The book provides a short review of the classical approach to planning and decision making under uncertainty. It further explains the concept of state-contingent goods, and it extends earlier work on the state-contingent approach to describing production under uncertainty to the problems involved in optimising production under uncertainty.
Many decision problems in Operations Research are defined on temporal networks, that is, workflows of time-consuming tasks whose processing order is constrained by precedence relations. For example, temporal networks are used to model projects, computer applications, digital circuits and production processes. Optimization problems arise in temporal networks when a decision maker wishes to determine a temporal arrangement of the tasks and/or a resource assignment that optimizes some network characteristic (e.g. the time required to complete all tasks). The parameters of these optimization problems (e.g. the task durations) are typically unknown at the time the decision problem arises. This monograph investigates solution techniques for optimization problems in temporal networks that explicitly account for this parameter uncertainty. We study several formulations, each of which requires different information about the uncertain problem parameters.
This book covers the basic theory of how, what and when firms should produce to maximise profits. Based on the neoclassical theory of the firm presented in most general microeconomic textbooks, it extends the general treatment and focuses on the application of the theory to specific problems that the firm faces when making production decisions to maximise profits. Increasing level of government regulation and the use of specialised and often very expensive equipment in modern production motivates the following focus areas: 1) How to optimise production under restrictions., 2) Treatment of fixed inputs and the process of input fixation, 3) Optimisation of production over time, 4) Linear and Mixed Integer Programming as tools for optimisation in practice. This updated second edition includes a more comprehensive introduction to the theory of decision making under risk and uncertainty as well as a new chapter on how to use linear programming to generate the supply function of the firm.
Drawing on cutting-edge research, this book proposes a new 'Supply Chain Optimization under Uncertainty', technology. Its application can bring many proven benefits to supply chain entities, any associated service providers, and, of course, the customers. The technology can provide the best design and operating solution for a Supply Chain Network (SCN) that is subject to any prevailing conditions of Operational Uncertainty (OU). A SCN is defined as a network of production facilities, distribution centers and retail sales outlets. OU is defined as any relevant combination of i) multiple process objectives e.g. a business needs to maximize operating profits and to minimize inventory levels, ii) fuzziness (, or >=) e.g. sales
Drawing on cutting-edge research, this book proposes a new 'Supply Chain Optimization under Uncertainty’, technology. Its application can bring many proven benefits to supply chain entities, any associated service providers, and, of course, the customers. The technology can provide the best design and operating solution for a Supply Chain Network (SCN) that is subject to any prevailing conditions of Operational Uncertainty (OU). A SCN is defined as a network of production facilities, distribution centers and retail sales outlets. OU is defined as any relevant combination of i) multiple process objectives e.g. a business needs to maximize operating profits and to minimize inventory levels, ii) fuzziness (<, <=, >, or >=) e.g. sales <= 1500 t/mth and iii) probability e.g. sale of fertilizer is dependent on probabilistic rainfall. Following this method always enables the determination of realistic optimum supply chain solutions, since the effects of any operational uncertainties are always provided for. The book is arranged in two parts. The first part covers the theory and recent research into supply chain optimization under uncertainty. The second part documents the application of the newly proposed technology to an agricultural fertilizer’s (NPK, South Africa) supply chain.
