A Feasibility Pump and Local Search Based Heuristic for Bi-Objective Pure Integer Linear Programming

2019 ◽  
Vol 31 (1) ◽  
pp. 115-133 ◽  
Author(s):  
Aritra Pal ◽  
Hadi Charkhgard
2012 ◽  
pp. 867-879
Author(s):  
O. J. Ibarra-Rojas ◽  
Y. A. Rios-Solis ◽  
O. L. Chacon-Mondragon

This chapter studies a manufacturing process of pieces. These pieces are produced with molds which are mounted on machines. The authors describe this process as an optimization problem using an integer linear programming formulation which integrates the most important features of the system, and determines the quantities of pieces to produce, including the allocation of molds to machines. The objective function is to maximize the weighted production since the authors seek to minimize the non-fulfilled demand. First they show that the addressed problem belongs to the NP-hard class. After observing that solving the problem in an exact way is time consuming, they propose a solution methodology based on an Iterated Local Search Algorithm. Through computational experimentation they make conclusions about the difficulty of the decisions determined in this manufacturing planning.


Author(s):  
O. J. Ibarra-Rojas ◽  
Y. A. Rios-Solis ◽  
O. L. Chacon-Mondragon

This chapter studies a manufacturing process of pieces. These pieces are produced with molds which are mounted on machines. The authors describe this process as an optimization problem using an integer linear programming formulation which integrates the most important features of the system, and determines the quantities of pieces to produce, including the allocation of molds to machines. The objective function is to maximize the weighted production since the authors seek to minimize the non-fulfilled demand. First they show that the addressed problem belongs to the NP-hard class. After observing that solving the problem in an exact way is time consuming, they propose a solution methodology based on an Iterated Local Search Algorithm. Through computational experimentation they make conclusions about the difficulty of the decisions determined in this manufacturing planning.


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