Showing papers on "Job shop published in 2013"

Training and assignment of multi-skilled workers for implementing seru production systems

Abstract: Confronted with high variety and low volume market demands, many companies, especially the Japanese electronics manufacturing companies, have reconfigured their conveyor assembly lines and adopted seru production systems. Seru production system is a new type of work-cell-based manufacturing system. A lot of successful practices and experience show that seru production system can gain considerable flexibility of job shop and high efficiency of conveyor assembly line. In implementing seru production, the multi-skilled worker is the most important precondition, and some issues about multi-skilled workers are central and foremost. In this paper, we investigate the training and assignment problem of workers when a conveyor assembly line is entirely reconfigured into several serus. We formulate a mathematical model with double objectives which aim to minimize the total training cost and to balance the total processing times among multi-skilled workers in each seru. To obtain the satisfied task-to-worker training plan and worker-to-seru assignment plan, a three-stage heuristic algorithm with nine steps is developed to solve this mathematical model. Then, several computational cases are taken and computed by MATLAB programming. The computation and analysis results validate the performances of the proposed mathematical model and heuristic algorithm.

A comparison of hybrid genetic algorithm and hybrid particle swarm optimization to minimize makespan for assembly job shop

Abstract: Very often, studies of job shop scheduling problem (JSSP) ignore assembly relationship and lot splitting. If an assembly stage is appended to JSSP for the final product, the problem then becomes assembly job shop scheduling problem (AJSSP). To allow lot splitting, lot streaming (LS) technique is examined in which jobs may be split into a number of smaller sub-jobs for parallel processing on different stages such that the system performance may be improved. In this study, the system objective is defined as the makespan minimization. In order to investigate the impact of LS on the system objective under different real-life operating conditions, part sharing ratio (PSR) and system congestion index (SCI) are considered. PSR is used to differentiate product-specific components from general-purpose, common components, and SCI for creating different starting conditions of the shop floor. Both PSR and CSI are useful as part sharing (also known as component commonality) is a common practice for manufacturing with assembly operations and system loading is a significant factor in influencing the shop floor performance. Since the complexity of AJSSP is NP-hard, a hybrid genetic algorithm (HGA) and a hybrid particle swarm optimization (HPSO) are proposed and developed to solve AJSSP in consideration of LS technique. Computational results show that for all test problems under various system conditions, HGA can significantly outperform HPSO. Also, equal-sized lot splitting is found to be the most beneficial LS strategy especially for medium-to-large problem size.

Scheduling flexible job shop problem subject to machine breakdown with route changing and right-shift strategies

Abstract: In this paper, flexible job shop scheduling problem with machine breakdown is of concern. Considering that there is a limitation in improving robust and stable performance of rescheduling with a single strategy, an approach with multi-strategies is proposed to make the scheduling more robust and stable. First, in prescheduling, a new idle time insertion strategy is put forward. In this new policy, idle time equal to repair time is inserted into an appropriate position of each machine according to the machine's breakdown nature. Second, route changing strategy combined with right-shift policy is proposed to keep the rescheduling as stable and robust as possible. In this policy, whether to right shift or route change is dependent on the cost of archiving robustness and stability. Based on the two strategies, new algorithms dealing with idle time insertion, right-shift scheduling, and route changing scheduling are designed. The computational results show the effectiveness of the new strategies and new algorithms compared with other strategies.

Learning iterative dispatching rules for job shop scheduling with genetic programming

Abstract: This study proposes a new type of dispatching rule for job shop scheduling problems. The novelty of these dispatching rules is that they can iteratively improve the schedules by utilising the information from completed schedules. While the quality of the schedule can be improved, the proposed iterative dispatching rules (IDRs) still maintain the easiness of implementation and low computational effort of the traditional dispatching rules. This feature makes them more attractive for large-scale manufacturing systems. A genetic programming (GP) method is developed in this paper to evolve IDRs for job shop scheduling problems. The results show that the proposed GP method is significantly better than the simple GP method for evolving composite dispatching rules. The evolved IDRs also show their superiority to the benchmark dispatching rules when tested on different problem instances with makespan and total weighted tardiness as the objectives. Different aspects of IDRs are also investigated and the insights from these analyses are used to enhance the performance of IDRs.

A Genetic Algorithm for Flexible Job Shop Scheduling

Abstract: Flexible Job Shop scheduling problem (FJSSP) is an important scheduling problem which has received considerable importance in the manufacturing domain. In this paper a genetic algorithm (GA) based scheduler is presented for flexible job shop problem to minimise makespan. The proposed approach implements a domain independent GA to solve this important class of problem. The scheduler is implemented in Microsoft Excel™ spreadsheet. The shop model is developed in the spreadsheet using the built in functions. Benchmark problems from the literature have been used to compare the performance of the proposed approach. The results show that the proposed approach is capable of achieving solutions comparable with earlier approaches used for the benchmark problems. It is also shown that the model can be easily customised to cater for any objective function without changing the basic GA routine thus making the proposed approach a robust and general purpose.

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

Abstract: 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-scheduli...

Ant colony optimization for job shop scheduling using multi-attribute dispatching rules

Abstract: This paper proposes a heuristic method based on ant colony optimization to determine the suboptimal allocation of dynamic multi-attribute dispatching rules to maximize job shop system performance (four measures were analyzed: mean flow time, max flow time, mean tardiness, and max tardiness). In order to assure high adequacy of the job shop system representation, modeling is carried out using discrete-event simulation. The proposed methodology constitutes a framework of integration of simulation and heuristic optimization. Simulation is used for evaluation of the local fitness function for ants. A case study is used in this paper to illustrate how performance of a job shop production system could be affected by dynamic multi-attribute dispatching rule assignment.

A novel genetic algorithm for flexible job shop scheduling problems with machine disruptions

Abstract: Either partial flexible job shop or total flexible job shop were studied and discussed in large amount. However, it is still far from a real-world manufacturing environment, in which disruptions such as machine failure must be taken into account. The goal of this paper is to create a genetic algorithm with very special chromosome encoding to handle flexible job shop scheduling that can adapt to disruption to reflect more closely the real-world manufacturing environment. We hope that by using just-in-time machine assignment and adapting scheduling rules, we can achieve the robustness and flexibility we desire. After detailed algorithm design and description, experiments were carried out. In the experiments, we compared our novel approach to two benchmark algorithms: a right-shifting rescheduler and a prescheduler. A right-shifting rescheduler repairs schedules by delaying affected operations until the disruption is over. A prescheduler works on each disruption scenario separately, treating disruptions like prescheduled downtime. Experiments showed that our approach was able to adapt to disruptions in a manner that minimized lost time than compared benchmark algorithms.

Scheduling job shop with lot streaming and transportation through a modified artificial bee colony

Abstract: We consider lot streaming problem in a job shop with consistent sub-lots and transportation, in which each lot is regarded as an individual job to reduce management complexity. A modified artificial bee colony (MABC) algorithm is proposed to minimise makespan. An effective two-phase decoding procedure is applied, in which a schedule is first built and then transportation tasks are dispatched. A swap and an insertion are used in the employed bee phase and the onlooker bee phase respectively to produce new solutions. No scouts are considered and the worst solution is replaced with the elite solution every certain cycles to enhance the diversity of the swarm. The testing results and the comparisons of MABC with some methods show that MABC performs better than the chosen algorithms on the considered problem.

Advanced dispatching rules for large-scale manufacturing systems

Abstract: Dispatching rules have been successfully applied to job sequencing and scheduling in large-scale manufacturing systems such as wafer fabrication plants, automatic guided vehicle systems, etc. Because they can be easily communicated and implemented, and because they can be speedily applied, dispatching rules are also one of the most prevalent approaches in this field. However, naysayers often criticize the sluggish performance levels of traditional dispatching rules. Furthermore, in many large-scale factories, scheduling systems have been installed and operational for more than 5 years with “satisfactory” results, but managers still believe that more beneficial modifications are possible. Specifically, better scheduling methods, dispatching rules, test environments, and reporting tools are needed. Over the years, a few new solutions have been proposed to address these issues. For instance, most traditional dispatching rules are based on historical data. With the emergence of data mining and online analytic processing, dispatching rules can now take predictive information into account. Further, rather than concentrating on a single performance measure, some dispatching rules are designed to optimize multiple objectives at the same time. Moreover, the content of a dispatching rule can be optimized for a largescale manufacturing system. In light of advanced computing systems, dispatching rules continue to be one of the most promising technologies for practical applications. This special issue focuses on innovative but practical dispatching rules rather than complex algorithms. This type of dispatching rule will continue to drive the mainstream of practical applications in factories for the foreseeable future. This special issue is intended to provide the details of advanced dispatching rule development and applications of those rules to job sequencing and scheduling in large-scale manufacturing systems. We are very grateful for the positive responses we have received from the authors who submitted papers and the marvelous help provided by a number of referees in the paper reviewing process. After a strict review, 25 papers were finally accepted for publication in this special issue. Zhang et al. used a genetic algorithm (GA) to optimize a set of dispatching rules for scheduling a job shop. Bayesian networks were also utilized to model the distribution of high-quality solutions in the population and to produce each new generation of individuals. In addition, some selected individuals were further improved by a special local search. One advantage of their method is that it can be readily applied in various dynamic scheduling environments which must be investigated with simulation. Lu and Romanowski considered a dynamic job shop problem in which job shops are disrupted by unforeseen events such as job arrivals and machine breakdowns. They used multi-contextual functions (MCFs) to describe the unique characteristics of a dynamic job shop at a specific time and examined 11 basic dispatching rules and 33 composite rules made with MCFs that describe machine idle time and job waiting time. The experimental data showed that schedules made by the composite rules outperformed schedules made by conventional rules. Lin et al. integrated an ant colony optimization (ACO) algorithm with a number of new ideas (heuristic initial solution, machine reselection step, and local search procedure) and T. Chen (*) Department of Industrial Engineering and Systems Management, Feng Chia University, Taichung, Taiwan e-mail: tolychen@ms37.hinet.net

An AIS-based hybrid algorithm with PDRs for multi-objective dynamic online job shop scheduling problem

Abstract: The dynamic online job shop scheduling problem (JSSP) is formulated based on the classical combinatorial optimization problem - JSSP with the assumption that new jobs continuously arrive at the job shop in a stochastic manner with the existence of unpredictable disturbances during the scheduling process. This problem is hard to solve due to its inherent uncertainty and complexity. This paper models this class of problem as a multi-objective problem and solves it by hybridizing the artificial intelligence method of artificial immune systems (AIS) and priority dispatching rules (PDRs). The immune network theory of AIS is applied to establish the idiotypic network model for priority dispatching rules to dynamically control the dispatching rule selection process for each operation under the dynamic environment. Based on the defined job shop situations, the dispatching rules that perform best under specific environment conditions are selected as antibodies, which are the key elements to construct the idiotypic network. Experiments are designed to demonstrate the efficiency and competitiveness of this model.

A linear programming-based method for job shop scheduling

Abstract: We present a decomposition heuristic for a large class of job shop scheduling problems. This heuristic utilizes information from the linear programming formulation of the associated optimal timing problem to solve subproblems, can be used for any objective function whose associated optimal timing problem can be expressed as a linear program (LP), and is particularly effective for objectives that include a component that is a function of individual operation completion times. Using the proposed heuristic framework, we address job shop scheduling problems with a variety of objectives where intermediate holding costs need to be explicitly considered. In computational testing, we demonstrate the performance of our proposed solution approach.

Particle Swarm Optimization Combined with Tabu Search in a Multi-agent Model for Flexible Job Shop Problem

Abstract: Flexible job shop scheduling problem (FJSP) is an important extension of the classical job shop scheduling problem, where the same operation could be processed on more than one machine and has a processing time depending on the machine used. The objective is to minimize the makespan, i.e., the total duration of the schedule. In this article, we propose a multi-agent model based on the hybridization of the tabu search (TS) method and particle swarm optimization (PSO) in order to solve FJSP. Different techniques of diversification have also been explored in order to improve the performance of our model. Our approach has been tested on a set of benchmarks existing in the literature. The results obtained show that the hybridization of TS and PSO led to promising results.

Parallel branch-and-bound and parallel PSO algorithms for job shop scheduling problem with blocking

Abstract: In this paper, we deal with the resolution of the scheduling problem with blocking which is known to be non-polynomial-hard. The nature of the workshop defines the type of issue to treat: flow shop problem and job shop problem (JSP). A lot of researches are dedicated in the literature to the resolution of the flow shop problem, whereas not enough works are found concerning JSP. We have oriented our efforts in this paper to the resolution of JSP with blocking. For that, we propose three methods: a parallel version of a branch-and-bound method based on an implicit enumeration, a sequential particle swarm optimisation (PSO) and a parallel PSO. The first one is an exact method but the complexity of the problem makes it useless for more than 10 jobs × 10 machines. For that, we investigate in meta-heuristics which procure good scheduling in real time. A comparison of these methods is presented at the end of this paper.

Scheduling of complex manufacturing systems with Petri nets and genetic algorithms: a case on plastic injection moulds

Abstract: This paper introduces significant improvements on a previous published work that addresses complex production scheduling problems using Petri nets (PNs) and genetic algorithms (GAs). The PN model allows a formal representation of the manufacturing system and of the special constraints of this kind of system, while the GA generates a near-optimal schedule through the structure provided by the PN. The corresponding manufacturing system is associated with a flexible job shop environment with features such as the fabrication of multiple parts and precedence relationships between such parts and assembly operations, in which the objective is the minimisation of the total weighted tardiness. As part of the modelling stage, a mixed integer linear programming formulation is proposed for this framework. The fabrication of a chess mould in a Colombian company is used in two ways: to introduce a proposed normalisation operator that improves the results by reducing the search space of the GA and to illustrate the use of PN modelling the special aforementioned constraints as well as the encoding of the chromosome used by the GA. The proposed approach was tested on randomly generated instances, and their performance was measure against optimal solutions or solutions provided by algorithms presented in previous work. The results confirm the relevance of this approach to schedule such complex manufacturing systems.

Cost-Time Profile Simulation for Job Shop Scheduling Decisions

Abstract: Manufacturers have to look constantly for new strategies and tools to improve processes, decrease cost and increase productivity and efficiency. Production scheduling is one of the crucial elements in manufacturing and has an impact on delivery deadlines and also on the production process in terms of its utilization. On the other hand, the value stream optimization is very important for lean manufacturing efforts. This paper is aimed to research the impact of job shop scheduling on value stream optimization and decreasing of cost-time investment. Value stream mapping represents a very efficient tool for visualization of activities within production flow focused on activity duration with the purpose to eliminate non-value added activities. Value stream costing is based on value stream and eliminates the need for overhead allocation and calculation. Cost-time profile is a powerful tool for visualization and calculation of cost accumulation during the time across the entire manufacturing flow. Software tools used in this paper are: Lekin scheduling system for constructing the schedules based on four different dispatching rules and Cost-Time Profiler software for simulating the impact of different schedules on total production cost and cost-time investment (representing the time value of money), which is proposed as a new scheduling objective function.

An efficient memetic algorithm for the flexible job shop with setup times

Abstract: This paper addresses the flexible job shop scheduling problem with sequence-dependent setup times (SDST-FJSP). This is an extension of the classical job shop scheduling problem with many applications in real production environments. We propose an effective neighborhood structure for the problem, including feasibility and non improving conditions, as well as procedures for fast neighbor estimation. This neighborhood is embedded into a genetic algorithm hybridized with tabu search. We conducted an experimental study to compare the proposed algorithm with the state-of-the-art in the SDST-FJSP and also in the standard FJSP. In this study, our algorithm has obtained better results than those from other methods. Moreover, it has established new upper bounds for a number of instances.

Towards an integrated Workload Control (WLC) concept:the performance of due date setting rules in job shops with contingent orders

Abstract: Workload control (WLC) is a production planning and control concept developed for make-to-order companies. Its customer enquiry management methodology supports due date setting, while its order release mechanism determines when to start production. For make-to-order companies, due date setting is a strategically important, complex task where unconfirmed jobs place demands on capacity which are contingent on a quotation being accepted by the customer. Yet most prior WLC research has begun at the order release stage with a set of confirmed orders with predetermined due dates. In contrast, this paper focuses specifically on customer enquiry management and uses simulation to compare and contrast the performance of 11 due date setting rules in a job shop where part of the workload consists of unconfirmed or contingent orders. The best results are achieved by a finite loading rule which explicitly considers the workload of contingent orders when estimating lead times. This enables demand to be levelled over tim...