A hybrid genetic algorithm and tabu search for minimizing makespan in flow shop scheduling problem

Umam, Moch Saiful; Mustafid, Mustafid; Suryono, Suryono

doi:10.1016/j.jksuci.2021.08.025

Cited by 52 publications

(27 citation statements)

References 39 publications

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“…Throughout this section, the multi-objective scenario is given first as the paper's contribution to developing an optimization algorithm based on partial opposed-based learning. Notably, the initialization uses a partial opposed-based approach, consistent with our previous work [16]. There are a few parallels between them.…”

Section: The Proposed Algorithmsmentioning

confidence: 63%

“…The best solution is presented in Table 2, which summarizes the findings. Additionally, for numerical analysis, we collect the Percentage of Relative Deviation (PRD) for the 120 instances over ten number runs to show the average error among a solution of the proposed algorithm and the lowest known upper bound values, where lower PRD indicates a better algorithm, formulated as: (5) Where: N : number of instances GATS2 : best solution from proposed GA-TS UB : Taillard upper bound, the best known solution for Taillard Regarding the hybrid strategy, GA has been hybridized to achieve one or more objectives with various algorithms, such as tabu search [16], which successfully improves the solution quality by 115 of 120 Taillard instances over hybrid genetic simulated annealing five other GA cooperation with PRD 3.05%. Thus, this proposed algorithm (GATS2) is compared to the algorithm in our previous work (GATS1), genetic algorithm variable neighborhood search (GAVNS) by [24], which is superior to the single variable neighborhood search algorithm, and finally compared to hybrid evolution strategy (HESSA) studied by [25].…”

Section: Resultsmentioning

confidence: 99%

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How to boost the flow shop manufacturing agility using hybrid Genetic Tabu Search in scheduling

Umam

Gondohanindijo²

2022

Sinergi

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The hybridization between evolutionary genetic algorithm and tabu search has been proposed in this paper to address flow shop scheduling. It accommodates jobs that need to be rearranged and executed on identical machines serially. High agility is required in the manufacturing process, especially for the garment industry to be able to stand facing competitors. The manufacturing related to scheduling to deliver a product as early as possible, the tardiness, and waiting time are also concerned. A Genetic Algorithm was widely used to deal with this; which finds an optimal solution to the problems because it can obtain a more optimal solution. Unfortunately, it is easy to get stuck in optimum local (early convergence is faster). The tabu search algorithm works as a local explorer to better find and exploit the optimum local area, which can be combined with a Genetic Algorithm. This study aims to minimize the three objectives mentioned above to increase production agility. These strategies are evaluated on Taillard benchmark problems to show the significance of the proposed algorithm. The outcomes prove that the hybrid mechanism can boost the solution quality by 2.75% compared to our previous work and can resolve all of Taillard instances better. It has been proven by a 0.28% percentage relative deviation, which shows the error rate is lower and means better.

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Section: The Proposed Algorithmsmentioning

confidence: 63%

Section: Resultsmentioning

confidence: 99%

How to boost the flow shop manufacturing agility using hybrid Genetic Tabu Search in scheduling

Umam

Gondohanindijo²

2022

Sinergi

Self Cite

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“…Rouky et al [10] proposed an ant colony optimization (ACO) hybridized with a variable neighborhood descent local search to solve the task sequences of quay cranes. Uman et al [11] tackled the flow shop scheduling problem and proposed the tabu search (TS) process with a genetic algorithm (GA) to minimize makespan. Another popular metaheuristic to solve SOPs is simulated annealing (SA).…”

Section: B Simulation-based Combinatorial Optimizationmentioning

confidence: 99%

Fast Simulation-Based Order Sequence Optimization Assisted by Pre-Trained Bayesian Recurrent Neural Network

Suemitsu

Bhamgara

Utsugi

et al. 2022

IEEE Robot. Autom. Lett.

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This paper presents a fast optimization method for the picking order sequence of automated order picking systems in logistics warehouses. In this order sequencing problem (OSP), the fulfillment sequence of the given picking order set is determined to optimize the performance measures such as makespan and deadlock occurrence. Simulation is generally necessary to evaluate these measures for complex automated systems. However, their order sequence cannot be optimized quickly due to the long calculation time. It may make the system productivity and flexibility lower than expected because its picking schedules cannot be updated frequently. We, therefore, propose a fast optimization method to solve these simulation-based OSPs by taking a pretrained surrogate-assisted optimization approach. Firstly, we utilized a Bayesian recurrent neural network (BRNN) as a surrogate model to accurately learn the relationship between picking order sequence and performances. Secondly, we developed the surrogate-assisted optimization method based on simulated annealing (SA) and BRNN. Numerical experiments show that the surrogate model can evaluate about 10000 times faster than the simulation. The proposed method also obtains an optimized solution 8.9 times faster than simulation-based optimization by the original SA.

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“…ACO is an algorithm that is inspired by the natural ability of ants to communicate with their fellow colonies by giving pheromones to the paths they travel when they find food [4]. While TS is an algorithm that performs searches starting from the initial solution and then explores the solution space through a move by paying attention to search history [5]. De La Cruz et al [6] used ACO and TS for Heterogeneous Vehicle Routing Problems with Time Windows and Multiple Products (HVRPTWMP).…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Process of Pick-Up and Delivery With Time Windows Using Ant Colony and Tabu Search Algorithms

Amalia¹,

Bakhtiar²,

Jaharuddin³

2022

BAREKENG: J. Il. Mat. & Ter.

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The provision of goods shuttle services sometimes faces several constraints, such as the limitation on the number of vehicles, vehicle capacity, and service time, or the vehicle used has single transport access. To avoid losses, a strategy is needed in determining the optimal route and policy for arranging goods in the vehicle especially if there are two types of goods involved. Traveling Salesman Problem and Pick-up and Delivery with Handling Costs and Time Windows (TSPPDHTW) is a model of an optimization problem that aims to minimize the total travel and goods handling costs in the goods pick-up and delivery with the constraints previously mentioned. Solving that model using the exact method requires a very long computation time so it’s not effective to be implemented in real-life. This study aims to develop a (meta)heuristic based on Ant Colony Optimization (ACO) and Tabu Search (TS) to be ACOTS to solve TSPPDHTW with reasonable computation time. The development is carried out by adding functions of clustering, evaluating constraints, cutting tours, arranging of goods, and evaluating moves on the TS, as well as modifying transition rules. The result has a deviation of about 22% and 99.99% less computational time than the exact method.

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A hybrid genetic algorithm and tabu search for minimizing makespan in flow shop scheduling problem

Cited by 52 publications

References 39 publications

How to boost the flow shop manufacturing agility using hybrid Genetic Tabu Search in scheduling

How to boost the flow shop manufacturing agility using hybrid Genetic Tabu Search in scheduling

Fast Simulation-Based Order Sequence Optimization Assisted by Pre-Trained Bayesian Recurrent Neural Network

Optimizing the Process of Pick-Up and Delivery With Time Windows Using Ant Colony and Tabu Search Algorithms

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