Practices for strategic capacity management in Malaysian manufacturing firms

While the notion of manufacturing capabilities is a long-standing notion in research on operations management, its actual implementation and management has been hardly researched. Five case studies in Malaysia offered the opportunity to examine the practice of manufacturing managers with regard to strategic capability management. The data collection and analysis was structured by using the notion of Strategic Capacity Management. Whereas traditionally literature has demonstrated the beneficial impact of an appropriate manufacturing strategy on the business strategy and performance, the study highlights the difficulty of managers to set the strategy, let alone implementing it. This is partly caused by the immense pressure of customers in these dominantly Make-To-Order environments for SMEs. Current concepts for manufacturing capabilities have insufficiently accounted this phenomenon and an outline of a research agenda is presented

A review of multi-factor capacity expansion models for manufacturing plants:searching for a holistic decision aid

Investment in capacity expansion remains one of the most critical decisions for a manufacturing organisation with global production facilities. Multiple factors need to be considered making the decision process very complex. The purpose of this paper is to establish the state-of-the-art in multi-factor models for capacity expansion of manufacturing plants within a corporation. The research programme consisting of an extensive literature review and a structured assessment of the strengths and weaknesses of the current research is presented. The study found that there is a wealth of mathematical multi-factor models for evaluating capacity expansion decisions however no single contribution captures all the different facets of the problem

Building Real Options into Physical Systems with Stochastic Mixed-Integer Programming

The problem of building real options into physical systems has three features: real options are not as easily defined as financial options; path-dependency and interdependencies among projects mean that the standard tools of options analysis tools are insufficient; and the focus is on identifying the best way to build flexibility into the design – not to value individual options. This paper suggests a framework for exploring real options in physical systems that especially addresses these two difficulties. This framework has two stages: options identification and options analysis. The options identification stage consists of screening and simulation models that focus attention on a small subset of the possible combination of projects. The options analysis stage uses stochastic mixed-integer programming to manage the path-dependency and interdependency features. This stochastic formulation enables the analyst to include more technical details and develop explicit plans for the execution of projects according to the contingencies that arise. The paper illustrates the approach with a case study of a water resources planning problem, but the framework is generally applicable to a variety of large-scale physical systems

Inexact fuzzy-stochastic constraint-softened programming - A case study for waste management

In this study, an inexact fuzzy-stochastic constraint-softened programming method is developed for municipal solid waste (MSW) management under uncertainty, The developed method can deal with multiple uncertainties presented in terms of fuzzy sets, interval values and random variables. Moreover, a number of violation levels for the system constraints are allowed. This is realized through introduction of violation variables to soften system constraints, such that the model's decision space can be expanded under demanding conditions. This can help generate a range of decision alternatives under various conditions, allowing in-depth analyses of tradeoffs among economic objective, satisfaction degree, and constraint-violation risk. The developed method is applied to a case study of planning a MSW management system. The uncertain and dynamic information can be incorporated within a multi-layer scenario tree; revised decisions are permitted in each time period based on the realized values of uncertain events. Solutions associated with different satisfaction degree levels have been generated, corresponding to different constraint-violation risks. They are useful for supporting decisions of waste flow allocation and system-capacity expansion within a multistage context. (C) 2008 Elsevier Ltd. All rights reserved

A two-stage planning model for power scheduling in a hydro-thermal system under uncertainty

Accepted versio

MODELOS Y MÉTODOS DE OPTIMIZACIÓN LINEAL CON INCERTIDUMBRE: UNA BREVE REVISIÓN DEL ESTADO DEL ARTE

In the modeling of many problems on linear optimization is not possible to consider the classic deterministic model because the set of parameters is not fully known due to the significant variation of the data along time or because there is no uniformity on the values. These kind of problems are known as problems with uncertainty and there are different approaches about modeling and methods of solution to resolve them. In this paper we make a review of such approaches focusing basically in stochastic optimization, fuzzy optimization, intervaling optimization and hybrid optimization. The difference between these approaches is perceived in the nature of the data, notions of feasibility and optimality and computational requirements, among others.En la modelación de muchos problemas de optimización lineal no es posible considerar el modelo clásico determinista, porque el conjunto de los parámetros no son completamente conocidos debido a que los datos varian en forma significativa a lo largo del tiempo o porque no hay homogeneidad en los valores.Estos problemas son conocidos como problemas con incertidumbre, para los cuales existen diversos enfoques en la modelación y en los métodos de solución. En este artículo se revisa tales enfoques, incidiendo fundamentalmente en la optimización estocástica, optimización difusa, optimización intervalar y optimización híbrida. La diferencia entre estos enfoques se perciben en la naturaleza de los datos, nociones de factibilidad y optimalidad, requerimientos computacionales, entre otros

Designing effective and efficient incentive policies for renewable in generation expansion planning

We present a bilevel optimization approach to designing effective and efficient incentive policies for promoting renewable energy. The effectiveness of an incentive policy is its capability to achieve a goal that would not be achievable without it. Renewable portfolio standard is used in this thesis as the policy goal. The efficiency of an incentive policy is measured by the amount of intervention, such as taxes collected and subsidies paid, to achieve the policy goal. We obtain the most effective and efficient incentive policies in the context of generation expansion planning, in which a planner makes investment decisions to serve project demand of electricity. A case study is conducted on an integrated coal and electricity network representing the contiguous United States. Numerical analysis from the case study provides insights on the comparison of various incentive policies. The sensitivity of the incentive policies with respect to coal production, energy investment costs, and transmission capacity is also studied

A probabilistic numerical method for optimal multiple switching problem and application to investments in electricity generation

In this paper, we present a probabilistic numerical algorithm combining dynamic programming, Monte Carlo simulations and local basis regressions to solve non-stationary optimal multiple switching problems in infinite horizon. We provide the rate of convergence of the method in terms of the time step used to discretize the problem, of the size of the local hypercubes involved in the regressions, and of the truncating time horizon. To make the method viable for problems in high dimension and long time horizon, we extend a memory reduction method to the general Euler scheme, so that, when performing the numerical resolution, the storage of the Monte Carlo simulation paths is not needed. Then, we apply this algorithm to a model of optimal investment in power plants. This model takes into account electricity demand, cointegrated fuel prices, carbon price and random outages of power plants. It computes the optimal level of investment in each generation technology, considered as a whole, w.r.t. the electricity spot price. This electricity price is itself built according to a new extended structural model. In particular, it is a function of several factors, among which the installed capacities. The evolution of the optimal generation mix is illustrated on a realistic numerical problem in dimension eight, i.e. with two different technologies and six random factors