Greybox Algorithm Configuration

The performance of state-of-the-art algorithms is highly dependent on their parameter values, and choosing the right configuration can make the difference between solving a problem in a few minutes or hours. Automated algorithm configurators have shown their efficiency on a wide range of applications. However, they still encounter limitations when confronted to a large number of parameters to tune or long algorithm running time. We believe that there is untapped knowledge that can be gathered from the elements of the configuration problem, such as the default value in the configuration space, the source code of the algorithm, and the distribution of the problem instances at hand. We aim at utilising this knowledge to improve algorithm configurators.

Proactive and reactive approach to employee competence configuration problem in planning and scheduling processes

At the time of commonplace automation, robotization and the rapid development of IT, high qualifications of employees have become the critical element of every industry system. This follows from their limited availability, frequently high costs of procurement and possible employee absenteeism. Moreover, the concept of Industry 4.0 will transform current industry employees into knowledge employees. This is due to the fact that hard and routine tasks will be executed by robots and computers. This constitutes change in the required employee competences. Unfortunately, the aspect of management and configuration of employee competences is often overlooked in industrial practice. In response to the existing problem, the article puts forward the original model of employee competence configuration which is a basis for responses to numerous questions of managers of production processes, both general ones, e.g., Do we have a sufficient set of competences to execute a production schedule? as well as detailed ones, e.g., Which and how many competences are missing? etc. An important novelty of the presented model is the possibility of its application with both proactive and reactive questions. Due to the discrete and combinatorial nature of the problem under consideration, the use of mathematical programming methods was limited only to small data instances. Therefore, a proprietary dedicated genetic algorithm was proposed to solve this problem, which turned out to be extremely effective. The use of this genetic algorithm has enabled finding a solution depending on the instance data up to 70 times faster than by use of the mathematical programming.

Replicated Computational Results (RCR) Report for“A Practical Approach to Subset Selection for Multi-Objective Optimization via Simulation”

In “A Practical Approach to Subset Selection for Multi-Objective Optimization via Simulation,” Currie and Monks propose an algorithm for multi-objective simulation-based optimization. In contrast to sequential ranking and selection schemes, their algorithm follows a two-stage scheme. The approach is evaluated by comparing the results to those obtained using the existing OCBA-m algorithm for synthetic problems and for a hospital ward configuration problem. The authors provide the Python code used in the experiments in the form of Jupyter notebooks. The code successfully reproduced the results shown in the article.

Compacting oblivious agents on dynamic rings

In this paper we investigate dynamic networks populated by autonomous mobile agents. Dynamic networks are networks whose topology can change continuously, at unpredictable locations and at unpredictable times. These changes are not considered to be faults, but rather an integral part of the nature of the system. The agents can autonomously move on the network, with the goal of solving cooperatively an assigned common task. Here, we focus on a specific network: the unoriented ring. More specifically, we study 1-interval connected dynamic rings (i.e., at any time, at most one of the edges might be missing). The agents move according to the widely used Look–Compute–Move life cycle, and can be homogenous (thus identical) or heterogenous (agents are assigned colors from a set of c > 1 colors). For identical agents, their goal is to form a compact segment, where agents occupy a continuous part of the ring and no two agents occupy the same node: we call this the Compact Configuration Problem. In the case of agents with colors, called the Colored Compact Configuration Problem, the goal is to group agents such that each group is formed by all agents having the same color, it occupies a continuous segment of the network, and groups of agents having different colors occupy distinct areas of the network. In this paper we determine the necessary conditions to solve both proposed problems. For all solvable cases, we provide algorithms for both the monochromatic and the colored version of the compact configuration problem. All our algorithms work even for the simplest model where agents have no persistent memory, no communication capabilities and do not agree on a common orientation within the network. To the best of our knowledge this is the first work on the compaction problem in a dynamic network.

Optimal design of tapered composite structures with a dynamic boundary subset blending model

Based on the classical bending rule that the plies composing the thinner region should be a subset of the ones of the thicker region for two adjacent laminates, a genetic algorithm–based dynamic boundary subset blending model is proposed to optimize the global stacking sequence of composite structures with ply-drops. Besides the stacking sequence chromosome of the guide laminate and ply number chromosome of each panel, a chromosome of a dynamic boundary subset factor is introduced for each panel to obtain a fully blended design. The lower and upper bounds of the dynamic boundary subset factor chromosome for each panel is determined by the ply number chromosomes of the panel and its adjacent panels. The stacking sequence of each panel can be determined by selection from combinations of various stacking sequences. The proposed blending model can solve the problem that laminates with identical thicknesses have the completely same layups even when they are not adjacent to each other. The optimal feasible designs outperform other published solutions for the 18-panel horseshoe configuration problem based on the classical bending rule.

Model of emergence evacuation route planning with contra flow and zone scheduling in disaster evacuation

Evacuation is characterized by rapid movement of people in unsafe locations or disaster sites to safer locations. The traffic management strategy for commonly used evacuations is the use of Shoulder-Lane, contraflowing traffic and gradual evacuation. Contra-flow has been commonly used in traffic management by changing traffic lanes during peak hours. To implement the contra-flow operation, there are two main problems that must be decided, namely Optimal Contraflow Lane Configuration Problem (OCLCP) and Optimal Contraflow Scheduling. Within the OCSP there are two approaches that can be used: zone scheduling and flow scheduling. Problem of Contra Flow and Zone Scheduling Problem is basically an Emergence Evacuation Route Planning (EERP) issue. This research will discuss EERP with ContraFlow and Zone Scheduling which can guarantee the movement of people in disaster area to safe area in emergency situation.

Configuration of smart phase-swapping switches in low-voltage distribution systems based on sequenced participation indices

The smart phase-swapping switches are used to rapidly change the phases of single-phase loads online in low-voltage distribution systems. They can reduce the three-phase imbalance indices. However, the effectiveness of phase-swapping operations is determined by not only the control strategy but also by the quantity and locations of smart phase-swapping switches. In this paper, a configuration method is proposed to determine the preferable quantity and locations of smart phase-swapping switches with considerations of economic benefits and operational requirements. Based on historical load information, the active and reactive powers of the loads are used to formulate the current imbalance index. The configuration problem is modeled as a multiobjective optimization that minimizes the current imbalance indices of all nodes and phase-swapping operations. The problem is solved by the particle swarm optimization algorithm to obtain the phase-swapping participation index of each single-phase load. The loads with high phase-swapping participation indices are preferably equipped with smart phase-swapping switches. The simulation results verify that the proposed method is effective and easy to be implemented in practical applications.