Bi-objective fuzzy batch scheduling for resource-constrained ship pipe hybrid flow shops: An enhanced hyper-heuristic algorithm

To ensure the competitiveness of manufacturing companies in the market, batching and batch scheduling are among the most important tasks. This paper presents a simulation-optimization approach that combines the discrete event simulation (DES) and the genetic algorithm (GA) to solve the batching and batch scheduling problem in a hybrid flow shop (HFS). HFS is widely used for the production of medium and large quantities of different technologically complex products. Based on a real-world manufacturing company, the HFS simulation model was developed using the Tecnomatix Plant Simulation software package. By analysing the influencing factors that represent production costs, a new formulation of the total cost of production was proposed. The purpose of this case study was to ensure timely delivery and minimize production costs by integrating simulation and optimization tools. This research considers sequence-dependent setup times, and availability of manufacturing and transportation equipment. The results of this research showed that the proposed simulation-optimization approach can be applied to solve the problem in many industrial case studies.

Batch scheduling and dispatching in the hybrid flow shop production mode is an inevitable development trend, as it effectively improves low resource utilization and reduces excessive work-in-process inventory. This study proposes a multi-objective batch scheduling technique for the hybrid flow shop based on the concept of batch flow. It establishes a multi-objective optimization model with the sub-batch as the smallest processing unit and utilizes an improved genetic algorithm to enhance search capability, resolving the optimization issues of work-in-process inventory and delivery time in the hybrid flow shop. The approach is further developed and analyzed using actual data from a specific workshop, demonstrating its effectiveness in enhancing production efficiency and reducing work-in-process inventory. The results validate the feasibility and efficacy of the proposed algorithm.

This paper investigates the multistage hybrid flow shop (HFS) scheduling problems using the new bat algorithm. A HFS is the generalisation of flowshop with multiple machines. HFS is one of the important scheduling problems that represent many industries like iron and steel, chemical, textile and ceramic industries. The HFS scheduling problems have been proved to be NP-hard. A recently developed bat meta-heuristic algorithm is proposed to solve the HFS problems. The proposed algorithm is validated with a well-chosen set of benchmark problems in the literature. Computational results indicate that the proposed bat algorithm is more efficient than the genetic algorithm and particle swarm optimisation.

PurposeHybrid flow shop with multiprocessor task (HFSMT) has received considerable attention in recent years. The purpose of this paper is to consider an HFSMT scheduling under the environment of a common time window. The window size and location are considered to be given parameters. The research deals with the criterion of total penalty cost minimization incurred by earliness and tardiness of jobs. In this research, a new memetic algorithm in which a global search algorithm is accompanied with the local search mechanism is developed to solve the HFSMT with jobs having a common time window. The operating parameters of memetic algorithm have an important role on the quality of solution. In this paper, a full factorial experimental design is used to determining the best parameters of memetic algorithm for each problem type. Memetic algorithm is tested using HFSMT problems.Design/methodology/approachFirst, hybrid flow shop scheduling system and hybrid flow shop scheduling with multiprocessor task are defined. The applications of the hybrid flow shop system are explained. Also the background of hybrid flow shop with multiprocessor is given in the introduction. The features of the proposed memetic algorithm are described in Section 2. The experiment results are presented in Section 3.FindingsComputational experiments show that the proposed new memetic algorithm is an effective and efficient approach for solving the HFSMT under the environment of a common time window.Originality/valueThere is only one study about HFSMT scheduling with time window. This is the first study which added the windows to the jobs in HFSMT problems.

The success of a product depends on the costs incurred through its entire processing. Indeed, an efficient schedule can significantly reduce the total costs. Most of the literature on scheduling assumes that machines are always available. However, due to maintenance activities machines cannot operate continuously without some unavailability periods. This chapter deals with scheduling a hybrid flow shop with availability constraints to minimize makespan. We investigate exact methods to solve two special cases of this problem to optimality. We formulate a dynamic programming to solve two-machine flow shop and a branch and bound algorithm to solve the two-stage hybrid flow shop.

This paper is survey of hybrid flow shop scheduling problems. An HFS scheduling problem is a classical flow-shop in which parallel machines are available to perform the same operation. Most real world scheduling problems are NP-hard in nature. The hybrid flow shop scheduling problems have received considerable research attention. The several optimization and heuristic solution procedures are available to solve a variety of hybrid flow shop scheduling problems. It discusses and reviews sustainability of several variants of the hybrid flow shop scheduling problem for economical analysis, each in turn considering different assumptions, constraints and objective functions. Sustainability is the long-term maintenance of responsibility, which analysis the economics and encompasses the concept of stewardship. The Hybrid flow shop problem has sustained for several decades with multi – objective constraints. The paper shows some fruitful directions for future research and opportunities in the area of hybrid flow shop.

Batch scheduling is a well-known topic that has been studied widely with various objectives, methods, and circumstances. Unfortunately, batch scheduling in a collaborative flow shop system is still unexplored. All studies about batch scheduling that are found were in a single flow shop system where all arriving jobs come from single door. In a collaborative flow shop system, every flow shop handles its own customers although joint production among flow shops to improve efficiency is possible. This work aims to develop a novel batch scheduling model for a collaborative multi-product flow shop system. Its objective is to minimize make-span and total production cost. This model is developed by using non-dominated sorting genetic algorithm (NSGA II) which is proven in many multi objective optimization models. This model is then compared with the non-collaborative models which use NSGA II and adjacent pairwise interchange algorithm. Due to the simulation result, the proposed model performs better than the existing models in minimizing the make-span and total production cost. The make-span of the proposed model is 10 to 17 percent lower than the existing non-collaborative models. The total production cost of the proposed model is 0.3 to 3.5 percent lower than the existing non-collaborative models.

The current work contributes to stochastic hybrid flow shop scheduling. After a thorough literature analysis, it is firstly evident that works on stochastic flow shop scheduling are still limited in number; moreover, they often rely on simplifying assumptions; eventually, they may lack in a full viability for industrial application of the proposed models or algorithms. Considering these limitations, the present work proposes a scheduling framework based on Discrete Event Simulation and on Genetic Algorithms. The work stems from a previously published work, therefore, contributes by identifying some inconsistencies in the original algorithm in the so called “limit cases”. Overall, the paper proposes an alternative fitness function to avoid the generation of such inconsistencies; besides, it considers a realistic probability distribution to describe the stochastic processing times for robust scheduling of a hybrid flow shop. The end purpose is to move towards a viable application of optimization algorithms in industrial environments.

A new concept of multi-shop (M ) is put forward which contains all basic shops including open shop (O), job shop (J ), flow shop (F ) and hybrid flow shop (H ) so that these basic shop can be scheduled together. Several algorithms including ant colony optimization (ACO), most work remaining (MWR), least work remaining (LWR), longest processing time (LPT) and shortest processing time (SPT) are used for scheduling the M. Numerical experiments of the M adopting data of some car and reC series benchmark instances are tested. The results show that the ACO algorithm has better performance for scheduling the M than the other algorithms, if minimizing the makespan ( C m*ax) is taken as the objective function. As a comparison, the separate shops contained in the M are also scheduled by the ACO algorithm for the same objective function, when the completing time of the jobs in the previous shop is taken as the ready time of these jobs in the following shop. The results show that the M has the advantage of shortening the makespan upon separate shops.

Hybrid flow shop (HFS) systems represent the typical flow shop production system with parallel machines on at least one stage of operation. This paper considers unrelated machines and anticipatory sequence-dependent setup times where job families can be formed based on similar setup characteristics. This results in the opportunity to save setups if two jobs of the same family are scheduled consecutively. Three constructive heuristic approaches, aiming at minimization of makespan, total completion time and the total number of setup procedures, are implemented based on the algorithm of Nawaz, Enscore and Ham (NEH).

In a recent paper by Fattahi et al. (Int J Adv Manuf Technol 65:787–802, 2013), a mathematical model is presented for a two-stage assembly scheduling problem containing a hybrid flow shop and an assembly stage. Some constraints in the model are not valid. This note shows that some new constraints are proposed for the mathematical model. An example is proposed to present and prove this new model by executing mathematical software.

Much of the research on operations scheduling problems has either ignored setup times or assumed that setup times on each machine are independent of the job sequence. Furthermore, most scheduling problems which have been discussed in the literature are under the assumption that machines are continuously available. Nevertheless, in most real life industries, a machine can be unavailable for many reasons, such as unanticipated breakdowns, i.e., stochastic unavailability, or due to a scheduled preventive maintenance where the periods of unavailability are known in advance, i.e., deterministic unavailability. This paper deals with the hybrid flow shop scheduling problems in which there are sequence-dependent setup times, commonly known as the SDST, and machines which suffer stochastic breakdown to optimize objectives based on expected makespan. This type of production system is found in industries such as chemical, textile, metallurgical, printed circuit board, and automobile manufacture. With the increase in manufacturing complexity, conventional scheduling techniques for generating a reasonable manufacturing schedule have become ineffective. The genetic algorithm can be used to tackle complex problems and produce a reasonable manufacturing schedule within an acceptable time. This paper describes how we can incorporate simulation into genetic algorithm approach to the scheduling of a SDST hybrid flow shop with machines that suffer stochastic breakdown. An overview of the hybrid flow shops and scheduling under stochastic unavailability of machines are presented. Subsequently, the details of incorporated simulation into genetic algorithm approach are described and implemented. Consequently, the results obtained are analyzed with Taguchi experimental design.

The hybrid flow shop (HFS) problem is solved in this study through composite dispatching rules (CDRs) generated automatically. CDR is a simple approach to solve the HFS problem rapidly and easily. Genetic programming (GP) is the most common methodology to generate CDRs to solve combinational optimization problems. The disruption in the main operations of GP, such as crossover and mutation, is high. Scatter programming is proposed in this paper to generate CDRs with improved scalability and flexibility for HFS problems. The proposed algorithm includes a novel local improvement method, one-point traversal shaking search, to accelerate convergence. Simulation studies demonstrate that the proposed algorithm outperforms genetic programming and scatter programming with shaking. The dispatching rules generated automatically can also be reused in similar instances and yield better results than existing dispatching rules. The rate of convergence of the proposed algorithm is higher than that of scatter programming with shaking.

Two-stage hybrid flow shop with precedence constraints and parallel machines at second stage

A hybrid flow shop (HFS) scheduling is characterized of “[Formula: see text]” jobs “[Formula: see text]” machines with “[Formula: see text]” stages by unidirectional flow of work with a variety of jobs being processed sequentially in a single-pass manner. The HFS scheduling problem is known to be strongly NP-hard in nature. Hence, the essential complexity of the problem necessitates the application of meta-heuristics to solve HFS scheduling problems. A population-based genetic algorithm (GA) and a simulated annealing (SA) algorithm have been proposed to solve the multi-stage HFS scheduling problem with missing operations to minimize the mean tardiness. The computational results observed that the GA is efficient in finding out good quality solutions.