A genetic algorithm approach for production scheduling with mould maintenance consideration

The traditional approach for maintenance scheduling concerns single-resource (machine) maintenance during production which may not be sufficient to improve production system reliability as a whole. Besides, in the literature many researchers schedule maintenance activities periodically with fixed maintenance duration. However, in a real manufacturing system maintenance activities can be executed earlier and the maintenance duration will become shorter since less time and effort are required. A practical example is that in a plastic production system, the proportion of machine-related downtime is even lower than mould-related downtime. The planned production operations are usually interrupted seriously because of the mismatch among the maintenance periods between injection machine and mould. In this connection, this paper proposes to jointly schedule production and maintenance tasks of multi-resources in order to improve production system reliability by reducing the mismatch among various processes. To integrate machine and mould maintenance tasks in production, this paper attempts to model the production scheduling with mould scheduling (PS-MS) problem with time-dependent deteriorating maintenance schemes. The objective of this paper is to propose a genetic algorithm approach to schedule maintenance tasks jointly with production jobs for the PS-MS problem, so as to minimise the makespan of production jobs.     

A priority scheduling approach for flexible job shops with multiple process plans

This paper considers the job shop scheduling problem with alternative operations and machines, called the flexible job shop scheduling problem. As an extension of previous studies, operation and routing flexibilities are considered at the same time in the form of multiple process plans, i.e. each job can be processed through alternative operations, each of which can be processed on alternative machines. The main decisions are: (a) selecting operation/machine pair; and (b) sequencing the jobs assigned to each machine. Since the problem is highly complicated, we suggest a practical priority scheduling approach in which the two decisions are done at the same time using a combination of operation/machine selection and job sequencing rules. The performance measures used are minimising makespan, total flow time, mean tardiness, the number of tardy jobs, and the maximum tardiness. To compare the performances of various rule combinations, simulation experiments were done on the data for hybrid systems with an advanced reconfigurable manufacturing system and a conventional legacy system, and the results are reported.     

Scheduling with alternatives: a link between process planning and scheduling

The objective of this research is to develop and evaluate effective, computationally efficient procedures for scheduling jobs in a large-scale manufacturing system involving, for example, over 1000 jobs and over 100 machines. The main performance measure is maximum lateness; and a useful lower bound on maximum lateness is derived from a relaxed scheduling problem in which preemption of jobs is based on the latest finish time of each job at each machine. To construct a production schedule that minimizes maximum lateness, an iterative simulation-based scheduling algorithm operates as follows: (a) job queuing times observed at each machine in the previous simulation iteration are used to compute a refined estimate of the effective due date (slack) for each job at each machine; and (b) in the current simulation iteration, jobs are dispatched at each machine in order of increasing slack. Iterations of the scheduling algorithm terminate when the lower bound on maximum lateness is achieved or the iteration limit is reached. This scheduling algorithm is implemented in Virtual Factory, a Windows-based software package. The performance of Virtual Factory is demonstrated in a suite of randomly generated test problems as well as in a large furniture manufacturing facility. To further reduce maximum lateness, a second scheduling algorithm also incorporates a tabu search procedure that identifies process plans with alternative operations and routings for jobs. This enhancement yields improved schedules that minimize manufacturing costs while satisfying job due dates. An extensive experimental performance evaluation indicates that in a broad range of industrial settings, the second scheduling algorithm can rapidly identify optimal or nearly optimal schedules.     

Reactive scheduling in a make-to-order flexible job shop with re-entrant process and assembly: a mathematical programming approach

A mixed-integer linear programming model is presented for the scheduling of flexible job shops, a production mode characteristic of make-to-order industries. Re-entrant process (multiple visits to the same machine group) and a final assembly stage are simultaneously considered in the model. The formulation uses a continuous time representation and optimises an objective function that is a weighted sum of order earliness, order tardiness and in-process inventory. An algorithm for predictive-reactive scheduling is derived from the proposed model to deal with the arrival of new orders. This is illustrated with a realistic example based on data from the mould making industry. Different reactive scheduling scenarios, ranging from unchanged schedule to full re-scheduling, are optimally generated for order insertion in a predictive schedule. Since choosing the most suitable scenario requires balancing criteria of scheduling efficiency and stability, measures of schedule changes were computed for each re-scheduling solution. The short computational times obtained are promising regarding future application of this approach in the manufacturing environment studied.     

Adaptive scheduling and tool flow control in flexible job shops

The recent manufacturing environment is characterized as having diverse products due to mass customization, short production lead-time, and ever-changing customer demand. Today, the need for flexibility, quick responsiveness, and robustness to system uncertainties in production scheduling decisions has dramatically increased. In traditional job shops, tooling is usually assumed as a fixed resource. However, when a tooling resource is shared among different machines, a greater product variety, routing flexibility with a smaller tool inventory can be realized. Such a strategy is usually enabled by an automatic tool changing mechanism and tool delivery system to reduce the time for tooling set-up, hence it allows parts to be processed in small batches. In this paper, a dynamic scheduling problem under flexible tooling resource constraints is studied and presented. An integrated approach is proposed to allow two levels of hierarchical, dynamic decision making for job scheduling and tool flow control in flexible job shops. It decomposes the overall problem into a series of static sub-problems for each scheduling horizon, handles random disruptions by updating job ready time, completion time, and machine status on a rolling horizon basis, and considers the machine availability explicitly in generating schedules. The effectiveness of the proposed dynamic scheduling approach is tested in simulation studies under a flexible job shop environment, where parts have alternative routings. The study results show that the proposed scheduling approach significantly outperforms other dispatching heuristics, including cost over time (COVERT), apparent tardiness cost (ATC), and bottleneck dynamics (BD), on due-date related performance measures. It is also found that the performance difference between the proposed scheduling approach and other heuristics tend to become more significant when the number of machines is increased. The more operation steps a system has, the better the proposed method performs, relative to the other heuristics.     

A genetic algorithm based approach for scheduling of jobs containing multiple orders in a three-machine flowshop

This paper investigates a new scheduling problem of multiple orders per job (MOJ) in a three-machine flowshop consisting of an item-processing machine, a lot-processing machine and a batch-processing machine, for a semiconductor manufacturing operation that must form a layer on the wafers. The three-machine flowshop MOJ scheduling problem deals with sequencing customer orders, assigning orders to jobs, and then combining the formed jobs into batches. Genetic algorithms are presented for this scheduling problem to minimise the total weighted tardiness (TWT), in the presence of non-zero order ready times, different order due dates, different order weights and unequal order sizes. Extensive experiments were performed to compare the proposed genetic algorithm (GA)-based approach with the benchmark heuristics presented in previous studies. The experiments led to the conclusions that the GA-based approach is significantly superior over other heuristics in terms of TWT and can find near-optimal solutions in an acceptable amount of computation time.     

Energy-Aware Production Scheduling in Flow Shop and Job Shop Environments Using a Multi-Objective Genetic Algorithm

Abstract

The energy-aware scheduling problem is a multi-objective optimization problem where the main goal is to achieve energy savings without affecting productivity in a manufacturing system. In this work, we present an approach for energy-aware flow shop scheduling problem and energy-aware job shop scheduling problem considering the process speed as the main energy-related decision variable. This approach allows one to set the appropriate process speed for every considered operation in the corresponding machine. When the speed is high, the processing time is short but the energy demand increases, and vice versa. Therefore, two objectives are worked together: a production objective, paired with an energy efficiency objective. A generic elitist multi-objective genetic algorithm was implemented to solve both problems. Results from a simple comparative design of experiments and a nonparametric test show that it is possible to smooth the energy demand profile and obtain reductions that average 19.8% in energy consumption. This helps to reduce peak loads and drops on applied energy sources demand, stabilizing the conversion units operational efficiency across the entire operational time with a minimum effect on the production maximum completion time (makespan).     

Applying Job Shop Scheduling to SMEs Manufacturing Platform to Revitalize B2B Relationship

A small and medium enterprises (SMEs) manufacturing platform aims to perform as a significant revenue to SMEs and vendors by providing scheduling and monitoring capabilities. The optimal job shop scheduling is generated by utilizing the scheduling system of the platform, and a minimum production time, i.e., makespan decides whether the scheduling is optimal or not. This scheduling result allows manufacturers to achieve high productivity, energy savings, and customer satisfaction. Manufacturing in Industry 4.0 requires dynamic, uncertain, complex production environments, and customer-centered services. This paper proposes a novel method for solving the difficulties of the SMEs manufacturing by applying and implementing the job shop scheduling system on a SMEs manufacturing platform. The primary purpose of the SMEs manufacturing platform is to improve the B2B relationship between manufacturing companies and vendors. The platform also serves qualified and satisfactory production opportunities for buyers and producers by meeting two key factors: early delivery date and fulfillment of processing as many orders as possible. The genetic algorithm (GA)-based scheduling method results indicated that the proposed platform enables SME manufacturers to obtain optimized schedules by solving the job shop scheduling problem (JSSP) by comparing with the real-world data from a textile weaving factory in South Korea. The proposed platform will provide producers with an optimal production schedule, introduce new producers to buyers, and eventually foster relationships and mutual economic interests.     

Knowledge-based scheduling in flexible manufacturing systems: An integration of pattern-directed inference and heuristic search

This paper presents a knowledge-based scheduling approach based on the problem-solving techniques developed in artificial intelligence. The approach is based on three key techniques. The first is the pattern-directed inference technique to capture the dynamic nature of the scheduling environment. The second is the non-linear planning technique to coordinate manufacturing processes and resource assignments. The third technique is the A? search algorithm to expedite the searching procedure. It models the scheduling process by state-space transitions; the job routing is obtained through selecting a sequence of scheduling operators guided by heuristics. Keeping track of the manufacturing system by a symbolic world model, this approach is adaptive to such environmental changes as new job arrivals and machine breakdowns, suitable for making real-time scheduling decisions.     

A computational study of heuristics for two-stage flexible flowshops

We consider the scheduling of two-stage flexible flowshops. This manufacturing environment involves two machine centres representing two consecutive stages of production. Each machine centre is composed of multiple parallel machines. Each job has to be processed serially through the two machine centres. In each machine centre, a job may be processed on any of the machines. There are n independent jobs to be scheduled without preemption. The jobs can wait in between the two machine centres and the intermediate storage is unlimited. Our objective will be to minimize the maximum completion time of the jobs. We formulate the problem as a mixed integer program. Given this problem class is NP-hard in the strong sense, we present three lower bounds to estimate the optimal solution. We then propose a sequence-first, allocate-second heuristic approach for its solution. We heuristically decompose the problem by first creating a priority list to order the jobs and then assign the jobs to the available machines in each machine centre based on this order. We describe seven rules for the sequencing phase. The assignment phase consists of a heuristic which attempts to minimize each partial schedule length while looking ahead at the future assignment of the currently unscheduled jobs. The computational performance of the heuristic approach was evaluated by comparing the value of each heuristic variant to the best among the three lower bounds. Its effectiveness was tested on scenarios pertinent to flexible flowshop environments, such as cellular manufacturing, by conducting a computational study of over 3400 problems. Our computational results indicate that the most effective approach used Johnson's rule to provide the priority list for job assignment. This provided integrality gaps which on the average were less than 0·73%.     

A constructive heuristic algorithm for concurrently selecting and sequencing jobs in an FMS environment

This paper deals with the concurrent solution of the loading and scheduling problems in a flexible manufacturing system ( FMS) environment. It is assumed that the FMS environment has production planned periodically and each job in the system has a number of operations to be processed on flexible machines. A heuristic approach using a constructive scheduling method is developed to solve the FMS loading and scheduling problems concurrently. The computational results are compared to an existing procedure that considers a hierarchical approach with a similar problem environment. The comparison study shows a significant improvement over the existing hierarchical procedure. This experiment indicates that a concurrent solution approach can solve the FMS loading and scheduling problems very effectively.     

A genetic algorithm for the multi-stage and parallel-machine scheduling problem with job splitting – A case study for the solar cell industry

This paper studies a multi-stage and parallel-machine scheduling problem with job splitting which is similar to the traditional hybrid flow shop scheduling (HFS) in the solar cell industry. The HFS has one common hypothesis, one job on one machine, among the research. Under the hypothesis, one order cannot be executed by numerous machines simultaneously. Therefore, multiprocessor task scheduling has been advocated by scholars. The machine allocation of each order should be scheduled in advance and then the optimal multiprocessor task scheduling in each stage is determined. However, machine allocation and production sequence decisions are highly interactive. As a result, this study, motivated from the solar cell industry, is going to explore these issues. The multi-stage and parallel-machine scheduling problem with job splitting simultaneously determines the optimal production sequence, multiprocessor task scheduling and machine configurations through dynamically splitting a job into several sublots to be processed on multiple machines. We formulate this problem as a mixed integer linear programming model considering practical characteristics and constraints. A hybrid-coded genetic algorithm is developed to find a near-optimal solution. A preliminary computational study indicates that the developed algorithm not only provides good quality solutions but outperforms the classic branch and bound method and the current heuristic in practice.     

Production scheduling optimisation with machine state and time-dependent energy costs

The increase of energy costs specially in manufacturing system encourages researchers to pay more attention to energy management in different ways. This paper investigates a non-preemptive single-machine manufacturing environment to reduce total energy costs of a production system. For this purpose, two new mathematical models are presented. The first contribution consists of an improvement of a mathematical formulation proposed in the literature which deals and deals with a scheduling problem at machine level to process the jobs in a predetermined order. The second model focuses on the generalisation of the previous one to deal simultaneously with the production scheduling at machine level as well as job level. So, the initial predetermined fixed sequence assumption is removed. Since this problem is NP-hard, an heuristic algorithm and a genetic algorithm based on the second model are developed to provide good solutions in reasonable computational time. Finally, the effectiveness of the proposed models and optimisation methods have been tested with different numerical experiments. In average, for small size instances which the mathematical model provides a solution in reasonable computational time, a gap of 2.2% for the heuristic and 1.82% for GA are achieved comparing to the exact method’s solution. These results demonstrate the accuracy and efficiency of both proposed algorithms.     

A scheduling problem in glass manufacturing

In this paper, a production scheduling problem in glass manufacturing is studied. The production facility consists of multiple identical production lines and each production line includes a number of serially arranged machines. The production is characterized by semi-ordered processing times in each product family, and the last machine in each production line is a bottleneck machine. Significant changeover times are required when products of different families are produced on a production line. The scheduling problem was modeled as a parallel no-delay flowshop scheduling problem (PNDFSP). The PNDFSP combines the parallel machine scheduling problem (PMSP) with the no-delay flowshop scheduling problem (NDFSP). While PMSP and NDFSP have received considerable attention in the literature, PNDFSP has not been well studied. A mixed-integer programming formulation is developed and an efficient heuristic algorithm is proposed. The sequential heuristic algorithm considers simultaneously the line changeover time, no-delay effect, and line utilization in assigning product families to the production lines. The computational results are reported.     

A decomposition-based approach to flexible flow shop scheduling under machine breakdown

Manufacturing systems in real-world production are generally dynamic and often subject to a wide range of uncertainties. Recently, research on production scheduling under uncertainty has attracted substantial attention. Although some methods have been developed to address this problem, scheduling under uncertainty remains inherently difficult to solve by any single approach. This article considers makespan optimisation of a flexible flow shop (FFS) scheduling problem under machine breakdown. It proposes a novel decomposition-based approach to decompose an FFS scheduling problem into several cluster scheduling problems which can be solved more easily by different approaches. A neighbouring K-means clustering algorithm is developed to first group the machines of an FFS into an appropriate number of machine clusters, based on a proposed machine allocation algorithm and weighted cluster validity indices. Two optimal back propagation networks, corresponding to the scenarios of simultaneous and non-simultaneous job arrivals, are then selectively adopted to assign either the shortest processing time (SPT) or the genetic algorithm (GA) to each machine cluster to solve cluster scheduling problems. If two neighbouring machine clusters are allocated with the same approach, they are subsequently merged. After machine grouping and approach assignment, an overall schedule is generated by integrating the solutions to the sub-problems. Computation results reveal that the proposed approach is superior to SPT and GA alone for FFS scheduling under machine breakdown.     

A genetic algorithm-based approach to machine assignment problem

Over the last few decades, production scheduling problems have received much attention. Due to global competition, it is important to have a vigorous control on production costs while keeping a reasonable level of production capability and customer satisfaction. One of the most important factors that continuously impacts on production performance is machining flexibility, which can reduce the overall production lead-time, work-in-progress inventories, overall job lateness, etc. It is also vital to balance various quantitative aspects of this flexibility which is commonly regarded as a major strategic objective of many firms. However, this aspect has not been studied in a practical way related to the present manufacturing environment.

In this paper, an assignment and scheduling model is developed to study the impact of machining flexibility on production issues such as job lateness and machine utilisation. A genetic algorithm-based approach is developed to solve a generic machine assignment problem using standard benchmark problems and real industrial problems in China. Computational results suggest that machining flexibility can improve the overall production performance if the equilibrium state can be quantified between scheduling performance and capital investment. Then production planners can determine the investment plan in order to achieve a desired level of scheduling performance.     

Applying ant colony system algorithm in the navigation process for plastic injection mould manufacturing scheduling optimisation

Under the computer-aided design (CAD) software architecture, this study aims to develop navigation processes for plastic injection mould manufacturing scheduling optimisation. Mould manufacturing is a job-shop scheduling problem, with components processing sequence under limited conditions. This study uses the search capabilities of the ant colony system (ACS) to determine a set of optimal schedules, under the condition of not violating the processing sequences, in order to minimise the total processing time and realise makespan minimisation. As the test results suggest, it can save up to 52% of manufacturing time, and also substantially shorten the processing time of the production plan. This study completes the algorithm steps and manufacturing process time estimation by operations on the navigation interface, and uses mould manufacturing scheduling to make optimised arrangements of finished components. The method can comply with the on-site manufacturing processes, improve scheduling prediction accuracy and consistently and efficiently integrate the optimisation scheduling system and mould manufacturing system. Visualised information of the scheduling results can be provided, thus allowing production management personnel to ensure smooth scheduling.     

On-line scheduling of multi-server batch operations

The batching of jobs in a manufacturing system is a very common policy in many industries. The main reasons for batching are the avoidance of setups and/or facilitation of material handling. Good examples of batch-wise production systems are the ovens that are found in the aircraft industry and in the manufacture of semiconductors. These systems often consist of multiple machines of different types for the range and volumes of products that have to be handled. Building on earlier research in the aircraft industry, where the process of hardening synthetic aircraft parts was studied, we propose a new heuristic for the dynamic scheduling of these types of systems. Our so-called look-ahead strategy bases its decision to schedule a job on a certain machine on the availability of information on a limited number of near future arrivals. The new control strategy distinguishes itself from existing heuristics by an integrated approach that involves all machines in the scheduling decision, instead of only considering idle machines. It is shown by an extensive series of simulation experiments that the new heuristic outperforms existing heuristics for most system configurations. Especially in the case of complex systems, where multiple products have to be handled by non-identical machines, the new heuristic proves its value as a practical scheduling tool. Important insight is obtained with regard to the relation between the system is configuration and its performance.     

Scheduling with non-decreasing deterioration jobs and variable maintenance activities on a single machine

There is a situation found in many manufacturing systems, such as steel rolling mills, fire fighting or single-server cycle-queues, where a job that is processed later consumes more time than that same job when processed earlier. The research finds that machine maintenance can improve the worsening of processing conditions. After maintenance activity, the machine will be restored. The maintenance duration is a positive and non-decreasing differentiable convex function of the total processing times of the jobs between maintenance activities. Motivated by this observation, the makespan and the total completion time minimization problems in the scheduling of jobs with non-decreasing rates of job processing time on a single machine are considered in this article. It is shown that both the makespan and the total completion time minimization problems are NP-hard in the strong sense when the number of maintenance activities is arbitrary, while the makespan minimization problem is NP-hard in the ordinary sense when the number of maintenance activities is fixed. If the deterioration rates of the jobs are identical and the maintenance duration is a linear function of the total processing times of the jobs between maintenance activities, then this article shows that the group balance principle is satisfied for the makespan minimization problem. Furthermore, two polynomial-time algorithms are presented for solving the makespan problem and the total completion time problem under identical deterioration rates, respectively.     

Two-stage hybrid flow shop scheduling with preventive maintenance using multi-objective tabu search method

This paper investigates an integrated bi-objective optimisation problem with non-resumable jobs for production scheduling and preventive maintenance in a two-stage hybrid flow shop with one machine on the first stage and m identical parallel machines on the second stage. Sequence-dependent set-up times and preventive maintenance (PM) on the first stage machine are considered. The scheduling objectives are to minimise the unavailability of the first stage machine and to minimise the makespan simultaneously. To solve this integrated problem, three decisions have to be made: determine the processing sequence of jobs on the first stage machine, determine whether or not to perform PM activity just after each job, and specify the processing machine of each job on the second stage. Due to the complexity of the problem, a multi-objective tabu search (MOTS) method is adapted with the implementation details. The method generates non-dominated solutions with several parallel tabu lists and Pareto dominance concept. The performance of the method is compared with that of a well-known multi-objective genetic algorithm, in terms of standard multi-objective metrics. Computational results show that the proposed MOTS yields a better approximation.