Output-driven information system planning: a case study

Information system (IS) plans can vary in length and detail. One must, therefore, be able to tailor the existing planning methodologies to produce the desirable outputs. This article proposes a framework of demand-centric adaptive IS planning process and applies it to a case study that demonstrates how to adapt the methodology to produce an IS plan for a small commercial bank. Following the output-driven adaptive approach, the project was completed on time with expected quality. The project document provides the bank’s management with guidelines for allocating their information resources to meet the current and future needs of business.     

Planning as incremental adaptation of a reactive system

The importance of solving the problem of integrating deliberative (“planning”) capabilities and reactive capabilities when building robust, ‘real-world’ robot systems is becoming widely accepted (Bresina and Drummond, 1990; Fraichard and Laugier, 1991; McDermott, 1991). This paper presents a solution to this problem: cast planning as the incremental adaptation of a reactive system to suit changes in goals or the environment. Our application domain is a manufacturing problem - robotic kitting. This paper represents an advance on existing work in two ways: It presents and formally examines an architecture that incorporates the benefits of a deliberative component without compromising the reactive component. Secondly, it provides the first set of performance statistics in the literature for this class of system. In our approach, the reactive system (the reactor) is a real-time system that continually interacts with the environment, and the planner is a separate and concurrent system that incrementally ‘tunes’ the behavior of the reactor to ensure that goals are achieved. We call this the planner-reactor approach. The reactor is described using a formal framework for representing flexible robot plans, the model (Lyons, 1990; Lyons and Arbib, 1989). Thus, the behavior of the reactor, and the rules by which the reactor can be modified, become open to mathematical analysis. We employ this to determine the constraints the planner must abide by to make safe adaptations and to ensure that incremental adaptations converge to a desired reactor. We discuss our current implementation of planner and reactor, work through an example from the kitting robot application, and present implementation results.     

Modeling expert problem solving in a game of chance: a Yahtzee© case study

Although developments on software agents have led to useful applications in automation of routine tasks such as electronic mail filtering, there is a scarcity of research that empirically evaluates the performance of a software agent versus that of a human reasoner, whose problem-solving capabilities the agent embodies. In the context of a game of a chance, namely Yahtzee©, we identified strategies deployed by expert human reasoners and developed a decision tree for agent development. This paper describes the computer implementation of the Yahtzee game as well as the software agent. It also presents a comparison of the performance of humans versus an automated agent. Results indicate that, in this context, the software agent embodies human expertise at a high level of fidelity.     

A case study of planning for smart factories

In this work, we propose the application of the SPIN software model checker to a multiagent system that controls the industrial production of goods. The flow of material is buffered on a production line with assembling stations. As the flow of material is asynchronous at each station, queuing is required as long as buffers provide waiting room. Besides validating the design of the system, the core objective of this work is to find concurrent plans that optimize the throughput of the system. In the mapping of the production system to the model checker, we model the production line as a set of communicating processes, with the movement of items modeled as channels. Experiments show that the model checker is able to analyze the system, subject to the partial ordering of the product parts. It derives valid and optimized plans with several thousands of steps using constraint branching in branch-and-bound search. We compare the results with a randomized exploration based on recent advances in Monte Carlo search.

     

The TRAINS project: a case study in building a conversational planning agent

Abstract

The TRAINS project is an effort to build a conversationally proficient planning assistant. A key part of the project is the construction of the TRAINS system, which provides the research platform for a wide range of issues in natural language understanding, mixed-initiative planning systems, and representing and reasoning about time, actions and events. Four years have now passed since the beginning of the project. Each year a demonstration system has been produced that focused on a dialogue that illustrates particular aspects of the research. The commitment to building complete integrated systems is a significant overhead on the research, but it is considered essential to guarantee that the results constitute real progress in the field. This paper describes the goals of the project, and the experience with the effort so far.     

Strategic planning for information systems – a case study of a UK metropolitan council

A case study of a UK metropolitan council is presented which examines, over an 11-year period using document analysis and staff interviews, the approach the council has evolved to develop a strategic information systems plan. The approach is analyzed using a planning reference model. The existence of a political, as opposed to a textbook, approach to planning is identified, the reasons for this are described, and some suggestions are made for research for the further understanding of planning, as well as suggestions for modifications to textbook planning approaches.     

Agile product line planning: A collaborative approach and a case study

Agile methods and product line engineering (PLE) have both proven successful in increasing customer satisfaction and decreasing time to market under certain conditions. Key characteristics of agile methods are lean and highly iterative development with a strong emphasis on stakeholder involvement. PLE leverages reuse through systematic approaches such as variability modeling or product derivation. Integrating agile approaches with product line engineering is an interesting proposition which – not surprisingly – entails several challenges: Product lines (PL) rely on complex plans and models to ensure their long-term evolution while agile methods emphasize simplicity and short-term value-creation for customers. When incorporating agility in product line engineering, it is thus essential to define carefully how agile principles can support particular PLE processes. For instance, the processes of defining and setting up a product line (domain engineering) and deriving products (application engineering) differ significantly in practices and focus with implications on the suitability of agile principles. This paper presents practical experiences of adopting agile principles in product line planning (a domain engineering activity). ThinkLets, i.e., collaborative practices from the area of collaboration engineering, are the building blocks of the presented approach as they codify agile principles such as stakeholder involvement, rapid feedback, or value-based prioritization. We discuss how our approach balances agility and the intrinsic needs of product line planning. A case study carried out with an industrial partner indicates that the approach is practicable, usable, and useful.     

Hierarchical production planning with flexibility in agroalimentary environment: a case study

In this paper we address to the planning problem in the agroalimentary domain. In such industry, several specific constraints should be taken into account for planning task such as the constraints of interdependencies between the products and variable production modes. Furthermore, we present the relationship between two large fields as the production hierarchical planning and the flexibility. Especially, we show that the flexibility planning should be made a priori and then integrated in the hierarchical planning process. Indeed, we have established a mathematical model according to different production levels. While taking into account real capacities of the shop and the interdependencies between the products, the results of our formulation are satisfactory in terms of quality of solution and time requirements. It??s shown that our model is able to reach all optimal solutions for all treated models and for all system levels.     

Trajectory planning for a manipulator with nonlinear Coulomb friction using a dynamically incremental genetic algorithm

Global warming and environmental destruction are caused in part by the mass consumption of energy by industries that use robotic manipulators. Hence, there is a need to minimize the energy used for manipulator control systems. It is relatively easy to analytically obtain an optimal solution for a linear system. However, a multi-link manipulator is governed by a nonlinear dynamical equation that is difficult to solve as a two-point boundary value problem. Here, the manipulator angles are approximated by Taylor and Fourier series, whose coefficients are sought by a genetic algorithm (GA) to optimize the objective function subject to the boundary conditions. A search method is proposed for planning the trajectory of a manipulator with nonlinear friction and geometrical constraints.     

The evolution of a production planning system: A 10-year case study

This paper describes the evolution of a production planning system (PPS) from a simple work sequence generation tool to a useful, sustained scheduling system. Three stages of evolution are described. In the first stage, a Gantt chart sequencing tool was converted to a scheduler's information system. This change was driven by the need to support the scheduler's daily task. The second stage of evolution was caused by an MRP–ERP conversion. The integration and conversion increased overhead and complexity in the job task and hence the tool, including the transformation of the previously integrated dispatching/scheduling task into separate dispatching and scheduling activities. The third stage of evolution has been small continuous improvements driven by management reporting requirements. PPS was developed in 1996 and has been fully operational since January 1997. Two major insights are discussed in this paper: the implications of supporting the scheduling task versus work sequence generation, and the software design requirements for evolutionary change as the software is used in an ever-changing situation.     

Multi-choice multi-objective mathematical programming model for integrated production planning: a case study

This article develops a multi-choice multi-objective linear programming model in order to solve an integrated production planning problem of a steel plant. The aim of the integrated production planning problem is to integrate the planning sub-functions into a single planning operation. The sub-functions are formulated by considering the capacity of different units of the plant, cost of raw materials from various territories, demands of customers in different geographical locations, time constraint for delivery the products, production cost and production rate at different stages of production process. Departure cost is also considered in the formulation of mathematical programming model. Some of the parameters are decided from a set of possible choices, therefore such parameters are considered as multi-choice type. Multi-choice mathematical programming problem cannot be solved directly. Therefore an equivalent multi-objective mathematical programming model is established in order to find the optimal solution of the problem. Computation of the mathematical programming model is performed with the practical production data of a plant to study the methodology.     

Multi-robot mission planning using evolutionary computation with incremental task addition

Intelligent Service Robotics - Mission planning asks the robot to solve complex missions, requiring the robot to execute several actions such that a complex goal condition (called mission) is...     

Personnel planning in concurrent engineering: A case study

Personnel planning is traditionally one of the last activities in the process of bringing a product to the market. This can lead to a lack of necessary knowledge, skills, and abilities (KSA) on the shop floor often causing delays at the start of production. In Concurrent Engineering (CE), planning activities are integrated and information is frequently exchanged to avoid such problems. As job design determines the requirements for personnel planning, this article presents a method for integrating job design and personnel planning. This method makes it possible to define personnel requirements in early phases as well as designing jobs to the availability of personnel. It is based on a tool for modeling the task structure in the early phases, when information is still poorly defined, continuously adapting the task model to increasing levels of detail, and a method of directly deducing from the task structure the necessary KSA. The method is well suited to deal with poorly defined information; it allows the design of jobs, and considers personnel selection, education, and training concurrently to other planning activities. A case study is presented to demonstrate its use. © 2000 John Wiley & Sons, Inc.     

Bayesian incremental inference update by re-using calculations from belief space planning: a new paradigm

Autonomous Robots - Inference and decision making under uncertainty are key processes in every autonomous system and numerous robotic problems. In recent years, the similarities between inference...     

A hybrid genetic tabu search algorithm for solving job shop scheduling problems: a case study

In recent decades many attempts have been made at the solution of Job Shop Scheduling Problem using a varied range of tools and techniques such as Branch and Bound at one end of the spectrum and Heuristics at the other end. However, the literature reviews suggest that none of these techniques are sufficient on their own to solve this stubborn NP-hard problem. Hence, it is postulated that a suitable solution method will have to exploit the key features of several strategies. We present here one such solution method incorporating Genetic Algorithm and Tabu Search. The rationale behind using such a hybrid method as in the case of other systems which use GA and TS is to combine the diversified global search and intensified local search capabilities of GA and TS respectively. The hybrid model proposed here surpasses most similar systems in solving many more traditional benchmark problems and real-life problems. This, the system achieves by the combined impact of several small but important features such as powerful chromosome representation, effective genetic operators, restricted neighbourhood strategies and efficient search strategies along with innovative initial solutions. These features combined with the hybrid strategy employed enabled the system to solve several benchmark problems optimally, which has been discussed elsewhere in Meeran and Morshed (8th Asia Pacific industrial engineering and management science conference, Kaohsiung, Taiwan, 2007). In this paper we bring out the system’s practical usage aspect and demonstrate that the system is equally capable of solving real life Job Shop problems.     

Negotiation as a metaphor for distributed problem solving

We describe the concept of distributed problem solving and define it as the cooperative solution of problems by a decentralized and loosely coupled collection of problem solvers. This approach to problem solving offers the promise of increased performance and provides a useful medium for exploring and developing new problem-solving techniques.We present a framework called the contract net that specifies communication and control in a distributed problem solver. Task distribution is viewed as an interactive process, a discussion carried on between a node with a task to be executed and a group of nodes that may be able to execute the task. We describe the kinds of information that must be passed between nodes during the discussion in order to obtain effective problem-solving behavior. This discussion is the origin of the negotiation metaphor: Task distribution is viewed as a form of contract negotiation.We emphasize that protocols for distributed problem solving should help determine the content of the information transmitted, rather than simply provide a means of sending bits from one node to another.The use of the contract net framework is demonstrated in the solution of a simulated problem in area surveillance, of the sort encountered in ship or air traffic control. We discuss the mode of operation of a distributed sensing system, a network of nodes extending throughout a relatively large geographic area, whose primary aim is the formation of a dynamic map of traffic in the area.From the results of this preliminary study we abstract features of the framework applicable to problem solving in general, examining in particular transfer of control. Comparisons with planner, conniver, hearsay-ii, and pup6 are used to demonstrate that negotiation—the two-way transfer of information—is a natural extension to the transfer of control mechanisms used in earlier problem-solving systems.     

Vehicle capacity planning system: a case study on vehicle routing problem with time windows

In this paper, we consider a local logistic company that provides transportation service for moving empty and laden containers within Singapore. Due to the limited capacity of its own fleet of vehicles, the company cannot handle all the job orders and have to outsource some orders to other smaller local transportation companies. The current operation of assigning jobs for outsourcing goes through two steps. In the first step, a certain percentage of jobs will be preselected for outsourcing according to some simple rules. Then at the second step, the rest of the jobs will be put into an in-house computer system which assigns jobs to its internal fleet of vehicles according to some greedy rules and the remaining jobs that cannot be served by the internal fleet of vehicles will be outsourced. This paper presents a vehicle capacity planning system (VCPS), which models the problem as a vehicle routing problem with time window constraints (VRPTW) and tabu search (TS) is applied to find a solution for the problem. From the simulation results, some new rules on how to assign jobs for outsourcing are derived, which are shown to be about 8% better than existing rules currently adopted by the company.     

A prototype-based method for classification with time constraints: a case study on automated planning

The main goal of Nearest Prototype Classification is to reduce storage space and retrieval time of classical Instance-Based Learning (IBL) algorithms. This motivation is higher in relational data since relational distance metrics are much more expensive to compute than classical distances like Euclidean distance. In this paper, we present an algorithm to build Relational Nearest Prototype Classifiers (RNPCs). When compared with Relational Instance-Based Learning (Relational IBL or RIBL) approaches, the algorithm is able to dramatically reduce the number of instances by selecting the most relevant prototypes, maintaining similar accuracy. The number of prototypes is obtained automatically by the algorithm, although it can also be bound by the user. In this work, we also show an application of RNPC for automated planning. Specifically, we describe a modeling task where a relational policy is built following an IBL approach. This approach uses the decisions taken by a planning system as learning examples. We show that when the number of learning examples is reduced with RNPC, the resulting policy is able to scale up better than the original planning system.     

Evaluating the mediator method: Prism as a case study

A software engineer's confidence in the profitability of a novel design technique depends to a significant degree on previous demonstrations of its profitability in practice. Trials of proposed techniques are thus of considerable value in providing factual bases for evaluation. We present our experience with a previously presented design approach as a basis for evaluating its promise and problems. Specifically, we report on our use of the mediator method to reconcile tight behavioral integration with ease of development and evolution of Prism, a system for planning radiation treatments for cancer patients. Prism is now in routine clinical use in several major research hospitals. Our work supports two claims. In comparison to more common design techniques, the mediator approach eases the development and evolution of integrated systems; and the method can be learned and used profitably by practising software engineers     

The Linux kernel as a case study in software evolution

We use 810 versions of the Linux kernel, released over a period of 14 years, to characterize the system’s evolution, using Lehman’s laws of software evolution as a basis. We investigate different possible interpretations of these laws, as reflected by different metrics that can be used to quantify them. For example, system growth has traditionally been quantified using lines of code or number of functions, but functional growth of an operating system like Linux can also be quantified using the number of system calls. In addition we use the availability of the source code to track metrics, such as McCabe’s cyclomatic complexity, that have not been tracked across so many versions previously. We find that the data supports several of Lehman’s laws, mainly those concerned with growth and with the stability of the process. We also make some novel observations, e.g. that the average complexity of functions is decreasing with time, but this is mainly due to the addition of many small functions.