Probabilistic cost benefit analysis of generation investment in a deregulated electricity market

A new methodology is proposed for the analysis of generation capacity investment in a deregulated market environment. This methodology proposes to make the investment appraisal using a probabilistic framework. The probabilistic production simulation (PPC) algorithm is used to compute the expected energy generated, taking into account system load variations and plant forced outage rates, while the Monte Carlo approach has been applied to model the electricity price variability seen in a realistic network. The model is able to capture the price and hence the profitability uncertainties for generator companies. Seasonal variation in the electricity prices and the system demand are independently modeled. The method is validated on IEEE RTS system, augmented with realistic market and plant data, by using it to compare the financial viability of several generator investments applying either conventional or directly connected generator (powerformer) technologies. The significance of the results is assessed using several financial risk measures.

Information system planning in drug service:a case study

The development of an information system in Caribbean public sector organisations is usually seen as a matter of installing hardware and software according to a directive from senior management, without much planning. This causes huge investment in procuring hardware and software without improving overall system performance. Increasingly, Caribbean organisations are looking for assurances on information system performance before making investment decisions not only to satisfy the funding agencies, but also to be competitive in this dynamic and global business world. This study demonstrates an information system planning approach using a process-reengineering framework. Firstly, the stakeholders for the business functions are identified along with their relationships and requirements. Secondly, process reengineering is carried out to develop the system requirements. Accordingly, information technology is selected through detailed system requirement analysis. Thirdly, cost-benefit analysis, identification of critical success factors and risk analysis are carried out to strengthen the selection. The entire methodology has been demonstrated through an information system project in the Barbados drug service, a public sector organisation in the Caribbean.

Optimal production planning for PCB assembly

It is indisputable that printed circuit boards (PCBs) play a vital role in our daily lives. With the ever-increasing applications of PCBs, one of the crucial ways to increase a PCB manufacturer’s competitiveness in terms of operation efficiency is to minimize the production time so that the products can be introduced to the market sooner. Optimal Production Planning for PCB Assembly is the first book to focus on the optimization of the PCB assembly lines’ efficiency. This is done by: • integrating the component sequencing and the feeder arrangement problems together for both the pick-and-place machine and the chip shooter machine; • constructing mathematical models and developing an efficient and effective heuristic solution approach for the integrated problems for both types of placement machines, the line assignment problem, and the component allocation problem; and • developing a prototype of the PCB assembly planning system. The techniques proposed in Optimal Production Planning for PCB Assembly will enable process planners in the electronics manufacturing industry to improve the assembly line’s efficiency in their companies. Graduate students in operations research can familiarise themselves with the techniques and the applications of mathematical modeling after reading this advanced introduction to optimal production planning for PCB assembly.

A production planning system for plastic footwear in a seasonal market

Modern injection-moulding machinery which produces several, pairs of plastic footwear at a time brought increased production planning problems to a factory. The demand for its footwear is seasonal but the company's manning policy keeps a fairly constant production level thus determining the aggregate stock. Production planning must therefore be done within the limitations of a specified total stock. The thesis proposes a new production planning system with four subsystems. These are sales forecasting, resource planning, and two levels of production scheduling: (a) aggregate decisions concerning the 'manufacturing group' (group of products) to be produced in each machine each week, and (b) detailed decisions concerning the products within a manufacturing group to be scheduled into each mould-place. The detailed scheduling is least dependent on improvements elsewhere so the sub-systems were tackled in reverse order. The thesis concentrates on the production scheduling sub-systems which will provide most. of the benefits. The aggregate scheduling solution depends principally on the aggregate stocks of each manufacturing group and their division into 'safety stocks' (to prevent shortages) and 'freestocks' (to permit batch production). The problem is too complex for exact solution but a good heuristic solution, which has yet to be implemented, is provided by minimising graphically immediate plus expected future costs. The detailed problem splits into determining the optimal safety stocks and batch quantities given the appropriate aggregate stocks. It.is found that the optimal safety stocks are proportional to the demand. The ideal batch quantities are based on a modified, formula for the Economic Batch Quantity and the product schedule is created week by week using a priority system which schedules to minimise expected future costs. This algorithm performs almost optimally. The detailed scheduling solution was implemented and achieved the target savings for the whole project in favourable circumstances. Future plans include full implementation.

Strategic development of transport systems: a study of the physical constraints on planning processes

Investment in transport infrastructure can be highly sensitive to uncertainty. The scale and lead time of strategic transport programmes are such that they require continuing policy support and accurate forecasting. Delay, cost escalation and abandonment of projects often result if these conditions are not present. In Part One the physical characteristics of infrastructure are identified as a major constraint on planning processes. The extent to which strategies and techniques acknowledge these constraints is examined. A simple simulation model is developed to evaluate the effects on system development of variations in the scale and lead time of investments. In Part Two, two case studies of strategic infrastructure investment are analysed. The absence of a policy consensus for airport location was an important factor in the delayed resolution of the Third London Airport issue. In London itself, the traffic and environmental effects of major highway investment ultimately resulted in the abandonment of plans to construct urban motorways. In both cases, the infrastructure implications of alternative strategies are reviewed with reference to the problems of uncertainty. In conclusion, the scale of infrastructure investment is considered the most important of the constraints on the processes of transport planning. Adequate appraisal of such constraints may best be achieved by evaluation more closely aligned to policy objectives.

Modelling manufacturing planning and control systems:the application of object-oriented principles and discrete-event simulation

Manufacturing planning and control systems are fundamental to the successful operations of a manufacturing organisation. 10 order to improve their business performance, significant investment is made by companies into planning and control systems; however, not all companies realise the benefits sought Many companies continue to suffer from high levels of inventory, shortages, obsolete parts, poor resource utilisation and poor delivery performance. This thesis argues that the fit between the planning and control system and the manufacturing organisation is a crucial element of success. The design of appropriate control systems is, therefore, important. The different approaches to the design of manufacturing planning and control systems are investigated. It is concluded that there is no provision within these design methodologies to properly assess the impact of a proposed design on the manufacturing facility. Consequently, an understanding of how a new (or modified) planning and control system will perform in the context of the complete manufacturing system is unlikely to be gained until after the system has been implemented and is running. There are many modelling techniques available, however discrete-event simulation is unique in its ability to model the complex dynamics inherent in manufacturing systems, of which the planning and control system is an integral component. The existing application of simulation to manufacturing control system issues is limited: although operational issues are addressed, application to the more fundamental design of control systems is rarely, if at all, considered. The lack of a suitable simulation-based modelling tool does not help matters. The requirements of a simulation tool capable of modelling a host of different planning and control systems is presented. It is argued that only through the application of object-oriented principles can these extensive requirements be achieved. This thesis reports on the development of an extensible class library called WBS/Control, which is based on object-oriented principles and discrete-event simulation. The functionality, both current and future, offered by WBS/Control means that different planning and control systems can be modelled: not only the more standard implementations but also hybrid systems and new designs. The flexibility implicit in the development of WBS/Control supports its application to design and operational issues. WBS/Control wholly integrates with an existing manufacturing simulator to provide a more complete modelling environment.

Organised for inward investment? Development agencies, local government, and firms in the inward investment process

Inward investment promotion and aftercare remain central aspects of local economic development for English Regional Development Agencies, Scottish and Welsh development bodies, and local authorities in Britain. In many cases, partnership and consultation mechanisms have become integral to attracting inward investment and providing aftercare. Inward investment is thus an important area in which to explore interinstitutional relations between agents operating along diverse spatial boundaries and with different responsibilities. In this paper we analyse the local and regional institutional structures and relations characterising the inward investment process in Britain using new survey data from local authorities, regional bodies, and inward investors. We find that promotional activities have clearly defined structures which are chiefly led by the regional level. Aftercare is characterised by more collaborative arrangements involving both regional bodies and local government. However, many bodies are little used, with competition and tension between partners remaining frequent within English regions, regardless of recent institutional changes designed to reduce such problems. In Scotland and Wales, however, their national institutions are not only widely used, but they create high levels of satisfaction from firms. Hence, England has yet to respond to the effective challenges of Scotland and Wales. The analysis also highlights the limited importance of all national, regional, and local public institutions in attracting inward investors and their subsequent aftercare. The critical inputs to business decisions appear to be driven chiefly by more general supply-side conditions (for example, general skills versus local public packages) and the general attractions of a particular location.

Strategic Planning: Implications and Applications for Line Managers

Strategic planning is the key to producing a realistic, attractive rate of growth and a respectable return on investment. The author analyzes the steps in the planning process and looks at the environmental and cultural values which influence the strategic planner in his/her work.

Branch and price solution approach for order acceptance and capacity planning in make -to -order operations

The increasing emphasis on mass customization, shortened product lifecycles, synchronized supply chains, when coupled with advances in information system, is driving most firms towards make-to-order (MTO) operations. Increasing global competition, lower profit margins, and higher customer expectations force the MTO firms to plan its capacity by managing the effective demand. The goal of this research was to maximize the operational profits of a make-to-order operation by selectively accepting incoming customer orders and simultaneously allocating capacity for them at the sales stage. ^ For integrating the two decisions, a Mixed-Integer Linear Program (MILP) was formulated which can aid an operations manager in an MTO environment to select a set of potential customer orders such that all the selected orders are fulfilled by their deadline. The proposed model combines order acceptance/rejection decision with detailed scheduling. Experiments with the formulation indicate that for larger problem sizes, the computational time required to determine an optimal solution is prohibitive. This formulation inherits a block diagonal structure, and can be decomposed into one or more sub-problems (i.e. one sub-problem for each customer order) and a master problem by applying Dantzig-Wolfe’s decomposition principles. To efficiently solve the original MILP, an exact Branch-and-Price algorithm was successfully developed. Various approximation algorithms were developed to further improve the runtime. Experiments conducted unequivocally show the efficiency of these algorithms compared to a commercial optimization solver.^ The existing literature addresses the static order acceptance problem for a single machine environment having regular capacity with an objective to maximize profits and a penalty for tardiness. This dissertation has solved the order acceptance and capacity planning problem for a job shop environment with multiple resources. Both regular and overtime resources is considered. ^ The Branch-and-Price algorithms developed in this dissertation are faster and can be incorporated in a decision support system which can be used on a daily basis to help make intelligent decisions in a MTO operation.^

Branch and Price Solution Approach for Order Acceptance and Capacity Planning in Make-to-Order Operations

The increasing emphasis on mass customization, shortened product lifecycles, synchronized supply chains, when coupled with advances in information system, is driving most firms towards make-to-order (MTO) operations. Increasing global competition, lower profit margins, and higher customer expectations force the MTO firms to plan its capacity by managing the effective demand. The goal of this research was to maximize the operational profits of a make-to-order operation by selectively accepting incoming customer orders and simultaneously allocating capacity for them at the sales stage. For integrating the two decisions, a Mixed-Integer Linear Program (MILP) was formulated which can aid an operations manager in an MTO environment to select a set of potential customer orders such that all the selected orders are fulfilled by their deadline. The proposed model combines order acceptance/rejection decision with detailed scheduling. Experiments with the formulation indicate that for larger problem sizes, the computational time required to determine an optimal solution is prohibitive. This formulation inherits a block diagonal structure, and can be decomposed into one or more sub-problems (i.e. one sub-problem for each customer order) and a master problem by applying Dantzig-Wolfe’s decomposition principles. To efficiently solve the original MILP, an exact Branch-and-Price algorithm was successfully developed. Various approximation algorithms were developed to further improve the runtime. Experiments conducted unequivocally show the efficiency of these algorithms compared to a commercial optimization solver. The existing literature addresses the static order acceptance problem for a single machine environment having regular capacity with an objective to maximize profits and a penalty for tardiness. This dissertation has solved the order acceptance and capacity planning problem for a job shop environment with multiple resources. Both regular and overtime resources is considered. The Branch-and-Price algorithms developed in this dissertation are faster and can be incorporated in a decision support system which can be used on a daily basis to help make intelligent decisions in a MTO operation.

A Study of the State of the Art of Process Planning for Additive Manufacturing

In the manufacturing industry the term Process Planning (PP) is concerned with determining the sequence of individual manufacturing operations needed to produce a given part or product with a certain machine. In this technical report we propose a preliminary analysis of scientific literature on the topic of process planning for Additive Manufacturing (AM) technologies (i.e. 3D printing). We observe that the process planning for additive manufacturing processes consists of a small set of standard operations (repairing, orientation, supports, slicing and toolpath generation). We analyze each of them in order to emphasize the most critical aspects of the current pipeline as well as highlight the future challenges for this emerging manufacturing technology.

Motion Planning and Controls with Safety and Temporal Constraints

Motion planning, or trajectory planning, commonly refers to a process of converting high-level task specifications into low-level control commands that can be executed on the system of interest. For different applications, the system will be different. It can be an autonomous vehicle, an Unmanned Aerial Vehicle(UAV), a humanoid robot, or an industrial robotic arm. As human machine interaction is essential in many of these systems, safety is fundamental and crucial. Many of the applications also involve performing a task in an optimal manner within a given time constraint. Therefore, in this thesis, we focus on two aspects of the motion planning problem. One is the verification and synthesis of the safe controls for autonomous ground and air vehicles in collision avoidance scenarios. The other part focuses on the high-level planning for the autonomous vehicles with the timed temporal constraints. In the first aspect of our work, we first propose a verification method to prove the safety and robustness of a path planner and the path following controls based on reachable sets. We demonstrate the method on quadrotor and automobile applications. Secondly, we propose a reachable set based collision avoidance algorithm for UAVs. Instead of the traditional approaches of collision avoidance between trajectories, we propose a collision avoidance scheme based on reachable sets and tubes. We then formulate the problem as a convex optimization problem seeking control set design for the aircraft to avoid collision. We apply our approach to collision avoidance scenarios of quadrotors and fixed-wing aircraft. In the second aspect of our work, we address the high level planning problems with timed temporal logic constraints. Firstly, we present an optimization based method for path planning of a mobile robot subject to timed temporal constraints, in a dynamic environment. Temporal logic (TL) can address very complex task specifications such as safety, coverage, motion sequencing etc. We use metric temporal logic (MTL) to encode the task specifications with timing constraints. We then translate the MTL formulae into mixed integer linear constraints and solve the associated optimization problem using a mixed integer linear program solver. We have applied our approach on several case studies in complex dynamical environments subjected to timed temporal specifications. Secondly, we also present a timed automaton based method for planning under the given timed temporal logic specifications. We use metric interval temporal logic (MITL), a member of the MTL family, to represent the task specification, and provide a constructive way to generate a timed automaton and methods to look for accepting runs on the automaton to find an optimal motion (or path) sequence for the robot to complete the task.