An enhanced hybrid genetic algorithm for solving traveling salesman problem

Ali, Zeravan Arif; Rasheed, Subhi Ahmed; Ali, Nabeel No’man

doi:10.11591/ijeecs.v18.i2.pp1035-1039

Cited by 6 publications

(4 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In low-level picker-to-part systems, pickers travel through the picking aisles and retrieve items placed in the first height level, so vertical movements are neglected. In this manner, the PRP is solved as a Steiner TSP (STSP) and is considered as a class ical TSP, which is NP-Hard [9], when the minimum distances for all nodes (picking locations) are computed [10]. These distances are measured by Manhattan distances.Therefore, the Manhattan distance between two picking locations i and j in a single-block warehouse is calculated using (1), where R and F respectively represent the y -coordinate of the rear and front of the warehouse layout.…”

Section: Proposed Methodsmentioning

confidence: 99%

Formulations for joint order picking problems in low-level picker-to-part systems

Cano¹

2020

Bulletin EEI

View full text Add to dashboard Cite

This article introduces several mathematical formulations for the joint order picking problem (JOPP) in low-level picker-to-part warehousing systems. In order to represent real warehousing environments, the proposed models minimize performance measures such as travel distance, travel time and tardiness, considering multi-block warehouses, due dates, and multiple pickers. The number of constraints and decision variables required for each proposed model is calculated, demonstrating the complexity of solving medium and long-sized problems in reasonable computing time using exact methods. The proposed models can be followed as a reference for new solution methods that yield efficient and fast solutions.

show abstract

Section: Proposed Methodsmentioning

confidence: 99%

Formulations for joint order picking problems in low-level picker-to-part systems

Cano¹

2020

Bulletin EEI

View full text Add to dashboard Cite

show abstract

“…In this section, the optimization result of the M-SCA has been compared with those of the genetic algorithm (GA), which is considered as one of the most powerful and widely used evolutionary algorithms [26]- [28], and the original sine cosine algorithm (SCA). Using the same comparison analysis of the previous section, 10 runs were made for each algorithm and the average of these runs was taken.…”

Section: A Comparsion Study With Other Optimization Techniquesmentioning

confidence: 99%

Control of a servo-hydraulic system utilizing an extended wavelet functional link neural network based on sine cosine algorithms

Mahdi

Lutfy

2022

IJEECS

View full text Add to dashboard Cite

Servo-hydraulic systems have been extensively employed in various industrial applications. However, these systems are characterized by their highly complex and nonlinear dynamics, which complicates the control design stage of such systems. In this paper, an extended wavelet functional link neural network (EWFLNN) is proposed to control the displacement response of the servo-hydraulic system. To optimize the controller's parameters, a recently developed optimization technique, which is called the modified sine cosine algorithm (M-SCA), is exploited as the training method. The proposed controller has achieved remarkable results in terms of tracking two different displacement signals and handling external disturbances. From a comparative study, the proposed EWFLNN controller has attained the best control precision compared with those of other controllers, namely, a proportional-integralderivative (PID) controller, an artificial neural network (ANN) controller, a wavelet neural network (WNN) controller, and the original wavelet functional link neural network (WFLNN) controller. Moreover, compared to the genetic algorithm (GA) and the original sine cosine algorithm (SCA), the M-SCA has shown better optimization results in finding the optimal values of the controller's parameters.

show abstract

“…The order batching problem is considered NP-Hard when the number of customer orders per batch is greater than two [15], which means it is impossible to obtain a polynomial-time solution for it [16], therefore, this type of problem requires to be solved using approximate methods such as metaheuristics [17,18], among which the particle swarm optimization [19], ant colony optimization [20], genetic algorithms (GA) [21], among others, can be mentioned. Specifically, group-oriented genetic algorithms (GGA) support the successful application to grouping problems because critical information from the chromosome is preserved and is correctly transferred in the crossover operators [22].…”

Section: Introductionmentioning

confidence: 99%

Solving the order batching and sequencing problem with multiple pickers: A grouped genetic algorithm

Cano

Cortés

Campo³

et al. 2021

IJECE

View full text Add to dashboard Cite

This paper introduces a grouped genetic algorithm (GGA) to solve the order batching and sequencing problem with multiple pickers (OBSPMP) with the objective of minimizing total completion time. To the best of our knowledge, for the first time, an OBSPMP is solved by means of GGA considering picking devices with heterogeneous load capacity. For this, an encoding scheme is proposed to represent in a chromosome the orders assigned to batches, and batches assigned to picking devices. Likewise, the operators of the proposed algorithm are adapted to the specific requirements of the OBSPMP. Computational experiments show that the GGA performs much better than six order batching and sequencing heuristics, leading to function objective savings of 18.3% on average. As a conclusion, the proposed algorithm provides feasible solutions for the operations planning in warehouses and distribution centers, improving margins by reducing operating time for order pickers, and improving customer service by reducing picking service times.

show abstract

An enhanced hybrid genetic algorithm for solving traveling salesman problem

Cited by 6 publications

References 21 publications

Formulations for joint order picking problems in low-level picker-to-part systems

Formulations for joint order picking problems in low-level picker-to-part systems

Control of a servo-hydraulic system utilizing an extended wavelet functional link neural network based on sine cosine algorithms

Solving the order batching and sequencing problem with multiple pickers: A grouped genetic algorithm

Contact Info

Product

Resources

About