Monika Kofler

Architecture and Design of the HeuristicLab Optimization Environment

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 tradeoff between runtime and solution quality and are therefore especially suitable for practical applications. In the last decades the success of heuristic optimization techniques in many different problem domains encouraged the development of a broad variety of optimization paradigms which often use natural processes as a source of inspiration (as for example evolutionary algorithms, simulated annealing, or ant colony optimization). For the development and application of heuristic optimization algorithms in science and industry, mature, flexible and usable software systems are required. These systems have to support scientists in the development of new algorithms and should also enable users to apply different optimization methods on specific problems easily. The architecture and design of such heuristic optimization software systems impose many challenges on developers due to the diversity of algorithms and problems as well as the heterogeneous requirements of the different user groups. In this chapter the authors describe the architecture and design of their optimization environment HeuristicLab which aims to provide a comprehensive system for algorithm development, testing, analysis and generally the application of heuristic optimization methods on complex problems.

Re-warehousing vs. healing: Strategies for warehouse storage location assignment

Since the 1960s researcher have developed various storage assignment strategies for different warehouse scenarios. Much of this research has been devoted to re-warehousing, which involves extensive re-arrangements akin to filling a warehouse from scratch. However, due to seasonal fluctuations in demand or product mix, operations managers need to periodically review item placement in practise and re-organize the warehouse to keep it operating efficiently. In this paper we demonstrate for a sample warehouse how applying a small number of cleanup tasks (=healing) every day will lead to a good total warehouse assignment over the course of many months and show that the results are competitive to re-warehousing.

Agent-Based Simulation of Dispatching Rules in Dynamic Pickup and Delivery Problems

This work treats the topic of solving dynamic pickup and delivery problems, also known as dial-a-ride problems. A simulation model is introduced that describes how an agent is able to satisfy the transportation requests. The agent behavior is given in form of a complex dispatching rule, which is optimized by metaheuristic approaches. For this purpose, a fitness function is described which is used to evaluate the quality of a solution. The rule to be optimized is a weighted sum of several primitive dispatching rules where each describes a small part of the information available in the system at a given time. Given a good configuration of the weights, we will show that the agents are able to serve the transportation requests. The optimization of the weights was conducted with the generic, open, and extensible optimization framework HeuristicLab. Index Terms—simulation, pickup and delivery, dispatching, optimization

Priority Rule Generation with a Genetic Algorithm to Minimize Sequence Dependent Setup Costs

Setup costs are a crucial factor in many branches of industry and frequently sequence dependent. However, the empirical acquisition of setup costs is inaccurate and not practicable for companies with large product portfolios operating in volatile markets. We therefore propose an abstract model for the estimation of such sequence dependent setup costs and subsequently apply dispatching and scheduling strategies to generate optimized production sequences. Both approaches are tested on randomly generated test instances and a real-world production scenario.

Simulation-Based Optimization with HeuristicLab: Practical Guidelines and Real-World Applications

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 configurations. Potential solutions are evaluated with a simulation model, which leads to new challenges regarding runtime performance, robustness, and distributed evaluation. In order to design, compare, and parameterize algorithmic approaches it is beneficial to use an optimization framework for algorithm design and evaluation. On the one hand, this chapter shows how arbitrary simulators can be coupled with the open-source HeuristicLab optimization framework. This coupling is implemented in a generic way so that the simulators act as external evaluators. On the other hand, we demonstrate how arbitrary optimizers available within HeuristicLab can be called from a simulator in order to perform complex optimization tasks within the simulation model. In order to illustrate the applicability of these approaches, real-world examples investigated by the authors are discussed. We show here application examples from different fields, namely logistics network design, vendor managed inventory routing, steel slab logistics, production optimization with dispatching rule scheduling, material flow simulation, and layout optimization.

Optimizing assembly line supply by integrating warehouse picking and forklift routing using simulation

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, picking and transport of work-in-process materials to and from the assembly lines, strongly influence each other. For instance, if the picking process batches orders that need to be transported to the same target, a reduction of travel distances can be achieved. The individual problems are coupled and validated via simulation, which leads to more robust and applicable results in practice. We test our approach on a scenario based on real-world data from Rosenbauer, one of the worlds largest suppliers of firefighting vehicles. Our results indicate that warehouse optimization can lead to a more efficient transport in an integrated problem formulation.

Coupling simulation with heuristiclab to solve facility layout problems

In this paper we describe the optimization of a facility layout scenario, which involves coupling simulation with the optimization environment HeuristicLab. For this purpose we show a problem formulation that acts as an interface between these two domains of problem modeling and optimization, and discuss optimization methodologies and their results for a number of artificial test problems as well as more complex real-world problems. HeuristicLab was designed with both practitioners and algorithm developers in mind. Practitioners benefit from a graphical user interface that facilitates so-called interactive algorithm engineering, where algorithms can be adjusted without actually writing code. Algorithm developers are aided in the development process by the plug-in based, easily extensible architecture and integrated parallelization functionality.

Modelling and optimizing storage assignment in a steel slab yard

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 the movement, also called shuffling, of all slabs above. We introduce a new model formulation, suitable neighbourhood and perturbation operators and discuss strategies for optimizing slab yard assignments with respect to shuffles and travel distance in picking.

Robust Storage Assignment in Warehouses with Correlated Demand

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 re-locations of products might be necessary to ensure that the quality of the storage assignment does not deteriorate over time. In this chapter we study how to generate more robust assignments that are suitable for volatile warehouse scenarios with correlated demand. In a case study based on 13 monthly snapshots from a real-world warehouse robust slotting outperformed greedy re-locations by up to 9.6 %.

Evolutionary Selection in Simulation-Based Optimization

In this work we examine the effect of elitist and non-elitist selection on a supply chain problem. The problem is characterized by an output constraint which in turn separates the search space in a feasible and a non-feasible region. Additionally the simulation output is noisy due to a stochastic demand model. We will show analyze which strategy is able to perform a walk on the boundary between the feasible and infeasible space. Additionally a new selection scheme is introduced based on a statistical test to evaluate the difference between two solutions given a number of noisy quality values. This selection scheme is described and evaluated on the problem situation.