Robust optimization and mixed-integer linear programming model for LNG supply chain planning problem

A robust green traffic‐based routing problem for perishable products distribution

“…For future studies, the following items are suggested based on the main limitations of the research. Heuristic and metaheuristic methods may be implemented to solve the problem in large scales efficiently, eg, as proposed by Song and Cheng, Hahmann et al, Babaee Tirkolaee et al, Goli et al, Tirkolaee et al, and Sangaiah et al Other uncertainty techniques such as fuzzy theory, grey systems, and stochastic optimal control could be compared to the current robust optimization approach for evaluating different policies under various well‐known techniques. Moreover, to make the model closer to the real‐world conditions, other objective functions including distribution reliability maximization or customers' satisfaction maximization can be studied as well.…”

Employing a Gaussian Particle Swarm Optimization method for tuning Multi Input Multi Output‐fuzzy system as an integrated controller of a micro‐grid with stability analysis

“…One of the constraints is expressed in Equation (26), which indicates energy constraint. In the energy management section, the limitation of boundary rate equalization is defined via Equation (27). The oscillation range of the parameters is shown in Equation (28).…”

“…Parikhani et al employed GA for obtaining the optimal thermal efficiency of a power generation system. A novel metaheuristic algorithm, namely cuckoo optimization algorithm, is designed by Sangaiah et al to solve the liquefied natural gas (LNG) sales planning over a given time horizon aiming to minimize the costs of the vendor. An efficient simulated annealing (SA) is proposed to solve a multitrip vehicle routing problem with time windows specifically related to urban waste collection .…”

Improved spotted hyena optimizer with space transformational search for training pi‐sigma higher order neural network

“…Also, nature inspired metaheuristic methods often lead to attain global optimum with reasonably little and limited number of reiterations. The high demand in the magnitude of the search region and the necessity for executing in real‐time inspired current research community, for resolving complicated real‐world difficulties by using nature inspired metaheuristic methods and applied in diversified fields including urban solid waste management and supply chain planning . Metaheuristic techniques are also cast off for answering job shop arranging and job selection complications.…”

“…The high demand in the magnitude of the search region and the necessity for executing in real-time inspired current research community, for resolving complicated real-world difficulties by using nature inspired metaheuristic methods and applied in diversified fields including urban solid waste management [22][23][24][25] and supply chain planning. 26 Metaheuristic techniques are also cast off for answering job shop arranging and job selection complications. The popularity and wide acceptance of neural network in computational optimization is due to its promising benefits over traditional strategies for diverse optimization tasks.…”