No abstract

Factories in the automotive supply industry paint a large number of items requested by car manufacturing companies on a daily basis. As these factories face numerous constraints and optimization objectives, finding a good schedule becomes a challenging task in practice, and full-time employees are expected to manually create feasible production plans. In this study, we propose novel constraint programming models for a real-life paint shop scheduling problem. We evaluate and compare our models experimentally by performing a series of benchmark experiments using real-life instances in the industry. We also show that the decision variant of the paint shop scheduling problem is NP-complete.

Employee scheduling is a well known problem that appears in a wide range of different areas including health care, air lines, transportation services, and basically any organization that has to deal with workforces. In this paper we model a collection of challenging staff scheduling instances as a weighted partial Boolean maximum satisfiability (maxSAT) problem. Using our formulation we conduct a comparison of four different cardinality constraint encodings and analyze their applicability on this problem. Additionally, we measure the performance of two leading solvers from the maxSAT evaluation 2015 in a series of benchmark experiments and compare their results to state of the art solutions. In the process we also generate a number of challenging maxSAT instances that are publicly available and can be used as benchmarks for the development and verification of modern SAT solvers.

The computation of string similarity measures has been thoroughly studied in the scientific literature and has applications in a wide variety of different areas. One of the most widely used measures is the so called string edit distance which captures the number of required edit operations to transform a string into another given string. Although polynomial time algorithms are known for calculating the edit distance between two strings, there also exist NP-hard problems from practical applications like scheduling or computational biology that constrain the minimum edit distance between arrays of decision variables. In this work, we propose a novel global constraint to formulate restrictions on the minimum edit distance for such problems. Furthermore, we describe a propagation algorithm and investigate an explanation strategy for an edit distance constraint propagator that can be incorporated into state of the art lazy clause generation solvers. Experimental results show that the proposed propagator is able to significantly improve the performance of existing exact methods regarding solution quality and computation speed for benchmark problems from the literature.

In this paper, we study an important real-life scheduling problem that can be formulated as an unrelated parallel machine scheduling problem with sequence-dependent setup times, due dates, and machine eligibility constraints. The objective is to minimise total tardiness and makespan. We adapt and extend a mathematical model to find optimal solutions for small instances. Additionally, we propose several variants of simulated annealing to solve very large-scale instances as they appear in practice. We utilise several different search neighbourhoods and additionally investigate the use of innovative heuristic move selection strategies. Further, we provide a set of real-life problem instances as well as a random instance generator that we use to generate a large number of test instances. We perform a thorough evaluation of the proposed techniques and analyse their performance. We also apply our metaheuristics to approach a similar problem from the literature. Experimental results show that our methods are able to improve the results produced with state-of-the-art approaches for a large number of instances.