Nawaz-Enscore-Ham Research Articles

Effective constructive heuristics for distributed no-wait flexible flow shop scheduling problem

The distributed shop scheduling has gained a great deal of attention due to its high-efficient collaboration and production. This paper studies a distributed no-wait flexible flow shop scheduling problem (DNWFFSP) with the makespan criterion, where each factory includes an identical flexible flow shop with no-wait constraint that requires each job to be processed without any waiting time between consecutive stages. A mixed-integer linear programming model for the DNWFFSP is presented. A machine selection method based on the first earliest available machine rule is presented. Three greedy factory assignment rules incorporating NEH (Nawaz-Enscore-Ham) heuristic are developed to allocate jobs into factories. Fourteen dispatch rules involving simple sorting and decomposition method are developed to construct a priority sequence of jobs. Thus, several constructive heuristics are obtained by combining dispatch rules and factory assignment rules. Furthermore, a constructive heuristic version of variable neighborhood descend is designed to address the DNWFFSP. One of constructive heuristics is selected to generate an initial solution, and five neighborhood search methods are used as the local search procedure. A comprehensive numerical comparison and statistical analysis are carried out to investigate the performance of the proposed approaches. The distributed NEH2 with smallest largest-medium rule obtains better results than other constructive heuristics for large-size instances.

A Comparative Analysis of Heuristic Metaheuristic and Exact Approach to Minimize Make Span of Permutation Flow Shop Scheduling

The study of permutation flow shop scheduling issues has proved fascinating. Make span reduction has been explored extensively throughout the years as one of the many factors. If there are more than two machines, finding a schedule that optimizes total completion times for Permutation Flow Shop issues is NP Hard. The well-known Nawaz–Enscore–Ham (NEH) heuristic is acknowledged as the top performing approach for the permutation flow shop scheduling issue till now, according to the make span minimization criteria. For large-scale problems, however, the NEH approach takes a long time to discover an estimated optimum solution. Many researchers have offered heuristics and meta-heuristics as the time it takes to compute grows exponentially in proportion to the size of the problem. For the permutation flow shop scheduling problem, we proposed a genetic algorithm with the criterion of minimizing make span as the criteria. The method's performance is compared to that of the well-known NEH algorithm. Computational experiments on benchmark data sets revealed that the proposed approach produces satisfactory solutions in short computational cycles. Furthermore, it just requires a few user-defined parameters, making it relevant to real-world flow shop scheduling challenges. The findings reveal that, when compared to an exact technique, the proposed genetic algorithm (GA) is capable of rapidly seeking optimal solutions for most small-sized instances. Furthermore, the proposed GA continues to function satisfactorily for medium and large-scale instances and generates solutions in a reasonable period.

No-Wait Job Shop Scheduling Using a Population-Based Iterated Greedy Algorithm

When no-wait constraint holds in job shops, a job has to be processed with no waiting time from the first to the last operation, and the start time of a job is greatly restricted. Using key elements of the iterated greedy algorithm, this paper proposes a population-based iterated greedy (PBIG) algorithm for finding high-quality schedules in no-wait job shops. Firstly, the Nawaz–Enscore–Ham (NEH) heuristic used for flow shop is extended in no-wait job shops, and an initialization scheme based on the NEH heuristic is developed to generate start solutions with a certain quality and diversity. Secondly, the iterated greedy procedure is introduced based on the destruction and construction perturbator and the insert-based local search. Furthermore, a population-based co-evolutionary scheme is presented by imposing the iterated greedy procedure in parallel and hybridizing both the left timetabling and inverse left timetabling methods. Computational results based on well-known benchmark instances show that the proposed algorithm outperforms two existing metaheuristics by a significant margin.

Solving Type-2 Fuzzy Distributed Hybrid Flowshop Scheduling Using an Improved Brain Storm Optimization Algorithm

The distributed hybrid flowshop scheduling (DHFS) problem is a common scheduling problem that has been researched in both academic and industrial fields during recent years. The uncertainty levels in realistic applications are generally too high to be represented by a deterministic value or a triangular fuzzy number (TFN) value. Considering the DHFS problem with type-2 fuzzy processing time and setup time constraints, an improved version of brain storm optimization was developed, where the objective is to minimize the maximum type-2 fuzzy completion time among all factories. The main contributions of this study are as follows: (1) each solution is represented by a two vectors, i.e., a scheduling vector and a factory assignment vector; (2) two realistic constraints, i.e., the type-2 fuzzy processing time in an uncertain environment and the setup time, make the problem more realistic; (3) a novel constructive heuristic based on the Nawaz-Enscore-Ham (NEH) method, called distributed NEH, is proposed; (4) several local search heuristics considering the problem features and the objective are developed to enhance the local search abilities; and (5) a simulated-annealing-based acceptance criterion is embedded to enhance the exploration abilities. The experimental results demonstrate that the proposed algorithm is more efficient and effective for solving the considered type-2 fuzzy DHFS problems in comparison with other recently published efficient algorithms.

Unified Multi-Objective Genetic Algorithm for Energy Efficient Job Shop Scheduling

In recent years, people have paid more and more attention to traditional manufacturing’s environmental impact, especially in terms of energy consumption and related emissions of carbon dioxide. Except for adopting new equipment, production scheduling could play an important role in reducing the total energy consumption of a manufacturing plant. Machine tools waste a considerable amount of energy because of their underutilization. Consequently, energy saving can be achieved by switching machines to standby or off when they lay idle for a comparatively long period. Herein, we first introduce the objectives of minimizing non-processing energy consumption, total weighted tardiness and earliness, and makespan into a typical production scheduling model-the job shop scheduling problem, based on a machine status switching framework. The multi-objective genetic algorithm U-NSGA-III combined with MME (a heuristic algorithm combined with the MinMax (MM) and Nawaz–Enscore–Ham (NEH) algorithms) population initialization method is used to solve the problem. The multi-objective optimization algorithm can generate a Pareto set of solutions so that production managers can flexibly select a schedule from these non-dominated schedules based on their priorities. Three sets of numerical experiments have been carried out on the extended Taillard benchmark to verify this three-objective model’s effectiveness and the multi-objective optimization algorithm. The results show that U-NSGA-III has obtained better Pareto solutions in most test problem instances than NSGA-II and NSGA-III. Furthermore, the non-processing energy consumption is reduced by 46%-69%, which is 13-83% of the total energy consumption.

An improved NEH heuristic to minimize makespan for flow shop scheduling problems

Flow shop scheduling problems with rudimentary criteria of minimum makespan are the most important investigated problems in the field of scheduling. Generally during the process of generating an optimal sequence, multiple partial sequences claiming the optimal value of makespan are observed. In this paper a novel tie-breaking rule to select one of the best optimal sequences out of all possible partial sequences is developed which then applied to Nawaz-Enscore-Ham (NEH) heuristic to solve the scheduling problems in permutation flowshop without increasing the computational complexity. The performance of proposed heuristic is tested with other existing tie-breaking heuristics of similar complexity over Taillard and VRF's instances. Computational results reveal that in terms of solution quality, the proposed heuristic outperforms over the other NEH based heuristics of the same complexity reported in literature.

A modified teaching learning metaheuristic algorithm with opposite-based learning for permutation flow-shop scheduling problem

Teaching-Learning-Based Optimization is one of the well-known metaheuristic algorithm in the research industry. Recently, various population-based algorithms have been developed for solving optimization problems. In this paper, a random scale factor approach is proposed to modify the simple TLBO algorithm. Modified Teaching-Learning-Based Optimization with Opposite-Based-Learning algorithm is applied to solve the Permutation Flow-Shop-Scheduling Problem with the purpose of minimizing the makespan. The OBL approach is used to enhance the quality of the initial population and convergence speed. PFSSP is used extensively for solving scheduling problem, which belongs to the category of NP-hard optimization problems. First, MTLBO is developed to effectively determine the PFSSP using the Largest Order Value rule-based random key, so that individual job schedules are converted into discrete schedules. Second, new initial populations are generated in MTLBO using the Nawaz–Enscore–Ham heuristic mechanism. Finally, the local exploitation ability is enhanced in the MTLBO using effective swap, insert and inverse structures. The performance of proposed algorithm is validated using ten benchmark functions and the Wilcoxon rank test. The computational results and comparisons indicate that the proposed algorithm outperformed over five well-known datasets such as Carlier, Reeves, Heller, Taillards and VRF benchmark test functions, compared to other metaheuristic algorithms. The p-value indicated the significance and superiority of the proposed algorithm over other metaheuristic algorithms.

Local search metaheuristic for solving hybrid flow shop problem in slabs and beams manufacturing

Solving optimization problems in the modern manufacturing industry is one of the most attractive areas of operational research. In this paper, we propose an industrial optimization case of slabs and beams production problem. The suggested approach consists in modeling the process as a hybrid flow shop with unrelated parallel machines under constraints of sequence dependent setup time. Jobs launched in production are of different characteristics and require a machine preparation time at each phase during their production cycle. The goal is to minimize the total tardiness of all jobs when they are completed on the last stage. The hybrid flow shop problem is considered as one of the NP-hard issues since it consists of at least two stages. To solve numerically this problem, we propose two methods, the iterative local search (ILS) and the iterated greedy (IG) metaheuristics. In addition, we suggest two new improvements, the first relates to the initial step while the second concerns the steps of the neighborhood exploration. In the initial phase, we consider an initial solution set generated from the priority rules whose scheduling is ensured by the Nawaz-Enscore-Ham (NEH) algorithm and the greedy randomized adaptive search procedure (GRASP). In the second phase, we suggest a new version of exploration of the neighborhood. In fact, from a single solution, we will generate a set of neighboring solutions and we will choose the best solution. In this new resolution approach, we develop a total of twelve algorithms based on neighborhood exploration. We note that the proposed algorithms are hybrid metaheuristics and are among the most powerful optimization algorithms in local search. A simulation study is conducted to verify the effectiveness of the suggested algorithms.This simulation is performed on a set of instances by varying the number of jobs, the number of machines per stage and the number of stages. We find that the IG algorithm based on NEH initialization heuristic gives good results in terms of quality and convergence time to the best solution.

Biogeography-based optimization algorithm for large-scale multistage batch plant scheduling

The batch process is characterized by many varieties, small batches, redundant production equipment, flexible production process, and high-added-value products. This process is widely used in chemical, plastic, rubber, pharmaceutical, fine chemical, metallurgical, steel, food, and other industries. The optimized scheduling scheme of the batch process can effectively enhance enterprise competitiveness and improve economic benefits. Multistage Multiproduct Scheduling Problem (MMSP) is an important branch of batch scheduling problems. It is difficult to solve MMSP within a reasonable time by traditional mathematical programming, because once the scale of scheduling problems increases, the solution space expands exponentially. This study proposes a metaheuristic approach based on a time key biogeography-based optimization algorithm to solve MMSP. This new time key representation contains two vectors, which represent the processing sequence and equipment allocation of orders respectively. In accordance with the time information in the new representation, we add the preference of equipment processing time to migration and calculate the probability of every mutation value. In addition, the elite solution is combined with the active scheduling technique and modified Nawaz-Enscore-Ham (NEH) algorithm to improve the search accuracy of the proposed algorithm. To test the performance of Improved Time Key Biogeography-Based Optimization (Improved-TKBBO) algorithm, its results are compared with computational results of mathematical programming, Genetic Algorithm (GA), and Line-up Competition Algorithm (LCA). Simulation results show that the proposed Improved-TKBBO can solve the large-scale MMSP with non-identical parallel units effectively.

A memetic algorithm with novel semi-constructive evolution operators for permutation flowshop scheduling problem

This paper proposes a memetic algorithm (MA) with novel semi-constructive crossover and mutation operators (MASC) to minimize makespan in permutation flowshop scheduling problem (PFSP). MASC combines the strengths of genetic algorithm (GA), simulated annealing (SA), and Nawaz–Enscore–Ham (NEH) algorithm. The aim is to enhance GA in identifying promising areas in the search space, whose local optima will be subsequently located by SA. This is achieved by means of novel crossover and mutation operators that construct chromosomes by using two different types of genes: static and dynamic genes. MASC is tested on the well-known Taillard’s benchmark instances. The proposed operators are compared with traditional operators. The results show that the proposed operators produce considerable improvements. These improvements reach up to 20.79% in the average relative error of best solution and 11.86% in the average relative error of average solution. MASC is compared with fourteen well-known and state-of-the-art algorithms. These algorithms include MA, whale optimization, ant colony optimization, particle swarm optimization, artificial bee colony, monkey search, and iterated greedy. The results show that MASC outperforms all the compared algorithms except three iterated greedy algorithms. Moreover, the improvement in the average relative error of best solution achieved on the best-so-far MA is 37.92%. Therefore, MASC can be considered as one of the best-so-far methods for PFSP.

A Knowledge-Based Cooperative Algorithm for Energy-Efficient Scheduling of Distributed Flow-Shop

Facing increasingly serious ecological problems, sustainable development and green manufacturing have attracted much attention. Meanwhile, with the development of globalization, distributed manufacturing is becoming widespread. This paper addresses an energy-efficient scheduling of the distributed permutation flow-shop (EEDPFSP) with the criteria of minimizing both makespan and total energy consumption. Considering the distributed and multiobjective optimization complexity, a knowledge-based cooperative algorithm (KCA) is proposed to solve the EEDPFSP. First, a cooperative initialization scheme is presented with both extended energy-efficient Nawaz–Enscore–Ham heuristic and slowest allowable speed rule that are specially designed to produce good initial solutions with certain diversity. Second, several properties of the nondominated solutions are investigated based on the characteristics of the bi-objective problem, which are used to develop the knowledge-based search operators. Third, a cooperative search strategy of multiple operators is designed for the solutions with different characteristics to tradeoff two objectives. Fourth, a knowledge-based local intensification is used for exploiting better nondominated solutions sufficiently. Moreover, an energy saving method based on the critical path is used to further improve the performance. The effect of parameter setting on the KCA is investigated with the Taguchi method of design-of-experiment. Extensive computational tests and comparisons are carried out, which verify the effectiveness of the special designs of the KCA in solving the EEDPFSP.

Total flow time minimization in no-wait job shop using a hybrid discrete group search optimizer

The no-wait job shop scheduling problem is a well-known NP-hard problem and it is typically decomposed into timetabling subproblem and sequencing subproblem. By adopting favorable features of the group search technique, a hybrid discrete group search optimizer is proposed for finding high quality schedules in the no-wait job shops with the total flow time criterion. In order to find more promising sequences, the producer operator is designed as a destruction and construction (DC) procedure and an insertion-based local search, the scrounger operator is implemented by differential evolution scheme, and the ranger operator is designed by hybridizing best insert moves. An efficient initialization scheme based on Nawaz–Enscore–Ham (NEH) heuristic is designed to construct the initial population with both quality and diversity. A speed-up method is developed to accelerate the evaluation of the insertion neighborhood. Computational results based on well-known benchmark instances show that the proposed algorithm clearly outperforms a hybrid differential evolution algorithm and an iterated greedy algorithm. In addition, the proposed algorithm is comparable to a local search method based on optimal job insertion, especially for large-size instances.

Nawaz, Enscore, Ham (NEH) Algorithm To Minimization Of Makespan In Furniture Company

This study describes production scheduling to minimize the delay in delivering products to consumers. Production scheduling using Nawaz, Enscore, Ham (NEH) Algorithm is done to minimize the total processing time of the product so that the product is finished on time. The fulfillment of due date affects the process of delivering products to consumers. Production scheduling is done in the furniture industry that runs the First-Come-First-Serve (FCFS) scheduling system. This study also compares the performance of the FCFS scheduling system with NEH Algorithm. FCFS scheduling is the existing scheduling systemthat has been implemented. The results of production scheduling with the NEH algorithm indicate the time required to complete all of orders (makespan) more shorter than the First Come First Serve method. the NEH Algorithm is smaller at 78.61 hours or 5.07% than than company’s actual method, the FCFS system. The performance of NEH algorithm also proves that it is better than company’s actual method with the efficiency index value of 1.05.

A Multi-Objective Memetic Algorithm for a Fuzzy Parallel Blocking Flow Shop Scheduling Problem of Panel Block Assembly in Shipbuilding

Scheduling plays an important role in improving the efficiency of panel block assembly. Because of the characteristics of the assembly lines and the imprecise and vague temporal parameters in real-world production, the scheduling of panel block assembly on parallel lines is formulated as a fuzzy parallel blocking flow shop scheduling problem with fuzzy processing time and fuzzy due date to minimize the fuzzy makespan and maximize the average agreement index. To solve this combinational optimization problem, a multiobjective memetic algorithm (MOMA) is proposed. In the MOMA, two novel heuristics are designed to generate several promising initial solutions, and a local search method is embedded to improve the exploitation capability. The performance of the MOMA is tested on the production instances of panel block assembly in shipbuilding. Computational comparisons of the MOMA with two other well-known multi-objective evolutionary algorithms demonstrate its feasibility and effectiveness in generating optimal solutions to the bi-criterion fuzzy scheduling problem of panel block assembly. 1. Introduction The development of large ships leads to an increased demand for panel blocks. Most large shipyards have established panel block assembly lines. The scheduling of panel block assembly belongs to a class of blocking flow shop scheduling problem (BFSP) because there is no buffer between consecutive stations (machines) in an assembly line. The BFSP has received increasing attention in the literature, and most studies consider minimizing the makespan. For more than a decade, various heuristics have been proposed for the scheduling problem with a makespan criterion, including the Nawaz-Enscore-Ham (NEH) heuristic (Nawaz et al. 1983), Min-Max enumeration heuristic (Ronconi 2004), and NEH-Wang-Pan-Tasgetiren (NEH-WPT) heuristic (Wang et al. 2011). Moreover, a growing number of meta-heuristics have also been developed for this type of problem, such as the genetic algorithm (GA) (Caraffa et al. 2001), tabu search approaches (TS and TS+M) (Grabowski & Pempera 2007), iterated greedy algorithm (Ribas et al. 2011), discrete particle swarm optimization algorithm (Wang & Tang 2012), memetic algorithm (MA) (Pan et al. 2013), and modified fruit fly optimization algorithm (Han et al. 2016).

A Hybrid Crow Search Algorithm for Solving Permutation Flow Shop Scheduling Problems

The permutation flow shop scheduling problem (PFSP) is a renowned problem in the scheduling research community. It is an NP-hard combinatorial optimization problem that has useful real-world applications. In this problem, finding a useful algorithm to handle the massive amounts of jobs required to retrieve an actionable permutation order in a reasonable amount of time is important. The recently developed crow search algorithm (CSA) is a novel swarm-based metaheuristic algorithm originally proposed to solve mathematical optimization problems. In this paper, a hybrid CSA (HCSA) is proposed to minimize the makespans of PFSPs. First, to make the CSA suitable for solving the PFSP, the smallest position value rule is applied to convert continuous numbers into job sequences. Then, the HCSA uses a Nawaz–Enscore–Ham (NEH) technique to create a population with the required levels of quality and diversity. We apply a local search to enhance the quality of the solutions and avoid premature convergence; simulated annealing enhances the local search of a method based on a variable neighborhood search. Computational tests are used to evaluate the algorithm using PFSP benchmarks with job sizes between 20 and 500. The tests indicate that the performance of the proposed HCSA is significantly superior to that of other algorithms.

A modified hybrid whale optimization algorithm for the scheduling problem in multimedia data objects

SummaryThe scheduling of the Multimedia Data Objects (MDO) is a difficult and extraordinary issue that faces us in the World Wide Web (WWW) to minimize the response time for clients to finish rapidly their tasks. MDO scheduling problem can be modeled as a Two‐Machine Flow Shop Scheduling Problem (T‐MFSSP) that minimizes the makespan. Notwithstanding another objective is to decrease the average lateness time of the clients' jobs. This paper proposes a Modified Hybrid Whale Algorithm (MHWA) to solve a standout among the most imperative applications of flow shop scheduling in the field of MDO. MHWA is hybridized with a local search strategy for solving the scheduling problem of MDO. To cope up with the combinatorial nature of the MDO scheduling, LRV maps the continuous search space into a sequence of jobs. To get better solutions, some operations are applied to the solutions such as swap mutation and reversed block insertion operations. Nawaz‐Enscore‐Ham (NEH) is added to MHWA to upgrade the performance of the algorithm. The solution of Johnson heuristic is added to the initial population of MHWA. It can be inferred that MHWA gives competitive outcomes appeared differently in relation to Johnson and Earliest Due Date (EDD) algorithms. MHWA can obtain the optimal makespan and can minimize the average lateness of the jobs.

Solving Distributed Hybrid Flowshop Scheduling Problems by a Hybrid Brain Storm Optimization Algorithm

With the trend of manufacturing globalization, distributed production has attracted wide attention from the industry and academia. Nevertheless, there has been little research on the distributed hybrid flowshop scheduling (DHFS) problem. To make up for the gap, this study aims to solve the DHFS problem, in which multiple factories with hybrid flowshop scheduling (HFS) problems are considered. This problem consists of two subproblems: 1) how to choose a factory for each job and 2) how to schedule all jobs within the assigned factories. To solve the DHFS problem, a mathematical model is formulated. Then, inspired by successful applications of brain storm optimization (BSO) algorithm in different fields, we try to solve the DHFS with a hybrid BSO (HBSO). In the proposed algorithm, firstly, a new approach to calculate the distance in the procedure of clustering is embedded. Then, a novel constructive heuristic based on the Nawaz-Enscore-Ham (NEH) method, called distributed NEH, is proposed. Moreover, an improved crossover operator based on the partial-mapped crossover (PMX) is designed for the distributed scheduling problem. Finally, the 20 large-scale instances based on the realistic production data are randomly generated to test the performance of the proposed algorithm. The experimental results verify that the proposed algorithm is efficient and effective for solving the considered DHFS problems in comparison with the other recently published efficient algorithms.

Hybrid Genetic Simulated Annealing Algorithm for Improved Flow Shop Scheduling with Makespan Criterion

Flow shop scheduling problems have a wide range of real-world applications in intelligent manufacturing. Since they are known to be NP-hard for more than two machines, we propose a hybrid genetic simulated annealing (HGSA) algorithm for flow shop scheduling problems. In the HGSA algorithm, in order to obtain high-quality initial solutions, an MME algorithm, combined with the MinMax (MM) and Nawaz–Enscore–Ham (NEH) algorithms, was used to generate the initial population. Meanwhile, a hormone regulation mechanism for a simulated annealing (SA) schedule was introduced as a cooling scheme. Using MME initialization, random crossover and mutation, and the cooling scheme, we improved the algorithm’s quality and performance. Extensive experiments have been carried out to verify the effectiveness of the combination approach of MME initialization, random crossover and mutation, and the cooling scheme for SA. The result on the Taillard benchmark showed that our HGSA algorithm achieved better performance relative to the best-known upper bounds on the makespan compared with five state-of-the-art algorithms in the literature. Ultimately, 109 out of 120 problem instances were further improved on makespan criterion.

An improved water wave optimization algorithm with the single wave mechanism for the no-wait flow-shop scheduling problem

ABSTRACTIn this article, a water wave optimization algorithm with a single wave mechanism, called single water wave optimization (SWWO), is proposed to solve the no-wait flow-shop scheduling problem (NWFSP) with the objective of minimizing the makespan. In the proposed SWWO, an improved Nawaz–Enscore–Ham (NEH) heuristic is applied to construct a high-quality initial candidate. In the propagation operation, a self-adaptive block-shift operation is employed. In the breaking operation, a variable neighbourhood search operation is utilized to explore the local optimal solution. According to the schema theory as presented in genetic algorithms, a crossover operation is adopted as the refraction operation. Finally, the computational results based on several benchmarks and statistical performance comparisons are presented. The experimental results demonstrate the effectiveness and efficiency of the proposed SWWO for solving the NWFSP.

RETRACTED: A hybrid whale optimization algorithm based on local search strategy for the permutation flow shop scheduling problem

Abstract The flow shop scheduling problem is one of the most important types of scheduling with a large number of real-world applications. In this paper, we propose a new algorithm that integrates the Whale Optimization Algorithm (WOA) with a local search strategy for tackling the permutation flow shop scheduling problem. The Largest Rank Value (LRV) requires the algorithm to deal with the discrete search space of the problem. The diversity of candidate schedules is improved using a swap mutation operation as well. In addition to the insert-reversed block operation is adopted to escape from the local optima. The proposed hybrid whale algorithm (HWA) is incorporated with Nawaz–Enscore–Ham (NEH) to improve the performance of the algorithm. It is observed that HWA gives competitive results compared to the existing algorithms.