic literature review 

Abbreviations

ACO

Ant Colony Optimization

APH

Author Proposed Heuristic

B&B

Branch and Bound

B&C

Branch and Cut

B&P

Branch and Price

BCA

Bee Colony Algorithm

BPP

Bin Packing Problem

BS

Beam Search

CCP

Chance Constrained Programming

CG

Column Generation

CLP

Container loading problem

CP

Cutting Problem

DP

Dynamic Programming

EA

Evolutionary Algorithm

ELS

Evolutionary Local Search

FFA

Firefly Algorithm

GA

Genetic Algorithm

GDS

Goal Driven Search

GHG

Green House Gas

GLS

Guided Local Search

GRASP

Greedy Randomized Adaptive Search Procedure

GSCM

Green Supply Chain Management

GVRP

Green VRP

HE

Heterogeneous items

HO

Homogeneous items

ILS

Iterated Local Search

IRP

Inventory Routing Problem

KP

Knapsack Problem

LIFO

Last In–First Out

LNS

Large Neighborhood Search

LP

Linear Programming

MA

Memetic Algorithm

MDVRP

Multi-Depot Vehicle Routing Problem

MIP

Mixed Integer Programming

MPNS

Multiple Phase Neighborhood Search

NLP

Non-Linear Programming

PLP

Pallet Loading Problem

PP

Packing Problem

PR

Path Relinking

PSO

Particle Swarm Optimization

SA

Simulated Annealing

SC

Supply chain

SCM

Supply Chain Management

SP

Stochastic Programming

SPP

Strip Packing Problem

SS

Scattered Search

SSCM

Sustainable Supply Chain Management

TBL

Triple-Bottom-Line

TRS

Tree Search

TS

Tabu Search

TSP

Traveling Salesman Problem

VNS

Variable Neighborhood Search

VRP

Vehicle Routing Problem

VRPLC

Vehicle Routing Problem with Loading Constraints

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© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Victoria Institute of Strategic Economic StudiesVictoria UniversityMelbourneAustralia
  2. 2.Operations and Supply Chain Management Research GroupUniversidad de La SabanaChíaColombia
  3. 3.Facultad de IngenieríaUniversidad de La SabanaChíaColombia

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