Production scheduling for blocking flowshop in distributed environment using effective heuristics and iterated greedy algorithm

Production scheduling plays an important role in the intelligent decision support system and intelligent optimization decision technology. In the context of the globalization trend, the current production and management may extend from a single factory to a distributed production network. In this paper, we study the distributed blocking flowshop scheduling problem (DBFSP) that is an important generalization of the traditional blocking flowshop scheduling problem in the distributed environment. Six constructive heuristics and an iterated greedy (IG) algorithm are proposed to minimize the makespan, which provides procedures for obtaining efficient and effective solutions to make decision-making sounder. The first five heuristics are developed based on the well-known NEH2 heuristic [B. Naderi, R. Ruiz, The distributed permutation flowshop scheduling problem, Computers & Operations Research, 37 (4) (2010) 754–768.] and the last heuristic is presented by extending the PW heuristic [Q.K. Pan, L. Wang, Effective heuristics for the blocking flowshop scheduling problem with makespan minimization, Omega, 40 (2) (2012) 218–229.] to DBFSP in an effective way. The composite heuristics that combining constructive heuristics and local searches are also studied. The proposed composite heuristics are chosen to generate an initial solution with a high level of quality. Keeping the simplicity of the IG algorithm, three local search procedures, two destruction procedures, an improved reconstruction procedure, and a simulated annealing-like acceptance criterion are well designed based on the problem-specific knowledge to enhance the IG algorithm. The computational experiments are carried out based on the 720 benchmark instances from the literature. The results show that the proposed heuristics are very effective for solving the problem under consideration and the presented IG algorithm performs significantly better than the other state-of-the-art metaheuristics from the literature.     

Using two-machine flowshop with maximum lateness objective to model multimedia data objects scheduling problem for WWW applications

The multimedia data objects scheduling problem for WWW applications is modeled using the two-machine flowshop problem of minimizing maximum lateness with separate setup times. We establish three dominance relations, and propose four heuristics. Also, we conduct computational experiments to compare the performance of the proposed heuristics and that of existing ones in the literature. The results of the computational experiments show that the proposed heuristics are quite efficient.Scope and purposeA two-machine flowshop scheduling problem involves scheduling a number of jobs on the machines in order to optimize a given criterion. The majority of research assumes that setup times are negligible or can be combined with the processing times. However, the latter assumption is invalid since it may lead to more idle time on the second machine. In the literature, the separate setup times problem has been mainly addressed with the completion-time-related objective functions such as makespan. However, there are many real-life situations in which a due-date-related objective function such as maximum lateness is more appropriate. The problem with maximum lateness objective has received limited attention from researchers as indicated by a recent survey paper. In this paper, we show a real-life situation in the Internet where the two-machine flowshop problem of minimizing maximum lateness with separate setup times can be used to model the multimedia object scheduling problem. We propose new improved heuristics for this problem and compare with existing ones in the literature.     

A memetic algorithm for the re-entrant permutation flowshop scheduling problem to minimize the makespan

A common assumption in the classical permutation flowshop scheduling model is that each job is processed on each machine at most once. However, this assumption does not hold for a re-entrant flowshop in which a job may be operated by one or more machines many times. Given that the re-entrant permutation flowshop scheduling problem to minimize the makespan is very complex, we adopt the CPLEX solver and develop a memetic algorithm (MA) to tackle the problem. We conduct computational experiments to test the effectiveness of the proposed algorithm and compare it with two existing heuristics. The results show that CPLEX can solve mid-size problem instances in a reasonable computing time, and the proposed MA is effective in treating the problem and outperforms the two existing heuristics.     

The three-stage assembly flowshop scheduling problem

We consider the three-stage assembly flowshop scheduling problem with the objective of minimizing the makespan. The three-stage assembly problem generalizes both the serial three machine flowshop problem and the two-stage assembly flowshop scheduling problem and is therefore strongly NP-hard. We analyze the worst-case ratio bound for several heuristics for this problem. We also analyze the worst-case absolute bound for a heuristic based on compact vector summation techniques and we point out that, for a large number of jobs, this heuristic becomes asymptotically optimal.Scope and purposeThe three-stage assembly flowshop scheduling problem models situations which arise frequently in manufacturing when various fabrication operations are performed concurrently and then collected and transported into an assembly area for a final assembly operation. The main criterion for this problem is the minimization of the maximum job completion time (makespan). The objective of this paper is to derive algorithms for minimizing the makespan. In doing so, we also demonstrate the reduction of assembly flowshop problems to their embedded serial flowshop problems.     

A PSO and a Tabu search heuristics for the assembly scheduling problem of the two-stage distributed database application

The assembly flowshop scheduling problem has been addressed recently in the literature. There are many problems that can be modeled as assembly flowshop scheduling problems including queries scheduling on distributed database systems and computer manufacturing. The problem has been addressed with respect to either makespan or total completion time criterion in the literature. In this paper, we address the problem with respect to a due date-based performance measure, i.e., maximum lateness. We formulate the problem and obtain a dominance relation. Moreover, we propose three heuristics for the problem: particle swarm optimization (PSO), Tabu search, and EDD. PSO has been used in the areas of function optimization, artificial neural network training, and fuzzy system control in the literature. In this paper, we show how it can be used for scheduling problems. We have conducted extensive computational experiments to compare the three heuristics along with a random solution. The computational analysis indicates that Tabu outperforms the others for the case when the due dates range is relatively wide. It also indicates that the PSO significantly outperforms the others for difficult problems, i.e., tight due dates. Moreover, for difficult problems, the developed dominance relation helps reduce error by 65%.     

A comprehensive review and evaluation of permutation flowshop heuristics to minimize flowtime

In recent years, a large number of heuristics have been proposed for the minimization of the total or mean flowtime/completion time of the well-known permutation flowshop scheduling problem. Although some literature reviews and comparisons have been made, they do not include the latest available heuristics and results are hard to compare as no common benchmarks and computing platforms have been employed. Furthermore, existing partial comparisons lack the application of powerful statistical tools. The result is that it is not clear which heuristics, especially among the recent ones, are the best. This paper presents a comprehensive review and computational evaluation as well as a statistical assessment of 22 existing heuristics. From the knowledge obtained after such a detailed comparison, five new heuristics are presented. Careful designs of experiments and analyses of variance (ANOVA) techniques are applied to guarantee sound conclusions. The comparison results identify the best existing methods and show that the five newly presented heuristics are competitive or better than the best performing ones in the literature for the permutation flowshop problem with the total completion time criterion.     

Minimizing total tardiness in a two-machine re-entrant flowshop with sequence-dependent setup times

We consider a two-machine re-entrant flowshop scheduling problem in which all jobs must be processed twice on each machine and there are sequence-dependent setup times on the second machine. For the problem with the objective of minimizing total tardiness, we develop dominance properties and a lower bound by extending those for a two-machine re-entrant flowshop problem (without sequence-dependent setup times) as well as heuristic algorithms, and present a branch and bound algorithm in which these dominance properties, lower bound, and heuristics are used. For evaluation of the performance of the branch and bound algorithm and heuristics, computational experiments are performed on randomly generated instances, and results are reported.     

An ant colony optimization approach to a permutational flowshop scheduling problem with outsourcing allowed

This paper deals with the scheduling problem of minimizing the makespan in a permutational flowshop environment with the possibility of outsourcing certain jobs. It addresses this problem by means of the development of an ant colony optimization-based algorithm. This new algorithm, here named as flowshop ant colony optimization is composed of two combined ACO heuristics. The results show that this new approach can be used to solve the problem efficiently and in a short computational time.     

Restarted Iterated Pareto Greedy algorithm for multi-objective flowshop scheduling problems

Multi-objective optimisation problems have seen a large impulse in the last decades. Many new techniques for solving distinct variants of multi-objective problems have been proposed. Production scheduling, as with other operations management fields, is no different. The flowshop problem is among the most widely studied scheduling settings. Recently, the Iterated Greedy methodology for solving the single-objective version of the flowshop problem has produced state-of-the-art results. This paper proposes a new algorithm based on Iterated Greedy technique for solving the multi-objective permutation flowshop problem. This algorithm is characterised by an effective initialisation of the population, management of the Pareto front, and a specially tailored local search, among other things. The proposed multi-objective Iterated Greedy method is shown to outperform other recent approaches in comprehensive computational and statistical tests that comprise a large number of instances with objectives involving makespan, tardiness and flowtime. Lastly, we use a novel graphical tool to compare the performances of stochastic Pareto fronts based on Empirical Attainment Functions.     

The Tricriteria Two-Machine Flowshop Scheduling Problem

The literature on the two-machine flowshop scheduling problem reveals that the problem has been addressed with bicriteria of either makespan and mean flowtime or makespan and maximum tardiness (lateness). This paper extends the problem to all the three criteria (tricriteria) where the objective is to minimize a weighted sum of makespan, mean flowtime, and maximum lateness. A dominance relation and a lower bound are established. The dominance relation and the lower bound are used to develop a branch-and-bound algorithm. The dominance relation is also used to develop several heuristics. An extensive computational analysis is conducted to evaluate the performance of the dominance relation and the heuristics. The analysis shows that the dominance relation is effective. The analysis also shows that the heuristics are quite efficient, and some heuristics have an error of less than 1%. Moreover, these heuristics have the desirable property that the error does not increase by the number of jobs.     

Scheduling in a two-machine flowshop for the minimization of the mean absolute deviation from a common due date

This paper addresses the minimization of the mean absolute deviation from a common due date in a two-machine flowshop scheduling problem. We present heuristics that use an algorithm, based on proposed properties, which obtains an optimal schedule for a given job sequence. A new set of benchmark problems is presented with the purpose of evaluating the heuristics. Computational experiments show that the developed heuristics outperform results found in the literature for problems up to 500 jobs.     

Some heuristics for no-wait flowshops with total tardiness criterion

No-wait flowshop scheduling problem is widely investigated because of its practical application and specific properties. However, the total tardiness criterion has not been much considered. In this paper, we propose six heuristic approaches for no-wait flowshops with total tardiness criterion, among which the modified NEH algorithm (MNEH) is verified to be the best. Also, a speed-up technique is introduced to MNEH to reduce the computational time in certain cases. By numeral experiments and analysis, we evaluate the performances of various heuristics. Finally we find out that MNEH is a satisfactory algorithm dealing with this problem.     

The distributed permutation flowshop scheduling problem

This paper studies a new generalization of the regular permutation flowshop scheduling problem (PFSP) referred to as the distributed permutation flowshop scheduling problem or DPFSP. Under this generalization, we assume that there are a total of F identical factories or shops, each one with m machines disposed in series. A set of n available jobs have to be distributed among the F factories and then a processing sequence has to be derived for the jobs assigned to each factory. The optimization criterion is the minimization of the maximum completion time or makespan among the factories. This production setting is necessary in today's decentralized and globalized economy where several production centers might be available for a firm. We characterize the DPFSP and propose six different alternative mixed integer linear programming (MILP) models that are carefully and statistically analyzed for performance. We also propose two simple factory assignment rules together with 14 heuristics based on dispatching rules, effective constructive heuristics and variable neighborhood descent methods. A comprehensive computational and statistical analysis is conducted in order to analyze the performance of the proposed methods.     

Two-stage hybrid flowshop scheduling with simultaneous processing machines

Simultaneous processing machines, common in processing industries such as steel and food production, can process several jobs simultaneously in the first-in, first-out manner. However, they are often highly energy-consuming. In this paper, we study a new two-stage hybrid flowshop scheduling problem, with simultaneous processing machines at the first stage and a single no-idle machine with predetermined job sequence at the second stage. A mixed integer programming model is proposed with the objective of minimizing the total processing time to reduce energy consumption and improve production efficiency. We give a sufficient and necessary condition to construct feasible sequencing solutions and present an effective approach to calculate the time variables for a feasible sequencing solution. Based on these results, we design a list scheduling heuristic algorithm and its improvement. Both heuristics can find an optimal solution under certain conditions with complexity O(nlogn), where n is the number of jobs. Our experiments verify the efficiency of these heuristics compared with classical heuristics in the literature and investigate the impacts of problem size and processing times.     

Efficient constructive and composite heuristics for the Permutation Flowshop to minimise total earliness and tardiness

In this paper we address the problem of scheduling jobs in a permutation flowshop with a just-in-time objective, i.e. the minimisation of the sum of total tardiness and total earliness. Since the problem is NP-hard, there are several approximate procedures available for the problem, although their performance largely depends on the due dates of the specific instance to be solved. After an in-depth analysis of the problem, different cases or sub-problems are identified and, by incorporating this knowledge, four heuristics are proposed: a fast constructive heuristic, and three different local search procedures that use the proposed constructive heuristic as initial solution.The proposed Prod. Type: FLPheuristics have been compared on an extensive set of instances with the best-so-far heuristic for the problem, as well as with adaptations of efficient heuristics for similar scheduling problems. The computational results show the excellent performance of the proposed algorithms. Finally, the positive impact of the efficient heuristics is evaluated by including them as seed sequences for one of the best metaheuristic for the problem.     

A comparison of scheduling algorithms for flexible flow shop problems with unrelated parallel machines,setup times,and dual criteria

This paper considers a flexible flow shop scheduling problem, where at least one production stage is made up of unrelated parallel machines. Moreover, sequence- and machine-dependent setup times are given. The objective is to find a schedule that minimizes a convex sum of makespan and the number of tardy jobs in a static flexible flow shop environment. For this problem, a 0–1 mixed integer program is formulated. The problem is, however, a combinatorial optimization problem which is too difficult to be solved optimally for large problem sizes, and hence heuristics are used to obtain good solutions in a reasonable time. The proposed constructive heuristics for sequencing the jobs start with the generation of the representatives of the operating time for each operation. Then some dispatching rules and flow shop makespan heuristics are developed. To improve the solutions obtained by the constructive algorithms, fast polynomial heuristic improvement algorithms based on shift moves and pairwise interchanges of jobs are applied. In addition, metaheuristics are suggested, namely simulated annealing (SA), tabu search (TS) and genetic algorithms. The basic parameters of each metaheuristic are briefly discussed in this paper. The performance of the heuristics is compared relative to each other on a set of test problems with up to 50 jobs and 20 stages and with an optimal solution for small-size problems. We have found that among the constructive algorithms the insertion-based approach is superior to the others, whereas the proposed SA algorithms are better than TS and genetic algorithms among the iterative metaheuristic algorithms.     

Ant colony optimization algorithm for a Bi-criteria 2-stage hybrid flowshop scheduling problem

We consider the problem of scheduling jobs in a hybrid flowshop with two stages. Our objective is to minimize both the makespan and the total completion time of jobs. This problem has been little studied in the literature. To solve the problem, we propose an ant colony optimization procedure. Computational experiments are conducted using random-generated instances from the literature. In comparison against other well-known heuristics from the literature, experimental results show that our algorithm outperforms such heuristics.     

A hybrid GA-SA algorithm for just-in-time scheduling of multi-level assemblies

The environment is a manufacturing facility that produces multi-level assemblies in a Just-In-Time (JIT) fashion. The due-dates and lot-sizes of the end-items are given, and the objective is to determine a lot-for-lot operations schedule that minimizes the cumulative production lead-time. The scheduling problem within such an environment is NP-hard, and therefore, the performance of heuristics may vary depending on the specific problem instance. To address this problem an effective hybrid Genetic Algorithm-Simulated Annealing (GA-SA) algorithm is developed. The GA starts with an initial population generated by well known scheduling heuristics, a critical path heuristic, and randomly generated schedules. The scheduling work is shared by the GA and SA in two phases that alternate until convergence: (1) Phase I is the GA that crosses over solutions for different work-centers, and (2) Phase II is the SA that improves the sequence of operations on individual work-centers. The effectiveness of the proposed heuristic is assessed via numerical studies.     

应用Agent理论的生产调度系统研究

生产调度问题,一般可根据生产流程的不同分为Job-shop调度和Flowshop调度两大类(也有学者认为,存在两者相结合的第三类—混合调度)。该文研究以最小化Makespan为目标的Flowshop调度问题。基于Agent理论,提出采用Flowshop复合代理体(Flowshop-Compound-Agent,FSCA)求解Flowshop调度问题的方法。在给出FSCA的结构及其实现的基础上,通过毛纺企业制条车间的实例说明了使用FSCA解决Flowshop调度问题的有效性。     

Heuristics for a two-stage assembly flowshop with bicriteria of maximum lateness and makespan

We consider a two-stage assembly flowshop scheduling problem with the objective of minimizing a weighted sum of makespan and maximum lateness. The problem is known to be NP-hard, and therefore, we propose heuristics to solve the problem. The proposed heuristics are Tabu search (Tabu), particle swarm optimization (PSO), and self-adaptive differential evolution (SDE). An extensive computational experiment is conducted to compare performances of the proposed heuristics. The computational experiment reveals that both PSO and SDE are much superior to Tabu. Moreover, it is statistically shown that PSO performs better than SDE. The computation times of both PSO and SDE are close to each other and they are less than 40 and 45 s, respectively, for the largest size problem considered.