We describe a preliminary implementation of the high-level modelling language Zinc. This language supports a modelling methodology in which the same Zinc model can be automatically mapped into different design models, thus allowing modellers to easily “plug and play” with different solving techniques and so choose the most appropriate for that problem. Currently, mappings to three very different design models based on constraint programming (CP), mixed integer programming (MIP) and local search are provided. Zinc is the first modelling language that we know of that supports such solver and technique-independent modelling. It does this by using an intermediate language called Flattened Zinc, and rewrite rules for transforming the Flattened Zinc model into one that is tailored to a particular solving technique.

Zinc is a new modelling language developed as part of the G12 project. It has four important characteristics. First, Zinc allows specification of models using a natural mathematicallike notation. To do so it supports overloaded functions and predicates and automatic coercion and provides arithmetic, finite domain and set constraints. Second, while Zinc is a relatively simple and small language, it can be readily extended to different application areas by means of powerful language constructs such as user-defined predicates and functions and constrained types. Third, Zinc provides sophisticated type and instantiation checking which allows early detection of errors in models. Finally, perhaps the main novelty in Zinc is that it is designed to support a modelling methodology in which the same conceptual model can be automatically mapped into different design models, thus allowing modellers to easily "plug and play" with different solving techniques and so choose the most appropriate for that problem. We describe in detail the various language features of Zinc and the many trade-offs we faced in its design.

This article describes applications of modern heuristic search methods to pattern sequencing problems, i.e., problems seeking for a permutation of the rows of a given matrix with respect to some given objective function. We consider two di#erent objectives: Minimization of the number of simultaneously open stacks and minimization of the average order spread. Both objectives require the adaptive evaluation of changed solutions to allow an e#cient application of neighbourhood search techniques.

Problems of sequencing cutting patterns arise in several industries. Given a set of cutting patterns and a given objective, a sequencing problem consists of finding a permutation of patterns that optimises the objective. Single objective problems are NP-hard. Heuristics and metaheuristics provide suitable solution approaches.

Abstract—In this paper, a two-phase algorithm was developed for the cutting sequencing problems in a modular manufacturing system. This system has a level of flexibility that depends on the cutting phase of raw material. This paper focuses primarily on the work station. In the first phase of the algorithm, an integer linear programming model is used to determine the number of hardboards that will be cut. The model was tested with two different objective functions which are to minimize waste and to minimize the number of hardboards used. In the second phase, a heuristic scheduling was developed to determine the pattern of cutting, by considering the due date and the number of customer demand. This algorithm is further implemented in a furniture manufacturer that operates using the make-to-order basis. Index Terms—EDD scheduling, integer linear programming, manufacturing system, sequential production. I.