Understanding Complexity in a Practical Combinatorial Problem Using Mathematical Programming and Constraint Programming

• Type: Article in International Conference Proceedings Book
• Date: 2018

Authors

Beatriz Brito Oliveira (Author) FEUP; Maria Antónia Carravilla (Author) FEUP

Conference proceedings International

Title: OPERATIONAL RESEARCH

Pages: 269-295

18th Congress of the Portuguese-Association-of-Operational-Research (APDIO)

Valenca, PORTUGAL, JUN 28-30, 2017

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Other information

Authenticus ID: P-00N-P78

DOI: 10.1007/978-3-319-71583-4_19

Abstract (EN): Optimization problems that are motivated by real-world settings are often complex to solve. Bridging the gap between theory and practice in this field starts by understanding the causes of complexity of each problem and measuring its impact in order to make better decisions on approaches and methods. The Job-Shop Scheduling Problem (JSSP) is a well-known complex combinatorial problem with several industrial applications. This problem is used to analyse what makes some instances difficult to solve for a commonly used solution approach - Mathematical Integer Programming (MIP) - and to compare the power of an alternative approach: Constraint Programming (CP). The causes of complexity are analysed and compared for both approaches and a measure of MIP complexity is proposed, based on the concept of load per machine. Also, the impact of problem-specific global constraints in CP modelling is analysed, making proof of the industrial practical interest of commercially available CP models for the JSSP.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 27