Minimization of Mean Tardiness in Hybrid Flow Shop with Missing Operations Using Genetic Algorithm

A multi-stage Hybrid Flow Shop (HFS) scheduling is characterized ‘n’ jobs ‘m’ machines with ‘M’ stages in series with unidirectional flow of work with a variety of jobs being processed sequentially in a single-pass manner. Most real world scheduling problems are NP-hard in nature. The essential complexity of the problem necessitates the use of meta-heuristics for solving hybrid flow shop scheduling problem. The paper addresses multi-stage hybrid flow shop scheduling problems with missing operations to minimize the makespan time with the specific batch size using Genetic Algorithm (GA) and Simulated Annealing algorithm (SA). It is observed that the GA algorithm yields good quality solutions than SA with lesser computational time.

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.

Improved cuckoo search algorithm for hybrid flow shop scheduling problems to minimize makespan

A well-planned schedule is essential to any organization’s growth. Thus, it is important for the literature to cover a more comprehensive range of scheduling problems. In this paper, energy-efficient hybrid flow shop (EEHFS) scheduling problems are considered. Researchers have developed several techniques to deal with EEHFS scheduling problems. Also, researchers have recently proposed several metaheuristics. Honey Badger Algorithm (HBA) is one of the most recent algorithms proposed to solve various optimization problems. The objective of the present work is to solve EEHFS scheduling problems using the Hybrid Honey Badger Algorithm (HHBA) to reduce the makespan (Cmax) and total energy cost (TEC). In the HHBA, a constructive heuristic known as the NEH heuristic was incorporated with the Honey Badger Algorithm. The suggested algorithm’s performance was verified using an actual industrial scheduling problem. The company’s results are compared with those of the HHBA. The HHBA could potentially result in an 8% decrease in total energy cost. Then, the proposed algorithm was applied to solve 54 random benchmark problems. The results of the proposed HHBA were compared with the FIFO dispatching rule, the NEH heuristic, and other metaheuristics such as the simulated annealing (SA) algorithm, the genetic algorithm (GA), the particle swarm optimization (PSO) algorithm, Honey Badger Algorithm, and the Ant Colony Optimization (ACO) algorithms addressed in the literature. Average percentage deviation (APD) was the performance measure used to compare different algorithms. The APD of the proposed HHBA was zero. This indicates that the proposed HHBA is more effective in solving EEHFS scheduling problems.

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.

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.

A novel discrete artificial bee colony algorithm for the hybrid flowshop scheduling problem with makespan minimisation

Genetic programming hyper-heuristic-based solution for dynamic energy-efficient scheduling of hybrid flow shop scheduling with machine breakdowns and random job arrivals

In this research, a bi-criteria group scheduling problem is investigated in a hybrid flow shop (HFS) environment, wherein the parallel machines are unrelated. The objective of the problem is to minimize a linear combination of the total weighted completion time being mindful of the producer and the total weighted tardiness being mindful of the customers. The underlying assumption is that the jobs are released into the system in dynamic times. The machine ready times are considered to be dynamic as well. Sequence-dependent setup times are required for changing the process between groups of jobs. The runtimes of jobs are assumed to be decreasing as the workers learn how to process similar jobs. A mixed-integer linear programing model is developed for the problem. However, since the problem is non-deterministic polynomial-time hard (NP-hard), it may not be solved to optimality within a reasonable time. This research comprehensively addresses the question of what type of meta-heuristic algorithm is more appropriate for solving these problems. In particular, local search algorithms are compared to the population-based algorithms with respect to the permutation or non-permutation properties of the optimal solution. Three algorithms based on tabu search as well as three algorithms based on simulated annealing are developed to represent the local search algorithms. Two other algorithms are developed based on genetic algorithm (GA) to exemplify the population-based algorithms. The results of a comprehensive experimental design reveal that GA-based algorithms have greater potential for identifying better quality solutions in HFS scheduling problems compared to the local search algorithms.

Hybrid flow shop scheduling (HFS) is an on sought problem modelling for production manufacturing. Due to its impact on productivity, researchers from different backgrounds have been attracted to solve its optimum solution. The HFS is a complex dilemma and provides ample solutions, thus inviting researchers to propose niche optimization methods for the problem. Recently, researchers have moved on to multi-objective solutions. In real-world situations, HFS is known for multi-objective problems, and consequently, the need for optimum solutions in multi-objective HFS is a necessity. Regarding sustainability topic, energy utilization is mainly considered as one of the objectives, including the common makespan criteria. This paper presents the existing multi-objective approach for solving energy utilization and makespan problems in HFS scheduling using Non-Dominated Sorting Genetic Algorithm-III (NSGA-III), and a comparison to other optimization models was subjected for analysis. The model was compared with the most sought algorithm and latest multi-objective algorithms, Strength Pareto Evolutionary Algorithm 2 (SPEA -II), Multi-Objective Algorithm Particle Swarm Optimization (MOPSO), Pareto Envelope-based Selection Algorithm II (PESA-II) and Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D). The research interest starts with problem modelling, followed by a computational experiment with an existing multi-objective approach conducted using twelve HFS benchmark problems. Then, a case study problem is presented to assess all models. The numerical results showed that the NSGA-III obtained 50% best overall for distribution performance metrics and 42% best in convergence performance metrics for HFS benchmark problems. In addition, the case study results show that NSGA-III obtained the best overall convergence and distribution performance metrics. The results show that NSGA-III can search for the best fitness solution without compromising makespan and total energy utilization. In the future, these multi-objective algorithms’ potential can be further investigated for hybrid flow shop scheduling problems.

Distributed scheduling is seldom investigated in hybrid flow shops. In this study, distributed two-stage hybrid flow shop scheduling problem (DTHFSP) with sequence-dependent setup times is considered. A collaborative variable neighborhood search (CVNS) is proposed to simultaneously minimize total tardiness and makespan. DTHFSP is simplified by incorporating factory assignment into machine assignment of a prefixed stage, and its solution is newly represented with a machine assignment string and a scheduling string. CVNS consists of two cooperated variable neighborhood search (VNS) algorithms, and neighborhood structures and global search have collaborated in each VNS. Eight neighborhood structures and two global search operators are defined to produce new solutions. The current solution is periodically replaced with a member of the archive farthest from it. Experiments are conducted , and the computational results validate that CVNS has good advantages over the considered DTHFSP.

With the rapid development of make-to-order pattern including E-commerce and takeout and catering service in restaurants, the study of integrated scheduling and distribution receives more and more attentions. Based on a practical order picking and distribution system, a three-stage hybrid flow shop scheduling problem with distribution is studied. Each order is processed on the hybrid flow shop which consists of identical parallel machines with sequence-dependent setup times at stage 1, identical parallel machines at stage 2 and dedicated machines at stage 3, followed by a multi-trip traveling salesman problem with capacitated vehicles for customers of different destination areas. A mixed-integer linear programming model is formulated to minimize the maximum delivery completion time. A variable neighborhood search (VNS)-based method, a four-layered constructive heuristic method (denoted by $$CH_{VNS}$$) and a hybrid heuristic method (denoted by $$CONS_{VNS}$$) which combines the VNS method and the $$CH_{VNS}$$ method are developed to solve the problems with practical size. Computational experiments show the effectiveness and efficiency of the proposed methods.

Multistage hybrid flow shop scheduling problems are considered in this paper. Each stage consists of several identical machines. The jobs are to be processed on any one of the machines at each stage. In most of the scheduling research works it is assumed that all jobs are to be processed at all stages. But in real-life industries some of the jobs may not be processed at some stages. Hence hybrid flow shop scheduling problems with missing operations are considered in this paper with the objective of determining a schedule that minimises makespan. The hybrid flow shop scheduling problems are non-deterministic polynomial time hard (NP-hard) problems. We propose a hybrid meta-heuristic algorithm, namely improved hybrid genetic scatter search (IHGSS) algorithm, based on genetic algorithm and scatter search algorithms. A case study problem of a leading steel furniture manufacturing company in India is presented to illustrate the proposed algorithm. Computational experiments show that the proposed IHGSS algorithm outperforms other heuristic and meta-heuristic algorithms.

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 that 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 (stochastic unavailability), or due to scheduled preventive maintenance where the periods of unavailability are known in advance (deterministic unavailability). This paper deals with hybrid flow shop scheduling problems in which there are sequence-dependent setup times (SDSTs), and machines suffer stochastic breakdowns, to optimise objectives based on the expected makespan. With the increase in manufacturing complexity, conventional scheduling techniques for generating a reasonable manufacturing schedule have become ineffective. An immune algorithm (IA) can be used to tackle complex problems and produce a reasonable manufacturing schedule within an acceptable time. In this research, a computational method based on a clonal selection principle and an affinity maturation mechanism of the immune response is used. This paper describes how we can incorporate simulation into an immune algorithm for the scheduling of a SDST hybrid flow shop with machines that suffer stochastic breakdowns. The results obtained are analysed using a Taguchi experimental design.

This article addresses the energy consumption optimization problems of the pickling process for titanium strip manufacturing. The hybrid flow shop scheduling schemes for the pickling process of titanium strips are designed, and a novel shop scheduling method based on reinforcement learning is proposed for the pickling process of titanium strips. In the scheduling scheme, the pickling chemical treatment process of titanium strips are described as an asymmetric hybrid flow shop scheduling problem (AHFSP), and a mathematical model containing a temperature structure is established with the optimization objectives of minimizing pickling time and energy consumption. Based on the proposed scheduling scheme, a novel shop scheduling method based on reinforcement learning for the titanium strip pickling process is proposed. First, a mixed integer linear programing model for the mixed flow shop scheduling problem is established. Second, the flow shop scheduling problem with sequential energy consumption decisions is approximated as an asymmetric traveling sales-man problem (ATSP). Finally, the ATSP is described as a Markov decision processes (MDP), and a Q-learning based scheduling method for titanium strip pickling shops is proposed. Finally, the effectiveness of the proposed method is verified by examples, and the scheduling scheme can reduce the energy consumption by 16.61% on average while maintaining the schedule, which improves the productivity and economic efficiency.