Job Shop

The primary objective of this study is to examine the performance of order-based dispatching rules in a general job shop, where the environmental factors are shop utilization and due date tightness. An order is defined as a collection of jobs that are shipped as a g r o u p a n order-to the customer, only on completion of the last job of the order. We specifically compare dispatching rules from past job-based studies to some rules adapted to encompass order characteristics. Standard flow time and tardiness measures are used, but in addition, we introduce measures that combine average performance with variation in an attempt to capture the performance of a majority of the orders processed in the shop. Of the 16 dispatching ruies tested, our results show that four of the simple rules dominate the others. We also found that order-based rules perform better than their job-based counterparts. The study makes use of multivariate statistical analysis, in addition to the usual univariate tests, which can provide additional insight to managers using multiple criteria in their decision process.

Genetic algorithms are a very popular heuristic which have been suc-cessfully applied to many optimization problems within the last 30 years. In this chapter, we give a survey on some genetic algorithms for shop scheduling problems. In a shop scheduling problem, a set of jobs has to be processed on a set of machines such that a specific optimization crite-rion is satisfied. According to the restrictions on the technological routes of the jobs, we distinguish a flow shop (each job is characterized by the same technological route), a job shop (each job has a specific route) and an open shop (no technological route is imposed on the jobs). We also consider some extensions of shop scheduling problems such as hybrid or flexible shops (at each processing stage, we may have a set of parallel machines) or the inclusion of additional processing constraints such as controllable processing times, release times, setup times or the no-wait condition. After giving an introduction into basic genet...

Job-release mechanisms in jobshop control are receiving increased recognition in recent research. However, there is considerable disagreement over the benefits of sophisticated job-release mechanisms, and they remain to be tested in various operating environments.

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Production Scheduling problems are typically named bases on the processing routes of their jobs on different processors and also the number of processors in each stage. In this paper, we consider the problem of scheduling a job shop (JSS) where set-up times are sequence-dependent (SDST) to minimize the maximum completion times of operations or makespan. Our problem is generally formulated as J/STsd/C max . To tackle such an NP-hard problem, a recent effective metaheuristic algorithm known as variable neighborhood search (VNS) is employed. VNS algorithms have shown excellent capability to solve scheduling problems to optimal or near-optimal schedule. Our proposed VNS is readily intelligible yet is a robust solution technique for the problem of SDST JSS. VNS is categorized as a local search-based algorithm armed with systematic neighborhood search structures. Our proposed VNS obviates the notorious myopic behavior of local search-based metaheuristic algorithms by the means of several systematic insertion neighborhood search structures. An experimental design based on Taillard's benchmark is conducted to evaluate the efficiency and effectiveness of our proposed algorithm against some effective algorithms in the literature. The obtained results strongly support the high performance of our proposed algorithm with respect to other well-known heuristic and metaheuristic algorithms.

Work flows in a job-shop are determined not only by the release load and the time between release factors, but also by the number of accepted orders. There has been extensive research on workload and input-output control aiming at improving the performance of manufacturing operations in job-shops. This paper explores the idea of controlling the workload since the acceptance/rejection of orders stage. A new acceptance/rejection rule is proposed, and tests are conducted to study the sensitivity of job-shop performance to different order acceptance parameters, like the tolerance of the workload limit and the due date extension acceptance. It also evaluates the effect of the negotiation phase of the proposed acceptance rule on the job-shop performance using a simulation model of a generic random job-shop. The extensive simulation experiments allow us to conclude that having a negotiation phase prior to rejection improves almost all workload performance measures. We also conclude that different tolerances of the workload limit affect slightly the performance of the job-shop.

Research on dispatching rules has focused upon deterministic job shop situations or small assembly environments and ignored operational factors. This work uses data obtained from a capital goods company that produces complex products. The paper first explores the influence of the data update period and the minimum setup, machining and transfer times under stochastic infinite capacity conditions. It then investigates the significance of these factors and the relative performance of eight dispatching rules with finite capacity and stochastic processing times. Dispatching rules and most operational parameters were statistically significant. With finite capacity, the 'best' dispatching rule was different at the component and product levels and varied according to the performance measure used. The shortest operation time first rule generally produced the best results, particularly at product level. r

The choice of an appropriate social rate of discount is critical in the decision-making process on public investments. In this paper we review the literature on social discounting, and address in particular a recently growing field of related research, that is, individual time preferences. We argue that an explicit consideration and analysis of the behaviour of individuals regarding the concept and the use of an appropriate social discount rate are essential for balanced decision making in the public sector, especially, though not exclusively, in the field of resource or environmental policy.

In this research, we model a semiconductor wafer fabrication process as a complex job shop, and adapt a Modified Shifting Bottleneck Heuristic (MSBH) to facilitate the multi-criteria optimization of makespan, cycle time, and total weighted tardiness using a desirability function. The desirability function is implemented at two different levels of the MSBH: the subproblem solution procedure level (SSP level) and the machine criticality measure level (MCM level). In addition, we suggest two different methods of choosing the critical toolgroup at the MCM level: (1) the Local MCM approach, which chooses the critical toolgroup based on local desirability values from the SSP level and (2) the Global MCM approach, which chooses the critical toolgroup based on its impact on the desirability of the entire disjunctive graph. Results demonstrate the desirabilitybased approaches' ability to simultaneously minimize all three objectives.

This paper proposes a new when-to-schedule policy in reactive scheduling, which considers timing of schedule revision based on the concept of a control limit policy. Under the proposed policy, schedule revision is carried out based on a cumulative task delay which can be a measure to determine suitable timing of schedule revision. Some computational experiments demonstrate the effectiveness of the proposed policy by applying it to job shop problems with machine breakdowns, where we minimize maximum tardiness as well as scheduling frequency. It is also shown that the proposed policy with even low scheduling frequency can outperform an event-driven rescheduling approach, which is one of the major effective reactive scheduling methods.

One of the basic and significant problems, that a shop or a factory manager is encountered, is a suitable scheduling and sequencing of jobs on machines. One type of scheduling problem is job shop scheduling. There are different machines in a shop of which a job may require some or all these machines in some specific sequence. For solving this problem, the objective may be to minimize the makespan. After optimizing the makespan, the jobs sequencing must be carried out for each machine. The above problem can be solved by a number of different methods such as branch and bound, cutting plane, heuristic methods, etc. In recent years, researches have used genetic algorithms, simulated annealing, and machine learning methods for solving such problems. In this paper, a simulation model is presented to work out job shop scheduling problems with the objective of minimizing makespan. The model has been coded by Visual SLAM which is a special simulation language. The structure of this language is based on the network modeling. After modeling the scheduling problem, the model is verified and validated. Then the computational results are presented and compared with other results reported in the literature. Finally, the model output is analyzed.

Due to the stubborn nature of the deterministic job-shop scheduling problem many solutions proposed are of hybrid construction cutting across the traditional disciplines. The problem has been investigated from a variety of perspectives resulting in several analytical techniques combining generic as well as problem specific strategies. We seek to assess a subclass of this problem in which the objective is minimising makespan, by providing an overview of the history, the techniques used and the researchers involved. The sense and direction of their work is evaluated by assessing the reported results of their techniques on the available benchmark problems. From these results the current situation and pointers for future work are provided. of manufacturing it involves finding a sequential allocation of competing resources that optimises a particular objective function. The deterministic job-shop scheduling problem, hereinafter referred to as Π J , is the most general of the classical scheduling problems. Π J consists of a finite set J of n jobs { } J i i n =1 to be processed on a finite set M of m machines { } . M k k m =1 Each job J i must be processed on every machine and consists of a chain or complex of m i operations O i1 , O i2 , …, O im i , which have to be scheduled in a predetermined given order (precedence constraint). There are N operations in total, N = m i i n .

The goal of process planning is to propose the routing of a previously designed part and results in a sequence of operations and their parameters. It concerns and requires detailed information about the process. The goal of production planning, on the other hand, is to schedule, sequence and launch the orders introduced on the routing sheet into the job-shop according to the enterprise's strategic goal and the actual conditions of the production plant. The goals, information and decisions taken in process planning and production planning and control are often very different and, because of that, it is very difficult to integrate them.

The Job Shop scheduling Problem (JSP) is one of the most known problems in the domain of the production task scheduling. The Job Shop scheduling Problem with Transportation resources (JSPT) is a generalization of the classical JSP consisting of two sub-problems: the job scheduling problem and the generic vehicle scheduling problem. In this paper, we make a state-of-the-art review of the different works proposed for the JSPT, where we present a new classification schema according to seven criteria such as the transportation resource number, the transportation resource type, the job complexity, the routing flexibility, the recir-culation constraint, the optimization criteria and the implemented approaches.

The ProModel Optimization Suite is a powerful yet easy-to-use simulation tool for modeling all types of manufacturing systems, ranging from small job shops and machining cells to large mass production, flexible manufacturing systems, and supply chain systems. ProModel is a Windows based application with an intuitive graphical interface and object-oriented modeling constructs that eliminate the need for programming. It combines the flexibility of a general-purpose simulation language with the convenience of a data-driven simulator. The ProModel Optimization Suite includes an optimization tool called SimRunner that performs sophisticated "what-if" analysis by running automatic factorial design of experiments on the model, providing the best answer possible. The paper provides an overview of the ProModel Optimization Suite and presents its modeling, analysis, and optimization capabilities

Tópicos Avanzados en producción y logística, profesor Elkin Rodríguez, Última versión: 16-09-2012 Universidad Nacional de Colombia RESUMEN: Este artículo trata acerca de los Algoritmos basados en colonia de hormigas, los cuales son unos modelos Metaheurísticos basados en el comportamiento autoorganizativo que algunas especies de hormigas presentan en la naturaleza para encontrar los caminos más cortos entre el nido y las fuentes de alimento. Inicialmente, se mencionan las características de las hormigas en la naturaleza que fundamentan este tipo de modelos, para así pasar, a partir de éstas características, a describir la Metaheurística de Optimización basada en colonia de Hormigas explicando el modo de operación general de éstos. Luego de ésto, se procede a estudiar el algoritmo Sistema de Hormigas, el cual fue el primer algoritmo de optimización de colonia de hormigas desarrollado por M. Dorigo, V. Maniezzo, A. Colorni en 1991, y el cual ha servido de base para muchos otros modelos, y cuyo entendimiento permitirá al lector tener una introducción para comprender la filosofía y funcionamiento de la mayoría de algoritmos basados en colonia de hormigas. Ya teniendo claro el funcionamiento de la metaheurística se estudia la aplicación de esta en un problema particular, en este caso un problema Job-Shop; para lo cual se introduce en qué consiste el problema de Job Shop, y se estudia cómo fue abordado con colonia de hormigas en este trabajo. Finalmente se mencionan las conclusiones a las que se ha llegado producto de la revisión bibliográfica y del análisis del funcionamiento de este tipo de algoritmos.

A manufacturing cell is a group of processes design to make a family of products in a flexible way. Cellular manufacturing concepts was developed to exploit the advantages of job shop layout and transfer line one. In these systems each cell (production unit) works separately and has its own recourses. Although these systems are more capital intensive but in factories where the variety and volume of production is comparatively high, these systems are recommended. Although each unit production lines are independent but the coordination between production, information and transportation system in huge cellular manufacturing plants is a major issue affecting total performance of the factory. This coordination is defined by the way material and information flows are established. A control policy in manufacturing systems imposes a particular pattern of information flow on the factory. Similarly, a specific implementation of transportation policies defines a pattern of material flow between two units. This paper studies the condition of material flow in INSIG. Furthermore, through simulation, proper flow of material with the aim of achieving company's development plan as well as increasing the production capacity of some units has been provided.

A life-cycle energy consumption analysis of a Bridgeport manual mill and a Mori Seiki DuraVertical 5060 has been conducted. The use phase incorporated three manufacturing environments: a community shop, a job shop, and a commercial facility. The CO2-equivalent emissions were presented per machined part. While the use phase comprised the majority of the overall emissions, the manufacturing phase emissions were significant especially for the job shop, which is not as efficient as the other facilities due to its inherent need for flexibility. Since the Mori Seiki is heavier, the manufacturing phase of this machine tool had a greater impact on emissions than the Bridgeport. Transportation was small relative to the use phase, which was dominated by cutting, HVAC, and lighting. These results highlight areas for energy reductions in machine tool design as well as the importance of facility type to the manufacture of any product.

An examination of the use of both frozen and replanning intervals for planning the master production schedule for a capacity-constrained job shop is presented. The results show that forecast error, demand lumpiness, setup time, planned lead time, and order size have a greater impact on the mean total backlog, total inventory, and number of setups than the frozen and replanning intervals.

The first comprehensive survey paper on scheduling problems with separate setup times or costs was conducted by Allahverdi et al. (1999), who reviewed the literature since the mid-1960s. Since the appearance of that survey paper, there has been an increasing interest in scheduling problems with setup times (costs) with an average of more than 40 papers per year being added to the literature. The objective of this paper is to provide an extensive review of the scheduling literature on models with setup times (costs) from then to date covering more than 300 papers. Given that so many papers have appeared in a short time, there are cases where different researchers addressed the same problem independently, and sometimes by using even the same technique, e.g., genetic algorithm. Throughout the paper we identify such areas where independently developed techniques need to be compared. The paper classifies scheduling problems into those with batching and non-batching considerations, and with sequence-independent and sequence-dependent setup times. It further categorizes the literature according to shop environments, including single-machine, parallel machines, flow shop, no-wait flow shop, flexible flow shop, job shop, open shop, and others. This is the Pre-Published Version.

This paper provides a simulation model to study the effect of the work-in-process control on due date performance in job shop environment. The due date performance is measured by both the number of tardy jobs and the total tardiness. The simulation runs include different shop configurations (flow shop and general job shop), workloads and sequencing rules. As expected, the results reveal that due date performance is highly dependent on the work-in-process, particularly after the system reaches saturation. Nevertheless, the model is very useful to show job shop managers the effect of the work-in-process control in the due date meeting performance.

Notice of Violation of IEEE Publication Principles"A Dynamic Distributed Diagnosis Algorithm for an Arbitrary Network Topology with Unreliable Nodes and Links,"by Pabitra Mohan Khilar and Sudipta Mahapatra,in the Proceedings of the International Conference on Advanced Computing and Communications, 2007. ADCOM, Dec. 2007, pp. 125-130After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.This paper contains significant portions of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:"A Distributed Network Connectivity Algorithm,"By E. Procopio Duarte Jr. and A.Weber,The Sixth International Symposium on Autonomous Decentralized Systems, 2003. ISADS 2003 April 2003, pp. 285-292This paper presents a distributed network diagnosis (DND) algorithm for an arbitrary network topology where every node needs to record the status of every other nodes and links assuming the nodes and links are subjected to crash and value faults in a dynamic fault environment (the node's or link's status may change during execution of algorithm). The algorithm operates correctly in each connected component if the network is partitioned due to a set of faulty links or faulty nodes. The worst-case bounds for diagnostic latency is at most O(td) rounds where t is the number of dissemination trees and d is the diameter of the network. The proposed approach uses non-broadcasting method of message dissemination that has similar diagnostic latency with flooding [4] and similar message co- mplexity with Chinese Agent [14] method of message dissemination respectively.

In this paper, we will address the cyclic scheduling problem with limited WIP (Work-in-process). In these systems, we have to minimize the cycle time of the production while working with a limited level of WIP. We consider here production systems with linear jobs (every operation was followed and preceded by only one operation). Many methods have been proposed to solve the cyclic scheduling problem. Among them, we focus on the exact approach, and more precisely the mathematical programming. In this paper, we will propose a Linear Program Model for cyclic scheduling with limited WIP and cycle time minimization.

The goal of process planning is to propose the routing of a previously designed part and results in a sequence of operations and their parameters. It concerns and requires detailed information about the process. The goal of production planning, on the other hand, is to schedule, sequence and launch the orders introduced on the routing sheet into the job-shop according to the enterprise's strategic goal and the actual conditions of the production plant. The goals, information and decisions taken in process planning and production planning and control are often very different and, because of that, it is very difficult to integrate them. The objective of this work is to develop a model that can be applied in the future to the development of an integrated process planning and scheduling tool using an integrated definition (IDEF) methodology to design an activity model, which integrates process and production planning in metal removal processes. An activity model will be used to develop a system that allows the user to plan the process and the production at the same time in collaborative engineering work. To design the activity model, a wide range of parts were evaluated and processed in an actual job-shop factory. Several activities were developed in detail to be tested in real cases, and an example of one of them is introduced in this article.

Notice of Violation of IEEE Publication Principles"Towards a Unified Framework for Complexity Measurement in Aspect-Oriented Systems,"by A. Kumar, R. Kumar, P.S. Groverin the Proceedings of the 2008 International Conference on Computer Science and Software Engineering, vol.2, pp.98-103, Dec. 2008After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.This paper contains significant portions of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:"Towards a Unified Coupling Framework for Measuring Aspect-oriented Programs"by T.T. Bartolomei, A. Garcia, C. Sant'Anna, C., E. Figueiredoin the Proceedings of the 3rd International Workshop on Software Quality Assurance (Portland, Oregon, November 06 - 06, 2006). SOQUA '06. ACMAspect-oriented programming (AOP) is an emerging technique that provides a mechanism to clearly encapsulate and implement concerns that crosscut other modules. It is claimed that this technique improves code modularization and therefore reduce complexity of object-oriented programs (OOP). Most of the proposed complexity measurement frameworks and metrics for AOP are for AspectJ programming language. In this paper we have defined a generic complexity measurement framework that takes into account three, the most well known families of available AOP languages, AspectJ, CaesarJ and Hyper/J. The proposed unified framework contributes to better understanding of complexity in AOP, which in turn help to (i) define new complexity metrics whic- h permit the analysis and comparison of Java, AspectJ, CaesarJ and Hyper/J implementations, and (ii) integrating different existing measures and examine same concepts in different ways.

Petri nets have been extensively used to model the dynamics of various types of manufacturing systems due to their power to capture the complex characteristics of many of such systems. However, the "state explosion" has prevented their use for solving real scheduling problems. To date, Petri net based algorithms have only been tested with relatively small problems with few machines and few jobs. This paper proposes an algorithm that combines the A* search with both an aggressive node-pruning strategy and improved evaluation functions to generate near-optimal schedules. Extensive computational tests were conducted on a wide vadety of scenarios ranging from classical job shop to complex FMS scheduling problems. The obtained results show very promising results in terms of solution quality and computing times. Journal of Mamtfitcturing Systems Vol. 24/No. 2 2005 Extensive computational tests were conducted on a wide variety of scenarios ranging from classical job shop to complex FMS scheduling problems. In particular, the Petri net based approach can handle alternative routings, dual resources, buffers of finite capacity, material handling machines, recirculation, and batch sizes. The obtained results show very promising results in terms of solution quality and computing times.

Determining the optimal sequence of jobs in job shop scheduling for small and medium enterprises, affect the machine productivity, earliness and tardiness costs of delivery. The deterministic variant of the problem is well-known to be NP-Hard. If random elements are introduced into the problem, the level of complexity goes higher. Hence, many priority rules have been developed to tackle stochastic job shop scheduling problem. However, to devise a better solution approach, simulation-optimization approach might be used. In this study, a mathematical model was developed for job shop scheduling with random process times and possible machine breakdowns. Then, a simulation-optimization model was applied to choose among a list of priority rules using Rockwell Arena 14. Finally, a numerical example was used to evaluate the quality of the model. Results showed that the rule Longest Processing Time (LPT) yields the lowest total earliness and tardiness costs. However, the total costs of the following rules are also acceptable: First in First out (FIFO), Last in First out (LIFO), Earliest Due Date (EDD) and Slack time (Slack).

In this paper we propose two time-indexed IP formulations for job-shop scheduling problems with a min-sum objective. The first model has variables associated to job scheduling patterns. The exponential number of variables calls for a column generation scheme which is carried out by a dynamic programming procedure. The second model is of network flow type with side constraints. This model can be strengthened by adding cutting inequalities of clique type. It turns out that the two models are equivalent, since the dual of the second formulation is equivalent to the compact dual of the first model. However, they require significantly different solution approaches and may behave differently in terms of computing time and memory usage. Good upper bounds are found by a heuristic procedure that randomly generates schedules from fractional solutions. These features allow for an effective pruning of the branch-and-bound tree and narrowing the gap between lower and upper bounds. However, the size of both models is critically affected by the time-indexed formulation which may heavily slow down the computation.

This paper presents a special case of the general shop called partial job shop problem. The partial job shop is a more realistic generalization of the mixed shop problem. The problem is formulated as a mixed integer programming model. A scatter search algorithm combined with tabu search and path relinking is used to tackle this problem with makespan criterion. The computational experiments are performed on some problem instances. The results are compared with a lower bound and the effectiveness of the algorithm is shown.

T his article proposes a supporting framework for the implementation of the material control system POLCA (paired-cell overlapping loops of cards with authorization). The POLCA system is particularly appropriate for environments that involve highly variable demand and large product variety, which force small batch (or even one-of-a-kind) production. We propose a load-based version of the POLCA control system (LB-POLCA), which determines the POLCA parameters (release authorizations, allowed workloads in the loops) according to an advanced resources planning (ARP) system that adequately captures the stochastic behavior of the production system and enables fine-tuning and high-level optimization of the manufacturing lot sizes. We also discuss the implementation of an electronic LB-POLCA system in a metal shop of Spicer Off-Highway Products Division, a subsidiary of the Dana Corporation.

The "job-shop scheduling problem" is known to be NP-complete. The version of interest m this paper concerns an assembly line designed to produce various cars, each of which reqmres a (possibly different) set of options. The combinatorlcs of the problem preclude seeking a maximal solution. Nevertheless, because of the underlying economic considerations, an approach that yields a "good' sequence of cars, given the specific required options for each, would be most valuable In this paper, we focus on an environment for seeking, studying, and evaluating approaches for yielding good sequences The environment we discuss rehes on the automated reasoning program ITP. Automated reasoning programs of this type offer a wide variety of ways to reason, strategies for controlling the reasoning, and auxfl,ary procedures that contribute to the effective study of problems of this k,nd. We view the study presented m this paper as a prototype of what can be accomplished with the assistance of an automated reasoning program.

Production Scheduling problems are typically named bases on the processing routes of their jobs on different processors and also the number of processors in each stage. In this paper, we consider the problem of scheduling a job shop (JSS) where set-up times are sequence-dependent (SDST) to minimize the maximum completion times of operations or makespan. Our problem is generally formulated as J/STsd/C max . To tackle such an NP-hard problem, a recent effective metaheuristic algorithm known as variable neighborhood search (VNS) is employed. VNS algorithms have shown excellent capability to solve scheduling problems to optimal or near-optimal schedule. Our proposed VNS is readily intelligible yet is a robust solution technique for the problem of SDST JSS. VNS is categorized as a local search-based algorithm armed with systematic neighborhood search structures. Our proposed VNS obviates the notorious myopic behavior of local search-based metaheuristic algorithms by the means of several systematic insertion neighborhood search structures. An experimental design based on Taillard's benchmark is conducted to evaluate the efficiency and effectiveness of our proposed algorithm against some effective algorithms in the literature. The obtained results strongly support the high performance of our proposed algorithm with respect to other well-known heuristic and metaheuristic algorithms.

In scheduling problems, taking the sequence-dependent setup times into account is one of the important issues that have recently been considered by researchers in the production scheduling field. In this paper, we consider flexible job-shop scheduling problem (FJSP) with sequence-dependent setup times to minimize makespan and mean tardiness. The FJSP consists of two sub-problems from which the first one is to assign each operation to a machine out of a set of capable machines, and the second one deals with sequencing the assigned operations on all machines. To solve this problem, a variable neighborhood search (VNS) algorithm based on integrated approach is proposed. In the presented optimization method, the external loop controlled the stop condition of algorithm and the internal loop executed the search process. To search the solution space, the internal loop used two main search engines, i.e. shake and local search procedures. In addition, neighborhood structures related to the sequencing problem and the assignment problem were employed to generate neighboring solutions. To evaluate the performance of the proposed algorithm, 20 test problems in different sizes are randomly generated. Consequently, computational results and comparisons validate the quality of the proposed approach.

Flexible manufacturing systems are not easy to control and it is difficult to generate controlling systems for this problem domain. Flexible job-shop scheduling problem (FJSP) is one of the instances in this domain. It is a problem which acquires the job-shop scheduling problems (JSP). FJSP has additional routing sub-problem in addition to JSP. In routing sub-problem each task is assigned to a machine out of a set of capable machines. In scheduling sub-problem, the sequence of assigned operations is obtained while optimizing the objective function(s). In this work an object-oriented (OO) approach with simulated annealing algorithm is used to simulate multi-objective FJSP. Solution approaches provided in the literature generally use two-string encoding scheme to represent this problem. However, OO analysis, design and programming methodology helps to present this problem on a single encoding scheme effectively which result in a practical integration of the problem solution to manufacturing control systems where OO paradigm is frequently used. Three parameters are considered in this paper: maximum completion time, workload of the most loaded machine and total workload of all machines which are the benchmark used to show the propose system achieve effective result. KEYWORDS Object-oriented manufacturing control, Object-oriented design, Multi-objective flexible job shop scheduling, simulated annealing algorithm.

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.

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.

A real-time operating system (RTOs) is often used in embedded system, to structure the application code and to ensure that the deadlines are met by reacting on events by executing the functions within precise time. Most embedded systems are bound to real-time constraints with determinism and latency as a critical metrics. RTOs are generally implemented in software, increases computational overheads, jitter and memory footprint. Modern FPGA technology, enables the implementation of a full featured and flexible hardware based RTOs, which helps in reducing to greater extent these overheads even if not remove completely. Scheduling algorithms play an important role in the design of real-time systems. An Adaptive Fuzzy Inference System (FIS) based scheduler framework proposed in this article is based on the study and conclusion drawn from the research over the years in HW SW co-design domain. The proposed novel two phase FIS based adaptive hardware task scheduler minimizes the processor time for scheduling activity which uses fuzzy logic to model the uncertainty at first stage along with adaptive framework that uses feedback which allows processors share of task running on multiprocessor to be controlled dynamically at runtime. This Fuzzy logic based adaptive hardware scheduler breakthroughs the limit of the number of total task and thus improves efficiency of the entire real-time system. The increased computation overheads resulted from proposed two phase FIS scheduler can be compensated by utilising the basic characteristics of parallelism of the hardware as scheduler being migrated to FPGA.

We explore use of data mining for lead time estimation in make-to-order manufacturing. The regression tree approach is chosen as the specific data mining method. Training and test data are generated from variations of a job shop simulation model. Starting with a large set of job and shop attributes, a reasonably small subset is selected based on their contribution to estimation performance. Data mining with the selected attributes is compared with linear regression and three other lead time estimation methods from the literature. Empirical results indicate that our data mining approach coupled with the attribute selection scheme outperforms these methods.

This article addresses the flexible job-shop scheduling problem (FJSP) to minimize makespan. The FJSP is strongly NP-hard and consists of two sub-problems. The first one is to assign each operation to a machine out of a set of capable machines, and the second one deals with sequencing the assigned operations on all machines. To solve this problem, an artificial immune algorithm (AIA) based on integrated approach is proposed. This algorithm uses several strategies for generating the initial population and selecting the individuals for reproduction. Different mutation operators are also utilized for reproducing new individuals. To show the effectiveness of the proposed method, numerical experiments by using benchmark problems are conducted. Consequently, the computational results validate the quality of the proposed approach.

This paper considers the maintenance of an unreliable M/G/1 queue-like job shop, integrating the maintenance process and the resulting queue operating characteristics. The system may breakdown, leading to unscheduled maintenance. Otherwise, preventive maintenance is done when n jobs have been processed-whichever comes first. Using arguments from renewal theory, basic results regarding the queue-maintenance policy are established. Both an analytical and numerical example are studied in detail.