Job Shop

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...

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.

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

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.

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.

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.

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.

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.

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.