Christopher D. Geiger

Rapid Modeling and Discovery of Priority Dispatching Rules: An Autonomous Learning Approach

Priority-dispatching rules have been studied for many decades, and they form the backbone of much industrial scheduling practice. Developing new dispatching rules for a given environment, however, is usually a tedious process involving implementing different rules in a simulation model of the facility under study and evaluating the rule through extensive simulation experiments. In this research, an innovative approach is presented, which is capable of automatically discovering effective dispatching rules. This is a significant step beyond current applications of artificial intelligence to production scheduling, which are mainly based on learning to select a given rule from among a number of candidates rather than identifying new and potentially more effective rules. The proposed approach is evaluated in a variety of single machine environments, and discovers rules that are competitive with those in the literature, which are the results of decades of research.

A note on the optimal sequence position for a rate-modifying activity under simple linear deterioration

This paper addresses the integration of two emerging classes of scheduling problems which, for the most part, have evolved independently. These problem classes are (i) scheduling problems with time-dependent processing times and (ii) scheduling problems with rate-modifying activities (RMAs). The integration of these two concepts is motivated by human operators who experience fatigue while carrying out tasks and take rest breaks for recovery, but is also applicable to machines that experience performance degradation over time and require maintenance in order to sustain acceptable production rates. We explore a sequence-independent, single processor makespan problem with position-dependent processing times and prove that under certain conditions, the optimal policy is to schedule the RMA in the middle of the task sequence.

The parcel hub scheduling problem: a simulation-based solution approach

This research presents an interesting scheduling problem common to freight consolidation terminals. This previously unstudied problem involves scheduling a set of inbound trailers to a fixed number of unload docks. The objective is to schedule the trailers to the unload docks to minimize the time span of the transfer operation. This study focuses on freight consolidation terminals in the parcel delivery industry. A simulation-based scheduling algorithm that uses a genetic algorithm to drive the search for new solutions is proposed. In addition to the introduction and discussion of the parcel hub scheduling problem, the contribution of this research is an approach that serves as the initial effort to solve this practical problem.

A fast Pareto genetic algorithm approach for solving expensive multiobjective optimization problems

Abstract We present a new multiobjective evolutionary algorithm (MOEA), called fast Pareto genetic algorithm (FastPGA), for the simultaneous optimization of multiple objectives where each solution evaluation is computationally- and/or financially-expensive. This is often the case when there are time or resource constraints involved in finding a solution. FastPGA utilizes a new ranking strategy that utilizes more information about Pareto dominance among solutions and niching relations. New genetic operators are employed to enhance the proposed algorithm’s performance in terms of convergence behavior and computational effort as rapid convergence is of utmost concern and highly desired when solving expensive multiobjective optimization problems (MOPs). Computational results for a number of test problems indicate that FastPGA is a promising approach. FastPGA yields similar performance to that of the improved nondominated sorting genetic algorithm (NSGA-II), a widely-accepted benchmark in the MOEA research community. However, FastPGA outperforms NSGA-II when only a small number of solution evaluations are permitted, as would be the case when solving expensive MOPs.

FastPGA: a dynamic population sizing approach for solving expensive multiobjective optimization problems

We present a new multiobjective evolutionary algorithm (MOEA), called fast Pareto genetic algorithm (FastPGA). FastPGA uses a new fitness assignment and ranking strategy for the simultaneous optimization of multiple objectives where each solution evaluation is computationally- and/or financially-expensive. This is often the case when there are time or resource constraints involved in finding a solution. A population regulation operator is introduced to dynamically adapt the population size as needed up to a user-specified maximum population size. Computational results for a number of well-known test problems indicate that FastPGA is a promising approach. FastPGA outperforms the improved nondominated sorting genetic algorithm (NSGA-II) within a relatively small number of solution evaluations.

Learning effective dispatching rules for batch processor scheduling

Batch processor scheduling, where machines can process multiple jobs simultaneously, is frequently harder than its unit-capacity counterpart because an effective scheduling procedure must not only decide how to group the individual jobs into batches, but also determine the sequence in which the batches are to be processed. We extend a previously developed genetic learning approach to automatically discover effective dispatching policies for several batch scheduling environments, and show that these rules yield good system performance. Computational results show the competitiveness of the learned rules with existing rules for different performance measures. The autonomous learning approach addresses a growing practical need for rapidly developing effective dispatching rules for these environments by automating the discovery of effective job dispatching procedures.

Minimizing the completion time of the transfer operations in a central parcel consolidation terminal with unequal-batch-size inbound trailers

This paper addresses the scheduling of inbound trailers to unload docks at central parcel consolidation terminals in the parcel delivery industry, an industry that operates in a time-critical environment. The scheduling function can have a significant impact on the amount of time required to unload the inbound trailers and sort and load the parcels to the outbound trailers. This problem is known as the parcel hub scheduling problem (PHSP). To solve the PHSP, a simulation-based scheduling approach with an embedded genetic algorithm is proposed. The results show that the proposed scheduling approach is able to reduce the amount of time required to unload the inbound trailers by approximately 3.5 percent compared to a previously developed algorithm and about 16.1 percent compared to an approach that is representative of current industry practice.

Evolutionary multiobjective optimization in noisy problem environments

This paper presents a multiobjective evolutionary algorithm (MOEA) capable of handling stochastic objective functions. We extend a previously developed approach to solve multiple objective optimization problems in deterministic environments by incorporating a stochastic nondomination-based solution ranking procedure. In this study, concepts of stochastic dominance and significant dominance are introduced in order to better discriminate among competing solutions. The MOEA is applied to a number of published test problems to assess its robustness and to evaluate its performance relative to NSGA-II. Moreover, a new stopping criterion is proposed, which is based on the convergence velocity of any MOEA to the true Pareto optimal front, even if the exact location of the true front is unknown. This stopping criterion is especially useful in real-world problems, where finding an appropriate point to terminate the search is crucial.

Handling uncertainty in evolutionary multiobjective optimization: SPGA

This paper presents an extension of the previously developed approach to solve multiobjective optimization problems in deterministic environments by incorporating a stochastic Pareto-based solution ranking procedure. The proposed approach, called stochastic Pareto genetic algorithm (SPGA), employs some statistical analysis on the solution dominance in stochastic problem environments to better discriminate among the competing solutions. Preliminary computational results on three published test problems for different levels of noise with SPGA and NSGA-II are discussed.

Improving the emergency department performance using simulation and mcdm methods

The main purpose of this paper is to introduce a new framework to more efficiently investigate the patient flow of the Emergency Department (ED) of a governmental hospital in Tehran, Iran, in order to find out improving scenarios for reducing waiting times of patients. The proposed framework integrates the simulation model of patients flow process with the group AHP and TOPSIS decision models in order to evaluate and rank scenarios based upon desired performance measures. TOPSIS decision model takes the weights of performance measures from the group AHP and the values of performance measures from simulation model, and ranks the improving scenarios. The results analysis indicates that the average waiting time of non-fast-track patients by taking new policies with reasonable expenditure can be reduced by 42.3%.