Multiple Criteria Optimization for Supply Chains – Analysis of Case Study

Completing of a production process in any enterprise requires designing of a proper supply chain. Its non-compliance with the ever-changing client needs may result in a variety of problems that are perceived in different ways by different stakeholders when attempting to resolve a decision problem. The paper presents an example solution of a problem related to planning of supplies of components of a final product. The proposed non-linear, deterministic mathematical model of a decision problem includes a set of 5 criteria: costs of warehousing, transport, stock in transit and the criterion of time of transport and warehouse efficiency. Such an approach allowed including various aspects of the enterprise operation and the operation of its individual departments such as supplies department, warehouse department, marketing/sales department and management.

[1]  C.-C. Chern,et al.  A heuristic algorithm for master planning that satisfies multiple objectives , 2007, Comput. Oper. Res..

[2]  Purushottam L. Meena,et al.  Joint planning and problem solving roles in supply chain collaboration , 2017 .

[3]  Yacov Y. Haimes,et al.  Multiobjective Decision Making: Theory and Methodology , 1983 .

[4]  Albert Corominas,et al.  Optimization and Decision Support Systems for Supply Chains , 2017 .

[5]  George Mavrotas,et al.  Effective implementation of the epsilon-constraint method in Multi-Objective Mathematical Programming problems , 2009, Appl. Math. Comput..

[6]  Matt Bassett,et al.  Optimizing the design of global supply chains at Dow AgroSciences , 2010, Comput. Chem. Eng..

[7]  Chelliah Sriskandarajah,et al.  Sequencing deliveries to minimize inventory holding cost with dominant upstream supply chain partner , 2009 .

[8]  Andrzej Jaszkiewicz,et al.  The 'Light Beam Search' approach - an overview of methodology and applications , 1999, Eur. J. Oper. Res..

[9]  Tadeusz Sawik.  Supply Chain Disruption Management Using Stochastic Mixed Integer Programming , 2017 .

[10]  Paweł Zmuda-Trzebiatowski,et al.  A Heuristic Approach to the Daily Delivery Scheduling Problem. Case Study: Alcohol Products Delivery Scheduling within Intra-Community Trade Legislation , 2014 .

[11]  Martin Christopher,et al.  Logistics and supply chain management : creating value-adding networks , 2005 .

[12]  Ilona Jacyna-Gołda,et al.  SOME ASPECTS OF RISK ASSESSMENT IN THE LOGISTICS CHAIN , 2014 .

[13]  Shahryar Rahnamayan,et al.  3D-RadVis: Visualization of Pareto front in many-objective optimization , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[14]  Carlos F. Daganzo,et al.  A theory of supply chains , 2003 .

[15]  R. Handfield,et al.  Purchasing and Supply Chain Management , 1997 .

[16]  Marcin Kiciński,et al.  MCDM Approach to the Excise Goods Daily Delivery Scheduling Problem. Case Study: Alcohol Products Delivery Scheduling under Intra-community Trade Regulations , 2014 .

[17]  D. Power.  Supply chain management integration and implementation: a literature review , 2005 .

[18]  A. Barbosa‐Póvoa.  Optimising Sustainable Supply Chains: A Summarised View of Current and Future Perspectives , 2017 .

[19]  Hoda A. ElMaraghy,et al.  Integrated supply chain design using multi-criteria optimization , 2008 .

[20]  Yacov Y. Haimes,et al.  Harmonizing the Omnipresence of MCDM in Technology, Society, and Policy , 2011 .

[21]  Kaisa Miettinen,et al.  Nonlinear multiobjective optimization , 1998, International series in operations research and management science.

[22]  Farzad Mahmoodi,et al.  Supplier selection model with contingency planning for supplier failures , 2013, Comput. Ind. Eng..

[23]  Kalyanmoy Deb,et al.  Multiobjective optimization , 1997 .

[24]  D. Lambert,et al.  Issues in Supply Chain Management , 2000 .