Optimization of operator allocation in a large multi product assembly shop through unique integration of simulation and genetic algorithm

Azadeh, A.; Asadzadeh, Seyed Mohammad; Tadayoun, S.

doi:10.1007/s00170-014-6213-9

Cited by 6 publications

(6 citation statements)

References 40 publications

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“…S will be divided into K ( K ≤ N) subsets {s 1 , s 2 , ..., s K } by clustering. These sets satisfy (8), (9) and 10:…”

Section: A Text Preprocess and Skus Clustering Based On Information mentioning

confidence: 99%

“…Dijkstra et al [8] proposed a dynamic programming (DP) storage strategy based on optimization to solve multi-aisle and multi-item problem. Azadeh et al [9] presented an algorithm based on the genetic algorithm to optimize the allocation of operators in a multi-product assembly shop. However, the full complement of robots has largely replaced human labor.…”

Section: Introductionmentioning

confidence: 99%

“…• A literature review of storage allocation approaches (section II) • Definition of the storage assignment and theories used in the algorithm TGCA, including: granulation, EI algebra [4] Extracting the relationship between products by using association rules DSAP [5] Combining the product affinity with the ABC classification ARIP [6] Mining the association relationship between orders through data mining and integer programming DMSA [7] Reducing the number of stops for picking one order DP [8] Using a dynamic programming storage strategy to solve multi-aisle and multi-item problem MOGA [9] Optimize the multi-product assembly shop based on the genetic algorithm ELECTRE TRI [10] Realize the classification storage by defining the shelf level and shelf position V-shaped and fishbone [11] Study a warehouse layout with V-shaped and fishbone channels TLEA [14] A two-layer evolutionary algorithm to solve the automatic warehouse layout design A literature overview [15] A literature overview of storage assignment policies and layout design PCA cluster [16] Clustering commodities with constraints on storage conditions. Principal component analysis is used to calculate the distance between clusters Different clustering [17] Clustering methods include principal component analysis, singular value decomposition, and two-step clustering Relationships analysis [18] Analyze the relationships among storage, batch processing, partitioning, and routing Distance estimation [19] Combining storage allocation and travel distance estimation, which is effective for different routing strategies Miniload and Kiva [20] Compare miniload system and Kiva system, and gave the advantages and limitations of each system Comparison four strategies [21] Compare four warehouse storage strategies: storage zoning, random, full turnover-based, and class-based storage Natural clustering [22] Measuring similarity between commodities to construct a natural clustering model FIGURE 1.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Text-Granulation Clustering Approach With Semantics for E-Commerce Intelligent Storage Allocation

2020

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With the hot rise and increasing popularity of e-commerce and online shopping, exponentially growing orders require to be picked up timely. In the order-based warehouse picking process, more intelligent optimization methods are urgently demanded to meet the maturity of robot technology. Due to the wide variety of SKUs (stock-keeping units) in warehouses, direct analysis based on orders can hardly get strong correlations among SKUs. Since orders and SKUs usually are presented in text form files, this paper proposes a clustering optimization algorithm based on text information granules which makes full use of context information and semantics. Storage assignment is determined by clustering SKUs first and then performing correlation analysis on the clustering results according to orders. The algorithm can give the semantic description of each cluster so that the clustering process is interpretable and transparent. Extensive experiments on a distribution center of a clothing production enterprise indicate 40.5 to 57.9% improvement in the average order picking distance compared with the ABC classification.

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“…S will be divided into K ( K ≤ N) subsets {s 1 , s 2 , ..., s K } by clustering. These sets satisfy (8), (9) and 10:…”

Section: A Text Preprocess and Skus Clustering Based On Information mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Text-Granulation Clustering Approach With Semantics for E-Commerce Intelligent Storage Allocation

2020

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“…Many performance measures have been used to achieve some objectives such as the average lead-time, the average labor utilization and the average waiting time. [11] used simulation and genetic algorithm to determine the optimum number and allocation of labor and the measure of the efficiency of their operation in an assembly shop. His aim is to maximize the throughput system.…”

Section: Labor Selection Problemmentioning

confidence: 99%

Improvement opportunities of a Simulation/Expert System Approach for Manufacturing System Sizing: A review and proposal

Imen¹,

Masmoudi²,

Elleuch³

et al. 2019

Adv. sci. technol. eng. syst. j.

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Manufacturing System (MS) sizing is a crucial task to complete in order to obtain the desired MS performance and efficiency. It involves selecting the required number of resources from each used type in a given planning horizon. In fact, different approaches coupling simulation/optimization tools have been developed to solve this issue and evaluate the MS performance. One of these approaches is the Simulation Expert System Approach (SESA). Unfortunately, the application domain of this approach is limited in sizing only the production resources (machines and labor) but neglects the material handling system (MHS) components. Moreover, omitting the transferring problem is not viable in the real world due to its importance in each shop floor. Thus, the aim of this paper is to describe the evolution of SESA, then, to check if the simulation optimization tools used in SESA are still relevant. This paper also investigates the importance of incorporating MHS in this approach and finally proposes some improvement opportunities for SESA including the tackling of the MHS fleet sizing problem. In fact, the wide literature review performed in this research indicates that SESA is still a pertinent approach but it must be improved. Therefore, it is expected that SESA improvement opportunities proposed in this work will greatly assist industrialists in enhancing the overall MS performance, providing a significant productivity increase and a minimization of the total production costs.

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“…Salido et al 26 developed GA to solve an extended version of the job shop scheduling problem in which machines can consume different amounts of energy to process tasks at different rates. Azadeh et al 27 presented an integrated simulation and GA for optimum operator allocation in a large multi-product assembly shop. Liang et al 28 studied a hybrid algorithm based on GA and SA to solve complex multiproduct scheduling problem with 0-wait constraint.…”

Section: Introductionmentioning

confidence: 99%

A computational efficient optimization of flow shop scheduling problems

Liang

Zhong

Liu

et al. 2022

Sci Rep

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Flow shop scheduling problems are NP-hard problems. Heuristic algorithms and evolutionary metaheuristic algorithms are commonly used to solve this kind of problem. Although heuristic algorithms have high solving speed, the solution quality is not good. Evolutionary algorithms make up for this defect in small-scale problems, but the solution performance will deteriorate with the expansion of the problem scale and there will be premature problems. In order to improve the solving accuracy of flow shop scheduling problems, a computational efficient optimization approach combining NEH and niche genetic algorithm (NEH-NGA) is developed. It is strengthened in the following three aspects: NEH algorithm is used to optimize the initial population, three crossover operators are used to enhance the genetic efficiency, and the niche mechanism is used to control the population distribution. A concrete application scheme of the proposed method is introduced. The results of compared with NEH heuristic algorithm and standard genetic algorithm (SGA) evolutionary metaheuristic algorithm after testing on 101 FSP benchmark instances show that the solution accuracy has been significantly improved.

show abstract

Optimization of operator allocation in a large multi product assembly shop through unique integration of simulation and genetic algorithm

Cited by 6 publications

References 40 publications

A Text-Granulation Clustering Approach With Semantics for E-Commerce Intelligent Storage Allocation

A Text-Granulation Clustering Approach With Semantics for E-Commerce Intelligent Storage Allocation

Improvement opportunities of a Simulation/Expert System Approach for Manufacturing System Sizing: A review and proposal

A computational efficient optimization of flow shop scheduling problems

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