Behavioral Factors in City Logistics from an Operations Research Perspective

In the face of sharp urbanization around the world, metropolitan areas have started different initiatives and projects to make cities more efficient and sustainable. Hereby logistics and transportation activities have a major impact in the development of so called 'Smart Cities'. By addressing complex decision making problems through simulation and optimization, the Operations Research community has contributed to the development of sustainable city logistic systems. While technical and structural problems have been extensively discussed in the literature, many models neglect the importance of behavioral issues arising from risk aversion, stakeholder interaction and human factors that play an important role in the consolidation and optimization of logistical activities. This paper reviews existing work considering behavioral factors from an OR perspective. Simulation and optimization models to major problem settings in City Logistics are discussed and methodologies to conquer real-life urban L&T challenges are presented.

[1]  Angel A. Juan,et al.  A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems , 2015 .

[2]  Jesús Muñuzuri,et al.  Estimating the extra costs imposed on delivery vehicles using access time windows in a city , 2013, Comput. Environ. Urban Syst..

[3]  Eiichi Taniguchi,et al.  A Microsimulation Based Analysis of Exact Solution of Dynamic Vehicle Routing with Soft Time Windows , 2012 .

[4]  Charles M. Macal,et al.  Tutorial on agent-based modeling and simulation , 2005 .

[5]  Teodor Gabriel Crainic,et al.  Lower and upper bounds for the two-echelon capacitated location-routing problem , 2012, Comput. Oper. Res..

[6]  Jan Fabian Ehmke,et al.  Floating car based travel times for city logistics , 2012 .

[7]  Raimo P. Hämäläinen,et al.  On the importance of behavioral operational research: The case of understanding and communicating about dynamic systems , 2013, Eur. J. Oper. Res..

[8]  S. K. Goyal,et al.  A multi-criteria decision making approach for location planning for urban distribution centers under uncertainty , 2011, Math. Comput. Model..

[9]  Luca Maria Gambardella,et al.  A survey on metaheuristics for stochastic combinatorial optimization , 2009, Natural Computing.

[10]  Soumaya Ben Letaifa.  How to strategize smart cities: Revealing the SMART model ☆ , 2015 .

[11]  J.H.R. van Duin,et al.  The Seventh International Conference on City Logistics Towards an agent-based modelling approach for the evaluation of dynamic usage of urban distribution centres , 2012 .

[12]  Eiichi Taniguchi,et al.  Evaluating city logistics measures using a multi-agent model , 2010 .

[13]  Eiichi Taniguchi,et al.  MULTI-AGENT MODELLING FOR EVALUATING DYNAMIC VEHICLE ROUTING AND SCHEDULING SYSTEMS , 2007 .

[14]  P. Van den Bossche,et al.  The Cell versus the System: Standardization challenges for electricity storage devices , 2009 .

[15]  Michael J. North,et al.  Tutorial on agent-based modelling and simulation , 2005, Proceedings of the Winter Simulation Conference, 2005..

[16]  Nilesh Anand,et al.  The Seventh International Conference on City Logistics City logistics modeling efforts : Trends and gaps-A review , 2012 .

[17]  Ramasamy Panneerselvam,et al.  A Survey on the Vehicle Routing Problem and Its Variants , 2012 .

[18]  Hongmei He,et al.  Analyzing key influence factors of city logistics development using the fuzzy decision making trial and evaluation laboratory (DEMATEL) method , 2012 .

[19]  Angel A. Juan,et al.  A simheuristic algorithm for solving the permutation flow shop problem with stochastic processing times , 2014, Simul. Model. Pract. Theory.

[20]  Javier Faulin,et al.  Electric Vehicles in Logistics and Transportation: A Survey on Emerging Environmental, Strategic, and Operational Challenges , 2016 .

[21]  Günther R. Raidl,et al.  A Variable Neighborhood Search Approach for the Two-Echelon Location-Routing Problem , 2012, EvoCOP.

[22]  T. V. Woensel,et al.  From managing urban freight to smart city logistics networks , 2017 .

[23]  Eiichi Taniguchi,et al.  Emerging Techniques for Enhancing the Practical Application of City Logistics Models , 2012 .

[24]  Annalisa Cocchia.  Smart and Digital City: A Systematic Literature Review , 2014 .

[25]  Joeri Van Mierlo,et al.  Implementing electric vehicles in urban distribution: A discrete event simulation , 2013, 2013 World Electric Vehicle Symposium and Exhibition (EVS27).

[26]  Angel A. Juan,et al.  Rich Vehicle Routing Problem , 2014, ACM Comput. Surv..

[27]  P. Nijkamp,et al.  Smart Cities in Europe , 2011 .

[28]  Eiichi Taniguchi,et al.  CITY LOGISTICS. NETWORK MODELLING AND INTELLIGENT TRANSPORT SYSTEMS , 2001 .

[29]  Mohammed Othman,et al.  Workforce scheduling: A new model incorporating human factors , 2012 .

[30]  Pascal Estraillier,et al.  Goods distribution with electric vans in cities: towards an agent-based simulation , 2009 .

[31]  Angel A. Juan,et al.  Routing fleets with multiple driving ranges: Is it possible to use greener fleet configurations? , 2014, Appl. Soft Comput..

[32]  Teodor Gabriel Crainic,et al.  Two-Echelon Vehicle Routing Problem: A satellite location analysis , 2010 .

[33]  Elliot Bendoly,et al.  Behavior in operations management: Assessing recent findings and revisiting old assumptions , 2006 .

[34]  Enno Siemsen,et al.  Behavioral operations: The state of the field , 2013 .

[35]  Joel S. E. Teo,et al.  Multi-agent Systems Modelling for Evaluating Joint Delivery Systems , 2014 .

[36]  Travis Tokar,et al.  Behavioral research in logistics and supply chain management , 2010 .

[37]  Vikas Sharma,et al.  A Study of Behavioural Perspective of Operations , 2015 .

[38]  Craig A. Knoblock,et al.  A Survey of Digital Map Processing Techniques , 2014, ACM Comput. Surv..

[39]  Katarzyna Nowicka,et al.  Smart City Logistics on Cloud Computing Model , 2014 .

[40]  A. Harrison,et al.  Supply chain management: theory, practice and future challenges , 2006 .

[41]  Christian Prins,et al.  Solving the two-echelon location routing problem by a GRASP reinforced by a learning process and path relinking , 2012, Eur. J. Oper. Res..

[42]  Michael Drexl,et al.  A survey of variants and extensions of the location-routing problem , 2015, Eur. J. Oper. Res..

[43]  S. Magand,et al.  Evaluation of EVs energy consumption influencing factors, driving conditions, auxiliaries use, driver's aggressiveness , 2013, 2013 World Electric Vehicle Symposium and Exhibition (EVS27).

[44]  Angel A. Juan,et al.  Using safety stocks and simulation to solve the vehicle routing problem with stochastic demands , 2011 .

[45]  Teodor Gabriel Crainic,et al.  Models for Evaluating and Planning City Logistics Systems , 2009, Transp. Sci..

[46]  H. J. Quak,et al.  Delivering Goods in Urban Areas: How to Deal with Urban Policy Restrictions and the Environment , 2009, Transp. Sci..

[47]  Tomer Toledo,et al.  The Role of Personality Factors in Repeated Route Choice Behavior: Behavioral Economics Perspective , 2011 .

[48]  Romeo Danielis,et al.  Urban freight policies and distribution channels: a discussion based on evidence from Italian cities , 2010 .

[49]  Jairo R. Montoya-Torres,et al.  Simulation-optimization approach for the stochastic location-routing problem , 2015, J. Simulation.

[50]  Wallace J. Hopp,et al.  On the Interface Between Operations and Human Resources Management , 2003, Manuf. Serv. Oper. Manag..

[51]  Eiichi Taniguchi,et al.  Evaluating City Logistics Measure in E-Commerce with Multiagent Systems , 2012 .

[52]  Teodor Gabriel Crainic,et al.  An adaptive large neighborhood search heuristic for Two-Echelon Vehicle Routing Problems arising in city logistics , 2012, Comput. Oper. Res..

[53]  Simona Mancini,et al.  Multi-echelon distribution systems in city logistics , 2013 .

[54]  Jesús González-Feliu,et al.  Vehicle routing problems for city logistics , 2017, EURO J. Transp. Logist..