This paper presents a new heuristic named GAPH based on a structural property for minimizing makespan in no-wait flowshop with sequence-dependent setup times. Experimental results demonstrate the superiority of the proposed approach over thhree of the best-know methods in the literature. Experimental and statistical analyses show that the new heuristic provides better solutions regarding the so...

We consider the NP-hard no-wait flowshop scheduling problem with sequence-dependent setup times to minimize makespan. We propose a hybrid heuristic which combines the iterated greedy process with local search method based on a block swap operator. Experimental results show that the proposed heuristic is comparatively more effective than the existing metaheuristics.

We address the 3-Machine Assembly-Type Flowshop Scheduling Problem (3MAF). This problem is known to be NP-complete in the strong sense. We propose an exact branch and bound method based on a recursive enumeration of potential inputs and outputs of the machines. Using this algorithm, several large size instances have been solved to optimality.

We consider in this paper the two-machine no-wait flowshop scheduling problem in which each machine may have an unavailable interval. We present a polynomial time approximation scheme for the problem when the unavailable interval is imposed on only one machine, or the unavailable intervals on the two machines overlap.

This paper addresses the jobshop and the flowshop scheduling problems with minimum and maximum time lags. To solve this kind of problems, we propose adaptations of Climbing Discrepancy Search (CDS). We study various parameter settings. Computational experiments are provided to evaluate the propositions.

This paper studies the two-machine permutation flowshop scheduling problem with anticipatory setup times and an availability constraint imposed only on the first machine. The objective is to minimize the makespan. Under the assumption that interrupted jobs can resume their operations, we present a polynomial-time approximation scheme (PTAS) for this problem.