Investigating Constraint Programming and Hybrid Methods for Real World Industrial Test Laboratory Scheduling

In this paper we deal with a complex real world scheduling problem closely related to the well-known Resource-Constrained Project Scheduling Problem (RCPSP). The problem concerns industrial test laboratories in which a large number of tests has to be performed by qualified personnel using specialised equipment, while respecting deadlines and other constraints. We present different constraint programming models and search strategies for this problem. Furthermore, we propose a Very Large Neighborhood Search approach based on our CP methods. Our models are evaluated using CP solvers and a MIP solver both on real-world test laboratory data and on a set of generated instances of different sizes based on the real-world data. Further, we compare the exact approaches with VLNS and a Simulated Annealing heuristic. We could find feasible solutions for all instances and several optimal solutions and we show that using VLNS we can improve upon the results of the other approaches

Algorithm Selection for the Graph Coloring Problem

Abstract. We present an automated algorithm selection method based on machine learning for the graph coloring problem (GCP). For this purpose, we identify 78 features for this problem and evaluate the performance of six state-of-the-art (meta)heuristics for the GCP. We use the obtained data to train several classification algorithms that are applied to predict on a new instance the algorithm with the highest expected performance. To achieve better performance for the machine learning algorithms, we investigate the impact of parameters, and evaluate different data discretization and feature selection methods. Finally, we evaluate our approach, which exploits the existing GCP techniques and the automated algorithm selection, and compare it with existing heuristic algorithms. Experimental results show that the GCP solver based on machine learning outperforms previous methods on benchmark instances

Minimizing Cumulative Batch Processing Time for an Industrial Oven Scheduling Problem

We introduce the Oven Scheduling Problem (OSP), a new parallel batch scheduling problem that arises in the area of electronic component manufacturing. Jobs need to be scheduled to one of several ovens and may be processed simultaneously in one batch if they have compatible requirements. The scheduling of jobs must respect several constraints concerning eligibility and availability of ovens, release dates of jobs, setup times between batches as well as oven capacities. Running the ovens is highly energy-intensive and thus the main objective, besides finishing jobs on time, is to minimize the cumulative batch processing time across all ovens. This objective distinguishes the OSP from other batch processing problems which typically minimize objectives related to makespan, tardiness or lateness. We propose to solve this NP-hard scheduling problem via constraint programming (CP) and integer linear programming (ILP) and present corresponding CP- and ILP-models. For an experimental evaluation, we introduce a multi-parameter random instance generator to provide a diverse set of problem instances. Using state-of-the-art solvers, we evaluate the quality and compare the performance of our CP- and ILP-models, which could find optimal solutions for many instances. Furthermore, using our models we are able to provide upper bounds for the whole benchmark set including large-scale instances

Heuristic Methods for Automatic Rotating Workforce Scheduling

Rotating workforce scheduling appears in different forms in a broad range of workplaces, such as industrial plants, call centers, public transportation, and airline companies. It is of a high practical relevance to find workforce schedules that fulfill the ergonomic criteria of the employees, and reduce costs for the organization. In this paper we propose new heuristic methods for automatic generation of rotating workforce schedules. To improve the quality of each heuristic method alone, we further propose the hybridization of these methods. The following methods are proposed: (1)A Tabu Search (TS) based algorithm, (2) A heuristic method based on min-conflicts heuristic (MC), (3) A method that includes in the tabu search algorithm the min-conflicts heuristic (TS-MC) and random walk (TS-RW), (4) A method that includes in the min-conflicts heuristic the tabu mechanism (MC-T), random walk (MC-RW), and both the tabu mechanism and the random walk (MC-T-RW). The appropriate neighborhood structure, tabu mechanism, and fitness function, based on the specifics of the problem are proposed. The proposed methods are implemented and experimentally evaluated on the benchmark examples given in the literature and on the real life test problems, which we collected from a broad range of organizations. Empirical results show that the combination of the min-conflicts heuristic with tabu search can be used to solve this problem very effectively. The hybrid methods improve the performance of the commercial system for generation of rotating workforce schedules and are currently in the procees of being included in a commercial package for automatic generation of rotating workforce schedules