A simulation-based multi-objective optimization framework: A case study on inventory management

We propose a simulation-based solution framework for tackling the multi-objective inventory optimization problem. The goal is to find appropriate settings of reorder point and order quantity to minimize three objective functions simultaneously, which are the expected values of the total inventory cost, the average inventory level, and the frequency of inventory shortage. We develop new algorithms that can exploit statistically valid ranking and selection (R&S) procedures and the desirable mechanics of conventional multi-objective optimization techniques. Two simulation algorithms are proposed to be applied in different scenarios depending on the preference information that is revealed either during or after the actual optimization process. Experimental results are provided to evaluate the efficiency of the developed algorithms and other existing solution frameworks.

[1]  Sigrún Andradóttir,et al.  Selection Procedures for Simulations with Multiple Constraints under Independent and Correlated Sampling , 2014, TOMC.

[2]  John E. Dennis,et al.  Normal-Boundary Intersection: A New Method for Generating the Pareto Surface in Nonlinear Multicriteria Optimization Problems , 1998, SIAM J. Optim..

[3]  Y. Rinott.  On two-stage selection procedures and related probability-inequalities , 1978 .

[4]  Peter J. Haas,et al.  Guest editors’ introduction to special issue on the third INFORMS simulation society research workshop , 2014, TOMC.

[5]  Alexandre Dolgui,et al.  Multi-objective optimization for inventory control in two-level assembly systems under uncertainty of lead times , 2010, Comput. Oper. Res..

[6]  Shing Chih Tsai,et al.  Rapid Screening Procedures for Zero-One Optimization via Simulation , 2013, INFORMS J. Comput..

[7]  José A. Sepúlveda,et al.  Multi-objective simulation optimization for a cancer treatment center , 2001, Proceeding of the 2001 Winter Simulation Conference (Cat. No.01CH37304).

[8]  Barry L. Nelson,et al.  Using Ranking and Selection to "Clean Up" after Simulation Optimization , 2003, Oper. Res..

[9]  Barry L. Nelson,et al.  Optimization via simulation over discrete decision variables , 2010 .

[10]  Erick C. Jones,et al.  Multi-objective stochastic supply chain modeling to evaluate tradeoffs between profit and quality , 2010 .

[11]  MavrotasGeorge.  Effective implementation of the ε-constraint method in Multi-Objective Mathematical Programming problems , 2009 .

[12]  Shing Chih Tsai,et al.  A simulation optimization approach for a two-echelon inventory system with service level constraints , 2013, Eur. J. Oper. Res..

[13]  Enver Yücesan,et al.  Guest editors' introduction to special issue on the first INFORMS simulation society research workshop , 2010, TOMC.

[14]  Dong-Hee Lee,et al.  An interactive method to multiresponse surface optimization based on pairwise comparisons , 2012 .

[15]  S. Andradóttir,et al.  Fully sequential procedures for comparing constrained systems via simulation , 2010 .

[16]  Murat Köksalan,et al.  Interactive Approaches for Discrete Alternative Multiple Criteria Decision Making with Monotone Utility Functions , 1995 .

[17]  Li Li,et al.  Customer satisfaction evaluation method for customized product development using Entropy weight and Analytic Hierarchy Process , 2014, Comput. Ind. Eng..

[18]  Marco Laumanns,et al.  An efficient, adaptive parameter variation scheme for metaheuristics based on the epsilon-constraint method , 2006, Eur. J. Oper. Res..

[19]  Seong-Hee Kim,et al.  Finding feasible systems in the presence of constraints on multiple performance measures , 2010, TOMC.

[20]  Joaquín Bautista,et al.  A multiobjective model and evolutionary algorithms for robust time and space assembly line balancing under uncertain demand , 2016 .

[21]  Sanja Petrovic,et al.  The falling tide algorithm: A new multi-objective approach for complex workforce scheduling , 2012 .

[22]  Shing Chih Tsai,et al.  Genetic-algorithm-based simulation optimization considering a single stochastic constraint , 2014, Eur. J. Oper. Res..

[23]  J S Hammond,et al.  Even swaps: a rational method for making trade-offs. , 1998, Harvard business review.

[24]  Douglas J. Morrice,et al.  A Multiple Attribute Utility Theory Approach to Ranking and Selection , 2001, Manag. Sci..

[25]  Mostafa Zandieh,et al.  Multi-objective supply planning for two-level assembly systems with stochastic lead times , 2012, Comput. Oper. Res..

[26]  M. Fu,et al.  Optimization of( s, S ) inventory systems with random lead times and a service level constraint , 1998 .

[27]  Douglas J. Morrice,et al.  Ranking and Selection with Multiple "Targets" , 2006, Proceedings of the 2006 Winter Simulation Conference.

[28]  Mustafa Y. Sir,et al.  Multi-objective simulation optimization using data envelopment analysis and genetic algorithm: Specific application to determining optimal resource levels in surgical services , 2013 .

[29]  Barry L. Nelson,et al.  A sequential procedure for neighborhood selection-of-the-best in optimization via simulation , 2006, Eur. J. Oper. Res..

[30]  Carolyn R. Boyle,et al.  An interactive multiple-response simulation optimization method , 1996 .

[31]  Gerald W. Evans,et al.  Multicriteria design of manufacturing systems through simulation optimization , 1994 .

[32]  Jehoshua Eliashberg,et al.  Arbitrating a dispute: a decision analytic approach , 1986 .

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