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

  • S.I.: OR in Transportation
  • Published:
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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.

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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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  1. Victoria Institute of Strategic Economic Studies, Victoria University, P.O. Box 14425, Melbourne, VIC, 8001, Australia

    Carlos A. Vega-Mejía, Jairo R. Montoya-Torres & Sardar M. N. Islam

  2. Operations and Supply Chain Management Research Group, Universidad de La Sabana, Campus del Puente del Común, Km. 7, Autopista Norte de Bogotá, Chía, Cundinamarca, Colombia

    Carlos A. Vega-Mejía

  3. Facultad de Ingeniería, Universidad de La Sabana, Campus del Puente del Común, Km. 7, Autopista Norte de Bogotá, Chía, Cundinamarca, Colombia

    Jairo R. Montoya-Torres

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Vega-Mejía, C.A., Montoya-Torres, J.R. & Islam, S.M.N. Consideration of triple bottom line objectives for sustainability in the optimization of vehicle routing and loading operations: a systematic literature review. Ann Oper Res 273, 311–375 (2019). https://doi.org/10.1007/s10479-017-2723-9

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