A hybrid multi-objective genetic algorithm for planning order release date in two-level assembly system with random lead times

Fallah-Jamshidi, Saeid; Karimi, N.; Zandieh, M.

doi:10.1016/j.eswa.2011.04.065

Cited by 10 publications

(11 citation statements)

References 21 publications

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“…The model calculates the mathematical expectation of the total cost composed of inventory holding costs for components at all levels and for the finished product and a backlogging cost for the finished product. The proposed lower and upper bounds and recursive function which expresses the dependence among levels, enabled us to study assembly systems with more than two levels and thus extend the results of (Yano 1987a, Chu et al 1993, Tang and Grubbström 2003, Hnaien et al 2008a, Fallah-Jamshidi et al 2011and Borodin et al 2016. Specific techniques were introduced to reduce the initial cardinality of research space, they considerably decreases the percentage of branches to be pruned.…”

Section: Discussionmentioning

confidence: 99%

“…An algorithm was developed to find optimal planned lead times, which minimize holding and backlogging costs. This study has been cited 148 times and in subsequent publications, models have been limited to one or two-level assembly systems (Chu et al 1993, Tang and Grubbström 2003, Mohebbi and Choobineh 2005, Hnaien et al 2008a, Fallah-Jamshidi et al 2011, Dolgui et al 2013and Borodin et al 2016. Chu et al (1993) addressed the same problem, but in the case of one-level assembly systems.…”

Section: Related Publicationsmentioning

confidence: 99%

“…The authors assumed that the components at level 1 of the BOM were stored and the finished product was assembled only after the given due date. Fallah-Jamshidi et al (2011) exploit the same problem in a multi-objective context. An electromagnetism-like mechanism is proposed to reinforce the GA and to determine minimal expected costs.…”

Section: Related Publicationsmentioning

confidence: 99%

“… to one-level assembly systems, continuous random lead times, real decision variables and one-off demand (Chu et al, 1993),  or to one-level assembly systems, discrete random lead times, discrete decision variables and a constant demand which is the same for all periods Dolgui 2013),  or to two-level systems with only one component at level 1 and two components at level 2, continuous random lead times and one period planning (Yano 1987a),  or to two-level assembly systems and several types of components at each level (Hnaien et al, 2008a, Fallah-Jamshidi et al, 2011.…”

Section: Related Publicationsmentioning

confidence: 99%

See 3 more Smart Citations

Planned lead times optimization for multi-level assembly systems under uncertainties

Ben-Ammar

Dolgui

2018

Omega

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Planned lead times are crucial parameters in management of supply networks that continue to be more and more extended with multiple levels of inventory of components and uncertainties. The object of this study is the problem of determining planned lead times in multi-level assembly systems with stochastic lead times of different partners of supply chains. A general probabilistic model with a recursive procedure to calculate all the necessary distributions of probability is proposed. A Branch and Bound algorithm is developed for this model to determine planned order release dates for components at the last level of a BOM which minimize the sum of inventory holding and backlogging costs. Experimental results show the behaviour of the proposed model and optimisation algorithm for different numbers of components at the last level of the BOM and for different numbers of levels and values of holding and backlogging costs. The model and algorithm can be used for assembly contracting in an assembly to order environment under lead time uncertainty.

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Section: Discussionmentioning

confidence: 99%

Section: Related Publicationsmentioning

confidence: 99%

Section: Related Publicationsmentioning

confidence: 99%

Section: Related Publicationsmentioning

confidence: 99%

See 2 more Smart Citations

Planned lead times optimization for multi-level assembly systems under uncertainties

Ben-Ammar

Dolgui

2018

Omega

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“…In terms of meta-heuristic approaches, only the work by Fallah-Jamshidi et al [61] was found in which order release dates are considered in the algorithm's encoding scheme. However, this approach was designed for an inventory control system in a two-level assembly, a less complex problem than the FJSSP.…”

Section: Integration Of Job Release Timesmentioning

confidence: 99%

Solving the flexible job-shop just-in-time scheduling problem with quadratic earliness and tardiness costs

Rey

Bekrar

Trentesaux

et al. 2015

Int J Adv Manuf Technol

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The flexible job-shop scheduling problem is known to be an (Non-deterministic Polynomial-time hard) combinatorial problem and has become a challenge in optimization and manufacturing control. Although flexibility is important in order to respond effectively to higher product variety, shorter lead times, and smaller batch sizes, industrialists also require just-in-time scheduling strategies to increase customer satisfaction. The aim of this paper is to find adequate job release times to meet production demands in relation to specific due dates. Since large deviations in job completion times are undesirable, the scheduling objective for just-in-time production is translated into the minimization of the mean-square due date deviation (MSD), quadratically penalizing inventory (earliness) costs and backlogging (tardiness) costs. Given the computational complexity of the problem, two meta-heuristics are proposed: a genetic algorithm (GA) and particle swarm optimization (PSO), as well as two different approaches to handle job release times. In the GA, job release times are treated as dependent variables, whereas the PSO enables the integration of job release times as independent variables within the particle encoding. These meta-heuristic approaches were compared using three benchmarks, two adapted from the literature and one inspired from a real manufacturing cell. The simulation results show that the GA and PSO attained similar performances, each one with advantages and disadvantages for constrained and unconstrained MSD problems.

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Reducing the Research Space of Possible Order Release Dates for Multi-level Assembly Systems under Stochastic Lead Times

Ben-Ammar

Marian

Dolgui

et al. 2014

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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A hybrid multi-objective genetic algorithm for planning order release date in two-level assembly system with random lead times

Cited by 10 publications

References 21 publications

Planned lead times optimization for multi-level assembly systems under uncertainties

Planned lead times optimization for multi-level assembly systems under uncertainties

Solving the flexible job-shop just-in-time scheduling problem with quadratic earliness and tardiness costs

Reducing the Research Space of Possible Order Release Dates for Multi-level Assembly Systems under Stochastic Lead Times

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