An improved stochastic programming model for supply chain planning of MRO spare parts

Abstract The maintenance, repair and operation (MRO) spare parts that are vital to machine operations are playing an increasingly important role in manufacturing enterprises. MRO spare parts supply chain management planning must be coordinated to ensure spare part availability while keeping the total cost to a minimum. Due to the specificity of MRO spare parts, randomness and uncertainties in production and storage should be quantified to formulate the problem in a mathematical model. Given these considerations, this paper proposes an improved stochastic programming model for the supply chain planning of MRO spare parts. In our stochastic programming model, the following improvements are made: First, we quantify the uncertain production time capacity as a random variable with a probability distribution. Second, the upper bound of the storage cost is modeled as a multi-choice variable in the constraint. To derive the equivalent deterministic model, the Lagrange interpolating polynomial approach is used. The results of the numerical examples validate the feasibility and efficiency of the proposed model. Finally, the model is tested in the supply chain planning of continuous caster (CC) bearings.

[1]  Timo Pirttilä,et al.  Improving global spare parts distribution chain performance through part categorization: A case study , 2011 .

[2]  van Geert-Jan Geert-Jan Houtum,et al.  System-oriented inventory models for spare parts , 2014 .

[3]  Thomas Spengler,et al.  Slab scheduling at parallel continuous casters , 2015 .

[4]  Chengbin Chu,et al.  Inspection and maintenance planning: an application of semi-Markov decision processes , 1997, J. Intell. Manuf..

[5]  Günther Schuh,et al.  Collaboration Mechanisms to Increase Productivity in the Context of Industrie 4.0 , 2014 .

[6]  Weiming Shen,et al.  A Discrete Stress–Strength Interference Theory-Based Dynamic Supplier Selection Model for Maintenance Service Outsourcing , 2016, IEEE Transactions on Engineering Management.

[7]  Yifan Zhou,et al.  Joint optimization of maintenance, buffer, and spare parts for a production system , 2015 .

[8]  R. Keith Mobley,et al.  An introduction to predictive maintenance , 1989 .

[9]  Ying Zhang,et al.  Single-machine scheduling problems with machine aging effect and an optional maintenance activity , 2016 .

[10]  Patrick Neumann,et al.  Considering human error in optimizing production and corrective and preventive maintenance policies for manufacturing systems , 2016 .

[11]  A. A. Kranenburg,et al.  A new partial pooling structure for spare parts networks , 2009, Eur. J. Oper. Res..

[12]  Kevin W. Li,et al.  Efficient aircraft spare parts inventory management under demand uncertainty , 2015 .

[13]  S. G. Li,et al.  The inventory management system for automobile spare parts in a central warehouse , 2008, Expert Syst. Appl..

[14]  Mordecai Avriel,et al.  The Value of Information and Stochastic Programming , 1970, Oper. Res..

[15]  M. Rönnqvist,et al.  Cost allocation in inventory pools of spare parts with service-differentiated demand classes , 2015 .

[16]  Mingxiang Jiang,et al.  Modeling of risk-based inspection, maintenance and life-cycle cost with partially observable Markov decision processes , 2005 .

[17]  Jan Holmström,et al.  Additive manufacturing in the spare parts supply chain , 2014, Comput. Ind..

[18]  Francesco Costantino,et al.  Multi-echelon, multi-indenture spare parts inventory control subject to system availability and budget constraints , 2013, Reliab. Eng. Syst. Saf..

[19]  Dirk Cattrysse,et al.  Multi-item spare parts systems with lateral transshipments and waiting time constraints , 2006, Eur. J. Oper. Res..

[20]  Loo Hay Lee,et al.  Multi-objective simulation-based evolutionary algorithm for an aircraft spare parts allocation problem , 2008, Eur. J. Oper. Res..

[21]  Lu Zhen,et al.  A stochastic programming model for multi-product oriented multi-channel component replenishment , 2015, Comput. Oper. Res..

[22]  Maghsud Solimanpur,et al.  An integrated supply chain configuration model and procurement management under uncertainty: A set-based robust optimization methodology , 2016 .

[23]  Janne Huiskonen,et al.  Maintenance spare parts logistics: Special characteristics and strategic choices , 2001 .

[24]  Mustapha Nourelfath,et al.  Integrated spare parts logistics and operations planning for maintenance service providers , 2014 .

[25]  Lu Zhen,et al.  Tactical berth allocation under uncertainty , 2015, Eur. J. Oper. Res..

[26]  Enzo Morosini Frazzon,et al.  Spare parts supply chains' operational planning using technical condition information from intelligent maintenance systems , 2014, Annu. Rev. Control..

[27]  Yun-Chin Chen,et al.  Applying moving back-propagation neural network and moving fuzzy neuron network to predict the requirement of critical spare parts , 2010, Expert Syst. Appl..

[28]  Marcello Braglia,et al.  The analytic hierarchy process applied to maintenance strategy selection , 2000, Reliab. Eng. Syst. Saf..

[29]  L. Pintelon,et al.  A framework for maintenance concept development , 2002 .

[30]  Eugene Levner,et al.  A network approach to modeling the multi-echelon spare-part inventory system with backorders and interval-valued demand , 2011 .

[31]  Seyed Reza Hejazi,et al.  A multi-objective approach to simultaneous determination of spare part numbers and preventive replacement times , 2011 .

[32]  M. Z. Babai,et al.  On the demand distributions of spare parts , 2012 .

[33]  Seyed Jafar Sadjadi,et al.  A robust optimization model for humanitarian relief chain design under uncertainty , 2016 .

[34]  Jo van Nunen,et al.  Dynamic demand fulfillment in spare parts networks with multiple customer classes , 2013, Eur. J. Oper. Res..