Vehicle Planning in Cross Chain Control Centers

To satisfy the growing need for efficient supply chain orchestration, a Cross Chain Control Center (4-C) might be an effective concept. A 4-C is a center from which multiple supply chains are controlled simultaneously, thereby aiming to exploit synergetic potential. This article proposes optimization approaches for transport timetabling and vehicle routing of multiple shippers that are controlled by a 4-C. We associate inconvenience with the requirement that shippers typically need to deviate from their individual schedules in order to make collaboration possible. This brings up a natural trade-off between inconvenience and transportation costs, which forms the essence of the proposed optimization models. Next, we pay attention to a fair redistribution of exploited synergy to the collaborating shippers. By considering several test cases, we find perfectly intuitively clear results supporting the applicability of this new approach to supply chain collaboration.

[1]  C. S. Lalwani,et al.  Combining vertical and horizontal collaboration for transport optimisation , 2007 .

[2]  Jean-François Cordeau,et al.  A Branch-and-Cut Algorithm for the Dial-a-Ride Problem , 2006, Oper. Res..

[3]  Leena Suhl,et al.  A time-space network based exact optimization model for multi-depot bus scheduling , 2006, Eur. J. Oper. Res..

[4]  Dan Stratila,et al.  Combinatorial optimization problems with concave costs , 2009 .

[5]  Gilbert Laporte,et al.  The Dial-a-Ride Problem (DARP): Variants, modeling issues and algorithms , 2003, 4OR.

[6]  Chandra Lalwani,et al.  Analysis of factory gate pricing in the UK grocery supply chain , 2007 .

[7]  Ali Haghani,et al.  Formulation and solution of a multi-commodity, multi-modal network flow model for disaster relief operations , 1996 .

[8]  Herbert Hamers,et al.  Production , Manufacturing and Logistics Supplier-initiated outsourcing : A methodology to exploit synergy in transportation , 2010 .

[9]  George L. Nemhauser,et al.  The fleet assignment problem: Solving a large-scale integer program , 1995, Math. Program..

[10]  Yahia Zare Mehrjerdi,et al.  The collaborative supply chain , 2009 .

[11]  M. Barratt.  Understanding the meaning of collaboration in the supply chain , 2004 .

[12]  Marc Salomon,et al.  Joint Route Planning Under Varying Market Conditions , 2006 .

[13]  Imma Curiel,et al.  Cooperative game theory and applications , 1997 .

[14]  Wendy Leavitt.  DATA, DATA EVERYWHERE , 2000 .

[15]  H. Young.  Monotonic solutions of cooperative games , 1985 .

[16]  L. Shapley.  A Value for n-person Games , 1988 .

[17]  Augusto Q. Novais,et al.  Simultaneous design and planning of supply chains with reverse flows: A generic modelling framework , 2010, Eur. J. Oper. Res..

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

[19]  E. Kohlberg.  On the Nucleolus of a Characteristic Function Game , 1971 .

[20]  Panos M. Pardalos,et al.  Minimum concave-cost network flow problems: Applications, complexity, and algorithms , 1991 .

[21]  Jean-François Cordeau,et al.  Branch and Cut and Price for the Pickup and Delivery Problem with Time Windows , 2009, Transp. Sci..

[22]  Nobuo Matsubayashi,et al.  A cost allocation problem arising in hub-spoke network systems , 2005, Eur. J. Oper. Res..

[23]  Panos E. Kourouthanassis,et al.  Towards ’smarter’ Supply and Demand-Chain Collaboration Practices Enabled by RFID Technology , 2005 .

[24]  Sophie N. Parragh,et al.  A survey on pickup and delivery models Part II : Transportation between pickup and delivery locations , 2007 .