Modeling a just-in-time plant using sequencing algorithms

An Australian automotive component company plans to assemble and deliver seats to customer on just-in-time basis. The company management has decided to model operations of the seat plant to help them make decisions on capital investment and labour requirements. There are four different areas in seat assembly and delivery areas. Each area is modeled independently to optimise its operations. All four areas are then combined into one model called the plant model to model operations of seat plant from assembly to delivery. Discrete event simulation software is used to model the assembly operations of seat plant.

Simulation-based input loading condition optimisation of airport baggage handling systems

Scheduling check-in station operations are a challenging problem within airport systems. Prior to determining check-in resource schedules, an important step is to estimate the Baggage Handling System (BHS) operating capacity under non-stationary conditions. This ensures that check-in stations are not overloaded with bags, which would adversely affect the system and cause cascade stops and blockages. Cascading blockages can potentially lead to a poor level of service and in worst scenario a customer may depart without their bags. This paper presents an empirical study of a multiobjective problem within a BHS system. The goal is to estimate near optimal input operating conditions, such that no blockages occurs at check-in stations, while minimising the baggage travel time and maximising the throughput performance measures. We provide a practical hybrid simulation and binary search technique to determine a near optimal input throughput operating condition. The algorithm generates capacity constraint information that may be used by a scheduler to plan check-in operations based on flight arrival schedules.

Development of an internet supportive platform for online teaching and learning of construction planning and scheduling

Construction Planning and Scheduling is taught for the fIrst time ill Semester 2, 2004 in the School of Architecture and Building, Deakin University. During the unit development process and the implementation of teaching activities, several issues arose in relation to implementing computer-aided construction scheduling and unit delivery in a unitary environment. Although various types of construction planning and scheduling software have been developed and applied, none of them can be run inside an online teaching software package, which provides powerful functions in administration. This research aims to explore the strategies to connect a project planning and scheduling software package and an online ~aching and learning software package by a Web-based support platform so that both the lecturer and students can draw up and communicate a construction plan or schedule with tables and fIgures. The key techniques of this supportive platfonn are idt;nlifies and they include a web-based graphically user-interfaced, dynamic and distributed multimedia data acquisition mechanism, which accepts users' drawings and retrieval information from canvas and stores the multimedia data ona server for further usage. This paper demonstrates the techniques and principals needed to construct such a multimedia data acquisition tool. This. research will fill the gap.in the literature in respect to an online pedagogical solution to an existing problem.

Modelling of birds and locust-control operations in Eastern Australia

Locust outbreaks provide an abundant, but unpredictable food source for many native species in Australia. For economic reasons, locust control is unavoidable and can affect a considerable area in Eastern Australia. Depending on the pesticide applied, locust control operations may affect birds in treated areas, either directly through intoxication of the predator, or indirectly, through elimination of its prey. As a preliminary step in identifying the potential impact of these operations on native species, the co-occurrence of birds and locust control operations was assessed using GIS mapping techniques. Data from the Birds of Australia New Atlas provided information about species' distribution between latitudes 17 and 37 degree S, and longitudes 136 and 152 degree E. Of the 834 species present in this region, 292 were chosen on the basis of their geographical distribution and occurrence west of the Great Dividing Range. Sightings for each species were mapped using reporting rates and number of observations per half-degree grid cells. Birds were categorised by habitat, distribution, movement and feeding habits and those species reported to consume Orthopterans were noted. APLC locust survey (1987–2000) and spraying data (1977–2002) were analysed and overlapped with soil and vegetation maps obtained from Geoscience Australia and Environment Australia to find significant hotspots for locust occurrence. These maps were then overlayed with bird distributions to identify the species most likely to be in areas of locust presence and spraying operations.

Influence operations and behavioural change

The intended outcome of Information Operations appears to be a favourable change (to the instigator) in attitudes or belief systems of the target, however, the relationship between attitude and behaviour is tenuous. Propaganda and other methods of ‘influence’ are difficult to assess as the cause and effect relationship is complicated. The short term effects of psychological warfare where force is used in conjunction with influence techniques can be easily assessed; at least at a superficial level. Even in the latter case, the actual causes and effects could be solely the force used or some other factors rather than the psychological techniques per se. Influence Operations attempt to win the hearts and minds of the target audience but, even if successful, the lasting effects of a campaign are problematic. It is further complicated because if a person has a particular view, it does not mean that the ensuing behaviours will reflect that view. Also, there is evidence that the use of force on one set of people produces attitudes and behaviours that instigate radical beliefs and behaviours in another set. So psychological warfare techniques on one group that may or may not produce compliant behaviour stimulates another group to empathise with the victims thus producing an overall practical negative influence. Influence campaigns cannot be separated from the physical environment in which they are executed. If good politics requires good influence campaigns then good influence campaigns require good politics to back them up. This paper will examine the relationships between short term influence campaigns and compare them with the more long term socialising effects such as early education, family and physical attributes that have on attitudes and beliefs which result in the development of such behaviours as terrorism.

Implications of short and long term critical flux experiments for laboratory-scale MBR operations

This paper evaluates the critical flux obtained by different techniques including tests with different flux step lengths (20 and 40 min and 7 days) and modes of operation (continuous and intermittent) under low and high MLSS concentrations. The paper also analyses a couple of long-term tests (flow rate of 40 and 20 L/day) to obtain the time required to reach the critical flux experimentally and compares those values with the results obtained numerically from a mathematical model. It was found that intermittent mode with membrane relaxation was useful in controlling the fouling of membrane and in restoring the membrane from fouling at lower MLSS.

An effective heuristic for stockyard planning and machinery scheduling at a coal handling facility

Coal handling is a complex process involving different correlated and highly dependent operations such as selecting appropriate product types, planning stockpiles, scheduling stacking and reclaiming activities and managing train loads. Planning these operations manually is time consuming and can result in non-optimized schedules as future impact of decisions may not be appropriately considered. This paper addresses the operational scheduling of the continuous coal handling problem with multiple conflicting objectives. As the problem is NP-hard in nature, an effective heuristic is presented for planning stockpiles and scheduling resources to minimize delays in production and the coal age in the stockyard. A model of stockyard operations within a coal mine is described and the problem is formulated as a Bi- Objective Optimization Problem (BOOP). The algorithm efficacy is demonstrated on different real-life data scenarios. Computational results show that the solution algorithm is effective and the coal throughput is substantially impacted by the conflicting objectives. Together, the model and the proposed heuristic, can act as a decision support system for the stockyard planner to explore the effects of alternative decisions, such as balancing age and volume of stockpiles, and minimizing conflicts due to stacker and reclaimer movements.

Simulation-based knowledge management in airport operations

Capturing and retaining knowledge in any organization is a major challenge. This talk describes how these challenges have been addressed through simulation and modeling techniques for complex engineered systems. A series of case studies that focus on airport processes are used to demonstrate the concepts. Furthermore, the additional benefits that a simulation model can bring, through online control and decision-making support, are discussed.

A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings

Electrical load forecasting plays a vital role in order to achieve the concept of next generation power system such as smart grid, efficient energy management and better power system planning. As a result, high forecast accuracy is required for multiple time horizons that are associated with regulation, dispatching, scheduling and unit commitment of power grid. Artificial Intelligence (AI) based techniques are being developed and deployed worldwide in on Varity of applications, because of its superior capability to handle the complex input and output relationship. This paper provides the comprehensive and systematic literature review of Artificial Intelligence based short term load forecasting techniques. The major objective of this study is to review, identify, evaluate and analyze the performance of Artificial Intelligence (AI) based load forecast models and research gaps. The accuracy of ANN based forecast model is found to be dependent on number of parameters such as forecast model architecture, input combination, activation functions and training algorithm of the network and other exogenous variables affecting on forecast model inputs. Published literature presented in this paper show the potential of AI techniques for effective load forecasting in order to achieve the concept of smart grid and buildings.

A multi-level approach to planning and scheduling resources for aviation training

This paper describes a multi-level system dynamics (SD) / discrete event simulation (DES) approach for assessing planning and scheduling problems within an aviation training continuum. The aviation training continuum is a complex system, consisting of multiple aviation schools interacting through interschool student and instructor flows that are affected by external triggers such as resource availability and the weather.
SD was used to model the overall training continuum at a macro level to ascertain relationships between system entities. SD also assisted in developing a shared understanding of the training continuum, which involves constructing the definitions of the training requirements, resources and policy objectives. An end-to-end model of the continuum is easy to relate to, while dynamic visualisation of system behaviour provides a method for exploration of the model.
DES was used for micro level exploration of an individual school within the training continuum to capture the physical aspects of the system including resource capacity requirements, bottlenecks and student waiting times. It was also used to model stochastic events such as weather and student availability. DES has the advantage of being able to represent system variability and accurately reflect the limitations imposed on a system by resource constraints.
Through sharing results between the models, we demonstrate a multi-level approach to the analysis of the overall continuum. The SD model provides the school’s targeted demand to the DES model. The detailed DES model is able to assess schedules in the presence of resource constraints and variability and provide the expected capacity of a school to the high level SD model, subjected to constraints such as instructor availability or budgeted number of training systems. The SD model allows stakeholders to assess how policy and planning affect the continuum, both in the short and the long term.
The development of this approach permits moving the analysis of the continuum between SD and DES models as appropriate for given system entities, scales and tasks. The resultant model outcomes are propagated between the continuum and the detailed DES model, iteratively generating an assessment of the entire set of plans and schedule across the continuum. Combining data and information between SD and DES models and techniques assures relevance to the stakeholder needs and effective problem scoping and scaling that can also evolve with dynamic architecture and policy requirements.
An example case study shows the combined use of the two models and how they are used to evaluate a typical scenario where increased demand is placed on the training continuum. The multi-level approach provides a high level indication of training requirements to the model of the new training school, where the detailed model indicates the resources required to achieve those particular student levels.

Data summarization techniques for big data - a survey

In current digital era according to (as far) massive progress and development of internet and online world technologies such as big and powerful data servers we face huge volume of information and data day by day from many different resources and services which was not available to human kind just a few decades ago. This data comes from available different online resources and services that are established to serve customers. Services and resources like Sensor Networks, Cloud Storages, Social Networks and etc., produce big volume of data and also need to manage and reuse that data or some analytical aspects of the data. Although this massive volume of data can be really useful for people and corporates it could be problematic as well. Therefore big volume of data or big data has its own deficiencies as well. They need big storage/s and this volume makes operations such as analytical operations, process operations, retrieval operations real difficult and hugely time consuming. One resolution to overcome these difficult problems is to have big data summarized so they would need less storage and extremely shorter time to get processed and retrieved. The summarized data will be then in "compact format" and still informative version of the entire data. Data summarization techniques aim then to produce a "good" quality of summaries. Therefore, they would hugely benefit everyone from ordinary users to researches and corporate world, as it can provide an efficient tool to deal with large data such as news (for new summarization).

Towards practical and near-optimal coflow scheduling for data center networks

In current data centers, an application (e.g., MapReduce, Dryad, search platform, etc.) usually generates a group of parallel flows to complete a job. These flows compose a coflow and only completing them all is meaningful to the application. Accordingly, minimizing the average Coflow Completion Time (CCT) becomes a critical objective of flow scheduling. However, achieving this goal in today's Data Center Networks (DCNs) is quite challenging, not only because the schedule problem is theoretically NP-hard, but also because it is tough to perform practical flow scheduling in large-scale DCNs. In this paper, we find that minimizing the average CCT of a set of coflows is equivalent to the well-known problem of minimizing the sum of completion times in a concurrent open shop. As there are abundant existing solutions for concurrent open shop, we open up a variety of techniques for coflow scheduling. Inspired by the best known result, we derive a 2-approximation algorithm for coflow scheduling, and further develop a decentralized coflow scheduling system, D-CAS, which avoids the system problems associated with current centralized proposals while addressing the performance challenges of decentralized suggestions. Trace-driven simulations indicate that D-CAS achieves a performance close to Varys, the state-of-the-art centralized method, and outperforms Baraat, the only existing decentralized method, significantly.