2012
DOI: 10.1016/j.eswa.2011.07.134
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An aggregate production planning model for two phase production systems: Solving with genetic algorithm and tabu search

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Cited by 90 publications
(65 citation statements)
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References 13 publications
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“…The results showed the proposed model can achieve an efficient production planning in a supply chain. Ramezanian et al [10] presented a mixed integer linear programming (MILP) model for general two-phase aggregate production planning systems. For solving this problem a genetic algorithm and tabu search was applied.…”
Section: Literature Reviewmentioning
confidence: 99%
“…The results showed the proposed model can achieve an efficient production planning in a supply chain. Ramezanian et al [10] presented a mixed integer linear programming (MILP) model for general two-phase aggregate production planning systems. For solving this problem a genetic algorithm and tabu search was applied.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Mutation refers to the modification of a genotype by some random process. 36 Not all mutations will result in increased fitness. For a binary genotype, the mutation operation can be as simple as inverting the value of one of the genes (flipping) or switching two adjacent values.…”
Section: Mutationmentioning
confidence: 99%
“…The approach can manage general product structures and consider the lead times of products, nonetheless deviations and uncertainties of the parameters cannot be treated. Similarly, the aggregate production planning problem of a two-stage system is solved by Ramezanian, Rahmani, and Barzinpour (2012), applying genetic algorithm and Tabu search. In contrast to heuristics-based approaches, decomposition-based solutions apply echelon-stock variables, simplifying the original multi-level problem to a series of single-item lot-sizing subproblems (Pochet and Wolsey 2006).…”
Section: Production Planning In Multi-stage Systemsmentioning
confidence: 99%