Machine Job Research Articles

A Flow Shop Scheduling Problem with Transportation Time and Separated Setup Time of Jobs

A flowshop scheduling problem has been one of the classical problems in production scheduling since Johnson proposed the well-known Johnson's rule in the two-stage flow-shop make-span scheduling problem. Mitten and Johnson (1959) separately gave solution algorithm of obtaining an optimal sequence for an ′n-job, 2-machine′ flow shop scheduling problem in which each job involves arbitrary time lags (start-lag, stop-lag). Also Maggu and Das (1980) gives solution algorithm of obtaining optimal sequence for a ′ n-job, 2-machine′ flow shop scheduling problem where in each job involves transportation time. Johnson (1959) presented a solution to the n-job, 2-machine flow-shop problem with an algorithm that produces an ordered sequence with minimum total elapsed time. Kim and Bobrowski (1994) present a computer simulation model for a limited machine job shop scheduling problem with sequence-dependent setup times. They study the influence of setup times and due date's information in priority rules performance for job-shop problem with setup times. S. M. Johnson (1954) presents optimal two and three stage production schedules with setup time included. Wlodzimierz Szwarc (1986) discussed the flow shop problem with time lags and separated setup times. C.T. Ng et al. (2007) presents the three-machine flow-shop scheduling problem to minimize maximum lateness with separate setup times. W. H. Yang et al. (1999) addressed a survey of scheduling research involving setup times. An overall view on set up times in sequencing problems is presented. In this paper, we addressed the three machine flow shop scheduling problem to minimize total completion time where setup times are treated as separate from processing times. Two cases are studied, taking into consideration these factors and the set up times separated from the processing times. This new algorithm provides an optimal scheduling sequence for flow-shop scheduling problems of 5-jobs on 3-machines and is proposed by using separated setup times of a job.The present paper is a modest attempt to investigate how the setup time is helpful for the production.

Ant Colony Intelligence in Flexible Manufacturing Scheduling Using Contract-Net Model

This paper builds an efficient agent-based flexible scheduling for real-world manufacturing systems. Considering the alternative processes and alternative machines, the allocation of manufacturing resources is achieved through negotiation among the job and machine agents in a multi-agent system (MAS). Ant Colony Intelligence (ACI) is proposed to be combined with Contract Net Protocol (CNP) so as to make agents adaptive to changing circumstances. ACI is integrated into both machine agents and job agents to solve the task allocation and sequencing problem. CNP is introduced to allow the agents to cooperate and coordinate their local schedules in order to find globally near-optimal robust schedules. The negotiation protocol is an interactive bidding mechanism based on the hybrid contract net protocol. The implementation of the issues using CNP model is discussed. Experimental results verify the effectiveness and efficiency of the proposed algorithm integrated with ant-inspired coordination.

A novel bi-vector encoding genetic algorithm for the simultaneous multiple resources scheduling problem

To improve capital effectiveness in light of demand fluctuation, it is increasingly important for high-tech companies to develop effective solutions for managing multiple resources involved in the production. To model and solve the simultaneous multiple resources scheduling problem in general, this study aims to develop a genetic algorithm (bvGA) incorporating with a novel bi-vector encoding method representing the chromosomes of operation sequence and seizing rules for resource assignment in tandem. The proposed model captured the crucial characteristics that the machines were dynamic configuration among multiple resources with limited availability and sequence-dependent setup times of machine configurations between operations would eventually affect performance of a scheduling plan. With the flexibility and computational intelligence that GA empowers, schedule planners can make advanced decisions on integrated machine configuration and job scheduling. According to a number of experiments with simulated data on the basis of a real semiconductor final testing facility, the proposed bvGA has shown practical viability in terms of solution quality as well as computation time.

Bicriteria supply chain scheduling on unrelated-parallel machines

This article addresses the unrelated-parallel machine scheduling problem with sequence-dependent setup times for minimizing the work-in-process inventory (WIP) while maximizing the customer service level in a supply chain. The machine availabilities and job releases are considered dynamic. In order to meet a variety of needs of the producer and customers, a mathematical model that conforms to a bicriteria objective has been developed. As the proposed research problem is shown to be NP-hard, a metasearch heuristic based upon tabu search is also developed to solve industry-size problems. Moreover, to evaluate the effectiveness of the proposed search algorithm, the search algorithmic solutions obtained for example problems are compared with that of the optimal solutions of the mathematical model obtained from CPLEX. To the best of our knowledge, this research is the first of its kind attempted for this scheduling problem in a supply chain.

Dynamic scheduling of a batch of parallel task jobs on heterogeneous clusters

This paper addresses the problem of minimizing the scheduling length (make-span) of a batch of jobs with different arrival times. A job is described by a direct acyclic graph (DAG) of parallel tasks. The paper proposes a dynamic scheduling method that adapts the schedule when new jobs are submitted and that may change the processors assigned to a job during its execution. The scheduling method is divided into a scheduling strategy and a scheduling algorithm. We also propose an adaptation of the Heterogeneous Earliest-Finish-Time (HEFT) algorithm, called here P-HEFT, to handle parallel tasks in heterogeneous clusters with good efficiency without compromising the makespan. The results of a comparison of this algorithm with another DAG scheduler using a simulation of several machine configurations and job types shows that P-HEFT gives a shorter makespan for a single DAG but scores worse for multiple DAGs. Finally, the results of the dynamic scheduling of a batch of jobs using the proposed scheduler method showed significant improvements for more heavily loaded machines when compared to the alternative resource reservation approach.

Scheduling multiple orders per job in a single machine to minimize total completion time

This paper deals with a single-machine scheduling problem with multiple orders per job (MOJ) considerations. Both lot processing machines and item processing machines are also examined. There are two primary decisions that must be made in the proposed problem: (1) how to group the orders together, and (2) how to schedule the jobs once they are formed. In order to obtain the optimal solution to a scheduling problem, these two decisions should be made simultaneously. The performance measure is the total completion time of all orders. Two mixed binary integer programming models are developed to optimally solve this problem. Also, two efficient heuristics are proposed for solving large-sized problems. Computational results are provided to demonstrate the efficiency of the models and the effectiveness of the heuristics.

Service control with the preemptive parallel job scheduler Scojo-PECT

User satisfaction and scheduling on grids makes predictability of response times and quality-of-service highly desirable. However, existing approaches for response-time prediction still show significant prediction errors, mostly due to problems in dynamic arrival of jobs with potentially higher priority and hard-to-anticipate packing and backfilling effects. The same problems imply that quality-of-service cannot be solved with standard approaches from communication systems. Thus, this paper presents a scheduling approach which provides a more suitable framework for service guarantees and predictability. The approach is based on coarse-grain preemption, combined with an innovative separation of job classes. Resource shares can be determined as necessary to meet target service levels. A further extension permits limited dynamic resource allocation to adapt to variations in machine load and job mixes. The feasibility of service control is demonstrated with various workloads.

Scheduling of a Job Shop with Two Machine Centers Having Parallel Machines

A job-shop with two machine centers with the second one consisting of k parallel machines is considered. An approximate algorithm for minimizing schedule length in the job shop with unit time operations in the first machine center and k time-units operations in the second is proposed. The algorithm has the absolute worst case error bound of k-1. Thus, the algorithm is optimal for k=1. Moreover, the algorithm can be modified to give optimal schedules for k=2. The paper extends considerably the earlier works on two machine job shops with unit time operations.

A Kind of JIT Management Mode for Discrete Machining Job Shop

It is one of the effective way to apply JIT (just in time) mode in the management of discrete job shop to improve the productivity of job shop. A kind of JIT management mode for discrete job shop is proposed in this paper. The machining tools such as cutters and frocks are distributed with materials in JIT mode, so that the inventory of machining tools is reduced, and productivity is improved. The process of distribution plan under this mode is studied. The JIT distribution management information system is developed based on the proposed studies, and is applied in a traditional machining job shop with satisfactory result.

An approach to the minimisation of makespan in the textile industry using ant colony optimisation

In modern industries, the optimisation of processes is very important to increase productivity. There are many optimisation techniques applied in the various processes of industries. In textile mills, cotton passes through a series of processes like carding, drawing, combing, etc. As various grades of cotton are processed in machines, the textile industry involves complex processes. Owing to this complexity, there is always a need for better optimisation techniques to reduce the makespan to increase productivity. Ant Colony Optimisation (ACO) is one of the most important optimisation techniques and is widely used in the process industry. This work overviews the application of ACO to minimise makespan in a textile industry by sequencing 'n' jobs of a spinning process in 'm' machines. A real-time problem from a textile industry has been analysed and the best optimal sequences for scheduling jobs have been found using ACO. In the first part of the paper, the need for a better optimisation technique in a textile industry is overviewed, along with some basic biological findings on real ants. In the second part, an algorithm for optimisation is derived, data collected from a textile industry are used to implement the algorithm and the sequencing of jobs in machines is identified. The obtained result depicts the need of ACO for makespan reduction in a textile industry.

RELIABILITY CENTERED MULTI OBJECTIVE HYBRID FLOW SHOP SCHEDULING

Real world scheduling problems are complex in nature. They require satisfying multiple objectives. To get a realistic schedule, consideration of machine reliability and availability is very important to allocate job in machine. This research aims to develop two fuzzy inference systems (FIS) for hybrid flow shop problem. First FIS is used to get priority of each job considering multiple objectives of processing time, due date and cost over time. Second FIS is used to get machine reliability and availability based priority using the information of mean time to failure (MTTF) & mean time to repair (MTTR) of each individual machine at each stage. To distribute the workload depending on their reliability and availability based priority of each machine, maximum utilization target is determined. An algorithm has been developed for grouping, sequencing & allocating the jobs to the machines at every stage in such a way that total percentage of over utilization will minimum. Based on this algorithm, a computing tool has been developed and, explained with a three stage hybrid flow shop scheduling problem.

An anticipative scheduling approach with controllable processing times

In practice, machine schedules are usually subject to disruptions which have to be repaired by reactive scheduling decisions. The most popular predictive approach in project management and machine scheduling literature is to leave idle times (time buffers) in schedules in coping with disruptions, i.e. the resources will be under-utilized. Therefore, preparing initial schedules by considering possible disruption times along with rescheduling objectives is critical for the performance of rescheduling decisions. In this paper, we show that if the processing times are controllable then an anticipative approach can be used to form an initial schedule so that the limited capacity of the production resources are utilized more effectively. To illustrate the anticipative scheduling idea, we consider a non-identical parallel machining environment, where processing times can be controlled at a certain compression cost. When there is a disruption during the execution of the initial schedule, a match-up time strategy is utilized such that a repaired schedule has to catch-up initial schedule at some point in future. This requires changing machine–job assignments and processing times for the rest of the schedule which implies increased manufacturing costs. We show that making anticipative job sequencing decisions, based on failure and repair time distributions and flexibility of jobs, one can repair schedules by incurring less manufacturing cost. Our computational results show that the match-up time strategy is very sensitive to initial schedule and the proposed anticipative scheduling algorithm can be very helpful to reduce rescheduling costs.

Dynamic job shop scheduling for missed due date performance

In this research, missed due date in terms of mean absolute lateness (MAL) and mean square lateness (MSL) has been considered as a performance criterion and a scheduling study has been performed to improve the missed due date performance in dynamic, stochastic, multi machine job shop environments. In the study, a new due date assignment model was proposed and a new dynamic dispatching rule was developed. The results indicate that the proposed due date assignment model is very successful for improving the missed due date performance and the developed dispatching rule is also very successful for meeting the assigned due dates.

Integrating a decomposition procedure with problem reduction for factory scheduling with disruptions: a simulation study

Dispatching rules are widely used in industry because schedules obtained from optimization procedures can be difficult to implement in the face of executional uncertainties. Barua et al. (Barua, A., Narasimhan, R., Upasani, A. and Uzsoy, R., Implementing global factory schedules in the face of stochastic disruptions. Int. J. Prod. Res., 2005, 43(4), 793–818) implement global schedules obtained from an optimization-based heuristic using a dispatching rule, and outperform myopic dispatching rules in the face of disruptions. However, the computation of the global schedules is still time-consuming for realistic instances. Upasani et al. (Upasani, A., Uzsoy, R. and Sourirajan, K., A problem reduction approach for scheduling semiconductor wafer fabrication facilities. IEEE Trans. Semicon. Manuf., 2006, 19, 216–225) develop a problem reduction scheme based on load disparity between work centres, and report significant reduction in CPU times with minimal loss of solution quality in deterministic experiments. In this paper we integrate the problem-reduction scheme to obtain global schedules with the dispatching approach of Barua et al. (Barua, A., Narasimhan, R., Upasani, A. and Uzsoy, R., Implementing global factory schedules in the face of stochastic disruptions. Int. J. Prod. Res., 2005, 43(4), 793–818) in a multi-product environment with stochastic machine breakdowns and job arrivals. A simulation model of a scaled-down wafer fabrication facility is used to evaluate the performance of the proposed procedures. Results show that the integrated procedure outperforms the benchmark dispatching rules while significantly reducing computation times.

Hepatocellular Carcinoma and Occupation in Men: A Case-Control Study

The principal objective of this work was to evaluate the association between the risk of hepatocellular carcinoma (HCC) and various occupations. This case-control study, conducted during 2000 to 2003 in four French hospitals, included 125 men younger than 75 years with incident HCC and 142 men as control subjects. An association with HCC was observed for machinery fitters and machine assemblers (odds ratio [OR] = 4.4 [1.0-19.0], eight exposed cases and three exposed controls). A higher percentage of cases than controls were metal machinists (OR = 2.2 [0.8-5.8], 14 exposed cases and 10 exposed controls), although the difference was not statistically significant. These associations were strongest for subjects with duration of employment greater than 20 years. Our findings suggest an association between metal machining jobs and hepatocellular carcinoma, although we cannot rule out the possibility that it was observed by chance.

EA for solving combined machine layout and job assignment problems

Machine layout and material flow between machines are crucial considerations for improving productivity in any manufacturing environment. The machine layout and the operations assignment problems are both known to be NP hard problems. In this paper, we consider a combined machine layout and job assignment problem and introduce an evolutionary algorithm to solve this combined problem. The usefulness of our approach is demonstrated through numerical examples.

Forming and scheduling jobs with capacitated containers in semiconductor manufacturing: Single machine problem

We study a scheduling problem motivated by the challenges observed in the newest semiconductor manufacturing wafer fabrication facilities. As wafers are larger and heavier in these wafer fabs, it is becoming more common to use specialized material handling containers that carry multiple orders coming from different customers and to schedule the containers as jobs in the fab. The system performance is a function of the completion times of orders, which ultimately depend on both (1) how the orders are assigned to such containers (“job formation”), and (2) how the containers are scheduled in the fab (“job scheduling”). The overall problem is to find the best way to form and schedule the jobs subject to complicating constraints, including the restrictions on the number of containers that can be used at one time and on the number of wafers the containers can carry. We focus on the single machine job formation and scheduling problem with the total completion time objective. We show that this problem is quite different from conventional parallel and serial batching scenarios and prove that the uncapacitated special case is polynomially solvable and the capacitated case is strongly NP-hard. We use a dynamic programming algorithm to solve the uncapacitated problem, which not only provides tight lower bounds for the capacitated problem, but also becomes a basis for a heuristic approach for the capacitated problem. The computational results show that this approach is very effective, leading to small optimality gaps that get even smaller as the problems become larger.

Minimizing total tardiness in parallel machine scheduling with setup times: An adaptive memory-based GRASP approach

This paper deals with the problem of scheduling jobs in uniform parallel machines with sequence-dependent setup times in order to minimize the total tardiness relative to job due dates. We propose GRASP versions that incorporate adaptive memory principles for solving this problem. Long-term memory is used in the construction of an initial solution and in a post-optimization procedure which connects high quality local optima by means of path relinking. Computational tests are carried out on a set of benchmark instances and the proposed GRASP versions are compared with heuristic methods from the literature.

Ant colony intelligence in multi-agent dynamic manufacturing scheduling

This study aims at building an efficient agent-based dynamic scheduling for real-world manufacturing systems with various products, processes, and disturbances. Ant colony intelligence (ACI) is proposed to be combined with local agent coordination so as to make autonomous agents adaptive to changing circumstances and to give rise to efficient global performance. The work here differs from other dynamic scheduling research in two areas: (1) a more generic and realistic manufacturing model with multiple product types, multiple/parallel multi-purpose machines with sequence-dependent setup constraints, and various dynamic disturbances is used, (2) ACI integrated with both machine agents and job agents to solve not only the task allocation problem, but also the task sequencing problem. The implementation of the aforementioned issues in a multi-agent system (MAS) is discussed. Simulation results show that, for most of the performance measures, a MAS integrated with well-designed ant-inspired coordination performs well compared to a MAS using dispatching rules.

Optimal booking of machines in a virtual job-shop with stochastic processing times to minimize total machine rental and job tardiness costs

We treat a virtual job-shop problem of sequencing n jobs on m unique machines. Each machine is outsourced with known renting cost per time unit. The job-operation processing times have random durations, and each job has its tardiness penalty function. The objective is to determine in advance the machine booking schedule that maximizes an economic gain. We present a simulation based on a cyclic coordinate descent search- algorithm and greedy priority dispatching rule that delivers a rent timetable that minimizes the expected total cost.