Journal Article10.1016/J.CIE.2020.106347
Mixed-integer linear programming and constraint programming formulations for solving distributed flexible job shop scheduling problem
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TL;DR: The results show that the sequence-based MILP model is the most efficient one, and the proposed CP model is effective in finding good quality solutions for the both the small-sized and large-sized instances.
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About: This article is published in Computers & Industrial Engineering. The article was published on 01 Apr 2020. The article focuses on the topics: Constraint programming & Job shop scheduling.
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Citations
Distributed Scheduling Problems in Intelligent Manufacturing Systems
TL;DR: The achievements and current research status in this field are analyzed, particularly swarm intelligence and evolutionary algorithms, which are used for managing distributed scheduling problems in manufacturing systems are discussed and future research directions are pointed out.
154
A discrete artificial bee colony algorithm for distributed hybrid flowshop scheduling problem with sequence-dependent setup times
TL;DR: A machine position-based mathematical model and a discrete artificial bee colony algorithm (DABC) for the DHFSP-SDST to optimise the makespan and results and statistical analyses validate that the DABC outperforms the best performing algorithm in the literature.
97
Decomposition-Based Multi-Objective Optimization for Energy-Aware Distributed Hybrid Flow Shop Scheduling with Multiprocessor Tasks
TL;DR: A mixed inter linear programming model and a Novel MultiObjective Evolutionary Algorithm based on Decomposition (NMOEA/D) are presented and proposed to address the Energy-Aware Distributed Hybrid Flow Shop Scheduling Problem with Multiprocessor Tasks.
95
Novel MILP and CP models for distributed hybrid flowshop scheduling problem with sequence-dependent setup times
TL;DR: In this article , three mixed-integer linear programming (MILP) models and a constraint programming (CP) model are formulated for distributed hybrid flow shop scheduling with sequence-dependent setup times (DHFSP-SDST).
92
Solving energy-efficient distributed job shop scheduling via multi-objective evolutionary algorithm with decomposition
TL;DR: A mathematical model is presented and an effective modified multi-objective evolutionary algorithm with decomposition (MMOEA/D) is proposed, which outperforms other algorithms in the energy-efficient distributed job shop scheduling problem.
92
References
Parallel machine scheduling with flexible resources
Emrah B. Edis,Ceyda Oğuz +1 more
TL;DR: This paper gives some extensions to the model of dynamic PMFRS problem and presents integer programming (IP) models for static and dynamic UPMFRS problems with the objective of minimizing makespan and proposes a relaxed IP based constraint programming (CP) approach.
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An efficient metaheuristics for a sequence-dependent disassembly planning
Yaping Ren,Leilei Meng,Chaoyong Zhang,Fu Zhao,Ulah Saif,Aihua Huang,Gamini P. Mendis,John W. Sutherland +7 more
TL;DR: A novel two-phase heuristic method is developed to effectively generate feasible disassembly sequence according to the AOG in reasonable computation time and an improved genetic algorithm (IGA) is proposed to solve the problem, in combination with the presented two- phase heuristic.
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A discrete time exact solution approach for a complex hybrid flow-shop scheduling problem with limited-wait constraints
TL;DR: The proposed exact solution approach for this optimization problem, based on a discrete time representation and a mixed-integer linear programming formulation, makes use of a new family of valid inequalities exploiting the fact that a limited waiting time is imposed on jobs between two successive production stages.
53
Modeling semiconductor testing job scheduling and dynamic testing machine configuration
Jei-Zheng Wu,Chen-Fu Chien +1 more
TL;DR: A hybrid approach including a mathematical programming model to optimize the testing job scheduling and an algorithm to specify the machine configuration of each job and allocate specific resources is developed to solve the problem in a short time for implementation.
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Effects of different chromosome representations in developing genetic algorithms to solve DFJS scheduling problems
TL;DR: This research advocates the importance of developing appropriate chromosome representations while applying genetic algorithms (or other meta-heuristic algorithms) to solve a space search problem, in particular when the solution space is high-dimensional.
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