Julian Scott Yeomans

An interval-parameter fuzzy two-stage stochastic program for water resources management under uncertainty

This study presents an interval-parameter fuzzy two-stage stochastic programming (IFTSP) method for the planning of water-resources-management systems under uncertainty. The model is derived by incorporating the concepts of interval-parameter and fuzzy programming techniques within a two-stage stochastic optimization framework. The approach has two major advantages in comparison to other optimization techniques. Firstly, the IFTSP method can incorporate pre-defined water policies directly into its optimization process and, secondly, it can readily integrate inherent system uncertainties expressed not only as possibility and probability distributions but also as discrete intervals directly into its solution procedure. The IFTSP process is applied to an earlier case study of regional water resources management and it is demonstrated how the method efficiently produces stable solutions together with different risk levels of violating pre-established allocation criteria. In addition, a variety of decision alternatives are generated under different combinations of water shortage.

OPTIMAL LEVEL SCHEDULES FOR MIXED-MODEL, MULTI-LEVEL JUST-IN-TIME ASSEMBLY SYSTEMS

Solving the level (or balanced) schedule problem is the most important scheduling goal for just-in-time production assembly systems. No previous methods have been presented for determining optimal balanced schedules in multi-level facilities. In this paper, it is shown that the multi-level, min-max problem is NP-hard in the strong sense. A dynamic programming algorithm (DP) is developed for both the min-max and min-sum problems which, for the first time, permits optimal schedules to be determined for large, multi-level problems. The time and space requirements of the DP are analyzed and several techniques for reducing the DPs computational requirements are described. A filtering scheme is proposed to eliminate dominated solutions from a problems potentially vast state space. Extensive computational testing of the min-max algorithm is reported and the conclusions from this testing are presented.

Optimal level schedules in mixed-model, multi-level JIT assembly systems with pegging

Abstract Solving the level (or balanced) schedule problem is the most important scheduling goal for just-in-time production assembly systems. For most industrial applications, determining the optimal balanced schedule in a multi-level facility is a very difficult combinatorial problem. However, if outputs at production levels which feed the final assembly level are dedicated or ‘pegged’ to the final product into which they will be assembled, then it can be shown that an efficient scheduling algorithm can be implemented to determine the optimal level schedule. It can also be shown that several symmetrical properties of the pegging model can be exploited to significantly reduce the computational requirements of the problem.

Supply planning for industrial ecology and remanufacturing under uncertainty: a numerical study of leaded-waste recovery from television disposal

The use of waste as a raw material for manufacturing is hampered by the uncertainty associated with the availability of supply. Technological change and obsolescence further complicates the ability of decision makers to consider discarded durable products as a potential source of raw materials. This uncertainty complicates remanufacturing and industrial ecology. A problem since remanufacturing and industrial ecology need to be (and can be) profitable as well as environmentally desirable if they are to be encouraged. To address this problem the modelling of the waste flow of durable goods is considered. The disposal of televisions in the United States is used to illustrate the challenges and requirements for forecasting in an environment with supply uncertainty. This example is timely since the diposal of cathode ray tubes (CRTs) in municipal landfills is being banned and an alternate technology trajectory for televisions exists—the flat panel display and phase-out of analogue broadcasting in the US. This paper estimates the waste stream resulting from three different scenarios of CRT leaded-waste disposal patterns. The reuse of lead-containing CRT glass is found to offer potential. The elimination of this controversial waste stream, as a result of replacement by the adoption of flat panel television technology, is still decades away. The findings in this study indicate the range of the quantity of waste that will require an alternative infrastructure as it is displaced from municipal landfills. This study provides important information for both developing a collection infrastructure and processing alternatives to extract the residual value of the disposed of televisions.

An Extension to a DEA Support System Used for Assessing R&D Projects

This paper describes an extension to the data envelopment analysis (DEA) support system that has been used for the assessment, rating, and ranking of diverse portfolios of research and development (R&D) projects at Lucent Technologies. The approach is illustrated through its application to a large portfolio of R&D projects considered by Lucents Advanced Technologies Group. The method proceeds by first stratifying the portfolio into comparably efficient groups of projects through the construction of a series of efficient DEA frontiers, and then by lexicographically ranking each project within these groups relative to DEA-based contextual attractiveness measures calculated from the different partitions. The advantages to this approach are provided not only from the perspective of the specific project rankings that are produced but also from the broader managerial insights that can be derived from any resulting differences between officially sanctioned, quantitative decision-making procedures, and the quality of the decisions that have actually been made by managers.

Methods for addressing climate change uncertainties in project environmental impact assessments

Climate change has important implications for assessing impacts of many types of project. If climate change is to be included in environmental assessments, then proponents must be able to incorporate its impacts and inherent uncertainties effectively into their analysis; many proponents do not possess sufficient grounding in how to accomplish this task successfully. In this paper, three basic analytical approaches to uncertainty analysis — scenario analysis, sensitivity analysis, and probabilistic analysis — are presented that proponents could use for integrating climate change induced impacts and their uncertainties into their environmental assessments, together with a framework for judging the circumstances that determine which method would be applicable. The use of these three approaches is illustrated on the environmental impacts of a run-of-the-river hydroelectric project.

Policy planning using genetic algorithms combined with simulation: the case of municipal solid waste

Previous research had introduced a genetic algorithm procedure for creating alternative policy options for municipal solid waste (MSW) management planning. These alternatives were generated during the design phase of planning, with the final policy determined in subsequent comparative analysis. However, because of the many uncertain factors that exist within MSW systems, this earlier procedure cannot be applied to situations containing such stochastic components. In this paper, it is shown that a generic algorithm approach can be simultaneously combined with simulation to incorporate these stochastic elements in the policy option generation phase; thereby permitting uncertainty to be directly integrated into the construction of the alternatives during the planning-design phase. This procedure is applied to case data taken from the Regional Municipality of Hamilton–Wentworth in the Province of Ontario, Canada. It can be shown that this procedure extends the earlier approach and provides many practical planning benefits for problems when uncertain conditions are present.

Automatic generation of efficient policy alternatives via simulation-optimization

Simulation-optimization methods can be used for many practical and industrial problems in which some or all of the system components are stochastic. These techniques can be applied to a wide variety of problem types, including those in which some functions cannot be represented analytically. In contrast to earlier function optimization approaches, in this paper, these techniques are used for generating several new policy options for planning applications. By using this approach, multiple policy alternatives can be created that meet established system criteria, while simultaneously remaining acceptable and implementable in practice. A subsequent comparative evaluation of the alternatives would be undertaken prior to final policy selection. An illustrative application of the method is provided to demonstrate the usefulness of this approach in the planning phase of policy design.

A Computational Analysis Of Balanced Jit Optimization Algorithms

Abstract The balanced schedule problem in mixed-model, JIT manufacturing is examined. Solving this problem is the cornerstone of production in any JIT facility. Although very efficient procedures have been demonstrated for the single-level problems, nobody has examined the nature of the solutions that these procedures actually produce. In this paper, the first large scale computational study is undertaken to examine these optimization algorithms. Furthermore, several open questions and conjectures have developed in the past few years. These open questions are formulated as propositions and answered by means of the extensive computational testing. The answers to these propositions are conclusive and, in some cases, quite unexpected. While it had appeared that most of the single-level problems had been efficiently solved, this study points to several interesting questions requiring additional study.

ADDRESSING AND COMMUNICATING CLIMATE CHANGE AND ITS UNCERTAINTIES IN PROJECT ENVIRONMENTAL IMPACT ASSESSMENTS

While climate change has become an important concern at both regional and global levels, its inherent uncertainties have often been cited as the main reason for delaying many actions to mitigate its potential impacts. Reviews of environmental assessments (EAs) have shown that impacts from climate change have been inadequately addressed within them and that the corresponding uncertainties have been addressed even more poorly. This paper describes several basic approaches for addressing and analysing climate change within the EAs of individual projects with a focus on its uncertainties. Subsequently, the paper describes how the results from this analysis can be effectively and comprehensively communicated to the EAs disparate set of technical and non-technical decision-makers and stakeholders. Based upon this overall approach, the paper proposes a general set of guidelines that enables proponents to incorporate climate change and its uncertainties into project EAs.