Production scheduling optimization in foundry using hybrid Particle Swarm Optimization algorithm

Bewoor, Laxmi A.; Prakash, V. Chandra; Sapkal, Sagar U.

doi:10.1016/j.promfg.2018.03.010

Cited by 34 publications

(9 citation statements)

References 11 publications

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“…The particles move frequently in the problem space with regard to the velocity vector in each iteration, and examine the new possible options by calculating the fitness value as a standard measurement. Two memories are assigned to store the best position of each particle in the past (the best local solution), and the best position among all particles (the best global solution) and finally, by considering the stopping algorithm conditions, the best global solution is determined as the result of the algorithm [64,65].…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Designing a resource-constrained project scheduling model considering multiple routes for flexible project activities: meta-heuristic algorithms

2019

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Resource constrained project scheduling problem with multiple routes for flexible project activities (RCPSP-MR) is a generalization of the RCPSP, in which for the implementation of each flexible activity in main structure of the project, several exclusive sub-networks are considered. Each sub-network is regarded as a route for the flexible activity. The routes are considered for each flexible activity that are varied in terms of: 1) Number of activities required to execute; 2) Precedence relationship between activates; 3) Allocation of different renewable and nonrenewable resources to each activity; and 4) Effectiveness on the duration and cost of project completion. In this paper, a new mathematical formulation of RCPSP-MR is firstly presented. Then, two solving approaches based on particle swarm optimization (PSO) and genetic algorithm (GA) are proposed to minimize costs of project completion. To evaluate the effectiveness of these proposed approaches, 50 problems (in very small, small, medium, and large-sized test problems) are designed and then are solved. Finally, comparisons are provided. Computational results show that the proposed GA generates high-quality solutions in a timely fashion. Keywords: Resource constrained project scheduling problem (RCPSP), flexible activities, multiple routes, particle swarm optimization (PSO), genetic algorithm (GA) 0,1, 2,..., 1 JN  1 J Set of total project fixed activities, 1 JJ  2 J Set of total flexible project activities, 21 J J J    Set of routes of each flexible activity 2 J   h  Set of total sub network for flexible activity

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Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Designing a resource-constrained project scheduling model considering multiple routes for flexible project activities: meta-heuristic algorithms

2019

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“…The heuristic method can construct the solution in a short time according to the characteristics of the problem, but it is difficult to guarantee the quality of the solution. With the development of computational intelligence, intelligent algorithms for solving PFSP have been widely studied, such as genetic algorithm, 4 particle swarm optimization, 5 differential evolution, 6 cuckoo search, 7 and various hybrid algorithms. 8 The cuckoo search (CS) algorithm is a new heuristic algorithm proposed by professor Xin-She Yang and S.…”

Section: Introductionmentioning

confidence: 99%

Improved cuckoo search algorithm and its application to permutation flow shop scheduling problem

Zhang

Luo

et al. 2020

Journal of Algorithms & Computational Technology

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Aiming at the problem that the standard cuckoo search algorithm relies on Levy flights, which leads to the step-size randomness of the search process, a self-adaptive step cuckoo search algorithm based on dynamic balance factor is proposed in our paper. First, two parameters are introduced in our paper, which were iteration number ratio parameter and adaptability ratio parameter. Then, a dynamic balance factor parameter is introduced to adjust the weight number of iteration number ratio parameter and adaptability ratio parameter. Finally, parameter skewness value calculation method and self-adaptive step strategy were proposed combined with the dynamic balance factor. Six typical test functions are used to test the performance of the proposed algorithm, the standard cuckoo search algorithm and the self-adaptive step cuckoo search algorithm which relies only on the iterative times. The test results show that the proposed algorithm had good convergence speed and accuracy. Meanwhile, taking the permutation flow shop scheduling problem as an example, eight operators of Car benchmark class are used as the test data to compare the performance of three algorithms, the effectiveness and superiority of the algorithm in solving the permutation flow shop scheduling problem are verified.

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“…The application of metaheuristic algorithms for solving engineering problems has been developed significantly over the past few decades. The application of particle swarm optimization for optimizing product scheduling is reported in [10]. The application of a genetic algorithm for solving the offset in the chemical process is reported in [11].…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Parameter Tuning of PSS and Wide-Area POD in PV Plant using FPA

Setiadi¹,

Abdillah²

2019

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In the future power grid scenario, large-scale renewable energy based power plant will be one of the main generations. Among renewable based power plant types, large-scale photovoltaic (PV) plant becoming more popular as they could provide zero-emission and sustainable energy. However, even though PV plants could contribute a positive impact on the environment, they could also contribute negatively to the power system. Large-scale PV generation came with a different dynamic and zero inertia characteristic due to the application of the power electronics devices. Furthermore, the PV plant has also a drawback in terms of intermittent power output due to the uncertainty of the sources. Those handicaps could deteriorate the stability performance of the power system especially oscillatory stability. Adding a power system stabilizer (PSS) to the systems is one of the ways for handling the oscillatory stability. However, with the integration of PV plants in the systems, PSS alone is not enough to handle the oscillatory problems coming from various sources such us from PV plant dynamic. Hence, utilizing wide-area power oscillation damping (POD) as a PV plant additional controller is inevitable. Hence, this paper proposed simultaneous parameter tuning between PSS and wide-area POD in PV plant using an imperialist competitive algorithm as the optimization method. The two-area power system is used to evaluate the performance of PSS and POD using FPA. From the results, it is found that the proposed method could enhance the oscillatory stability of the systems.

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Production scheduling optimization in foundry using hybrid Particle Swarm Optimization algorithm

Cited by 34 publications

References 11 publications

Designing a resource-constrained project scheduling model considering multiple routes for flexible project activities: meta-heuristic algorithms

Designing a resource-constrained project scheduling model considering multiple routes for flexible project activities: meta-heuristic algorithms

Improved cuckoo search algorithm and its application to permutation flow shop scheduling problem

Simultaneous Parameter Tuning of PSS and Wide-Area POD in PV Plant using FPA

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