Showing papers on "Job shop published in 2011"

Bi-objective optimization research on integrated fixed time interval preventive maintenance and production for scheduling flexible job-shop problem

Abstract: Research highlights? Integrated flexible job-shop and preventive maintenance is probed in this study. ? Makespan as an objective for production part and reliability as an objective for maintenance part are optimized simultaneously. ? A mathematic model and a solving method are introduced in this paper and results are compared carefully. This paper investigates integrated flexible job shop problem (FJSP) with preventive maintenance (PM) activities under the multi-objective optimization approaches. Finding compromise solutions between the production objectives and maintenance ones is under consideration. In order to carry out the maintenance activities, reliability models are employed. This paper attempts to simultaneously optimize two objectives: the minimization of the makespan for the production part and the minimization of the system unavailability for the maintenance part. For doing it so, two decisions are taken at the same time: finding the appropriate assignment of n jobs on m machines in order to minimize the makespan and deciding when to execute the PM activities in order to minimize the system unavailability. Both the maintenance activity numbers and maintenance intervals are not fixed in advance. Four multi-objective optimization methods are compared to find the Pareto-optimal front in the flexible job-shop problem case. Promising the obtained results, a benchmark with a large number of test instances (more than 4800) and meticulous care is employed.

Dynamic flexible job shop scheduling with alternative process plans: an agent-based approach

Abstract: This paper studies a flexible job shop problem considering dynamic events such as stochastic job arrivals, uncertain processing times, and unexpected machine breakdowns. Also, the considered job shop problem has routing flexibility and process flexibility. A multi-agent scheduling system has been developed for solution with good quality and robustness. A pheromone-based approach is proposed for coordination among agents. The proposed multi-agent approach is compared with five dispatching rules from literature via simulation experiments to statistical analysis. The simulation experiments are performed under various experimental settings such as shop utilization level, due date tightness, breakdown level, and mean time to repair. The results show that the proposed agent-based approach performs well under all problem settings.

Integrating order review/release and dispatching rules for assembly job shop scheduling using a simulation approach

Abstract: This paper deals with an assembly job shop scheduling problem considering two phases of control: order review/release (ORR) and dispatching rules. Dispatching rules have been intensively used in earlier job shop research. Such rules determine the processing sequence of jobs waiting in the queues of corresponding machines. In recent years, ORR emerges as another option for job shop control and has received increasing attention. Such control determines when to release jobs to the shop floor. Different ORR mechanisms have been devised and are reported to bring several advantages such as reduced inventory cost of early finished orders, controlled and balanced shop load levels and shorter order flow time. Previous studies on ORR often assume a simplified job shop without assembly operations, while this research applies ORR to an assembly job shop. The aim of this research is to evaluate the ability of different combinations of ORR-dispatching rules in optimising due date and flow time related performance measu...

Scheduling Trains with Priorities: A No-Wait Blocking Parallel-Machine Job-Shop Scheduling Model

Abstract: The paper investigates train scheduling problems when prioritised trains and nonprioritised trains are simultaneously traversed in a single-line rail network. In this case, no-wait conditions arise because the prioritised trains such as express passenger trains should traverse continuously without any interruption. In comparison, nonprioritised trains such as freight trains are allowed to enter the next section immediately if possible or to remain in a section until the next section on the routing becomes available, which is thought of as a relaxation of no-wait conditions. With thorough analysis of the structural properties of the No-Wait Blocking Parallel-Machine Job-Shop Scheduling (NWBPMJSS) problem that is originated in this research, an innovative generic constructive algorithm (called NWBPMJSS_Liu-Kozan) is proposed to construct the feasible train timetable in terms of a given order of trains. In particular, the proposed NWBPMJSS_Liu-Kozan constructive algorithm comprises several recursively used subalgorithms (i.e., Best-Starting-Time-Determination Procedure, Blocking-Time-Determination Procedure, Conflict-Checking Procedure, Conflict-Eliminating Procedure, Tune-Up Procedure, and Fine-Tune Procedure) to guarantee feasibility by satisfying the blocking, no-wait, deadlock-free, and conflict-free constraints. A two-stage hybrid heuristic algorithm (NWBPMJSS_Liu-Kozan-BIH) is developed by combining the NWBPMJSS_Liu-Kozan constructive algorithm and the Best-Insertion-Heuristic (BIH) algorithm to find the preferable train schedule in an efficient and economical way. Extensive computational experiments show that the proposed methodology is promising because it can be applied as a standard and fundamental toolbox for identifying, analysing, modelling, and solving real-world scheduling problems.

Genetic algorithm for job-shop scheduling with machine unavailability and breakdowns

Abstract: The job-shop scheduling problem (JSSP) is considered to be one of the most complex combinatorial optimisation problems. In our previous attempt, we hybridised a Genetic Algorithm (GA) with a local search technique to solve JSSPs. In this research, we propose an improved local search technique, Shifted Gap-Reduction (SGR), which improves the performance of GAs when solving relatively difficult test problems. We also modify the new algorithm for JSSPs with machine unavailability and breakdowns. We consider two scenarios of machine unavailability. First, where the unavailability information is available in advance (predictive) and, secondly, where the information is known after a real breakdown (reactive). We show that the revised schedule is mostly able to recover if the interruptions occur during the early stages of the schedules.

Advice Complexity and Barely Random Algorithms

Abstract: Recently, a new measurement – the advice complexity – was introduced for measuring the information content of online problems. The aim is to measure the bitwise information that online algorithms lack, causing them to perform worse than offline algorithms. Among a large number of problems, a well-known scheduling problem, job shop scheduling with unit length tasks , and the paging problem were analyzed within this model. We observe some connections between advice complexity and randomization. Our special focus goes to barely random algorithms, i.e. , randomized algorithms that use only a constant number of random bits, regardless of the input size. We adapt the results on advice complexity to obtain efficient barely random algorithms for both the job shop scheduling and the paging problem. Furthermore, so far, it has not yet been investigated for job shop scheduling how good an online algorithm may perform when only using a very small (e.g. , constant) number of advice bits. In this paper, we answer this question by giving both lower and upper bounds, and also improve the best known upper bound for optimal algorithms.

Coordinating Aggregate and Detailed Scheduling Decisions in the one Machine Job-shop; I-theory

Abstract: This paper presents a formal model of the one-machine job-shop scheduling problem with variable capacity. Its primary interest focuses on the trade-off between overtime and detailed scheduling costs. The scheduling problem considered is minimizing the sum of weighted tardiness and weighted flow-time costs for a given capacity plan i.e., a given overtime plan. The paper generalizes sequence-theory results to this case where possible, analyzes various lower-bounding structures for the problem, outlines a preliminary branch-and-bound algorithm, and illustrates several interesting features of the algorithm and bounding structures by an example. Extensions of the results to more complex environments are discussed.

Simultaneous scheduling of production and maintenance tasks in the job shop

Abstract: This paper deals with the job shop problem of simultaneous scheduling of production operations and preventive maintenance tasks. To solve this problem, we develop an elitist multi-objective genetic algorithm that provides a set of Pareto optimal solutions minimising the makespan and the total maintenance cost. A deep study was made to choose the best encoding, operators, and the different probabilities. Some lower bounds of the adopted criteria are developed. The computational experiments carried out on a set of published instances validate the efficiency of the proposed algorithm.

A job-shop problem with one additional resource type

Abstract: We consider a job-shop scheduling problem with n jobs and the constraint that at most p

Optimization process planning using hybrid genetic algorithm and intelligent search for job shop machining

Abstract: Optimization of process planning is considered as the key technology for computer-aided process planning which is a rather complex and difficult procedure. A good process plan of a part is built up based on two elements: (1) the optimized sequence of the operations of the part; and (2) the optimized selection of the machine, cutting tool and Tool Access Direction (TAD) for each operation. In the present work, the process planning is divided into preliminary planning, and secondary/detailed planning. In the preliminary stage, based on the analysis of order and clustering constraints as a compulsive constraint aggregation in operation sequencing and using an intelligent searching strategy, the feasible sequences are generated. Then, in the detailed planning stage, using the genetic algorithm which prunes the initial feasible sequences, the optimized operation sequence and the optimized selection of the machine, cutting tool and TAD for each operation based on optimization constraints as an additive constraint aggregation are obtained. The main contribution of this work is the optimization of sequence of the operations of the part, and optimization of machine selection, cutting tool and TAD for each operation using the intelligent search and genetic algorithm simultaneously.

A hybrid approach based on the genetic algorithm and neural network to design an incremental cellular manufacturing system

Abstract: One important issue related to the implementation of cellular manufacturing systems (CMSs) is to decide whether to convert an existing job shop into a CMS comprehensively in a single run, or in stages incrementally by forming cells one after the other, taking the advantage of the experiences of implementation. This paper presents a new nonlinear programming model in a dynamic environment. Furthermore, a novel hybrid approach based on the genetic algorithm and artificial neural network is proposed to solve the presented model. From the computational analyses, the proposed algorithm is found much more efficient than the genetic algorithm and simulated annealing in generating optimal solutions.

Application of value stream mapping using simulation to decrease production lead time: a Malaysian manufacturing case

Abstract: Lean concept has been applied across many companies which offer value and eliminate wastes. Value stream map (VSM) as one of the fundamental tools in lean concept outlines the material and information flows for a product family to reduce wastes at discrete event production routine. In this paper, the improvement of the production lead time using VSM as a technique in a Malaysian supplier, with a job shop production system, is investigated. The main contribution of this paper is reducing production lead time when the Takt Time is much higher than the highest station's cycle time, and reducing unplanned released orders. This paper evaluates the present routing events using current state map and the future state is created answering the eight standard questions. Then, a detailed simulation model was developed to verify the result from future state map and answering the questions that are unable to be addressed by VSM.

A computational study of a shifting bottleneck heuristic for multi-product complex job shops

Abstract: In this article, we present the results of a performance evaluation of a shifting bottleneck heuristic (SBH) applied to complex multi-product job shops. This type of job shop can be found in semiconductor wafer fabrication facilities (wafer fabs) that produce application-specific integrated circuits for a wide array of customers. The SBH decomposes the overall scheduling problem into scheduling problems for single tool groups. The solutions of these scheduling problems are connected via a disjunctive graph. We perform simulation experiments with (reference) models of wafer fabs to assess the performance of a rolling horizon approach in a dynamic environment. We compare the results of the SBH for multi-product situations with results for local and global dispatching rules. The SBH outperforms these dispatching rules with respect to total weighted tardiness. The application of the SBH provides the most benefit in the case of manufacturing systems that have to cope with many different products.

An adaptive annealing genetic algorithm for the job-shop planning and scheduling problem

Abstract: The genetic algorithm, the simulated annealing algorithm and the optimum individual protecting algorithm are based on the order of nature, and there exist some application limitations on global astringency, population precocity and convergence rapidity. An adaptive annealing genetic algorithm is proposed to deal with the job-shop planning and scheduling problem for the single-piece, small-batch, custom production mode. In the AAGA, the adaptive mutation probability is included to improve upon the convergence rapidity of the genetic algorithm, and to avoid local optimization, the Boltzmann probability selection mechanism from the simulated annealing algorithm, which solves the population precocity and the local convergence problems, is applied to select the crossover parents. Finally, the AAGA-based job-shop planning and scheduling problem is discussed, and the computing results of AAGA and GA are depicted and compared.

The flexible blocking job shop with transfer and set-up times

Abstract: The Flexible Blocking Job Shop (FBJS) considered here is a job shop scheduling problem characterized by the availability of alternative machines for each operation and the absence of buffers. The latter implies that a job, after completing an operation, has to remain on the machine until its next operation starts. Additional features are sequence-dependent transfer and set-up times, the first for passing a job from a machine to the next, the second for change-over on a machine from an operation to the next. The objective is to assign machines and schedule the operations in order to minimize the makespan. We give a problem formulation in a disjunctive graph and develop a heuristic local search approach. A feasible neighborhood is constructed, where typically a critical operation is moved (keeping or changing its machine) together with some other operations whose moves are "implied". For this purpose, we develop the theoretical framework of job insertion with local flexibility, based on earlier work of Groflin and Klinkert on insertion. A tabu search that consistently generates feasible neighbor solutions is then proposed and tested on a larger test set. Numerical results support the validity of our approach and establish first benchmarks for the FBJS.

Optimising Workload Norms: The Influence of Shop Floor Characteristics on Setting Workload Norms for the Workload Control Concept

Abstract: Workload control (WLC) is a leading production planning and control (PPC) solution for small to medium sized enterprises (SMEs) and make-to-order (MTO) companies, but when WLC is implemented, practitioners find it difficult to determine suitable workload norms to obtain optimum performance. Theory has provided some solutions (e.g., based on linear programming) but, to remain optimal, these require the regular feedback of detailed information from the shop floor about the status of work-in-process (WIP), and are therefore often impractical. This paper seeks to predict workload norms without such feedback requirements, analysing the influence of shop floor characteristics on the workload norm. The shop parameters considered are flow characteristics (from an undirected pure job shop to a directed general flow shop), and the number of possible work centres in the routing of a job (i.e., the routing length). Using simulation and optimisation software, the workload norm resulting in optimum performance is deter...

A genetic algorithm-based scheduler for multiproduct parallel machine sheet metal job shop

Abstract: This paper presents a genetic algorithm-based job-shop scheduler for a flexible multi-product, parallel machine sheet metal job shop. Most of the existing research has focused only on permutation job shops in which the manufacturing sequence and routings are strictly in a predefined order. This effectively meant that only the jobs shops with little or no flexibility could be modeled using these models. The real life job shops may have flexibility of routing and sequencing. Our paper proposes one such model where variable sequences and multiple routings are possible. Another limitation of the existing literature was found to be negligence of the setup times. In many job shops like sheet metal shops, setup time may be a very sizable portion of the total make-span of the jobs, hence setup times will be considered in this work. One more flexibility type arises as a direct consequence of the routing flexibility. When there are multiple machines (parallel machines) to perform the same operation, the job could be routed to one or more of these machines to reduce the make-span. This is possible in situations where each job consists of a pre-defined quantity of a specified product. In other words, same job is quantity-wise split into two or more parts whenever it reduces the makespan. This effectively assumes that the setup cost is negligible. This model has been implemented on a real-life industry problem using VB.Net programming language. The results from the scheduler are found to be better than those obtained by simple sequencing rules.

A genetic algorithm for dynamic facility planning in job shop manufacturing

Abstract: Product variety, process improvement, and technology improvement can make the original layout plan inefficient. Therefore, improving the current facility layout appears to be a vital mission. This study proposes a model to address the dynamic facility planning problem in job shop manufacturing environment. Since the facility layout problem is an NP-hard problem, an optimal solution is difficult to obtain. This study apply a genetic algorithm (GA) in the proposed model to solve the facility layout problem, considering the handling cost, the facility moving cost, and the facility setup cost. The computational results show that the GA-based approach performs well. Based on the computational results, this study also applies cost–benefit analysis by management perspective for considering whether or not the planners rearrange the original layout.

A study of production smoothing in a job shop environment

Abstract: We consider the problem of smoothing production in a job shop in which all production is to customer order and the demand process is a stationary stochastic process. We present an approach to production smoothing based on the concept of a planning window. A planning window is the difference between the promised delivery time and the planned production time for a product. It represents the degree of flexibility available for planning the production of committed orders. We characterize the production smoothing benefits for a range of planning windows by means of an approximate analytic model and a simulation study. These analyses show that substantial smoothing benefits result from small changes in the length of the planning window. We discuss the implementation of the production smoothing approach and illustrate this implementation with an industrial case study that was the motivation for this work.

A two-stage hybrid memetic algorithm for multiobjective job shop scheduling

Abstract: In this paper we address multiobjective job shop scheduling problems. After several decades of research in scheduling problems, a variety of heuristics have been developed. The proposed algorithm is a hybrid of three frequently applied ones: the dispatching rule, the shifting bottleneck procedure, and the evolutionary algorithm. It is a two-stage algorithm, which integrates a rule-based memetic algorithm in the first stage and a re-optimization procedure of shifting bottleneck in the second. We conduct experiments using benchmark instances found in the literature to assess the performance of the proposed method. The experimental results show that the proposed method is effective and efficient for multiobjective scheduling problems.

Scheduling stochastic job shop subject to random breakdown to minimize makespan

Abstract: The problem of scheduling stochastic job shop subject to breakdown is seldom considered. This paper proposes an efficient genetic algorithm (GA) for the problem with exponential processing time and non-resumable jobs. The objective is to minimize the stochastic makespan itself. In the proposed GA, a novel random key representation is suggested to represent the schedule of the problem and a discrete event-driven decoding method is applied to build the schedule and handle breakdown. Probability stochastic order and the addition operation of exponential random variables are also used to calculate the objective value. The proposed GA is applied to some test problems and compared with a simulated annealing and a particle swarm optimization. The computational results show the effectiveness of the GA and its promising advantage on stochastic scheduling.