Production planning: An improved hybrid approach

Byrne, Mike; Hossain, Mohataz

doi:10.1016/j.ijpe.2004.06.021

Cited by 79 publications

(41 citation statements)

References 10 publications

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“…If the simulation results are different from the analytical model, they are then used to adjust the constraints of the analytical models. Byrne and Hossain (2005) developed an extended linear programming model for the hybrid modeling approach first proposed by Byrne and Bakir (1999). Their hybrid solution approach iteratively applies simulation and LP to solve a multi-period multi-product production planning problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal design of supply chain network under uncertainty environment using hybrid analytical and simulation modeling approach

Chiadamrong

Piyathanavong

2017

J Ind Eng Int

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Models that aim to optimize the design of supply chain networks have gained more interest in the supply chain literature. Mixed-integer linear programming and discrete-event simulation are widely used for such an optimization problem. We present a hybrid approach to support decisions for supply chain network design using a combination of analytical and discrete-event simulation models. The proposed approach is based on iterative procedures until the difference between subsequent solutions satisfies the pre-determined termination criteria. The effectiveness of proposed approach is illustrated by an example, which shows closer to optimal results with much faster solving time than the results obtained from the conventional simulation-based optimization model. The efficacy of this proposed hybrid approach is promising and can be applied as a powerful tool in designing a real supply chain network. It also provides the possibility to model and solve more realistic problems, which incorporate dynamism and uncertainty.

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Section: Literature Reviewmentioning

confidence: 99%

Optimal design of supply chain network under uncertainty environment using hybrid analytical and simulation modeling approach

Chiadamrong

Piyathanavong

2017

J Ind Eng Int

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“…The reason for applying simulation in these cases is the computational complexity or the lack of analytical form of the objective function and/or constraints. In production planning, simulation-based optimisation is mostly applied by iteratively adjusting parameter values according to the results of simulation experiments, until the target values of the performance indicators are reached (Byrne and Hossain 2005;Laroque et al 2012;Melouk et al 2013;Irdem et al 2010;Gansterer, Almeder, and Hartl 2014).…”

Section: Robust Production Planningmentioning

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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“…The parameters can be iteratively adjusted according to the results of the simulation, until the target values of the performance indicators are reached (Byrne and Hossain, 2005;Laroque et al, 2012). In contrast, a simulation-based optimization method is proposed in the paper that relies on linear regression models instead of iterations, thus requires less computation and always relies on up-to-date data.…”

Section: Simulation-based Optimization With Regression Modelsmentioning

confidence: 99%

Robust production planning and capacity control for flexible assembly lines

2015

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The frequently changing order stream and high product variety require robust planning and control approaches, as well as a flexible system structure in order to fulfill the highest possible customer service level and to keep the production costs on a reasonable level. In the paper, a combined production planning and capacity control method for assembly lines is proposed aiming at balancing the workload of the human operators and decreasing the overall production costs on a given time horizon. Instead of using the idealistic cycle times and simple manufacturing control rules, the proposed planning and control methodology is based on adaptive calculations taken from continuously updated historical production data. The manufacturing execution-level data is applied for building regression models predicting the capacity requirements of the future production scenarios. Besides, the historical data is also used as direct input of discrete-event simulations, to determine the proper control policies of human operator allocations for the different scenarios mentioned above. In order to calculate reliable and feasible production plans, the regression models and control policies are integrated in a mathematical programming model that minimizes a cost function representing the total production costs.

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Production planning: An improved hybrid approach

Cited by 79 publications

References 10 publications

Optimal design of supply chain network under uncertainty environment using hybrid analytical and simulation modeling approach

Optimal design of supply chain network under uncertainty environment using hybrid analytical and simulation modeling approach

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

Robust production planning and capacity control for flexible assembly lines

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