Andreas Beham

AbYSS: Adapting Scatter Search to Multiobjective Optimization

We propose the use of a new algorithm to solve multiobjective optimization problems. Our proposal adapts the well-known scatter search template for single-objective optimization to the multiobjective domain. The result is a hybrid metaheuristic algorithm called Archive-Based hYbrid Scatter Search (AbYSS), which follows the scatter search structure but uses mutation and crossover operators from evolutionary algorithms. AbYSS incorporates typical concepts from the multiobjective field, such as Pareto dominance, density estimation, and an external archive to store the nondominated solutions. We evaluate AbYSS with a standard benchmark including both unconstrained and constrained problems, and it is compared with two state-of-the-art multiobjective optimizers, NSGA-II and SPEA2. The results obtained indicate that, according to the benchmark and parameter settings used, AbYSS outperforms the other two algorithms as regards the diversity of the solutions, and it obtains very competitive results according to the convergence to the true Pareto fronts and the hypervolume metric.

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.

Benefits of plugin-based heuristic optimization software systems

Plugin-based software systems are the next step of evolution in application development. By supporting fine grained modularity not only on the source code but also on the post-compilation level, plugin frameworks help to handle complexity, simplify application configuration and deployment, and enable users or third parties to easily enhance existing applications with self-developed modules without having access to the whole source code. In spite of these benefits, plugin-based software systems are seldom found in the area of heuristic optimization. Some reasons for this drawback are discussed, several benefits of a plugin-based heuristic optimization software system are highlighted and some ideas are shown, how a heuristic optimization meta-model as the basis of a thorough plugin infrastructure for heuristic optimization could be defined.

A comparative study of genetic algorithm components in simulation-based optimisation

In this paper, we present a comparative study of different stochastic components of genetic algorithms for simulation-based optimisation of the buffer allocation problem. We explore the effects of elements such as operators, fitness assignment strategies and elitism. Three different recombination operators, incorporated with constraint handling mechanisms such as repair and penalty functions, are examined. Under the shed of the experiments, we incorporate problem specific knowledge to further enhance the practicality of GA in decision making for buffer allocation problem.

A genetic programming approach to solve scheduling problems with parallel simulation

Scheduling and dispatching are two ways of solving production planning problems. In this work, based on preceding works, it is explained how these two approaches can be combined by the means of an automated rule generation procedure and simulation. Genetic programming is applied as the creator and optimizer of the rules. A simulator is used for the fitness evaluation and distributed over a number of machines. Some example results suggest that the approach could be successfully applied in the real world as the results are more than human competitive.

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.

Optimization Strategies for Integrated Knapsack and Traveling Salesman Problems

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 activities can be described by a combination of simple, existing, problems. In this work we aim to discuss strategies to combine existing algorithms for simple problems in order to solve a more complex master problem. New challenges arise in such an integrated optimization approach.

Simulation-based evolution of resupply and routing policies in rich vendor-managed inventory scenarios

Vendor managed inventory combines inventory management and transportation. Compared to classical inventory management approaches, this strategy offers various degrees of freedom for the vendor while providing a certain service quality level for the customers. To capture the characteristics of rich real-world scenarios, our problem formulation consists of multiple customers, many products and stochastic product usages. Additionally, we also consider mixed formulations, where only a certain part of the customers is switched to a vendor managed inventory to allow a stepwise transition. We show that resupply and routing policies can be evolved autonomously for those scenarios using a simulation-based optimization approach. By combining inventory management and routing, the resulting policies aim to minimize costs and to maximize resource usage while maintaining a given service level. In order to validate our approach, we perform case studies and apply the evolved rules on a large-scale vendor managed inventory scenario for supermarkets. Furthermore, we show that our methodology can be used to perform a sensitivity analysis by considering the influence of exogenous and endogenous factors on the decision process, if a customer base should be transitioned to a vendor managed inventory.

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

Production fine planning using a solution archive of priority rules

Production Fine Planning is often performed directly using all information and assuming that it is fixed. In practice, however, this information changes regularly and the plan has to be adapted. This often means a complete rescheduling of all operations. We present a new approach to this problem by optimizing priority rules that can sort the available next actions. These priority rules often yield similar results even though they do not resemble each other. By using genetic programming to build these priority rules, a distributed system to compute the simulations and a solution archive with a cache of hundreds of thousands of priority rules, new insights into priority rule-based optimization are gained. This archive does not only speed up calculation by avoiding re-simulation of the same rule but can provide a pseudo Pareto front of shorter sub-optimal solutions that facilitate interpretation of the more complex rules and their evolution during the optimization process.