The IDP knowledge base system currently usesMiniSAT(ID) as its backend Constraint Programming (CP) solver. A few similar systems have used a Mixed Integer Programming (MIP) solver as backend. However, so far little is known about when the MIP solver is preferable. This paper explores this question. It describes the use of CPLEX as a backend for IDP and reports on experiments comparing both back...

In this paper, we address the scheduling problem involving batch processing machines. The presented mixed integer programming formulation first provides an elegant model for the problem under study. Furthermore, it enables solutions to the problem instances beyond the capability of exact methods developed so far. In order to alleviate computational burden, we propose MIP-based approaches which ...

research interests have been focused on the concept of penalizing jobs both for being early and for being tardy because not only of modern competitive industrial challenges of providing a variety of products at a very low cost by smoothing productions but also of its increasing and exciting computer applications. here, sequencing approaches of the mixed- model just-in-time production systems is...

Most industrial model predictive controllers (MPC) use the traditional two-layer structure developed in the early 1980’s, where the upper layer defines optimal steady-state targets for inputs and outputs, while the lower layer calculates the control moves that drive the system towards these steady-state targets. Typically both layers use continuous quadratic programming (QP) formulations to der...

We consider mixed-integer linear programming (MIP) models of production planning problems known as the small bucket lot-sizing and scheduling problems. We present an application of a class of valid inequalities to the case with lost demand (stock-out) costs. Presented results of numerical experiments made for the the Proportional Lot-sizing and Scheduling Problem (PLSP) confirm benefits of such...

Most industrial model predictive controllers (MPC) use the traditional two-layer structure developed in the early 1980’s, where the upper layer defines optimal steady-state targets for inputs and outputs, while the lower layer calculates the control moves that drive the system towards these steady-state targets. As a rule, both layers use continuous quadratic programming (QP) formulations to de...

Mixed Integer Programming (MIP) models are known to be sensitive to slight changes. In this study, we measure the perturbation to a production plan that occurs when re-planning is carried-on due to new orders arrival. Perturbation level is measured using well known metrics: (1) the Hamming distance, (2) the edit distance, and (3) the Damerau-Levenshtein distance. We propose three alternative fo...

This thesis introduces the novel paradigm of constraint integer programming (CIP), which integrates constraint programming (CP) andmixed integer programming (MIP) modeling and solving techniques. It is supplemented by the software SCIP, which is a solver and framework for constraint integer programming that also features SAT solving techniques. SCIP is freely available in source code for academ...

We present a computational study of parametric tabu search for solving 0-1 mixed integer programming (MIP) problems, a generic heuristic for general MIP problems. This approach solves a series of linear programming problems by incorporating branching inequalities as weighted terms in the objective function. New strategies are proposed for uncovering feasible and high-quality solutions and exten...

Many optimisation problems contain substructures involving constraints on sequences of decision variables. Such constraints can be very complex to express with mixed integer programming (MIP), while in constraint programming (CP), the global constraint regular easily represents this kind of substructure with deterministic finite automata (DFA). In this paper, we use DFAs and the associated laye...