Managing variants of a personalized product

作者:Albert Haag

摘要

A product variant table is a table that lists legal combinations of product features. Variant tables can be used to constrain the variability offered for a personalized product. The concept of such a table is easy to understand. Hence, variant tables are natural to use when ensuring the completeness and correctness of a quote/order for a customizable product. They are also used to filter out inadmissible choices for features in an interactive specification (configuration) process. Variant tables can be maintained as relational (database) tables, using spreadsheets, or in proprietary ways offered by the product modeling environment. Variant tables can become quite large. A way of compressing them is then sought that supports a space-efficient representation and a time-efficient evaluation. The motivation of this work is to develop a simple approach to compress/compile a variant table into an easy to read, but possibly hard to write form that can be deployed in a business setting at acceptable cost and risk in a similar manner as a database. The main result is a simple compression and evaluation scheme for an individual variant table called a Variant Decomposition Diagram (VDD). A VDD supports efficient consistency checks, the filtering of inadmissible features, and iteration over the table. A simple static heuristic for decomposition order is proposed that suggests itself from a “column oriented viewpoint”. This heuristic is not always optimal, but it has the advantage of allowing fast compilation of a variant table into a VDD. Compression results for a publicly available model of a Renault Megane are given. With the proposed heuristic the VDD is a specialization of a Zero-suppressed (binary) Decision Diagram (ZDD) (Knuth 2011) and also maps to a Multi-valued Decision Diagram (MDD) (Andersen et al. 2007; Berndt et al. 2012).

论文关键词:Variant tables, Arc consistency, Decision diagrams, Table compression, Column-oriented decomposition, VDD, ZDD, BDD, MDD

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论文官网地址:https://doi.org/10.1007/s10844-016-0432-5