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.
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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
A constraint programming approach for solving unrelated parallel machine scheduling problem
TL;DR: In this article, a noval constraint programming (CP) model with two customized branching strategies that utilize CP's global constraints, interval decision variables, and domain filtering algorithms is presented.
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Sequence-dependent setup time flexible job shop scheduling problem to minimise total tardiness
TL;DR: In this paper, the problem of scheduling flexible job shops with setup times where the setups are sequence-dependent is studied and an effective metaheuristic algorithm based on iterated local search is proposed and compared with a tabu search and variable neighbourhood search algorithms.
69
Algorithms for the unrelated parallel machine scheduling problem with a resource constraint
TL;DR: The overall algorithm, which combines the two-stage heuristic with a full CP model, finds for the smaller instances from the literature better than or the same solutions as all previously proposed methods in considerably less computation time.
64
A genetic algorithm for the proportionate multiprocessor open shop
TL;DR: A compu-search methodology (a genetic algorithm (GA) is developed to schedule the shop with the objective of minimizing the makespan and successful experiments on large-scale problem instances suggest the readiness of the GA for industrial use.
63
Analysis of a parallel machine scheduling problem with sequence dependent setup times and job availability intervals
TL;DR: The initial investigations show that the pure CP model is very efficient in obtaining good quality feasible solutions but, fails to report the optimal solution for the majority of the problem instances, while the two logic-based Benders decomposition algorithms are able to obtain near optimal solutions.
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