No-idle Flow Shop Scheduling Problems Research Articles

A Self-Learning Discrete Jaya Algorithm for Multiobjective Energy-Efficient Distributed No-Idle Flow-Shop Scheduling Problem in Heterogeneous Factory System.

In this study, a self-learning discrete Jaya algorithm (SD-Jaya) is proposed to address the energy-efficient distributed no-idle flow-shop scheduling problem (FSP) in a heterogeneous factory system (HFS-EEDNIFSP) with the criteria of minimizing the total tardiness (TTD), total energy consumption (TEC), and factory load balancing (FLB). First, the mixed-integer programming model of HFS-EEDNIFSP is presented. An evaluation criterion of FLB combining the energy consumption and the completion time is introduced. Second, a self-learning operators selection strategy, in which the success rate of each operator is summarized as knowledge, is designed for guiding the selection of operators. Third, the energy-saving strategy is proposed for reducing the TEC. The energy-efficient no-idle FSP is transformed to be an energy-efficient permutation FSP to search the idle times. The speed of operations which adjacent are idle times is reduced. The effectiveness of SD-Jaya is tested on 60 benchmark instances. On the quality of the solution, the experimental results reveal that the efficacy of the SD-Jaya algorithm outperforms the other algorithms for addressing HFS-EEDNIFSP.

A Hybrid Discrete Bacterial Memetic Algorithm with Simulated Annealing for Optimization of the Flow Shop Scheduling Problem

This paper deals with the flow shop scheduling problem. To find the optimal solution is an NP-hard problem. The paper reviews some algorithms from the literature and applies a benchmark dataset to evaluate their efficiency. In this research work, the discrete bacterial memetic evolutionary algorithm (DBMEA) as a global searcher was investigated. The proposed algorithm improves the local search by applying the simulated annealing algorithm (SA). This paper presents the experimental results of solving the no-idle flow shop scheduling problem. To compare the proposed algorithm with other researchers’ work, a benchmark problem set was used. The calculated makespan times were compared against the best-known solutions in the literature. The proposed hybrid algorithm has provided better results than methods using genetic algorithm variants, thus it is a major improvement for the memetic algorithm family solving production scheduling problems.

A cooperative water wave optimization algorithm with reinforcement learning for the distributed assembly no-idle flowshop scheduling problem

The distributed assembly no-idle flow-shop scheduling problem (DANIFSP) is a novel and considerable model, which is suitable for the modern supply chains and manufacturing systems. In this study, a cooperative water wave optimization algorithm, named CWWO, is proposed to address the DANIFSP with the goal of minimizing the maximum assembly completion time. In the propagation phase, a reinforcement learning mechanism based on the framework of the VNS is designed to balance the exploration and exploitation of the CWWO algorithm. Afterwards, the path-relinking combined with the VNS method as the modified breaking operator is introduced to enhance the capability of local search. Furthermore, a multi-neighborhood perturbation strategy in the refraction phase is applied to extract knowledge information to increase the probability of escaping the local optimal. Moreover, the comprehensive experimental program is executed to calibrate the control parameters of the CWWO algorithm and illustrate the cooperative effect of the three modified operations. The performance of the CWWO algorithm is verified on the benchmark set, and the experimental results demonstrated the stability and validity of the CWWO algorithm.

Local Search Methods for a Distributed Assembly No-Idle Flow Shop Scheduling Problem

Due to the complexity of a real-practice manufacturing process, various complex constraints should be considered to make the conventional model more suitable for the realistic production. This paper proposes a distributed assembly no-idle flow shop scheduling problem (DANIFSP) with the objective of minimizing the makespan at the assembly stage. The DANIFSP consists of two stages, i.e., production and assembly. The production stage contains several identical flow shops working in parallel, in which all jobs with series of operations that should be allocated to one of these factories and all operations of jobs should be performed in the allocated factories. To satisfy the no-idle constraint, each machine must process jobs without any interruption from the start of processing the job to the completion of processing the last job. In the second assembly stage, the processed jobs are assembled by a single machine. For addressing the DANIFSP, this paper extends three constructive heuristics based on a new job assignment rule and proposes two simple meta-heuristics including iterated local search (ILS) and variable neighborhood search (VNS). A comprehensive calibration and analysis for the proposed algorithms through a design of experiments are carried out. The comparison with recently published algorithms demonstrates the high effectiveness of the proposed ILS and VNS.

A hybrid discrete teaching-learning based meta-heuristic for solving no-idle flow shop scheduling problem with total tardiness criterion

This paper proposes a hybrid discrete teaching-learning based meta-heuristic (HDTLM) to solve the no-idle flow shop scheduling problem (NIFSP) with the total tardiness criterion. To imitate the teaching-learning phenomenon in the real world, the HDTLM is composed of three phases, i.e. discrete teaching phase based on probabilistic model, discrete learning phase based on hierarchical structure, and reinforcement learning. In the discrete teaching phase, a probabilistic model based on the elite learners and the best learner is used to generate a series of position sequences, and the concept of consensus permutation is employed to replace the mean individual in the teaching-learning based optimization (TLBO) algorithm. Each job of the consensus permutation is inserted into a new sequence according to the position sequence. In the discrete learning phase, according to different levels of learners, all learners are divided into three layers, i.e. top layer, middle layer, bottom layer, and then the proposed learning phase adopts the order of top-down to spread the knowledge. The reinforcement learning phase is applied to the best learner to further improve the knowledge level of teacher. The parameters of the HDTLM are calibrated by a design of experiments (DOE) on randomly generated testing instances. The computational results on Taillard and Ruiz's benchmark sets and statistical analyses show that the HDTLM is an efficient and effective method for solving the NIFSP.

Memetic algorithm with node and edge histogram for no-idle flow shop scheduling problem to minimize the makespan criterion

This paper presents a memetic algorithm with hybrid node and edge histogram (MANEH) to solve no-idle permutation flow shop scheduling problem (NIPFSP) with the criterion to minimize the maximum completion time (the makespan criterion). The MANEH mainly composes of two components: population-based global search and local refinements for individuals. At the initialization stage, a modified speed-up NEH method and the random initialization are utilized to generate more promising solutions with a reasonable running time. At the population-based global search stage, a random sample crossover is first proposed to construct a hybrid node and edge histogram matrix (NEHM) with superior solutions in the population, and then a new sequence is generated by sampling the NEHM or selecting jobs from a template sequence. At the local refinements stage, an improved general variable neighborhood search with the simulated annealing acceptance (GVNS-SA) is developed to improve the current best individual. The GVNS-SA adopts a random referenced local search in the inner loop and the probability of SA to decide whether accept the incumbent solution for the next iteration. Moreover, the influence of key parameters in the MANEH is investigated based on the approach of a design of experiments (DOE). Finally, numerical simulation based on the benchmark of Ruiz and thorough statistical analysis are provided. The comparisons between MANEH and some existing algorithms as well as MA-based algorithms demonstrate the effectiveness and superiority of the proposed MANEH in solving the NIPFSP. Furthermore, the MANEH improves 89 out of the 250 current best solutions reported in the literature.

Invasive weed optimization algorithm for optimization no-idle flow shop scheduling problem

In this paper, an invasive weed optimization (IWO) scheduling algorithm is presented for optimization no-idle flow-shop scheduling problem (NFSP) with the criterion to minimize the maximum completion time (makespan). Firstly, a simple approach is put forward to calculate the makespan of job sequence. Secondly, the most position value (MPV) method is used to code the weed individuals so that fitness values can be calculated. Then, use the global exploration capacity of IWO to select the best fitness value and its corresponding processing sequence of job by evaluating the fitness of individuals. The results of 12 different scale NFSP benchmarks compared with other algorithms show that NFSP can be effectively solved by IWO with stronger robustness.

Evolutionary heuristic for makespan minimization in no-idle flow shop production systems

This paper deals with no-idle flow shop scheduling problem with the objective of minimizing makespan. A new hybrid metaheuristic is proposed for the scheduling problem solution. The proposed method is compared with the best method reported in the literature. Experimental results show that the new method provides better solutions regarding the solution quality to set of problems evaluated.

Flow shop makespan minimization scheduling with deteriorating jobs under dominating machines

Abstract This paper studies general, no-wait or no-idle flow shop scheduling problems with deteriorating jobs, that is, jobs whose processing times are an increasing function of their start times. A linear deterioration function with identical normal processing times of all jobs is assumed and some dominating relationships between machines can be satisfied. For the makespan minimization problems, the following dominant machines constraint: idm, ddm, idm–ddm and ddm–idm are considered. We show that these problems can be solved in polynomial time, although these problems are more complicated than the classical ones.

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

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 combinatorial optimisation problem where some efficient methods of converting a continuous vector to a discrete job permutation and vice versa are presented. These methods are not problem specific and can be employed in any continuous algorithm to tackle the permutation type of optimisation problems. Secondly, a variable parameter search is introduced for the differential evolution algorithm which significantly accelerates the search process for global optimisation and enhances the solution quality. Thirdly, some novel ways of calculating the total tardiness from makespan are introduced for the NIPFS problem. The performance of vpsDE is evaluated against a well-known RKGA from the literature. The computational results show its highly competitive performance when compared to RKGA. It is shown in this paper that the vpsDE performs better than the RKGA, thus providing an alternative solution approach to the literature that the RKGA can be well suited.

Flow shop scheduling problems with deteriorating jobs under dominating machines

This paper considers the general, no-wait and no-idle flow shop scheduling problems with deteriorating jobs. By a deteriorating job we mean that the processing time is an increasing function of its execution starting time. A linear deterioration function is assumed and some dominating relationships between machines can be satisfied. It is shown that for the problems to minimize the makespan or the weighted sum of completion time, polynomial algorithms still exist, although these problems are more complicated than the classical ones. When the objective is to minimize the maximum lateness, the solutions of a classical version may not hold.

Flow shop scheduling with deteriorating jobs under dominating machines

This paper addresses no-wait or no-idle flow shop scheduling problems with deteriorating jobs, i.e., jobs whose processing times are an increasing function of their starting time. A simple linear deterioration function is assumed and some dominating relationships between machines can be satisfied. It is shown that for the problems to minimize makespan or weighted sum of completion time, polynomial algorithms still exist, although these problems are more complicated than the classical ones. When the objective is to minimize maximum lateness or maximum tardiness, the solutions of a classical version may not hold.

Flow shop scheduling problems with decreasing linear deterioration under dominant machines

This paper considers the general, no-wait and no-idle flow shop scheduling problems with deteriorating jobs, respectively. By a deteriorating job, we mean that the processing time is a decreasing function of its execution starting time. The normal processing time proportional to its decreasing rate is assumed and some dominant relationships between machines can be satisfied. It is shown that for the problems to minimize the makespan or the weighted sum of completion time, polynomial algorithms still exist, although these problems are more complicated than the classical ones. When the objective is to minimize the maximum lateness, the solutions of a classical version may not hold.