Interconnected logistic networks and protocols: simulation-based efficiency assessment

Logistic networks intensely use means of transportation and storage facilities to deliver goods. However, these logistic networks are still poorly interconnected and this fragmentation is responsible for a lack of consolidation and thus efficiency. To cope with the seeming contradiction of just-in-time deliveries and challenging emissions targets, a major improvement in supply networks is sought here. This new organisation is based on the universal interconnection of logistics services, namely a Physical Internet where goods travel in modular containers for the sake of interconnection in open networks. If from a logical point of view, merging container flows should improve efficiency, no demonstration of its potential has been carried out prior to the here reported research. To reach this potentiality assessment goal, we model the asynchronous shipment and creation of containers within an interconnected network of services, find the best path routing for each container and minimise the use of transportations means. To carry out the demonstration and assess the associated stakes, we use a set of actual flows from the fast-moving consumer goods sector in France. Various transportation protocols and scenarios are tested, revealing encouraging results for efficiency indicators such as CO2 emissions, cost, lead time, delivery travel time, and so forth. As this is a first work in the field of flows transportation, the simulation model and experiment exposes many further research avenues.

[1]  Ralph E. Gomory,et al.  A Linear Programming Approach to the Cutting Stock Problem---Part II , 1963 .

[2]  R. Joumard Methods of estimation of atmospheric emissions from transport: European scientist network and scientific state-of-the art , 1999 .

[3]  Eric Ballot,et al.  The reduction of greenhouse gas emissions from freight transport by pooling supply chains , 2013 .

[4]  W. Dullaert,et al.  Horizontal cooperation in logistics : Opportunities and impediments , 2007 .

[5]  FuL.,et al.  Heuristic shortest path algorithms for transportation applications , 2006 .

[6]  D. Simchi-Levi New worst‐case results for the bin‐packing problem , 1994 .

[7]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[8]  W. Dullaert,et al.  Horizontal Cooperation in Transport and Logistics: A Literature Review , 2007, Transportation Journal.

[9]  Morton W Jørgensen,et al.  Estimating emissions from railway traffic , 1998 .

[10]  Muddassar Farooq,et al.  Routing Protocols for Next-Generation Networks Inspired by Collective Behaviors of Insect Societies: An Overview , 2008, Swarm Intelligence.

[11]  D. J. van der Zee,et al.  Modeling decision making and control in manufacturing simulation , 2006 .

[12]  Zissis Samaras,et al.  METHODOLOGY FOR CALCULATING TRANSPORT EMISSIONS AND ENERGY CONSUMPTION , 1999 .

[13]  Benoît Montreuil,et al.  Analogies between Internet network and logistics service networks: challenges involved in the interconnection , 2014, J. Intell. Manuf..

[14]  Shenle Pan,et al.  Contribution à la définition et à l'évaluation de la mutualisation de chaînes logistiques pour réduire les émissions de CO2 du transport : application au cas de la grande distribution , 2010 .

[15]  Ludo Gelders,et al.  A swift response framework for measuring the strategic fit for a horizontal collaborative initiative , 2009 .

[16]  Sai Ho Chung,et al.  A heuristic methodology for order distribution in a demand driven collaborative supply chain , 2004 .

[17]  Moshe Ben-Horim,et al.  A linear programming approach , 1977 .

[18]  Panos M. Pardalos,et al.  Handbook of Optimization in Telecommunications , 2006 .

[19]  Laurence R. Rilett,et al.  Heuristic shortest path algorithms for transportation applications: State of the art , 2006, Comput. Oper. Res..

[20]  Edward G. Coffman,et al.  Approximation algorithms for bin packing: a survey , 1996 .

[21]  A. McKinnon,et al.  Forecasting the carbon footprint of road freight transport in 2020 , 2010 .

[22]  Margaret O'Mahony,et al.  Parallel implementation of a transportation network model , 2005, J. Parallel Distributed Comput..

[23]  T. Lindvall ON A ROUTING PROBLEM , 2004, Probability in the Engineering and Informational Sciences.

[24]  Benoît Montreuil,et al.  Toward a Physical Internet: meeting the global logistics sustainability grand challenge , 2011, Logist. Res..

[25]  Chang Ouk Kim,et al.  Multi-agent systems applications in manufacturing systems and supply chain management: a review paper , 2008 .

[26]  R. Gomory,et al.  A Linear Programming Approach to the Cutting-Stock Problem , 1961 .

[27]  Adriana Giret,et al.  Agent-supported simulation environment for intelligent manufacturing and warehouse management systems , 2011 .

[28]  Rina Dechter,et al.  Generalized best-first search strategies and the optimality of A* , 1985, JACM.

[29]  Benoit Montreuil,et al.  Towards a Physical Internet: the Impact on Logistics Facilities and Material Handling Systems Design and Innovation , 2010 .

[30]  Alan McKinnon,et al.  Analysis of Transport Efficiency in the UK Food Supply Chain , 2003 .

[31]  Lee Schipper,et al.  Trends in Truck Freight Energy Use and Carbon Emissions in Selected OECD Countries from 1973 to 2003 , 2009 .

[32]  Jeffrey D. Ullman,et al.  Worst-Case Performance Bounds for Simple One-Dimensional Packing Algorithms , 1974, SIAM J. Comput..

[33]  Benoît Montreuil,et al.  Physical Internet Enabled Open Hub Network Design for Distributed Networked Operations , 2012, Service Orientation in Holonic and Multi-Agent Manufacturing Control.

[34]  Benoît Montreuil,et al.  Physical Internet Foundations , 2012, Service Orientation in Holonic and Multi Agent Manufacturing and Robotics.

[35]  Deeparnab Chakrabarty,et al.  Knapsack Problems , 2008 .