Showing papers on "Job shop published in 2005"

Embedded ties and the acquisition of competitive capabilities

Abstract: We build on previous research that explores the external acquisition of competitive capabilities through the embedded ties that firms form in networks and alliances. While information sharing and trust have been theorized to be key features of the interorganizational ties that facilitate the acquisition of competitive capabilities, we argue that these mechanisms provide an incomplete explanation because they do not fully address the partially tacit nature of the knowledge that underlies competitive capabilities. Joint problem-solving arrangements play a prominent role in capability acquisition by promoting the transfer of complex and difficult-to-codify knowledge. Drawing on a set of case studies and a survey of 234 job shop manufacturers we find support for the role of joint problem solving with suppliers in facilitating the acquisition of competitive capabilities. Copyright © 2005 John Wiley & Sons, Ltd.

A review of production planning and control: the applicability of key concepts to the make-to-order industry

Abstract: The paper reviews ‘classic approaches’ to Production Planning and Control (PPC) such as Kanban, Manufacturing Resource Planning (MRP II) and Theory of Constrains (TOC), and elaborates upon the emergence of techniques such as Workload Control (WLC), Constant Work In Process (CONWIP), Paired cell Overlapping Loops of Cards with Authorization (POLCA) and web- or e-based Supply Chain Management (SCM) solutions. A critical assessment of the approaches from the point of view of various sectors of the Make-To-Order (MTO) Industry is presented. The paper considers factors such as the importance of the customer enquiry stage, company size, degree of customization and shop floor configuration and shows them to play a large role in the applicability of planning and control concepts. The paper heightens the awareness of researchers and practitioners to the PPC options, aids managerial system selection decision-making, and highlights the importance of a clear implementation strategy. WLC emerges as the most effective Job Shop solution; whilst for other configurations there are several alternatives depending on individual company characteristics and objectives. The paper outlines key areas for future research, including the need for empirical research into the use of Workload Control in small and medium sized MTO companies.

An Advanced Tabu Search Algorithm for the Job Shop Problem

Abstract: The job shop scheduling problem with the makespan criterion is a certain NP-hard case from OR theory having excellent practical applications. This problem, having been examined for years, is also regarded as an indicator of the quality of advanced scheduling algorithms. In this paper we provide a new approximate algorithm that is based on the big valley phenomenon, and uses some elements of so-called path relinking technique as well as new theoretical properties of neighbourhoods. The proposed algorithm owns, unprecedented up to now, accuracy, obtainable in a quick time on a PC, which has been confirmed after wide computer tests.

A model for the assessment of waste in job shop environments

Abstract: Purpose – The paper aims to investigate the waste in a job shop environment and proposes an assessment method aimed at helping companies to identify root causes of waste.Design/methodology/approach – The seven wastes (overproducing; processing; inventory; transporting; producing defects; time waiting; and motion waste) and their relationships were explored. A waste matrix was developed to quantify in a percentage form the relationships among wastes and represents a probability that a certain type of waste will affect others or be affected by others. An assessment questionnaire was employed to allocate the source of waste and differentiate between the levels of waste. The waste matrix and the assessment questionnaire were incorporated in the assessment method to rank the existing waste in a job shop.Findings – The developed model serves as guidelines for simplifying the search of waste problems and identifies opportunities for waste elimination. A case study was conducted to validate the model; and the res...

Anticipation and flexibility in dynamic scheduling

Abstract: Many real-world optimization problems change over time and require frequent re-optimization. We suggest that in such environments, an optimization algorithm should reflect the problem's dynamics and explicitly take into account that changes to the current solution are to be expected. We claim that this can be achieved by having the optimization algorithm search for solutions that are not only good, but also flexible, i.e. easily adjustable if necessary in the case of problem changes. For the example of a job-shop with jobs arriving non-deterministically over time, we demonstrate that avoiding early idle times increases flexibility, and thus that the incorporation of an early idle time penalty as secondary objective into the scheduling algorithm can greatly enhance the overall system performance.

Workload based order acceptance in job shop environments

Abstract: In practice, order acceptance and production planning are often functionally separated As a result, order acceptance decisions are made without considering the actual workload in the production system, or by only regarding the aggregate workload We investigate the importance of a good workload based order acceptance method in over-demanded job shop environments, and study approaches that integrate order acceptance and resource capacity loading We present sophisticated methods that consider technological restrictions, such as precedence relations, and release and due dates of orders We use a simulation model of a generic job shop to compare these methods with straightforward methods, which consider capacity restrictions at an aggregate level and ignore precedence relations We compare the performance of the approaches based on criteria such as capacity utilisation The simulation results show that the sophisticated approaches significantly outperform the straightforward approaches in case of tight due dates (little slack) In that case, improvements of up to 30% in utilisation rate can be achieved In case of much slack, a sophisticated order acceptance method is less important

Using Data Mining for Due Date Assignment in a Dynamic Job Shop Environment

Abstract: Due date assignment is an important task in shop floor control, affecting both timely delivery and customer satisfaction. Due date related performances are impacted by the quality of the due date assignment methods. Among the simple and easy to implement due date assignment methods, the total work content (TWK) method achieves the best performance for tardiness related performance criteria and is most widely used in practice and in study. The performance of the TWK method can be improved if the due date allowance factor k could render a more precise and accurate flowtime estimation of each individual job. In this study, in order to improve the performance of the TWK method, we have presented a model that incorporated a data mining tool – Decision Tree – for mining the knowledge of job scheduling about due date assignment in a dynamic job shop environment, which is represented by IF-THEN rules and is able to adjust an appropriate factor k according to the condition of the shop at the instant of job arrival, thereby reducing the due date prediction errors of the TWK method. Simulation results show that our proposed rule-based TWK due date assignment (RTWK) model is significantly better than its static and dynamic counterparts (i.e., TWK and Dynamic TWK methods). In addition, the RTWK model also extracted comprehensive scheduling knowledge about due date assignment, expressed in the form of IF-THEN rules, allowing production managers to easily understand the principles of due date assignment .

Integrated optimization of production planning and scheduling for a kind of job-shop

Abstract: This paper addresses an integrated job-shop production planning and scheduling problem with setup time and batches. It not only considers the setup cost, work-in-process inventory, product demand, and the load of equipment, but also the detailed scheduling constraints. That is a way different from the traditional hierarchical production planning method. The hierarchical methods do not consider the detailed scheduling constraints, so it cannot guarantee to obtain a feasible production plan. Here the integrated problem is formulated as a nonlinear mixed integer program model. And in order to simultaneously optimize the production plan and the schedule, an improved hybrid genetic algorithm (HGA) is given. In the model, the detailed scheduling constraints are used to compute the accurate load of a device in order to obtain a feasible production plan. The heuristic scheduling rules such as the shortest processing time (SPT) and the longest processing time (LPT) are used to generate a better initial solution. Also, a subsection coding strategy is offered to convert the planning and scheduling solution into a chromosome. At last, a comparison is made between the hybrid algorithm and a hierarchical production planning and scheduling method, showing that the hybrid algorithm can solve the problem effectively.

A fuzzy genetic algorithm for real-world job shop scheduling

Abstract: In this paper, a multi-objective genetic algorithm is proposed to deal with a real-world fuzzy job shop scheduling problem. Fuzzy sets are used to model uncertain due dates and processing times of jobs. The objectives considered are average tardiness and the number of tardy jobs. Fuzzy sets are used to represent satisfaction grades for the objectives taking into consideration the preferences of the decision maker. A genetic algorithm is developed to search for the solution with maximum satisfaction grades for the objectives. The developed algorithm is tested on real-world data from a printing company. The experiments include different aggregation operators for combining the objectives.

Evolving dispatching rules for solving the flexible job-shop problem

Abstract: We solve the flexible job shop problem (FJSP) by using dispatching rules discovered through genetic programming (GP). While simple priority rules (SPR) have been widely applied in practice, their efficacy remains poor due to lack of a global view. Composite dispatching rules (CDR) have been shown to be more effective as they are constructed through human experience. In this paper, we employ suitable parameter and operator spaces for evolving CDRs using GP, with an aim towards greater scalability and flexibility. Experimental results show that CDRs generated by our GP framework outperforms the SPRs and CDRs selected from literature in 74% to 85% of FJSP problem instances.

Optimal scheduling for flexible job shop operation

Abstract: This paper presents a new integer linear programming (ILP) model to schedule flexible job shop, discrete parts manufacturing industries that operate on a make-to-order basis. The model considers groups of parallel homogeneous machines, limited intermediate buffers and negligible set-up effects. Orders consist of a number of discrete units to be produced and follow one of a given number of processing routes. The model allows re-circulation to take place, an important issue in practice that has received scant treatment in the scheduling literature. Good solution times were obtained using commercial mixed-integer linear programming (MILP) software to solve realistic examples of flexible job shops to optimality. This supports the claim that recent advances in computational power and MILP solution algorithms are making this approach competitive with others traditionally applied in job shop scheduling.

Multiagent scheduling method with earliness and tardiness objectives in flexible job shops

Abstract: Flexible job-shop scheduling problems are an important extension of the classical job-shop scheduling problems and present additional complexity. Such problems are mainly due to the existence of a considerable amount of overlapping capacities with modern machines. Classical scheduling methods are generally incapable of addressing such capacity overlapping. We propose a multiagent scheduling method with job earliness and tardiness objectives in a flexible job-shop environment. The earliness and tardiness objectives are consistent with the just-in-time production philosophy which has attracted significant attention in both industry and academic community. A new job-routing and sequencing mechanism is proposed. In this mechanism, two kinds of jobs are defined to distinguish jobs with one operation left from jobs with more than one operation left. Different criteria are proposed to route these two kinds of jobs. Job sequencing enables to hold a job that may be completed too early. Two heuristic algorithms for job sequencing are developed to deal with these two kinds of jobs. The computational experiments show that the proposed multiagent scheduling method significantly outperforms the existing scheduling methods in the literature. In addition, the proposed method is quite fast. In fact, the simulation time to find a complete schedule with over 2000 jobs on ten machines is less than 1.5 min.

Applying the clonal selection principle to find flexible job-shop schedules

Abstract: We apply the Clonal Selection principle of the human immune system to solve the Flexible Job-Shop Problem with recirculation. Various practical design issues are addressed in the implemented algorithm, ClonaFLEX; first, an efficient antibody representation which creates only feasible solutions and a bootstrapping antibody initialization method to reduce the search time required. Second, the assignment of suitable mutation rates for antibodies based on their affinity. To this end, a simple yet effective visual method of determining the optimal mutation value is proposed. And third, to prevent premature convergence, a novel way of using elite pools to incubate antibodies is presented. Performance results of ClonaFLEX are obtained against benchmark FJSP instances by Kacem and Brandimarte. On average, ClonaFLEX outperforms a cultural evolutionary algorithm (EA) in 7 out of 12 problem sets, equivalent results for 4 and poorer in 1.

Schedule Generation Schemes for the Job-Shop Problem with Sequence-Dependent Setup Times: Dominance Properties and Computational Analysis

Abstract: We consider the job-shop problem with sequence-dependent setup times. We focus on the formal definition of schedule generation schemes (SGSs) based on the semi-active, active, and non-delay schedule categories. We study dominance properties of the sets of schedules obtainable with each SGS. We show how the proposed SGSs can be used within single-pass and multi-pass priority rule based heuristics. We study several priority rules for the problem and provide a comparative computational analysis of the different SGSs on sets of instances taken from the literature. The proposed SGSs significantly improve previously best-known results on a set of hard benchmark instances.

A new hybrid GA/SA algorithm for the job shop scheduling problem

Abstract: Among the modern heuristic methods, simulated annealing (SA) and genetic algorithms (GA) represent powerful combinatorial optimization methods with complementary strengths and weaknesses. Borrowing from the respective advantages of the two paradigms, an effective combination of GA and SA, called Genetic Simulated Algorithm (GASA), is developed to solve the job shop scheduling problem (JSP). This new algorithm incorporates metropolis acceptance criterion into crossover operator, which could maintain the good characteristics of the previous generation and reduce the disruptive effects of genetic operators. Furthermore, we present two novel features for this algorithm to solve JSP. Firstly, a new full active schedule (FAS) based on the operation-based representation is presented to construct schedule, which can further reduce the search space. Secondly, we propose a new crossover operator, named Precedence Operation Crossover (POX), for the operation-based representation. The approach is tested on a set of standard instances and compared with other approaches. The Simulation results validate the effectiveness of the proposed algorithm.

Heuristics for minimizing total weighted tardiness in complex job shops

Abstract: Semi-conductor manufacturing is arguably one of the most complex manufacturing processes in existence today. A semi-conductor wafer fabrication facility is comprised of batching machines, parallel machines, machines with sequence-dependent set-ups, and re-circulating product flow. The individual job release times and due dates combine with the other processing environment characteristics to form a ‘complex’ job shop scheduling problem. We first present a mixed-integer program (MIP) to minimize total weighted tardiness in a complex job shop. Since the problem is NP-hard, we compare a heuristic based on the MIP (MIP heuristic) with both a tuned version of a modified shifting bottleneck heuristic (SB heuristic) and three dispatching rules using random problem instances of a representative model from the literature. While the MIP heuristic typically produces superior schedules for problem instances with a small number of jobs, the SB heuristic consistently outperforms the MIP heuristic for larger problem inst...

Capacity-driven acceptance of customer orders for a multi-stage batch manufacturing system: models and algorithms

Abstract: An automotive parts manufacturer produces a wide variety of parts in a job shop environment. Many of the manufacturing operations have substantial setups. When a client phones in an order, the manufacturer must decide quickly whether or not it has the capacity required to accept the order. We develop a simplified formulation of the order acceptance problem. We formulate the discrete-time version as an integer program. The problem is NP-hard, but in 51 out of 51 test problems the LP relaxation is tight. For larger problems we test several heuristics. Three of the heuristics look promising: simulated annealing, a genetic algorithm, and a linear-programming-based heuristic.

Reactive repair tool for job shop schedules

Abstract: Reactive schedule repair is a better alternative to total rescheduling of impaired job shop schedules. For ease of implementation in the job shops, heuristic-based schedule repair methods are preferred. However, the majority of the repair heuristics reported in the literature are capable of handling only a singular disruption to the schedule. On the contrary, real-world job shops are subjected to multiple complex disruptions that occur randomly over the span of the schedule. A new heuristic, modified affected operation rescheduling (mAOR), has been successfully used for repairing a majority of typical job shop disruptions such as absenteeism of workers, process time variations and arrival of unexpected jobs using a combination of generic repair steps. In the present work, the mAOR heuristic has been applied for repairing randomly occurring multiple disruptions under rigorous shop floor conditions. The relationship between the variation of shop floor conditions and the performance of the schedule repair he...

A novel approach to lean control for Taiwan-funded enterprises in mainland China

Abstract: The lean control approach has been successfully applied to reduce waste and improve customer service in numerous Taiwan-funded enterprises. Although numerous models have been developed to overcome its limitations, such as determining unnecessary moving, unnecessary inventory, and redundant transportation, they do not, however, identify focused stages in which to start lean control. To secure Taiwan-Funded Enterprises in Mainland China (TFEMC), in this paper, after using value stream mapping (VSM) to show the current state of manufacturing processes, rough set theory is adopted and used to identify types of focused stages, where lean controls are most required. A generalised label-correcting algorithm is then developed to determine the desired stages of lean manufacturing, which are difficult to show in the VSM. This methodology is suitable for a repetitive manufacturing environment of mixed type, i.e., job shop and flow shop, and achieves the following objectives: 1. Decreasing work in progress (WIP) inve...