A Hybrid Algorithm for Job Shop Scheduling Problem

Toader, Florentina Alina

doi:10.24846/v24i2y201505

Cited by 11 publications

(7 citation statements)

References 13 publications

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“…This speed, which is represented in real numbers, is subsequently transformed into a set of probabilities by equation (19). Then, the new position of the particle i is calculated by means of equations (20) and (21). The best positions obtained for the particle and the swarm are also calculated like in the continuous version using equations (15) and (16).…”

Section: Discrete Pso For the Optimization Of M-mdpdptwmentioning

confidence: 99%

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Optimization of m-MDPDPTW Using the Continuous and Discrete PSO

Dridi¹,

Alaïa²,

Borne³

et al. 2019

SIC

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This paper presents a variant of the vehicle routing problem (VRP) that combines several constraints. This variant addresses the pickup and delivery problem (PDP), the use of multiple vehicles (m), the multi-depots (MD) and the time constraint (TW): m-MDPDPTW. In the m-MDPDPTW, one should build a route made up of several pairs (customer / supplier), which starts and ends at the same depot and respects the precedence and capacity constraints. For solving this problem, two approaches based on particle swarms are proposed with a view to minimizing the total distance travelled by all vehicles. The results yielded by these two algorithms, the continuous PSO and the discrete PSO, are then compared by making use of the benchmarks generated by Li and Lim.

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Section: Discrete Pso For the Optimization Of M-mdpdptwmentioning

confidence: 99%

“…The PSO algorithm has been used in various areas, namely, supply problems, (Mousavi et al, 2017), quadratic allocation problems, (Pradeepmon et al, 2018), technical design issues, (Dhiman & Kaur, 2019), as well as for solving the problems of industrial and assembly workshops (Toader, 2015).…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Optimization of m-MDPDPTW Using the Continuous and Discrete PSO

Dridi¹,

Alaïa²,

Borne³

et al. 2019

SIC

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“…with ρ a user defined parameter ( 0 ≤ ρ ≤ 1 ). The general ACS structure as in [18,40,41] is presented in Algorithm 1.…”

Section: Ant Colony Optimizationmentioning

confidence: 99%

Solving a Distribution Network Design Problem by Combining Ant Colony Systems and Lagrangian Relaxation

Lagos¹,

Paredes²,

Niklander³

et al. 2015

SIC

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Distribution network design (DND) attempts to integrate tactical issues such as inventory policies and/or vehicle routing decisions with strategic ones such as the problem of locating facilities and allocate customers to such facilities. When inventory policy decision making is considered the problem is also known as inventory location modelling (ILM) problem. During the last two decades, mathematical programming as well as (meta-)heuristic approaches have been considered to address different DND problem. In this article we consider a hybrid algorithm of Lagrangian Relaxation and artificial ants to solve an ILM problem previously proposed in the literature. We use ACS to allocate customers to a subset of warehouses that is previously generated by the Lagrangian relaxation. Results show that the hybrid approach is quite competitive, obtaining near-optimal solutions within an acceptable time.

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“…Nowadays, thanks to the easy coding and implementation of PSO, various versions of PSO have been employed in different areas of research, such as machine control [13] and fault diagnosis [14]. However, for a large range of industrial actuators, it is often necessary to respect some constraints on control and its increments [15].…”

Section: Introductionmentioning

confidence: 99%

Constrained multiobjective PSO and T-S fuzzy models for predictive control

Thamallah¹,

Sakly²,

M’Śahli³

2018

Turk J Elec Eng & Comp Sci

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Multiobjective optimization problems are still a challenging area in the field of control system engineering. In this context, the current study describes a new multivariable predictive control scheme formulated by using the T-S fuzzy modeling method and a new constrained multiobjective PSO algorithm. The T-S fuzzy modeling technique is applied to forecast the behaviors of the nonlinear system. It also aims at establishing some conditions so that the proposed control loop is asymptotically stable. The obtained experimental results show that the combination of the philosophy of the T-S fuzzy model and multiobjective PSO is very good in the controlling of nonlinear multivariable processes. The satisfactory tracking results with small values of MRE demonstrate the proof of capability of the proposed algorithm and the accuracy of the T-S modeling approach. Meanwhile, experimental results show that, compared with the ones obtained with standard MPC, the proposed method is of high effectiveness in term of the control increments optimization and the errors in the presence of disturbances.

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A Hybrid Algorithm for Job Shop Scheduling Problem

Cited by 11 publications

References 13 publications

Optimization of m-MDPDPTW Using the Continuous and Discrete PSO

Optimization of m-MDPDPTW Using the Continuous and Discrete PSO

Solving a Distribution Network Design Problem by Combining Ant Colony Systems and Lagrangian Relaxation

Constrained multiobjective PSO and T-S fuzzy models for predictive control

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