An Adaptive Genetic Algorithm for Production Scheduling on Manufacturing Systems with Simultaneous Use of Machines and AGVs

Sanches, Danilo Sipoli; Rocha, Josimar da Silva; Castoldi, Marcelo Favoretto; Morandin, Orides; Kato, E.R.R.

doi:10.1007/s40313-015-0174-6

Cited by 13 publications

(5 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Taking the makespan as the optimization objective, they proposed a two-stage ant colony algorithm to first solve the allocation and path selection problems of an AGV. Sanchesi et al [18] constructed a planning model with the goal of shortening the manufacturing cycle to the maximum extent under the minimum running time and adopted an adaptive genetic algorithm to solve the optimal scheduling scheme. Nageswararao [19] proposed a meta-heuristic gravity search algorithm to solve the scheduling problems of processing equipment and AGVs in a job shop.…”

Section: Literature Reviewmentioning

confidence: 99%

“…(1) Nonlinear convergence factor According to Equations ( 15) and (18), A affects the optimization accuracy and convergence speed of the whale. According to Equation ( 16), a has a direct influence on A, and a decreases linearly from 2 to 0, which makes the search speed of the algorithm decline in the later period and is not conducive to the fast convergence of the algorithm.…”

Section: Improved Whale Optimization Algorithm (Iwoa)mentioning

confidence: 99%

See 1 more Smart Citation

Modeling an Optimal Environmentally Friendly Energy-Saving Flexible Workshop

Zhang,

Wei,

Gao

2023

Applied Sciences

View full text Add to dashboard Cite

From the perspective of energy efficiency and environmental sustainability, the scheduling problem in a flexible workshop with the utilization of automated guided vehicles (AGVs) was investigated for material transportation. Addressing the dual-constrained integrated scheduling challenge involving machining machines and AGVs, a scheduling optimization model was established with makespan, workshop energy consumption, and processing quality as the optimization objectives. To effectively solve this model, an enhanced whale optimization algorithm (IWOA) was proposed. Specifically, nonlinear convergence factors, adaptive inertia weights, and improved helix positions were introduced into the standard whale optimization algorithm to update the model. Furthermore, a loss function was constructed based on fuzzy membership theory to obtain the optimal compromise solution of the multi-objective model. The research results indicate that: (1) The IWOA obtained the optimal solutions on benchmark instances MK01, MK02, MK04, MK07, and MK08; (2) The IWOA outperformed the WOA(1), WOA(2), WOA-LEDE, and NSGA-II algorithms in the two instances provided in this paper, demonstrating strong robustness of the model; (3) Although the multi-objective model constructed in this paper could not surpass the single-objective optimal solution in individual objectives, it achieved compensation in other objectives, effectively balancing the trade-offs among the makespan, workshop energy consumption, and processing quality of the three objectives. This research offers an effective practical approach to address green flexible workshop scheduling with AGV transportation.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Improved Whale Optimization Algorithm (Iwoa)mentioning

confidence: 99%

Modeling an Optimal Environmentally Friendly Energy-Saving Flexible Workshop

Zhang,

Wei,

Gao

2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…ON 5,4 [5,4,79,17,103,3], ON 8,2 [3,5112,0,115,6], ON 4,5 [6,1,0,0,0,1], ON 3,2 [6,5, 0,0,0,5], ON 9,2 [6,5,0,0,0,5], ON 1,1 [6,0,119,0, 123,1],…”

Section: Case Studymentioning

confidence: 99%

“…Therefore, it is proposed to have hybrid heuristic (ALS) which uses combined breadth-first algorithmic deepening A* with suboptimal breadth-first branch-and bound can find a quick solution, then which is improved by backtracking. Sanches Sipoli et al 17 developed an adoptive GTA to work out the scheduling of production systems considering the concurrent use of computers and AGVs for minimum MSN. Reddy et al 18 addressed the machines and AGVs concurrent scheduling in a MMFMS by using symbiotic organisms search algorithm (SOSA) for minimization of MSN and it is made known that the results of SOSA are promising.…”

mentioning

confidence: 99%

Optimum scheduling of machines, automated guided vehicles and tools without tool delay in a multi‐machine flexible manufacturing system using symbiotic organisms search algorithm

Lalitha

Narapureddy

Dwivedula³

et al. 2022

Concurrency and Computation

View full text Add to dashboard Cite

This article deals with machines, automated guided vehicles (AGVs) and tools simultaneous scheduling in multimachine flexible manufacturing system (FMS) with the lowest possible number of copies of every tool type without tool delay considering jobs' transport times among machines to minimize makespan (MSN). The tools are kept in a central tool magazine (CTM), which is shared by and serves many machines. Tool transporter (TT) and AGVs shift tools and jobs among machines respectively. This concurrent scheduling problem is extremely complex in nature as it involves determining the lowest possible tool copies of every type of tool, allocation of AGVs and tools copies to job-operations, job-operations sequencing on machines, and associated trip operations including the dead heading trip and loaded trip times of AGVs. This paper proposes nonlinear mixed integer programming (MIP) formulation to model this problem and symbiotic organisms search algorithm (SOSA) for solving this problem. Verification is carried out using an industrial problem in a manufacturing firm. The results show that employing two copies for one tool type and one copy each for the remaining tool types results in no tool delay and reduction in MSN which causes a reduction in cost, and SOSA is providing better solution than Jaya algorithm.

show abstract

“…For optimal scheduling, the path conflict problem is solved by swarm intelligence optimization algorithms 5 such as the PSO algorithm [6][7][8][9][10] and genetic algorithm. [11][12][13][14][15][16][17][18] Umar et al 19 proposed a genetic algorithm based on task priority to detect and avoid AGV deadlocks and path conflicts. Tanaka et al 20 proposed a method for assigning tasks by considering the subsequent path planning.…”

Section: Introductionmentioning

confidence: 99%

Research on global optimization method for multiple AGV collision avoidance in hybrid path

Cao

Meng

2021

Optim Control Appl Methods

View full text Add to dashboard Cite

Due to the increasing number of automated guided vehicles (AGVs) in the multi-AGV system and the limitation of working environment, path conflicts often occur in the working process of AGVs, which affects the working efficiency of the multi-AGV system. Thus, a optimization method by arranging the AGVs' traffic sequence is proposed in this paper. First, an AGV working map is reconstructed with graph theory, and then the corresponding collision avoidance rules are formulated for different types of conflicts. In multi-AGV system, each collision avoidance decision has an impact on the efficiency of the system, so it is crucial to adopt appropriate decisions. To optimize the decisions, the system fitness of different collision avoidance decisions are calculated based on the global state of the system, and the particle swarm optimization (PSO) algorithm is used to optimize the decisions. Furthermore, the PSO algorithm is improved by planning the direction of particle motion in the solution space and introducing mutation operation, so as to improve the search ability of the particle in the solution space. To verify the feasibility and effectiveness of the improved particle swarm optimization (IPSO) algorithm, an experiment system is built based on.NET platform. Results show that the IPSO algorithm than the traditional algorithms experimental performs better. The IPSO algorithm can effectively reduce congestion caused by path conflict and enhance the efficiency of the multi-AGV system.

show abstract

An Adaptive Genetic Algorithm for Production Scheduling on Manufacturing Systems with Simultaneous Use of Machines and AGVs

Cited by 13 publications

References 18 publications

Modeling an Optimal Environmentally Friendly Energy-Saving Flexible Workshop

Modeling an Optimal Environmentally Friendly Energy-Saving Flexible Workshop

Optimum scheduling of machines, automated guided vehicles and tools without tool delay in a multi‐machine flexible manufacturing system using symbiotic organisms search algorithm

Research on global optimization method for multiple AGV collision avoidance in hybrid path

Contact Info

Product

Resources

About