Dirk C. Mattfeld

Production scheduling and rescheduling with genetic algorithms

A general model for job shop scheduling is described which applies to static, dynamic and non-deterministic production environments. Next, a Genetic Algorithm is presented which solves the job shop scheduling problem. This algorithm is tested in a dynamic environment under different workload situations. Thereby, a highly efficient decoding procedure is proposed which strongly improves the quality of schedules. Finally, this technique is tested for scheduling and rescheduling in a non-deterministic environment. It is shown by experiment that conventional methods of production control are clearly outperformed atreasonable runtime costs.

A Computational Study

In this chapter we give a survey on the GA approaches considered so far. We continue with a detailed computational study of the most powerful algorithm on 162 benchmark problems. Finally we discuss the suitability of the algorithm for either very large or very difficult JSP instances.

An efficient genetic algorithm for job shop scheduling with tardiness objectives

Abstract We consider job shop scheduling problems with release and due-dates, as well as various tardiness objectives. To date, no efficient general-purpose heuristics have been developed for these problems. genetic algorithms can be applied almost directly, but come along with apparent weaknesses. We show that a heuristic reduction of the search space can help the algorithm to find better solutions in a shorter computation time. Two ways of reducing a search space are investigated by considering short-term decisions made at the machine level and by long-term decisions made at the shop floor level.

On Permutation Representations for Scheduling Problems

In this paper we concentrate on job shop scheduling as a representative of constrained combinatorial problems. We introduce a new permutation representation for this problem. Three crossover operators, different in tending to preserve the relative order, the absolute order, and the position in the permutation, are defined. By experiment we observe the strongest phenotypical correlation between parents and offspring when respecting the absolute order. It is shown that a genetic algorithm using an operator which preserves the absolute order also obtains a superior solution quality.

Evolutionary Search and the Job Shop

1. Introduction.- 2. Job Shop Scheduling.- 3. Local Search Techniques.- 4. Evolutionary Algorithms.- 5. Perspectives on Adaptive Scheduling..- 6. Population Flow in Adaptive Scheduling.- 7. Adaptation of Structured Populations.- 8. A Computational Study.- 9. Conclusions and Outlook.- References.

Anticipation and flexibility in dynamic scheduling

Many real-world optimization problems change over time and require frequent re-optimization. We suggest that in such environments, an optimization algorithm should reflect the problems dynamics and explicitly take into account that changes to the current solution are to be expected. We claim that this can be achieved by having the optimization algorithm search for solutions that are not only good, but also flexible, i.e. easily adjustable if necessary in the case of problem changes. For the example of a job-shop with jobs arriving non-deterministically over time, we demonstrate that avoiding early idle times increases flexibility, and thus that the incorporation of an early idle time penalty as secondary objective into the scheduling algorithm can greatly enhance the overall system performance.

A search space analysis of the Job Shop Scheduling Problem

A computational study for the Job Shop Scheduling Problem is presented. Thereby,emphasis is put on the structure of the search space as it appears for local search. A statisticalanalysis of the search space reveals the impact of inherent properties of the problem onlocal search based heuristics.

Advanced routing for city logistics service providers based on time-dependent travel times

Abstract Increasing traffic demand, recurring congestion and sophisticated e-commerce business models lead to enormous challenges for routing in city logistics. We introduce a planning system for city logistics service providers, which faces those challenges by more realistic vehicle routing considering time-dependent travel times. Time-dependent travel times arise from telematics-based traffic data collection city-wide. Well-known heuristics for the Traveling Salesman Problem and for the Vehicle Routing Problem are adapted to time-dependent planning data. Computational experiments allow for insights into the efficiency of individual heuristics, their adaptability to time-dependent planning data sets, and the quality and structure of resulting delivery tours.

Synergies of Operations Research and Data Mining

In this contribution we identify the synergies of Operations Research and Data Mining. Synergies can be achieved by integration of optimization techniques into Data Mining and vice versa. In particular, we define three classes of synergies and illustrate each of them by examples. The classification is based on a generic description of aims, preconditions as well as process models of Operations Research and Data Mining. It serves as a framework for the assessment of approaches at the intersection of the two procedures.

Control of Parallel Population Dynamics by Social-Like Behavior of GA-Individuals

A frequently observed difficulty in the application of genetic algorithms to the domain of optimization arises from premature convergence. In order to preserve genotype diversity we develop a new model of auto-adaptive behavior for individuals. In this model a population member is an active individual that assumes social-like behavior patterns. Different individuals living in the same population can assume different patterns. By moving in a hierarchy of “social states” individuals change their behavior. Changes of social state are controlled by arguments of plausibility. These arguments are implemented as a rule set for a massively-parallel genetic algorithm. Computational experiments on 12 large-scale job shop benchmark problems show that the results of the new approach dominate the ordinary genetic algorithm significantly.