scholarly journals No-Idle Flowshop Scheduling for Energy-Efficient Production: An Improved Optimization Framework

Mathematics ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1335
Author(s):  
Chen-Yang Cheng ◽  
Shih-Wei Lin ◽  
Pourya Pourhejazy ◽  
Kuo-Ching Ying ◽  
Yu-Zhe Lin

Production environment in modern industries, like integrated circuits manufacturing, fiberglass processing, steelmaking, and ceramic frit, is characterized by zero idle-time between inbound and outbound jobs on every machine; this technical requirement improves energy efficiency, hence, has implications for cleaner production in other production situations. An exhaustive review of literature is first conducted to shed light on the development of no-idle flowshops. Considering the intractable nature of the problem, this research also develops an extended solution method for optimizing the Bi-objective No-Idle Permutation Flowshop Scheduling Problem (BNIPFSP). Extensive numerical tests and statistical analysis are conducted to evaluate the developed method, comparing it with the best-performing algorithm developed to solve the BNIPFSP. Overall, the proposed extension outperforms in terms of solution quality at the expense of a longer computational time. This research is concluded by providing suggestions for the future development of this understudied scheduling extension.

Scheduling problems are NP-hard in nature. Flowshop scheduling problems, are consist of sets of machines with number of resources. It matins the continuous flow of task with minimum time. There are various traditional algorithms to maintain the order of resources. Here, in this paper a new stochastic Ant Colony optimization technique based on Pareto optimal (PA-ACO) is implemented for solving the permutation flowshop scheduling problem (PFSP) sets. The proposed technique is employed with a novel local path search technique for initializing and pheromone trails. Pareto optimal mechanism is used to select the best optimal path solution form generated solution sets. A comparative study of the results obtained from simulations shows that the proposed PA-ACO provides minimum makespan and computational time for the Taillard dataset. This work will applied on large scale manufacturing production problem for efficient energy utilization.


Author(s):  
Andromachi Taxidou ◽  
Ioannis Karafyllidis ◽  
Magdalene Marinaki ◽  
Yannis Marinakis ◽  
Athanasios Migdalas

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