Journal Article10.1016/J.ASOC.2020.106343
Discrete evolutionary multi-objective optimization for energy-efficient blocking flow shop scheduling with setup time
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TL;DR: Simulation results show that DEMO outperforms the three state-of-the-art algorithms with respect to hypervolume, coverage rate and distance metrics.
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About: This article is published in Applied Soft Computing. The article was published on 01 Aug 2020. The article focuses on the topics: Flow shop scheduling & Job shop scheduling.
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Citations
A distributed heterogeneous permutation flowshop scheduling problem with lot-streaming and carryover sequence-dependent setup time
TL;DR: An enhanced artificial bee colony algorithm (NEABC) with strong intensification is proposed to generate promising swarm and a restart strategy is designed in the scout bee stage with the consideration of the special onlooker bee stage of the algorithm.
113
A Hyperheuristic With Q-Learning for the Multiobjective Energy-Efficient Distributed Blocking Flow Shop Scheduling Problem
01 May 2023
TL;DR: In this article , a hyperheuristic with low-level learning (HHQL) was proposed to solve the distributed blocking flow shop scheduling problem (EEDBFSP), which considers both total tardiness (TTD) and total energy consumption (TEC).
103
An improved Jaya algorithm for solving the flexible job shop scheduling problem with transportation and setup times
TL;DR: This work modeled the flexible job shop scheduling problem by utilizing an integer programming method, wherein the energy consumption and makespan objectives are optimized simultaneously and an improved Jaya (IJaya) algorithm was proposed to solve the problem.
94
Energy-efficient distributed heterogeneous welding flow shop scheduling problem using a modified MOEA/D
TL;DR: In this paper, a multi-objective evolutionary algorithm based on decomposition (MOEA/D) is proposed for energy-efficient scheduling of distributed heterogeneous welding flow shop (DHWFSP).
87
Solving Biobjective Distributed Flow-Shop Scheduling Problems With Lot-Streaming Using an Improved Jaya Algorithm
01 Jun 2023
TL;DR: In this article , a distributed flow-shop scheduling problem with lot-streaming that considers completion time and total energy consumption is addressed, and an improved Jaya algorithm is proposed to solve it.
86
References
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
Qingfu Zhang,Hui Li +1 more
TL;DR: Experimental results have demonstrated that MOEA/D with simple decomposition methods outperforms or performs similarly to MOGLS and NSGA-II on multiobjective 0-1 knapsack problems and continuous multiobjectives optimization problems.
An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints
Kalyanmoy Deb,Himanshu Jain +1 more
TL;DR: A reference-point-based many-objective evolutionary algorithm that emphasizes population members that are nondominated, yet close to a set of supplied reference points is suggested that is found to produce satisfactory results on all problems considered in this paper.
Benchmarks for basic scheduling problems
TL;DR: This paper proposes 260 randomly generated scheduling problems whose size is greater than that of the rare examples published, and the objective is the minimization of the makespan.
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A novel hybrid discrete differential evolution algorithm for blocking flow shop scheduling problems
TL;DR: A novel hybrid discrete differential evolution (HDDE) algorithm for solving blocking flow shop scheduling problems to minimize the maximum completion time (i.e. makespan) and a local search algorithm based on insert neighborhood structure is embedded in the algorithm to balance the exploration and exploitation by enhancing the local searching ability.
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A Many-Objective Evolutionary Algorithm Using A One-by-One Selection Strategy
TL;DR: The main idea is that in the environmental selection, offspring individuals are selected one by one based on a computationally efficient convergence indicator to increase the selection pressure toward the Pareto optimal front.
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