Solving Job Shop Scheduling Problem Using an Ant Colony Algorithm

Nazif, Habibeh

doi:10.18488/journal.2/2015.5.5/2.5.261.268

Cited by 7 publications

(3 citation statements)

References 23 publications

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“…For the sake of verifying the feasibility and effectiveness of the improved GA-TS algorithm, the genetic algorithm and particle swarm algorithm that have been widely used in solving vehicle scheduling problems were adopted to solve this problem [43]. In addition, considering that different customer sizes have a great impact on the solution performance of the algorithm, in order to verify that the genetic tabu algorithm proposed in this study can effectively solve the number of customer nodes of different sizes, references [56,57] divide customers into three sizes, 30, 60, and 90, and run each algorithm 30 times, respectively, to obtain the optimal solutions of each algorithm, so as to verify the solution performance of the algorithm under the conditions of customers of different sizes. The parameter settings of each algorithm are shown in Table 6, where P is the population size, G is the number of iterations, CP is the crossover rate, MP is the mutation rate, TG is the tabu algebra, CS is the candidate set, TL is the tabu length, W is the inertia weight, and C is the acceleration factor.…”

Section: Algorithm Comparison and Analysismentioning

confidence: 99%

Research on Optimization Method and Algorithm Design of Green Simultaneous Pick-up and Delivery Vehicle Scheduling under Uncertain Demand

et al. 2022

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In order to solve the problem that the existing low-carbon vehicle scheduling model ignores the economic benefits of enterprises and cannot fully reflect the fuzzy needs of customers, the green simultaneous pick-up and delivery vehicle scheduling problem is studied here. With the goal of minimizing the total cost composed of service cost, fuel consumption cost, and carbon emission cost, a multi-objective comprehensive model of green simultaneous pick-up and delivery under fuzzy demand is established. In order to fully consider the objective uncertainty of customer demand and customer service time, triangular fuzzy numbers are introduced and simultaneous delivery demand is considered. An improved genetic tabu search algorithm is proposed to solve this problem. In the improved GA-TS algorithm, the penalty factor is introduced into the fitness function, the selection operator combined with elite strategy is adopted, and a mutation operator combined with tabu search algorithm is proposed. The Taguchi analysis method is used to obtain reasonable parameter settings of the GA-TS algorithm. Finally, a case study is used to verify the effectiveness of the model and hybrid algorithm. The experimental results show that the proposed comprehensive model can effectively optimize the scheduling of low-carbon simultaneous pick-up and delivery vehicles under fuzzy demand, and the effectiveness and feasibility of genetic tabu search algorithm are verified by comparing the experimental results of different algorithms and different case sizes.

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Section: Algorithm Comparison and Analysismentioning

confidence: 99%

Research on Optimization Method and Algorithm Design of Green Simultaneous Pick-up and Delivery Vehicle Scheduling under Uncertain Demand

et al. 2022

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“…Habibeh Nazif [5] outlined the ACO for job shop scheduling which provides the best solutions through the directed search near the neighborhood. A good solution has generated in negligible computation time.…”

Section: Related Workmentioning

confidence: 99%

An Improved Ant Colony Optimized Tabu Search Algorithm for Makespan Improvement in Job Shop

sundari*,

Eshwaramurthy

2019

IJITEE

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In industries, the completion time of job problems is increased drastically in the production unit. In many existing kinds of research, the completion time i.e. makespan of the job is minimized using straight paths which is time-consuming. In this paper, we addressed this problem using an Improved Ant Colony Optimization and Tabu Search (ACOTS) algorithm by identifying the fault occurrence position exactly to rollback. Also, we used a short term memory-based rollback recovery technique to roll back to its own short term memory to reduce the completion time of the job. Short term memory is used to visit the recent movements in Tabu search. Our proposed ACOTS-Cmax approach is efficient and consumed less completion time compared to the ACO algorithm

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“…Historically, several algorithms are proposed in literature to solve the job shop scheduling problem by optimizing the makespan such as: branch and bound (BB) [3], simulated annealing (SA) [4], Tabu search (TS) [5] [6], genetic algorithms (GA) [7][8] [9],neural networks (NN) [10],ant colony optimization (ACO) [11],Particle swarm optimization (PSO) [12], Bee colony optimization (BCO) [13] and firefly algorithm(FA) [14]. Additionally, some researchers have developed an hybrid optimization strategy for JSSP such as parallel GRASP with path-relinking [15] and new hybrid genetic algorithm [16].…”

Section: Introductionmentioning

confidence: 99%

Memetic chicken swarm algorithm for job shop scheduling problem

Semlali

Riffi

Chebihi

2019

IJECE

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<p>This paper presents a Memetic Chicken swarm optimization (MeCSO) to solve job shop scheduling problem (JSSP). The aim is to find a better solution which minimizes the maximum of the completion time also called Makespan. In this paper, we adapt the chicken swarm algorithm which take into consideration the hierarchical order of chicken swarm while seeking for food. Moreover, we integrate 2-opt method to improve the movement of the rooster. The new algorithm is applied on some instances of ORLibrary. The empirical results show the forcefulness of MeCSO comparing to other metaheuristics from literature in term of run time and quality of solution.</p>

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Solving Job Shop Scheduling Problem Using an Ant Colony Algorithm

Cited by 7 publications

References 23 publications

Research on Optimization Method and Algorithm Design of Green Simultaneous Pick-up and Delivery Vehicle Scheduling under Uncertain Demand

Research on Optimization Method and Algorithm Design of Green Simultaneous Pick-up and Delivery Vehicle Scheduling under Uncertain Demand

An Improved Ant Colony Optimized Tabu Search Algorithm for Makespan Improvement in Job Shop

Memetic chicken swarm algorithm for job shop scheduling problem

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