Andreas Beham

Fachhochschule Oberösterreich | fh-ooe · School of Informatics, Communications and Media

Changes in dynamic optimization problems entail updates to the problem model, which in turn can result in changes to the problem’s fitness landscape and even its solution encoding. In order to yield valid solutions that are applicable to the current problem state, optimization algorithms must be able to cope with such dynamic problem updates. Furth...

The performance of modern production systems often depends upon automated production planning strategies such as material requirements planning. Parametrizing, evaluating and comparing these strategies by testing them in the real world is often difficult and prohibitively resource intensive. State-of-the-art computer simulation can be used to adequ...

In dynamic warehouse operations, factory crane scheduling is a challenging problem to be solved. The interplay between a number of cranes requires good coordination to efficiently handle requested transport orders within the warehouse. The objective is to maximize the throughput and therefore minimize the makespan of crane schedules for a given set...

Many real-world processes are of dynamic nature and therefore subject to change. In this paper, dynamic warehouse operations are taken care of, more specifically crane operations that involve moving steel coils between storage locations within a large warehouse. An open-ended optimization approach is employed to create an optimal schedule of crane...

Fitness Landscape Analysis (FLA) denotes the task of analyzing black-box optimization problems and capturing their characteristic features with the goal of providing additional information, that may help in algorithm selection, parametrization or guidance. Many real-world optimization tasks require dynamic on-going optimization and a plethora of me...

Dynamic optimization is of high practical relevance for many production and logistics processes. Often however, in research, the dynamics are neglected and an algorithm or optimization is presented for a static decision scenario. The effects that occur with implementing decisions one by one in a dynamic environment subject to other dynamic events h...

Multi-objective symbolic regression has the advantage that while the accuracy of the learned models is maximized, the complexity is automatically adapted and need not be specified a-priori. The result of the optimization is not a single solution anymore, but a whole Pareto-front describing the trade-off between accuracy and complexity. In this cont...

Optimization networks are a new methodology for holistically solving interrelated problems that have been developed with combinatorial optimization problems in mind. In this contribution we revisit the core principles of optimization networks and demonstrate their suitability for solving machine learning problems. We use feature selection in combin...

Solving manufacturing optimization problems in the context of intelligent production involves the consideration of continuously changing events of the respective enterprise environment in real time. Smart solution methods are needed which are able to cope with such necessary reactions to uncertainty and dynamics. In general, this field of research...

Worker cross-training is a problem arising in many companies that involve human work. To perform certain activities, workers are required to possess certain skills. Cross-trained workers possess even multiple skills, which enables a more flexible deployment, but also incurs higher costs. Thus, companies seek to balance the available skills such tha...

Project scheduling in manufacturing environments often requires flexibility in terms of the selection and the exact length of alternative production activities. Moreover, the simultaneous scheduling of multiple lots is mandatory in many production planning applications. To meet these requirements, a new resource-constrained project scheduling probl...

To react on increasing customer demand uncertainty, production systems have to be flexible concerning the provided capacity. With respect to labour, one opportunity to gain such flexibility is to assign workers to different work stations which often require different skills to be operated. Therefore, cross-trained workers are needed to enable this...

Efficient global optimization is, even after over two decades of research, still considered as one of the best approaches to surrogate-assisted optimization. In this paper, material requirements planning parameters are optimized and two different versions of EGO, implemented as optimization networks in HeuristicLab, are applied and compared. The fi...

In the context of real-world optimization problems in the area of production and logistics, multiple objectives have to be considered very often. Precisely such a situation is also regarded in this work. For a resource-constrained project scheduling problem with activity selection and time flexibility, a new bi-objective extension is developed. Mot...

The dynamic block relocation problem is a variant of the BRP where the initial configuration and retrieval priorities are known but are subject to change during the implementation of an optimized solution. This paper investigates two kinds of potential changes. The exchange of assigned priorities between two blocks and the arrival of new blocks. Fo...

In the steel industry, logistics is very often part of the value chain since storage processes and therefore cooling processes contribute to the product quality to a very larger degree. As a result, steel logistics is concerned with the storage and movement of – in our case – work in process (WIP) materials. Thousands of tons of steel are transport...

In this position paper we describe challenges related to uncertainty handling when solving stacking problems within storage zones in the steel production value chain. Manipulations in those zones are often relocations of materials performed with gantry cranes. Thereby the crane operators themselves or dispatchers constantly solve a complex stacking...

Real-world project scheduling often requires flexibility in terms of the selection and the exact length of alternative production activities. Moreover, the simultaneous scheduling of multiple lots is mandatory in many production planning applications. To meet these requirements, a new flexible resource-constrained multi-project scheduling problem i...

In the era of commonly available problem-solving tools for, it is especially important to choose the best available method. We use local optima network analysis and machine learning to select appropriate algorithms on the instance-to-instance basis. The preliminary results show that such method can be successfully applied for sufficiently distinct...

In this initial research work we show the industrial need to analyze production systems with respect to their resilience. In the LOISI project enterprise models will be developed to support the analysis and management of resilient production processes for half-finished steel products. We describe the software models currently developed and the conc...

Finding the optimal workforce qualification is important in many different industries. Cross-qualifications play an important role if workers are required to perform various tasks during working hours. This is especially crucial in production plants where workforce has to be trained to operate multiple machines throughout the day, and is closely co...

This paper presents for the first time a reinforcement learning algorithm with function approximation for stacking problems with continuous production and retrieval. The stacking problem is a hard combinatorial optimization problem. It deals with the arrangement of items in a localized area, where they are organized into stacks to allow a delivery...

This paper introduces a new, highly asynchronous method for surrogate-assisted optimization where it is possible to concurrently create surrogate models, evaluate fitness functions and do parameter optimization for the underlying problem, effectively eliminating sequential workflows of other surrogate-assisted algorithms. Using optimization network...

Combinatorial optimization problems come in a wide variety of types but five common problem components can be identified. This categorization can aid the selection of interesting and diverse set of problems for inclusion in the combinatorial black-box problem benchmark. We suggest two real-world problems for inclusion into the benchmark. One is a t...

Algorithm selection is useful in decision situations where among many alternative algorithm instances one has to be chosen. This is often the case in heuristic optimization and is detailed by the well-known no-free-lunch (NFL) theorem. A consequence of the NFL is that a heuristic algorithm may only gain a performance improvement in a subset of the...

Conventional solution methods for logistics optimization problems often have to be adapted when objectives or restrictions of organizations in logistics environments are changing. In this paper, a new, generic solution approach called optimization network (ON) is developed and applied to a logistics optimization problem, the Location Routing Proble...

Optimization networks are a new methodology for holistically solving interrelated problems that have been developed with combinatorial optimization problems in mind. In this contribution we revisit the core principles of optimization networks and demonstrate their suitability for solving machine learning problems. We use feature selection in combin...

The no free lunch (NFL) theorem puts a limit to the range of problems a certain metaheuristic algorithm can be applied to successfully. For many methods these limits are unknown a priori and have to be discovered by experimentation. With the use of fitness landscape analysis (FLA) it is possible to obtain characteristic data and understand why meth...

Optimization problems can sometimes be divided into multiple subproblems. Working on these subproblems instead of the actual master problem can have some advantages, e.g. if they are standard problems, it is possible to use already existing algorithms, whereas specialized algorithms would have to be implemented for the master problem. In this paper...

Evolutionary algorithms are generic and flexible optimization algorithms which can be applied to many optimization problems in different domains. Depending on the specific type of evolutionary algorithm, they offer several parameters such as population size, mutation probability, crossover and mutation operators, or number of elite solutions. How t...

We present a new hybrid model-based algorithm called Memetic Path Relinking (MemPR). MemPR incorporates ideas of memetic, evolutionary, model-based algorithms and path relinking. It uses different operators that compete to fill a small population of high quality solutions. We present a new hard grouping problem derived from a real world transport l...

This article presents new methods for the block relocation problem (BRP). Although much of the existing work focuses on the restricted BRP, we tackle the unrestricted BRP, which yields more opportunities for optimisation. Our contributions include fast heuristics able to tackle very large instances within seconds, fast metaheuristics that provide v...

Among the many applications of fitness landscape analysis a prominent example is algorithm selection. The no-free-lunch (NFL) theorem has put a limit on the general applicability of heuristic search methods. Improved methods can only be found by specialization to certain problem characteristics which limits their application to other problems. This...

Combining multiple algorithms to cooperate in solving different optimization problems or process other workflows can be done in various problem domains, e.g. combinatorial optimization and data analysis. Optimization networks allow us to create such cooperative approaches by connecting multiple algorithms and letting them work together. In this pap...

With the continuous advancement of industry 4.0, also in the area of production and logistics optimization, a more holistic consideration of problems is required. Therefore, in contrary to the traditional sequential optimization approach in the area of operations research, in this paper, an integrated solution approach called optimization networks...

In this paper we are identifying enterprise interoperability as a possible framework for knowledge management in projects. In particular we are reflecting on research projects, involving small groups of companies and research institutions with heterogeneous backgrounds. The nature of research raises several issues due to the heterogeneous expertise...

Due to the current structural economic transformation towards smart production and logistics, a holistic and interactive connection between involved agents and departments becomes essential. Therefore, also in the field of operations research, an innovative approach of performance measurement is necessary to ensure increasing efficiency in smart en...

Model building is a fundamental task in simulation-based optimisation. In this paper, we demonstrate the application of Sim# in combination with HeuristicLab to optimise semi-automated machinery. On top of Sim#, custom simulation extensions have been implemented and are used to create a simulation model of real world machinery. These extensions ena...

The paper deals with human-computer interaction in which the cooperation leads to solve a difficult issue of discrete optimization. Considered jobs scheduling problem in a robotic cell consisting of two machines and a robotic operator. Only one of two machines can work at a time and there are setup times between successive operations on a machine....

The task of selecting an appropriate algorithm instance for a given optimization problem instance often requires significant experience. Efficient optimization requires a different set of parameters or an entirely different algorithmic approach for some characteristics of problem instances. Obtaining such experience takes significant amount of time...

In the optimization of real-world activities the effects of solutions on related activities need to be considered. The use of isolated problem models that do not adequately consider related processes does not allow addressing system-wide consequences. However, sometimes the complexity of the real-world model and its interplay with related activitie...

Stacking and shuffling problems are key logistics problems in various areas such as container shipping or steel industry. The aim of this paper is motivated by a real world instance arised in steel production. Slabs, continously but randomly casted, need to be arranged for transport while having a certain number of buffer stacks available. The opti...

Multi-objective symbolic regression has the advantage that while the accuracy of the learned models is maximized, the complexity is automatically adapted and need not be specified a-priori. The result of the optimization is not a single solution anymore, but a whole Pareto-front describing the trade-off between accuracy and complexity. In this cont...

Software frameworks for metaheuristic optimization take the burden off researchers and practitioners to start from scratch and implement their own algorithms and problems. One such framework is HeuristicLab. While it allows using existing, already implemented algorithms and problems comfortably and provides an extensive range of tools for analyzing...

Dynamic and stochastic problem environments are often difficult to model using standard problem formulations and algorithms. One way to model and then solve them is simulation-based optimization: Simulations are integrated into the optimization process in order to evaluate the quality of solution candidates and to identify optimized system configur...

In many warehouses manual order picking is one of the most time and labour intensive processes. Products that are often ordered together are said to be correlated or affine and order picking performance may be improved by placing correlated products close to each other. In industries with strong seasonality patterns and fluctuating demand regular r...

The quadratic assignment problem is among the harder combinatorial optimization problems in that even small instances might be difficult to solve and for different algorithms different instances pose challenges to solve. Research on the quadratic assignment problem has thus focused on developing methods that defy the problem’s variety and that can...

Rapid prototyping and testing of new ideas has been a major argument for evolutionary computation frameworks. These frameworks facilitate the application of evolutionary computation and allow experimenting with new and modified algorithms and problems by building on existing, well tested code. However, one could argue, that despite the many framewo...

Actual developments in power grid research, analysis, and operation are dominated clearly by the strong convergence of electrical engineering with information technology. Hence, new control abilities in power grids come up that revolutionize traditional optimization issues, requiring novel solution methods. At the same time, heuristic algorithms ha...

Estimating hardness based on intrinsic characteristics of problem instances plays an important role in algorithm selection and parameter tuning. We have compiled an extensive study of different fitness landscape and problem specific measures for the quadratic assignment problem to predict and correlate problem instance hardness for several differen...

There has been a wealth of research on warehouse optimization since the 1960s, and in particular on increasing order picking efficiency, which is one of the most labor intensive processes in many logistics centers. In the last ten years, affinity based slotting strategies, which place materials that are frequently ordered/picked together close to e...

Many optimization problems cannot be solved by classical mathematical optimization techniques due to their complexity and the size of the solution space. In order to achieve solutions of high quality though, heuristic optimization algorithms are frequently used. These algorithms do not claim to find global optimal solutions, but offer a reasonable...

In the last few years, fitness landscape analysis has seen an increase in interest due to the availability of large problem collections and research groups focusing on the development of a wide array of different optimization algorithms for diverse tasks. Instead of being able to rely on a single trusted method that is tuned and tweaked to the appl...

The dial-a-ride problem consists of designing vehicle routes in the area of passenger transportation. Assuming that each vehicle can act autonomously, the problem can be modeled as a multi-agent system. In that context, it is a complex decision process for each agent to determine what action to perform next. In this work, the agent function is evol...

Metaheuristic optimization algorithms are general optimization strategies suited to solve a range of real-world relevant optimization problems. Many metaheuristics expose parameters that allow to tune the effort that these algorithms are allowed to make and also the strategy and search behavior [1]. Adjusting these parameters allows to increase the...

In this paper we investigate how to stack products on a storage yard for efficient retrieval. The objective is to minimise both the transport distance and the number of stack shuffles. Previous research on yard storage assignment indicated that the fitness landscape of the problem features a high degree of neutrality, meaning that there are many ne...

Scheduling transport activities in the steel production process is a challenging task since there are many interdependencies with upstream and downstream activities. In this work, we present a simulation optimization approach to increase the efficiency of the transport activities within cold charge. We present a simulation model which captures the...

The significance of system orientation in production and logistics optimization has often been neglected in the past. An isolated view on single activities may result in globally suboptimal performance. We consider a manufacturing process where assembly lines are supplied from a central logistics center. The different steps, such as storage, pickin...

This contribution describes how symbolic regression can be used for knowledge discovery with the open-source software HeuristicLab. HeuristicLab includes a large set of algorithms and problems for combinatorial optimization and for regression and classification, including symbolic regression with genetic programming. It provides a rich GUI to analy...

Steel slabs are intermediates in the production of sheets, plates or coils in the steel industry. In this paper we consider cold charge slabs which are stored in stacks on a slab yard for a couple of hours, days or weeks until they are assigned to a rolling schedule and retrieved. When a slab is not positioned on top of a stack, retrieval requires...

Fitness landscape analysis methods have become an increasingly popular topic for research. The future application of these methods to metaheuristics can yield advanced self-adaptive metaheuristics and knowledge bases that can take the role of expert systems in the field of optimization. One important feature of such an expert system would be the pr...

Optimization of simulation parameters is an important task in many different sciences where simulation is used to model and analyze complex processes and behaviors. In this work it is shown how users, such as researchers, students, and practitioners can benefit from the integration of data-exchange-interfaces in optimization software system. The de...