Scheduling of jobs and maintenance in a textile company

In this paper we consider a periodic maintenance scheduling problem in a textile company. In our research, a periodic maintenance schedule consists of several maintenance periods and each maintenance period is scheduled after a periodic time interval. The purpose of this paper is to find a schedule that minimizes the maximum tardiness subject to periodic maintenance and nonresumable jobs. An efficient heuristic is developed to provide the near optimal schedule for the problem. A branch-and-bound algorithm is also proposed to find the optimal schedule. Some important theorems associated with the problem are implemented in the algorithm. Computational results show that the proposed heuristic is highly accurate and efficient.     

Multi-objective flexible flow lines with due window, time lag, and job rejection

This paper presents a multi-objective flexible flow shop scheduling problem with limited time lag between stages. n jobs are available to schedule in a predetermined due window. A mixed integer linear programming model with the objectives of maximizing the total profit gained from scheduled jobs and minimizing deviation from the due date is introduced. Due to non-deterministic polynomial-time-hard complexity, the problem is solved with an efficient genetic algorithm. A heuristic mechanism is devised in every generation of the genetic algorithm to assure the solution feasibility. This heuristic also decreases the total solution time by reducing the search space. Computational results show that the presented approach finds solutions of good quality in reasonable runtimes.     

Minimization of maximum tardiness on unrelated parallel machines with process restrictions and setups

We consider the problem of scheduling N jobs on M unrelated parallel machines to minimize maximum tardiness. Each job has a due date and requires a single stage of processing. A setup for dies is incurred if the type of the job scheduled is different from the previous one on that machine. For each die type, the number of dies is restricted. Because of the mechanical structure of the machines and the fitness of dies to each machine, the processing time depends on both the job and the machine. In this paper, an efficient heuristic based on guided search, record-to-record travel, and tabu lists is presented to minimize maximum tardiness. Computational characteristics of the proposed heuristic are evaluated through extensive experiments, which show that the proposed heuristic outperforms a simulated annealing method tested and is able to prescribe the optimal solutions for problems in small scales.     

CNC机床中刀具交换问题的启发式算法

刀具交换问题是指在一台CNC机床上加工N个作业时，在优化作业加工次序和刀具装载策略的过程中，使CNC机床的换刀次数最少。为了有效地搜索到刀具交换问题的优化解，本文提出了基于定向搜索的启发式算法的求解方法。通过实验数据的验证，本文提出的算法具有简单、计算速度快以及效率高的特点。     

Minimization of maximum tardiness on unrelated parallel machines with process restrictions and setups

We consider the problem of schedulingN jobs onM unrelated parallel machines to minimize maximum tardiness. Each job has a due date and requires a single stage of processing. A setup for dies is incurred if the type of the job scheduled is different from the previous one on that machine. For each die type, the number of dies is restricted. Because of the mechanical structure of the machines and the fitness of dies to each machine, the processing time depends on both the job and the machine. In this paper, an efficient heuristic based on guided search, record-to-record travel, and tabu lists is presented to minimize maximum tardiness. Computational characteristics of the proposed heuristic are evaluated through extensive experiments, which show that the proposed heuristic outperforms a simulated annealing method tested and is able to prescribe the optimal solutions for problems in small scales.     

A genetic algorithm for solving no-wait flexible flow lines with due window and job rejection

This paper addresses a no-wait multi-stage flexible flow shop problem. There are n jobs to complete in a predetermined due window; hence, some jobs may be rejected. A mixed integer linear programming model with the objective of maximizing the total profit gained from scheduled jobs is introduced. Since the problem is NP-hard and difficult to find an optimal solution in a reasonable computational time, an efficient genetic algorithm is presented as the solution procedure. A heuristic mechanism is proposed to use in each generation of the genetic algorithms to assure the feasibility and superior quality of the obtained solutions. Computational results show that the presented approach performs considerably in terms of both quality of solutions and required runtimes.     

A Novel Representation Scheme for Disassembly Sequence Planning

The representation of the disassembly sequence is a key issue in maintenance planning. It involves a highly constrained combinatory problem, which is coupled with varying start and end nodes. These start and end nodes are dependent upon the nature of a maintenance task. In this paper, different representation and modelling schemes for disassembly sequence planning are first reviewed. Then, a novel representation scheme for disassembly sequence, which is generic and can be used to represent both the geometrical and precedence constraints dynamically in product disassembly, is proposed. Based on such a representation scheme, the process for the determination of possible disassembly sequences can be simplified. By taking into consideration disassembly constraints, the optimal disassembly sequence in relation to the component to be maintained (target component) can be quickly derived. This is achieved by pruning the search space of disassembly sequences, grouping related components into subassemblies, and identifying free components to facilitate disassembly oper-ations. Subsequently, the optimal disassembly sequence in relation to the target component can be obtained using genetic algorithms. In this manner, the disassembly sequences for a complex product comprising a relatively large number of components can be derived within a short time. A case study is used to illustrate the effectiveness of the representation scheme. Comparisons are made using the same case study with the AND/OR graph representation and the Petri net approach for disassembly sequence planning. The results show that the proposed representation scheme is simpler and is more efficient than the rest. ID="A1" Correspondence and offprint requests to: Dr L. P. Khoo, School of Mechanical and Production Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798. E-mail: mlpkhoo@nyu.edu.sg     

Sequencing heuristic for scheduling jobs with dependent setups in a manufacturing system

In this paper we discuss a single-machine scheduling problem with machine maintenance. In many production systems, the sequence-dependent setup time of a job cannot be ignored when a switch between two different jobs occurs. In our research, we develop a heuristic to minimize the completion time, or equivalently the total setup time subject to maintenance and due dates. The heuristic consists of three procedures: the initialization procedure, the Stinson heuristic procedure and the iterative procedure. Computational performance of the heuristic is evaluated by comparing its solution with the solution of the branch-and-bound algorithm. The performance of the heuristic on various sizes problems is provided.     

A beam-search-based algorithm for the tool switching problem on a flexible machine

The tool switching problem is used to determine a job sequence and the tools to be loaded on a machine with the objective of minimising the total number of tool switches. In this paper, in order to develop a practical and efficient approach to solving this problem, a beam-search-based algorithm is introduced to formulate the solution space of the problem. The performance of the heuristic algorithm is compared with the heuristic developed by Bard. The proposed algorithm is then tested on some random test problems. The results show that the beam-search-based algorithm performs well in terms of computational efficiency and solution quality.     

A job-insertion heuristic for minimizing the mean flowtime in dynamic flowshops

A new adaptive job-insertion based heuristic is presented to minimize the mean flowtime in a dynamic flowshop consisting of m machines. Job orders arrive to the system randomly, and the job arrival or release dates are not known in advance. The heuristic is derived by inserting new jobs into the scheduled sequence as needed when the machine becomes free. Computation results indicate that the proposed heuristic performs 2.7%–10.8% better than the SPT dispatching rule, which is currently one of the most effective methods for minimizing the mean flowtime in dynamic flowshops.     

Rule-based process planning by grouping features

A macro-level CAPP system is proposed to plan the complicated mechanical prismatic parts efficiently. The system creates the efficient machining sequence of the features in a part by analyzing the feature information. Because the planning with the individual features is very complicated, feature groups are formed for effective planning using the nested relations of the features of a part, and special feature groups are determined for sequencing. The process plan is generated based on the sequences of the feature groups and features. When multiple machines are required, efficient machine assignment is performed. A series of heuristic rules are developed to accomplish it.     

Scheduling identical parallel machines with machine eligibility restrictions to minimize total weighted flowtime in automobile gear manufacturing

In this paper, we address a scheduling problem for minimizing total weighted flowtime, observed in automobile gear manufacturing. Specifically, the bottleneck operation of the pre-heat treatment stage of gear manufacturing process has been dealt with in scheduling. Many real-life scenarios like unequal release times, sequence dependent setup times, and machine eligibility restrictions have been considered. A mathematical model taking into account dynamic starting conditions has been proposed. The problem is derived to be NP-hard. To approach the problem, a few heuristic algorithms have been proposed. Based on planned computational experiments, the performance of the proposed heuristic algorithms is evaluated: (a) in comparison with optimal solution for small-size problem instances and (b) in comparison with the estimated optimal solution for large-size problem instances. Extensive computational analyses reveal that the proposed heuristic algorithms are capable of consistently yielding near-statistically estimated optimal solutions in a reasonable computational time.     

No-wait flowshop scheduling problem to minimize the number of tardy jobs

This research addresses the m-machine no-wait flowshop scheduling problem with the objective of minimizing the number of tardy jobs. The problem is known to be NP-hard even for the two machines, and literature reveals that no heuristics have been developed for this problem. In this paper, we propose an efficient heuristic based on simulated annealing, where we first adapt the single-machine optimal algorithm to our problem to develop two new heuristics, NOTA and NOTM. An improved simulated annealing heuristic, called SA-GI, is then developed by feeding the best performing heuristic among NOTA, NOTM, and EDD into the simulated annealing algorithm. A second proposed heuristic, called SA-IP, further improves the SA-GI solution by using insertion and pair-wise exchange techniques. Based on the computational experiments, the overall relative percentage errors of the heuristic SA, SA-GI, and SA-IP are 8.848, 8.428, and 0.207, respectively. The computational times of the three heuristics are close to each other, and the largest average time is less than one second, and hence, the computational time is not an issue. Therefore, the heuristic SA-IP is the best one.     

A general flow shop scheduling problem with consideration of position-based learning effect and multiple availability constraints

In this paper, a more general version of the flow shop scheduling problem with the objective of minimizing the total flow time is investigated. In order to get closer to the actual conditions of the problem, some realistic assumptions including non-permutation scheduling, learning effect, multiple availability constraints, and release times are considered. It is assumed that the real processing time of each job on a machine depends on the position of that job in the sequence, and after processing a specified number of jobs at each machine, an unavailability period is occurring because of maintenance activities. Moreover, it is supposed that each job may not be ready for processing at time zero and may have a release time. According to these assumptions, a new mixed integer linear programming (MILP) model is proposed to formulate the problem. Due to the high complexity of the problem, a heuristic method and a simulated annealing algorithm are presented to find the nearly optimal solutions for medium- and large-sized problems. To obtain better and more robust solutions, the Taguchi method is used in order to calibrate the simulated annealing algorithm parameters. Finally, the computational results are provided for evaluating the performance and effectiveness of the proposed solution methods.     

Scheduling on unrelated parallel machines with sequence- and machine-dependent setup times and due-date constraints

We consider the unrelated parallel-machine scheduling problem with sequence- and machine-dependent setup times and due-date constraints. There are N jobs, each having a due date and requiring a single operation on one of the M machines. A setup is required if there is a switch from processing one type of job to another. Due to the characteristics of machines, the processing time depends upon the job and machine on which the job is processed, and the setup time is sequence and machine dependent. In addition, certain jobs have strict due-date constraints. An effective heuristic based on a modified apparent-tardiness-cost-with-setup procedure, the simulated annealing method, and designed improvement procedures is proposed to minimize the total tardiness of this scheduling problem. Computational characteristics of the proposed heuristic are evaluated through an extensive experiment using a newly created data set. Computational results show that the proposed heuristic is able to effectively improve the initial solutions, obtained by a modified apparent-tardiness-cost-with-setup procedure, and obtains better results than a random descent heuristic.     

Setup reduction in printed circuit board assembly by efficient sequencing

Component setup time in printed circuit board (PCB) assembly makes up a significant part of the total production time in PCB manufacturing. The sequence in which PCBs are assembled is one of the fundamental factors determining the number of component switches that are needed. group technology techniques have been used to group PCB boards into similar groups to determine the assembly sequence. The measure of similarity is the key to the grouping problem. In PCB assembly, the similarity must be considered not only amongst different PCBs but must also taking into account the varying contents of the feeder rack on which the components are mounted in an incremental mode. In this paper, a new grouping strategy that combines the feeder rack contents into the similarity measure for efficient grouping is proposed. The groups of PCBs are then sequenced efficiently using a heuristic that resembles greedy tree traversal. The new grouping and group sequencing strategy outperforms existing methods of grouping particularly for large PCB component incidence matrices.     

An efficient bi-objective heuristic for scheduling of hybrid flow shops

This paper considers the problem of scheduling n independent jobs in hybrid flow shop environment with sequence-dependent setup times to minimize the makespan and total tardiness. For the optimization problem, an algorithm namely; bi-objective heuristic (BOH) is proposed for searching Pareto-optimal frontier. The aim of the proposed algorithm is to generate a good approximation of the set of efficient solutions. The BOH procedure initiates by generating a seed sequence. Since the output results are strongly dependent on the initial solution and in order to increase the quality of output results algorithm, we have considered how the generation of seed sequence with random way and particular sequencing rules. Two methods named Euclidean distance and percent error have been proposed to compare non-dominated solution sets obtain of each seed sequence. It is perceived from these methods that the generation of seed sequence using earliest due date rule is more effective. Then, the performance of the proposed BOH is compared with a simulated annealing proposed in the literature and a VNS heuristic on a set of test problems. The data envelopment analysis is used to evaluate the performance of approximation methods. From the results obtained, it can be seen that the proposed algorithm is efficient and effective.     

Set-up reduction in PCB automated assembly

An heuristic approach to reduce set-up time in printed circuit board assembly is given. A single insertion/ placement machine is considered, and set-up time is taken as directly proportional to the number of component replacements. The problem is formulated as an IP model, for which a heuristic method is developed. The method includes two algorithms: sequence and mix. The algorithms are implemented in LISP, and an extensive series of experiments is performed and their results are presented.     

Integrated knowledge-based assembly sequence planning

This paper presents a novel approach and system for the automatic generation, selection and evaluation, optimisation, and simulation of assembly plans. The information and knowledge about a product and its assembly (e.g. assembly constraints, solid model and CAD database, heuristic rules) are described using a hybrid approach and model with numeric and symbolic representation. A new methodology is presented to generate all feasible assembly sequences of the product by reasoning and decomposing the feasible subassemblies, and representing them by the assembly Petri net modelling. Qualitative strategic constraints are then used to evaluate the feasible assembly sequences. In order to obtain a good assembly sequence, some quantitative criteria such as assembly time and cost, workstation number, operator number, and part priority index are applied to select the optimal assembly sequence. Based on DFA analysis, MTM time analysis, and assemblability analysis, estimates are made of the assembly time and cost of the product when each of these sequences is used. A knowledge-based system KAPSS has been developed to achieve the integration of generation, selection evaluation, and visualisation of the assembly sequences.     

Sequencing the mixed-model assembly line to minimize the total utility and idle costs with variable launching interval

A mixed-model assembly line (MMAL) is a type of a production line where a variety of products is assembled on it. A mixed-model assembly line problem involves not only solving the traditional problems of the assembly line design (i.e., determining the cycle time, the number and sequence of stations, and the balancing problem) but also determining the sequence of products in assembly line. The product sequencing has a high effect on the mixed-model assembly line efficiency. In this paper, we consider sequencing problem with a variable launching interval between products on the assembly line. A mathematical model is presented, which is capable to solve the small-sized ones of these problems. The considered problem involves two optimization problems (the sequencing problem and launching interval problem). Since this problem is strongly NP-hard, a hybrid metaheuristic algorithm based on the simulated annealing approach and a heuristic approach is developed. The heuristic approach (launching interval between products algorithm) is presented to solve the launching interval problem for each sequence. Numerical experiments are used to evaluate the performance and effectiveness of the proposed algorithm. Variable launching interval consideration in MMAL problem causes the higher complexity of this problem. However, this assumption improves the considered goals for this problem. Not only a power algorithm for MMAL is presented in this paper but also the effect of this assumption is discussed. Numerical experiments are used to evaluate the performance and effectiveness of the proposed algorithm.