Showing papers on "Job shop published in 2004"

An Ant Colony Optimization Algorithm for Shop Scheduling Problems

Abstract: We deal with the application of ant colony optimization to group shop scheduling, which is a general shop scheduling problem that includes, among others, the open shop scheduling problem and the job shop scheduling problem as special cases. The contributions of this paper are twofold. First, we propose a neighborhood structure for this problem by extending the well-known neighborhood structure derived by Nowicki and Smutnicki for the job shop scheduling problem. Then, we develop an ant colony optimization approach, which uses a strong non-delay guidance for constructing solutions and which employs black-box local search procedures to improve the constructed solutions. We compare this algorithm to an adaptation of the tabu search by Nowicki and Smutnicki to group shop scheduling. Despite its general nature, our algorithm works particularly well when applied to open shop scheduling instances, where it improves the best known solutions for 15 of the 28 tested instances. Moreover, our algorithm is the first competitive ant colony optimization approach for job shop scheduling instances.

GENACE: an efficient cultural algorithm for solving the flexible job-shop problem

Abstract: This work presents an efficient methodology called GENACE for solving the flexible job-shop scheduling problem (or FJSP) with recirculation. We show how CDRs are used to solve the FJSP with recirculation by themselves and to provide a bootstrapping mechanism to initialize GENACE. We then adopt a cultural evolutionary architecture to maintain knowledge of schemata and resource allocations learned over each generation. The belief spaces influence mutation and selection over a feasible chromosome representation. Experimental results show that GENACE obtains better upper bounds for 11 out of 13 benchmark problems, with improvement factors of 2 to 48 percent when compared to results by Kacem et al. (2002), Brandimarte (1993) and of using CDRs alone.

Finite capacity scheduling using job prioritization and machine selection

Abstract: In a method, device, and computer-readable medium for finite capacity scheduling, heuristic rules are applied in two integrated stages: Job Prioritization and Machine Selection. During Job Prioritization (“JP”), jobs are prioritized based on a set of JP rules which are machine independent. During Machine Selection (“MS”), jobs are scheduled for execution at machines that are deemed to be best suited based on a set of MS rules. The two-stage approach allows scheduling goals to be achieved for performance measures relating to both jobs and machines. For example, machine utilization may be improved while product cycle time objectives are still met. Two user-configurable options, namely scheduling model (job shop or flow shop) and scheduling methodology (forward, backward, or bottleneck), govern the scheduling process. A memory may store a three-dimensional linked list data structure for use in scheduling work orders for execution at machines assigned to work centers.

Tardiness minimization in a flexible job shop: A tabu search approach

Abstract: This paper addresses the problem of scheduling jobs in a flexible job shop with the objective of minimizing total tardiness. The flexible job shop differs from the classical job shop in that each of the operations associated with a job can be processed on any of a set of alternative machines. Two heuristics based on tabu search are developed for this problem: a hierarchical procedure and a multiple start procedure. The procedures use dispatching rules to obtain an initial solution and then search for improved solutions in neighborhoods generated by the critical paths of the jobs in a disjunctive graph representation. Diversification strategies are also implemented and tested. The outcomes of extensive computational results are reported.

Using multiple objective tabu search and grammars to model and solve multi-objective flexible job shop scheduling problems

Abstract: A linguistic-based meta-heuristic modeling and solution approach for solving the flexible job shop scheduling problem (FJSSP) is presented in this study. FJSSP is an extension of the classical job-shop scheduling problem. The problem definition is to assign each operation to a machine out of a set of capable machines (the routing problem) and to order the operations on the machines (the sequencing problem), such that predefined performance measures are optimized. In this research, the scope of the problem is widened by taking into account the alternative process plans for each part (process plan selection problem). Probabilistic selection of alternative process plans and machines are also considered. The FJSSP is presented as a grammar and the productions in the grammar are defined as controls (Baykasoglu, 2002). Using these controls and Giffler and Thompson's (1960) priority rule-based heuristic along with the multiple objective tabu search algorithm of Baykasoglu et al. (1999) FJSSP is solved. This novel approach simplifies the modeling process of the FJSSP and enables usage of existing job shop scheduling algorithms for its fast solution. Instead of scheduling job shops with inflexible algorithms that cannot take into account the flexibility which is available in the job shop, the present algorithm is developed which can take into account the flexibility during scheduling. Such an approach will considerably increase the responsiveness of the job shops.

A Rollout Metaheuristic for Job Shop Scheduling Problems

Abstract: In this paper we deal with solution algorithms for a general formulation of the job shop problem, called alternative graph. We study in particular the job shop scheduling problem with blocking and/or no-wait constraints. Most of the key properties developed for solving the job shop problem with infinite capacity buffer do not hold in the more general alternative graph model. In this paper we report on an extensive study on the applicability of a metaheuristic approach, called rollout or pilot method. Its basic idea is a look-ahead strategy, guided by one or more subheuristics, called pilot heuristics. Our results indicate that this method is competitive and very promising for solving complex scheduling problems.

Workload control in job shops, grasping the tap

Abstract: The term job shops is used to indicate companies that produce customer-specific components in small batches. Jobs (production orders) in a job shop are characterised by a large variety of routings and operation processing times. This variety, combined with irregular order arrivals, generally leads to long waiting times on the shop floor. The Workload Control (WLC) concept is specifically designed to create a match between capacity requirements and capacity availability in such an environment. The key decision within this concept is the release of jobs to the shop floor. This decision is often illustrated by the metaphor of a tap controlling the flow into a bathtub (the shop floor). This thesis focuses on grasping the tap, with the double meaning of holding it firmly and understanding it. Although WLC can be typified as a simple and practical concept, the exact influences of the release decision are difficult to understand. Nevertheless, a detailed understanding of these influences is essential for the application and development of WLC. Therefore, a twofold objective has been formulated for this research project: 1. To deepen knowledge on the functioning of release methods within the WLC concept; 2. To improve release methods within the WLC concept. The research project has been subdivided in four phases: a comparison and analysis of release methods, a simulation study to test the methods and to provide an in-depth analysis, the redesign of methods, and finally an evaluation of the redesign.

An Effective Chromosome Representation for Evolving Flexible Job Shop Schedules

Abstract: As the Flexible Job Shop Scheduling Problem (or FJSP) is strongly NP-hard, using an evolutionary approach to find near-optimal solutions re- quires effective chromosome representations as well as carefully designed pa- rameters for crossover and mutation to achieve efficient search This paper pro- poses a new chromosome representation and a design of related parameters to solve the FJSP efficiently The results of applying the new chromosome repre- sentation for solving the 10 jobs x 10 machines FJSP are compared with three other chromosome representations Empirical experiments show that the pro- posed chromosome representation obtains better results than the others in both quality and processing time required

Benefits of lot splitting in job-shop scheduling

Abstract: As the lot-streaming concept has been used widely to reduce the makespan in a production system, most research has investigated the flow shop production systems; however, job-shop production systems have received much less attention, relatively. This study more thoroughly investigates the application of lot splitting in a job shop production system with setup times, which cannot be omitted. The objective investigated not only minimises the makespan but also minimises the total production cost, which includes the material handling cost, the setup cost and the inventory cost. A disjunctive graph is first used to describe the addressed scheduling problem, and an integer programming model is then constructed to obtain an optimal solution. In order to investigate the influence of the number of sublots and sublot sizes on a job-shop production system with regard to the corresponding objective considered, some experiments are conducted and results presented as well.

Lean production in the furniture industry: the double D assembly cell.

Abstract: There are five generally recognized manufacturing system types and the Lean Production system is the newest manufacturing system design. Lean Production is functionally and operationally different from any other manufacturing system. It uses less of everything when compared to the most common functional job shop manufacturing system: less labor, less manufacturing space, less tooling investment, and less design hours to develop a new product. Lean Production normally means holding less than half the regularly needed stock on-hand. In addition, the implementation of Lean Production by a manufacturer results in fewer defects, and therefore, an increase in quality. The fundamentals of this newest manufacturing system are: manufacturing and assembly cells, pull system methodology, 100 percent good quality, on-time delivery every time, respect for people, and maximum utilization of non-depreciable resources, i.e., people and raw materials. Lean Production uses the cellular manufacturing system for one-piece flow. This system is flexible and designed to produce superior quality products, on-time, at the lowest possible cost, and on a continuous basis. This research utilizes the basic manufacturing philosophies and methodologies for the design and implementation of a Lean Production subassembly manufacturing cell, a non-typical double D configuration, in the furniture industry. A systematic detailed case study illustrates and proves the flexibility of lean manufacturing; it is adaptive and cost effective, improves quality, and is ergonomically correct for workers in the furniture manufacture sector.

Development and evaluation of cross-training policies for manufacturing teams

Abstract: This study addresses the problem of developing and evaluating cross-training policies for manufacturing teams from a Human Resource Management (HRM) and Operations Management (OM) viewpoint. A cross-training policy can be regarded as a set of rules to determine the distribution of workers' skills. The specific way in which workers and machines are connected determines the agility of the workforce. In this article, we develop an integer goal programming model to support a consequent application of alternative cross-training policies. A simulation study is performed to assess the performance of the resulting cross-training configurations within three routing structures: a parallel structure, a serial structure, and a job shop structure. Results indicate that within all routing structures, the focus of cross-training policies depends on whether a HRM or an OM viewpoint is considered. Within the parallel and the serial structures, however, HRM and OM goals are compatible and can be integrated within a single ...

Metaheuristics for the mixed shop scheduling problem

Abstract: In this paper, three metaheuristics are proposed for solving a class of job shop, open shop, and mixed shop scheduling problems. We evaluate the performance of the proposed algorithms by means of a set of Lawrence’s benchmark instances for the job shop problem, a set of randomly generated instances for the open shop problem, and a combined job shop and open shop test data for the mixed shop problem. The computational results show that the proposed algorithms perform extremely well on all these three types of shop scheduling problems. The results also reveal that the mixed shop problem is relatively easier to solve than the job shop problem due to the fact that the scheduling procedure becomes more flexible by the inclusion of more open shop jobs in the mixed shop.

A New Exact Solution Algorithm for the Job Shop Problem with Sequence-Dependent Setup Times

Abstract: We propose a new solution approach for the Job Shop Problem with Sequence Dependent Setup Times (SDST-JSP) The problem consists in scheduling jobs, each job being a set of elementary operations to be processed on different machines The objective pursued is to minimize the completion time of the set of operations We investigate a relaxation of the problem related to the traveling salesman problem with time windows (TSPTW) Our approach is based on a Branch and Bound procedure, including constraint propagation and using this relaxation It compares favorably over the best available approaches from the literature on a set of benchmark instances

Dynamic rescheduling using a simulation‐based expert system

Abstract: The development of a rule‐based expert system (ES), driven by a discrete event simulation model, that performs dynamic shop scheduling is described. Based on a flowtime prediction heuristic that has been developed and base‐line runs to establish the efficacy of scheduling strategies such as shortest processing time (SPT), critical ratio, total work, etc., a rescheduling‐based dispatching strategy is investigated in a dynamic job shop environment. The results are discussed and analyzed.

Metaheuristics for the mixed shop scheduling problem

Abstract: In this paper, three metaheuristics are proposed for solving a class of job shop, open shop, and mixed shop scheduling problems. We evaluate the performance of the proposed algorithms by means of a set of Lawrence’s benchmark instances for the job shop problem, a set of randomly generated instances for the open shop problem, and a combined job shop and open shop test data for the mixed shop problem. The computational results show that the proposed algorithms perform extremely well on all these three types of shop scheduling problems. The results also reveal that the mixed shop problem is relatively easier to solve than the job shop problem due to the fact that the scheduling procedure becomes more flexible by the inclusion of more open shop jobs in the mixed shop.

Genetic algorithm application on the job shop scheduling problem

Abstract: Based on the concepts of operation template and virtual job shop, this paper attempts to solve several job shop scheduling problems with different scale and analyzes the relationship among the population size, mutation probability, the number of evolving generations and the complexity of the undertaking problem visually by using the trend chart of the fitness curves. This visual analysis could provide some referencing information for the adjustment of genetic algorithm running parameters.

Learning and labour assignment in a dual resource constrained cellular shop

Abstract: Recent studies have shown that in a cellular shop that benefits from learning due to repetitive processing, limitations attributable to routing flexibility can be more than offset. Moreover, the shop can respond more quickly to changes in demand than a job shop. This study examines the impact of labour assignments in a dual constrained cellular shop in which processing times decrease with operator task repetition. Results indicate that in the presence of operator learning, shop performance is significantly affected by the flexibility permitted in labour assignments. Moreover, the sensitivity of performance to labour assignments is significantly impacted by staffing levels and the magnitude of learning effects.

Landscape Regularity and Random Walks for the Job-Shop Scheduling Problem

Abstract: We perform a novel analysis of the fitness landscape of the job-shop scheduling problem (JSP) In contrast to other well-known combinatorial optimization problems, we show that the landscape of the JSP is non-regular, in that the connectivity of solutions is variable As a consequence, we argue that random walks performed on such a landscape will be biased We conjecture that such a bias should affect both random walks and local search algorithms, and may provide a partial explanation for the remarkable success of the latter in solving the JSP

A genetic algorithm based approach for integration of process planning and production scheduling

Abstract: Process planning and production scheduling play important roles in manufacturing systems Their roles are to ensure the availability of manufacturing resources needed to accomplish production tasks result from a demand forecast In this paper, a fuzzy inference system(FI9) in choosing alternative machines for integrated process planning and scheduling bf a job shop manufacturing system are proposed Instead of choosing alternative machines randomly, machines are being selected based on the machines reliability The mean time to failure (MTF) values are input in a fuzzy inference mechanism, which outputs the machine reliability The machine is then being penalized based on the fuzzy output The most reliable machine will have the higher priority to be chosen In order to overcome the problem of un-utilization machines, sometimes faced by unreliable machine, the genetic algorithms have been used to balance the load for all the machines Simulation study shows that the system can be used as an alternative way of choosing machines in integrated process planning and scheduling

An empirical study of policies to integrate reactive scheduling and control in just-in-time job shop environments

Abstract: In this paper, we compare the performance of policies for integrating reactive scheduling and control that differ in the way they interpret and dynamically reoptimize schedules in the face of contingencies. We conduct our analysis in the context of just-in-time job shop environments ( job shop problems with an objective of minimizing the sum of tardiness and inventory costs), subject to machine failures. We empirically evaluate the tradeoffs in schedule quality and computational time of different scheduling policies under different load conditions and different levels of uncertainty. Our results show that reactive procedures that selectively reoptimize a subset of the scheduling problems are capable of producing high-quality solutions in a fraction of the time required to generate brand new schedules.

Simulated Annealing with Restart to Job Shop Scheduling Problem Using Upper Bounds

Abstract: An algorithm of simulated annealing for the job shop scheduling problem is presented. The proposed algorithm restarts with a new value every time the previous algorithm finishes. To begin the process of annealing, the starting point is a randomly generated schedule with the condition that the initial value of the makespan of the schedule does not surpass a previously established upper bound. The experimental results show the importance of using upper bounds in simulated annealing in order to more quickly approach good solutions.