Heuristics for multilevel lot-sizing with a bottleneck
Management Science
Optimal lot-sizing algorithms for complex product structures
Operations Research
Solving multi-item capacitated lot-sizing problems using variable redefinition
Operations Research
Capacitated lot sizing with setup times
Management Science
Improved algorithms for economic lot size problems
Operations Research
bc -- prod: A Specialized Branch-and-Cut System for Lot-Sizing Problems
Management Science
Single-point stochastic search algorithms for the multi-level lot-sizing problem
Computers and Operations Research
A MAX-MIN ant system for unconstrained multi-level lot-sizing problems
Computers and Operations Research
An application of swarm optimization to nonlinear programming
Computers & Mathematics with Applications
Variable neighborhood search based approach for solving multilevel lot-sizing problems
ACE'10 Proceedings of the 9th WSEAS international conference on Applications of computer engineering
Integer linear programming model for multidimensional two-way number partitioning problem
Computers & Mathematics with Applications
Incorporating a database approach into the large-scale multi-level lot sizing problem
Computers & Mathematics with Applications
A variable neighborhood search based approach for uncapacitated multilevel lot-sizing problems
Computers and Industrial Engineering
Neighborhood search techniques for solving uncapacitated multilevel lot-sizing problems
Computers and Operations Research
A novel parallel hybrid intelligence optimization algorithm for a function approximation problem
Computers & Mathematics with Applications
Discrete Particle Swarm Optimization for the Multi-Level Lot-Sizing Problem
International Journal of Applied Metaheuristic Computing
Computers and Industrial Engineering
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The multilevel lot-sizing (MLLS) problem is a key production planning problem in materials requirements planning (MRP) system. The MLLS problem deals with determining the production lot-sizes of various items appearing in the product structure over a given finite planning horizon to minimize the production cost, the inventory carrying cost, the back ordering cost and etc. This paper proposed a particle swarm optimization (PSO) algorithm for solving the uncapacitated MLLS problem with assembly structure. All the mathematical operators in our algorithm are redefined and the inertial weight parameter can be either a negative real number or a positive one. The feasibility and effectiveness of our algorithm are investigated by comparing the experimental results with those of a genetic algorithm (GA).