Uncertainty management by relaxation of conflicting constraints in production process scheduling

Mathematical-analytical methods as used in Operations Research approaches are often insufficient for scheduling problems. This is due to three reasons: the combinatorial complexity of the search space, conflicting objectives for production optimization, and the uncertainty in the production process. Knowledge-based techniques, especially approximate reasoning and constraint relaxation, are promising ways to overcome these problems. A case study from an industrial CIM environment, namely high-grade steel production, is presented to demonstrate how knowledge-based scheduling with the desired capabilities could work. By using fuzzy set theory, the applied knowledge representation technique covers the uncertainty inherent in the problem domain. Based on this knowledge representation, a classification of jobs according to their importance is defined which is then used for the straightforward generation of a schedule. A control strategy which comprises organizational, spatial, temporal, and chemical constraints is introduced. The strategy supports the dynamic relaxation of conflicting constraints in order to improve tentative schedules

Local search: A guide for the information retrieval practitioner

There are a number of combinatorial optimisation problems in information retrieval in which the use of local search methods are worthwhile. The purpose of this paper is to show how local search can be used to solve some well known tasks in information retrieval (IR), how previous research in the field is piecemeal, bereft of a structure and methodologically flawed, and to suggest more rigorous ways of applying local search methods to solve IR problems. We provide a query based taxonomy for analysing the use of local search in IR tasks and an overview of issues such as fitness functions, statistical significance and test collections when conducting experiments on combinatorial optimisation problems. The paper gives a guide on the pitfalls and problems for IR practitioners who wish to use local search to solve their research issues, and gives practical advice on the use of such methods. The query based taxonomy is a novel structure which can be used by the IR practitioner in order to examine the use of local search in IR

Validation of spatial microsimulation models: a proposal to adopt the Bland-Altman method

Model validation is recognised as crucial to microsimulation modelling. However, modellers encounter difficulty in choosing the most meaningful methods to compare simulated and actual values. The aim of this paper is to introduce and demonstrate a method employed widely in healthcare calibration studies. The ‘Bland-Altman plot’ consists of a plot of the difference between two methods against the mean (x-y versus x+y/2). A case study is presented to illustrate the method in practice for spatial microsimulation validation. The study features a deterministic combinatorial model (SimObesity), which modelled a synthetic population for England at the ward level using survey (ELSA) and Census 2011 data. Bland-Altman plots were generated, plotting simulated and census ward-level totals for each category of all constraint (benchmark) variables. Other validation metrics, such as R2, SEI, TAE and RMSE, are also presented for comparison. The case study demonstrates how the Bland-Altman plots are interpreted. The simple visualisation of both individual- (ward-) level difference and total variation gives the method an advantage over existing tools used in model validation. There still remains the question of what constitutes a valid or well-fitting model. However, the Bland Altman method can usefully be added to the canon of calibration methods

Uncertainties due to imperfect knowledge of systematic effects: general considerations and approximate formulae

Starting from considerations about meaning and subsequent use of asymmetric uncertainty intervals of experimental results, we review the issue of uncertainty propagation. We show that, using a probabilistic approach (the so-called Bayesian approach), all sources of uncertainty can be included in a logically consistent way. Practical formulae for the first moments of the probability distribution are derived up to second-order approximations.Comment: 23 pages, 6 figures. This paper and related work are also available at http://www-zeus.roma1.infn.it/~agostini/prob+stat.htm