An aggregate production planning model for two phase production systems: Solving with genetic algorithm and tabu search

Ramezanian, Reza; Rahmani, Donya; Barzinpour, Farnaz

doi:10.1016/j.eswa.2011.07.134

Cited by 90 publications

(65 citation statements)

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%

Proposing an Aggregate Production Planning Model by Goal Programming Approach, a Case Study

Rad¹,

Shirouyehzad²

2014

Data Envelopment Analysis and Decision Science

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Production planning is one of the most important functions in the process of production management. Production planning in the intermediate range of time is termed as "aggregate production planning (APP)". Aggregate production planning is an important upper level planning activity in a production management system. The present study tries to suggest an aggregate production planning model for products of Hafez tile factory during one year. Due to this fact that the director of the company seeks 3 main objectives to determine the optimal production rate, the linear goal planning method was employed. After solving the problem, in order to examine the efficiency and the distinctiveness of this method in compare to linear programming, the problem was modeled just by considering one objective then was solved by linear programming approach. The findings revealed the goal programming with multi objectives resulted more appropriate solution rather than linear programming with just one objective.

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Section: Literature Reviewmentioning

confidence: 99%

Proposing an Aggregate Production Planning Model by Goal Programming Approach, a Case Study

Rad¹,

Shirouyehzad²

2014

Data Envelopment Analysis and Decision Science

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“…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%

Naturally selecting solutions

2013

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For decades, computer scientists have looked to nature for biologically inspired solutions to computational problems; ranging from robotic control to scheduling optimization. Paradoxically, as we move deeper into the postgenomics era, the reverse is occurring, as biologists and bioinformaticians look to computational techniques, to solve a variety of biological problems. One of the most common biologically inspired techniques are genetic algorithms (GAs), which take the Darwinian concept of natural selection as the driving force behind systems for solving real world problems, including those in the bioinformatics domain. Herein, we provide an overview of genetic algorithms and survey some of the most recent applications of this approach to bioinformatics based problems.

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“…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%

Robust production planning and control for multi-stage systems with flexible final assembly lines

Gyulai

Pfeiffer

Monostori

2016

International Journal of Production Research

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Production planning of final assembly systems is a challenging task, as the often fluctuating order volumes require flexible solutions. Besides, the calculated plans need to be robust against the process-level disturbances and stochastic nature of some parameters like manual processing times or machine availability. In the paper, a simulation-based optimisation method is proposed that utilises lower level shop floor data to calculate robust production plans for final assembly lines of a flexible, multi-stage production system. In order to minimise the idle times when executing the plans, the capacity control that specifies the proper operator-task assignments is also determined. The analysed multi-stage system is operated with a pull strategy, which means that the production at the final assembly lines generates demands for the preceding stages providing the assembled components. In order to guarantee the feasibility of the plans calculated for the final assembly lines, a decomposition approach is proposed to optimise the production plan of preceding stages. By this way, the robust production can be ensured resulting in reduced losses and overall production costs even though the system is exposed to changes and disturbances.

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An aggregate production planning model for two phase production systems: Solving with genetic algorithm and tabu search

Cited by 90 publications

References 13 publications

Proposing an Aggregate Production Planning Model by Goal Programming Approach, a Case Study

Proposing an Aggregate Production Planning Model by Goal Programming Approach, a Case Study

Naturally selecting solutions

Robust production planning and control for multi-stage systems with flexible final assembly lines

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