Journal of Advanced Transportation

Research Article

Multimodal Green Logistics Network Design of Urban Agglomeration with Stochastic Demand

Table 1

Characteristics of the reviewed models proposed for the green logistics network design problem.
StudyModel characteristicsObjective functionsDecision variablesSolution procedure
Research areasdecision makersTransport modesDemandsChoice behaviorsNumberTypeCapacityRoutingCarbon taxExactHeuristic
SMSMSMDeStTypeUtilitySM
Xiong and Wang [12]✓ ✓  ✓✓ -- ✓Cost, time✓✓  MOGA
Harris et al. [25]✓ ✓ ✓ ✓ -- ✓Cost, CO2 emissions✓✓  SEAMO2
Yang et al. [26]✓ ✓ ✓ ✓ --✓ Cost✓✓ Cplex 
Turken et al. [24]✓ ✓ ✓ ✓ --✓ Cost✓✓ FLAA 
Rezaee et al. [2]✓ ✓  ✓ ✓--✓ Cost✓✓ Cplex 
Gao et al. [28]✓ ✓ ✓ ✓ --✓ Cost✓✓ Cplex 
Yamada et al. [37]✓ ✓  ✓✓ UECost, time✓ Benefit–cost ratio✓✓  GA,GLS
Meng and Wang [38] ✓✓  ✓✓ UECost, time✓ Cost✓✓  GA
Wang and Meng [39] ✓✓  ✓✓ UECost, time✓ Cost✓✓ B&B 
Zhang et al. [35]✓ ✓  ✓✓ UECost, time✓ Benefit–cost ratio✓✓  GA+ FWA
This study ✓ ✓ ✓ ✓SUECost, time, CO2 emission✓ Social benefit✓✓✓ QPSO+ MSA
S: single, M: multiple, De: deterministic, St: stochastic, UE: user equilibrium, SUE: stochastic user equilibrium, GA: Genetic Algorithm, and MOGA: Multiobjective Genetic Algorithm.
SEAMO2: Simple Evolutionary Multiobjective Optimization 2, FLAA: Facility Linear Approximation Algorithm, GLS: Genetic Local Search, and B&B: Branch-and-Bound.
FW: Frank–Wolfe Algorithm, QPSO: Quantum behaved Particle Swarm Optimization, and MSA: Method of Successive Averages.