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Production scheduling is an interdisciplinary challenge of addressing optimality criteria such as minimizing makespan, mean flow time, idle machine time, total tardiness, number of tardy jobs, in-process inventory cost, cost of being late. Research till date used various AI techniques, heuristics and metaheuristics to optimize scheduling criteria. If(More)
— Flowshop scheduling is an interdisciplinary challenge of addressing major optimality criteria of minimizing makespan and total-flow-time. The enumerations for finding the probabilities for improving the utilization of resources turn this problem towards NP-Hard. Particle Swam Optimization (PSO) has proven its ability to find of near optimal solution when(More)
based on the assumption that the priority of a job in the sequence is given by the sum of its processing times on the bottleneck machine(s) for selecting the initial sequence of jobs. The computational experimentations show that there is a significant improvement in solution quality over the existing heuristic, especially for large problem sizes while not(More)
Since the no-wait flow shop scheduling problems have been proved to be NP-hard, heuristic procedures are considered as the most suitable ones for their solution, especially for large — sized problems. We present a constructive heuristic for minimizing total flow time criterion in no-wait flow shop scheduling problems. The proposed heuristic is based(More)
This paper proposes a penalty-shift-insertion (PSI)-based algorithm for the no-wait flow shop scheduling problem to minimize total flow time. In the first phase, a penalty-based heuristic, derived from Vogel's approximation method used for the classic transportation problem is used to generate an initial schedule. In the second phase, a known solution is(More)
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