An Evolutionary Neighborhood Search Algorithm for Flexible Job-Shop Scheduling Problem in Steel Tube Production

2012 ◽  
Vol 566 ◽  
pp. 620-627
Author(s):  
Can Tao Shi ◽  
Guo Jun Yang ◽  
Tie Ke Li
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Search Algorithm ◽  
Steel Tube ◽  
Neighborhood Search ◽  
Flexible Job Shop ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Neighborhood Search Algorithm

This paper focuses on the tube processing context in steel tube production and constructs a flexible job-shop scheduling problem (FJSP) model with consideration of the typical features and constraints. An evolutionary neighborhood search algorithm which consists of process consolidation (PC) strategy, production line adjustment (LA) strategy and elimination mechanism (EM) strategy is proposed. The computational tests show that the proposed algorithm has good performance.

scholarly journals A global-local neighborhood search algorithm and tabu search for flexible job shop scheduling problem

2021 ◽  
Vol 7 ◽  
pp. e574
Author(s):  
Nayeli Jazmin Escamilla Serna ◽  
Juan Carlos Seck-Tuoh-Mora ◽  
Joselito Medina-Marin ◽  
Norberto Hernandez-Romero ◽  
Irving Barragan-Vite ◽  
...  
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Search Algorithm ◽  
Neighborhood Search ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Neighborhood Search Algorithm

The Flexible Job Shop Scheduling Problem (FJSP) is a combinatorial problem that continues to be studied extensively due to its practical implications in manufacturing systems and emerging new variants, in order to model and optimize more complex situations that reflect the current needs of the industry better. This work presents a new metaheuristic algorithm called the global-local neighborhood search algorithm (GLNSA), in which the neighborhood concepts of a cellular automaton are used, so that a set of leading solutions called smart-cells generates and shares information that helps to optimize instances of the FJSP. The GLNSA algorithm is accompanied by a tabu search that implements a simplified version of the Nopt1 neighborhood defined in Mastrolilli & Gambardella (2000) to complement the optimization task. The experiments carried out show a satisfactory performance of the proposed algorithm, compared with other results published in recent algorithms, using four benchmark sets and 101 test problems.

An improved SFLA for flexible job shop scheduling problem considering energy consumption

2018 ◽  
Vol 32 (34n36) ◽  
pp. 1840112 ◽  
Author(s):  
Xiaoxing Zhang ◽  
Zhicheng Ji ◽  
Yan Wang
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Neighborhood Search ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

In this paper, a multi-objective flexible job shop scheduling problem (MOFJSP) was studied systematically. A novel energy-saving scheduling model was established based on considering makespan and total energy consumption simultaneously. Different from previous studies, four types of energy consumption were considered in this model, including processing energy, idle energy, transport energy, and turn-on/off energy. In addition, a turn-off strategy is adopted for energy-saving. A modified shuffled frog-leaping algorithm (SFLA) was applied to solve this model. Moreover, operators of multi-point crossover and neighborhood search were both employed to obtain optimal solutions. Experiments were conducted to verify the performance of the SFLA compared with a non-dominated sorting genetic algorithm with blood variation (BVNSGA-II). The results show that this algorithm and strategy are very effective.

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