Numerical schemes and genetic algorithms for the optimal control of a continuous model of supply chains

Rarità, Luigi; Stamova, Ivanka; Tomasiello, Stefania

doi:10.1016/j.amc.2020.125464

Cited by 32 publications

(11 citation statements)

References 22 publications

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“…The multiobjective optimization problem remains relevant in recent years, especially considering the number of works in the production, engineering, education, and other research fields. Moreover, many such publications provide information on the use of GA, including the analyzed ones [14][15][16] where the researchers apply GA to solve scheduling and similar problems. Amjad et al [14] made a detailed review of recent achievements in the application of GA to solve the flexible job-shop scheduling problem (JSSP).…”

Section: Optimization Algorithms For Solving Multicriteria Problemsmentioning

confidence: 99%

“…Viana, Junior, and Contreras [15] proposed a new GA with improved crossover and mutation operators for JSSP. Rarità et al [16] developed a supply chains model through partial and ordinary differential equations. The authors of this article proposed to use GA to control the outflow of the supply chain.…”

Section: Optimization Algorithms For Solving Multicriteria Problemsmentioning

confidence: 99%

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Evaluation of the Forms of Education of High School Students Using a Hybrid Model Based on Various Optimization Methods and a Neural Network

et al. 2021

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This article deals with the multicriteria programming model to optimize the time of completing home assignments by school students in both in-class and online forms of teaching. To develop a solution, we defined 12 criteria influencing the school exercises’ effectiveness. In this amount, five criteria describe exercises themselves and seven others the conditions at which the exercises are completed. We used these criteria to design a neural network, which output influences target function and the search for optimal values with three optimization techniques: backtracking search optimization algorithm (BSA), particle swarm optimization algorithm (PSO), and genetic algorithm (GA). We propose to represent the findings for the optimal time to complete homework as a Pareto set.

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Section: Optimization Algorithms For Solving Multicriteria Problemsmentioning

confidence: 99%

Section: Optimization Algorithms For Solving Multicriteria Problemsmentioning

confidence: 99%

Evaluation of the Forms of Education of High School Students Using a Hybrid Model Based on Various Optimization Methods and a Neural Network

et al. 2021

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“…Only the information of G T and COMRATE m is needed to calculate the control output of SMC-P, which impose a great flexibility to real applications. [33], quasi-sliding mode control (QSMC) and ISMC, are employed to compare with SMC-P. In this nonlinear HCS in container ports, T G =2, T Q =1.5, T F =3, T U =0.5, T I =0.5, and COMRATE m = 1.3.…”

Section: B Power Integral Reaching Lawmentioning

confidence: 99%

“…In QSMC and ISMC, the exponential reaching law in (10) is employed and the saturation function is used to replace sign function to reduce chattering. According to (10), ε=0.5, ∆=0.05, k=1 in QSMC and ISMC; and according to (33), α=0.8, β=4 in SMC-P.…”

Section: B Power Integral Reaching Lawmentioning

confidence: 99%

“…x c x dt , c 2 =2 in ISMC; and according to(33), k i =1.6 in SMC-P.The comparisons of ufr and control signal u (in this uncertain nonlinear HCS, control signal is arate) among five methods are plotted in FIGUREs 3 and 4 respectively. In the 1 st and 2 nd subfigures of FIGURE 4, the ordinate of the curve for the first 10 days is on the left, while the ordinate of the curve for the last 40 days is on the right.…”

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confidence: 99%

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Exploring the Resilience of Uncertain Nonlinear Handling Chain Systems in Container Ports With a Novel Sliding Mode Control

Yang

et al. 2021

IEEE Access

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Uncertain handling chain system (HCS) of container ports brings steady-state error to the original control decisions, and even worse, dramatically degrades the system performance. The steady-state error will cause unsatisfied freight requirement to be much higher than the expected value for a long time, resulting in the decrease of system robustness and resilience. In this work, a novel sliding mode control with power integral reaching law (SMC-P) is presented for nonlinear HCS of container ports under uncertainty. Specifically, the integral of system state variable, the exponential reaching law and the power of the switching function are integrated to the traditional reaching law. And it is proven that the eliminated steady-state error, the accelerated approach speed, and the reduced chattering can be effectively obtained by SMC-P. A nonlinear HCS in container ports with uncertain freight requirement and handling ability is considered. SMC-P is compared with traditional method, genetic algorithm, quasi-sliding mode control and integral sliding mode control. Simulation results show that SMC-P does not only balance both steady-state error reduction and chattering avoidance caused by uncertainty, but also optimize the performance, robustness, and resilience of the uncertain nonlinear HCS. This study also brings economic and sustainability contributions for port authorities. INDEX TERMS Resilience, uncertain nonlinear handling chain system, sliding mode control, power integral reaching law

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Hybrid improved sine cosine algorithm for mixed-integer nonlinear programming problems

et al. 2023

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Numerical schemes and genetic algorithms for the optimal control of a continuous model of supply chains

Cited by 32 publications

References 22 publications

Evaluation of the Forms of Education of High School Students Using a Hybrid Model Based on Various Optimization Methods and a Neural Network

Evaluation of the Forms of Education of High School Students Using a Hybrid Model Based on Various Optimization Methods and a Neural Network

Exploring the Resilience of Uncertain Nonlinear Handling Chain Systems in Container Ports With a Novel Sliding Mode Control

Hybrid improved sine cosine algorithm for mixed-integer nonlinear programming problems

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