Abstract
In this paper, we analyse a complex production planning problem in a Make-To-Order company, involving quoting due dates, along with production scheduling, some plant layout decisions and line balancing issues. A general framework has been developed that identifies the main levels for decision making, and tackles these problems in a hierarchical fashion, the resulting interactions being carefully taken into consideration. This framework, based on considering discretised planning horizons, is implemented on a Decision Support System developed around an interface specially designed to involve the various participants in the planning process. In practice, the system is composed by a package of software products used as add-ons to the main management information system of the company. A comprehensive and detailed description of the approach, techniques used and developed algorithms is presented. The experience in using a first prototype of the system shows this may become a highly valuable computer tool in production planning, layout design and assignment of orders.

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A theoretical and experimental investigation, which allows the quantification of the general error function relative to the synthetic-heterodyne signal processing in interferometric fibre-optic sensors, is performed. The implications on the performance of interferometric sensing arrays are addressed. Considering the decrease of the readout phase errors, potentially more favourable implementations of this processing technique are proposed and compared.

Abstract
Vector quantization of images raises problems of complexity in codebook search and subjective quality of images. The family of image vector quantieation algorithms proposed in this paper addresses both of those problems. The Fuzzy Classified Vector Quantizer (FCVQ) is based on fuzzy set theory and consists basically in a method of extracting a subcodebook from the original codebook, biased by the features of the block to be coded. The incidence of each feature on the blocks is represented by a fuzzy set that captures its (possibly subjective) nature. Unlike the Classified Vector Quantizer (CVQ), in the FCVQ a specific subcodebook is extracted for each block to be coded, allowing a better adaptation to the block. The CVQ may be regarded as a special case of the FCVQ. In order to explore the possible correlation between blocks, an estimator for the degree of incidence of features on the block to be coded is included. The estimate is based on previously coded blocks and is obtained by maximizing a possibility; a distribution that intends to represent the subjective knowledge on the feature's possibility of occurrence conditioned to the coded blocks is used. Some examples of the application of a FCVQ coder to two test images are presented. A slight improvement on the subjective quality of the coded images is obtained, together with a significant reduction on the codebook search complexity and, when applying the estimator, a reduction of the bit rate.