In the no-idle flowshop, machines cannot be idle after finishing one job and before starting the next one. Therefore, start times of jobs must be delayed to guarantee this constraint. In practice machines show this behavior as it might be technically unfeasible or uneconomical to stop a machine in between jobs. This has important ramifications in the modern industry including fiber glass processing, foundries, production of integrated circuits and the steel making industry, among others. However, to assume that all machines in the shop have this no-idle constraint is not realistic. To the best of our knowledge, this is the first paper to study the mixed no-idle extension where only some machines have the no-idle constraint. We present a mixed integer programming model for this new problem and the equations to calculate the makespan. We also propose a set of formulas to accelerate the calculation of insertions that is used both in heuristics as well as in the local search procedures. An effective iterated greedy (IG) algorithm is proposed. We use an NEH-based heuristic to construct a high quality initial solution. A local search using the proposed accelerations is employed to emphasize intensification and exploration in the IG. A new destruction and construction procedure is also shown. To evaluate the proposed algorithm, we present several adaptations of other well-known and recent metaheuristics for the problem and conduct a comprehensive set of computational and statistical experiments with a total of 1750 instances. The results show that the proposed IG algorithm outperforms existing methods in the no-idle and in the mixed no-idle scenarios by a significant margin.

/pdf/an-effective-iterated-greedy-algorithm-for-the-mixed-no-idle-5r3agjjxc4.pdf

An effective iterated greedy algorithm for the mixed no-idle permutation flowshop scheduling problem

A discrete artificial bee colony algorithm for the no-idle permutation flowshop scheduling problem with the total tardiness criterion

A variable iterated greedy algorithm with differential evolution for the no-idle permutation flowshop scheduling problem

A novel hybrid discrete particle swarm optimization (HDPSO) algorithm is proposed in this paper to solve the no-idle permutation flow shop scheduling problems with the criterion to minimize the maximum completion time (makespan). Firstly, two simple approaches are presented to calculate the makespan of a job permutation. Secondly, a speed-up method is proposed to evaluate the whole insert neighborhood of a job permutation with (n−1)2 neighbors in time O(mn
 2), where n and m denote the number of jobs and machines, respectively. Thirdly, a discrete particle swarm optimization (DPSO) algorithm based on permutation representation and a local search algorithm based on the insert neighborhood are fused to enhance the searching ability and to balance the exploration and exploitation. Then, computational simulation results based on the well-known benchmarks and statistical performance comparisons are provided. It is concluded that the proposed HDPSO algorithm is not only superior to two recently published heuristics, the improved greedy (IG) heuristic and Kalczynski–Kamburowski (KK) heuristic, in terms of searching quality, but also superior to the single DPSO algorithm and the PSO algorithm with variable neighborhood search (PSOvns) in terms of searching quality, robustness and efficiency.

No-idle permutation flow shop scheduling based on a hybrid discrete particle swarm optimization algorithm

In this paper, we investigate the use of a continuous algorithm for the no-idle permutation flowshop scheduling (NIPFS) problem with tardiness criterion. For this purpose, a differential evolution algorithm with variable parameter search (vpsDE) is developed to be compared to a well-known random key genetic algorithm (RKGA) from the literature. The motivation is due to the fact that a continuous DE can be very competitive for the problems where RKGAs are well suited. As an application area, we choose the NIPFS problem with the total tardiness criterion in which there is no literature on it to the best of our knowledge. The NIPFS problem is a variant of the well-known permutation flowshop (PFSP) scheduling problem where idle time is not allowed on machines. In other words, the start time of processing the first job on a given machine must be delayed in order to satisfy the no-idle constraint. The paper presents the following contributions. First of all, a continuous optimisation algorithm is used to solve ...

A differential evolution algorithm for the no-idle flowshop scheduling problem with total tardiness criterion

This paper studies four n-job, m-machine flowshop problems when processing times of jobs on various machines follow certain conditions. The objective is to obtain a sequence, which minimizes total elapsed time under no-idle constraint. Under no-idle constraint, the machines work continuously without idle-interval. We prove two theorems. We introduce simple algorithms without using branch and bound technique. Numerical examples are also given to demonstrate the algorithms.

Flowshop/No-idle Scheduling to Minimize Total Elapsed Time

This paper deals with n-job, 2-machine flowshop/mean flowtime scheduling problems working under a "no-idle" constraint, that is, when machines work continuously without idle intervals. A branch and bound technique has been developed to solve the problem.

Flowshop/no-idle scheduling to minimise the mean flowtime

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Flowshop/No-idle Scheduling to Minimize Total Elapsed Time

Flowshop/no-idle scheduling to minimise the mean flowtime