Rolling Horizon Workforce Schedule to Assign Workers to Tasks in a Learning/Forgetting Environment

The variability in demand across the planning horizon and the presence of heterogeneous workforces where workers learn and forget at different rates make the process of building and managing a workforce challenging. When integrating learning and forgetting functions of workers into workforce scheduling, the previous experience of a worker on a task can have significant impact on productivity. While making assignments over an infinite planning horizon is ideal, the learning/forgetting function significantly increases problem complexity and solution difficulty as the length of planning horizon increases. In this thesis, a multi-period rolling horizon worker-task assignment framework is developed to overcome computational challenges associated with longer planning horizons. The non-linear learning/forgetting function is converted into an equivalent linear form (using an existing technique) to further reduce problem complexity. We design experiments to analyze the optimal planning horizon and the factors that affect it, questions that remain unanswered in literature. After testing the model under different scenarios (varying staffing level, variation in demand, learning rate, forgetting rate and workforce heterogeneity), we conclude variation in demand and staffing level to be the most significant factors in determining the optimal planning horizon. We also see a significant improvement in performance when comparing our proposed multi-period framework against a myopic model, especially in scenarios with higher workforce heterogeneity, higher variation in demand, and faster forgetting rate

When Does a Newcomer Contribute to a Better Performance? A Multi-Agent Study on Self-Organising Processes of Task Allocation

This paper describes how a work group and a newcomer mutually adapt. We study two types of simulated groups that need an extra worker, one group because a former employee had left the group and one group because of its workload. For both groups, we test three conditions, newcomers being specialists, newcomers being generalists, and a control condition with no newcomer. We hypothesise that the group that needs an extra worker because of its workload will perform the best with a newcomer being a generalist. The group that needs an extra worker because a former employee had left the group, will perform better with a specialist newcomer. We study the development of task allocation and performance, with expertise and motivation as process variables. We use two performance indicators, the performance time of the slowest agent that indicates the speed of the group and the sum of performance of all agents to indicate labour costs. Both are indicative for the potential benefit of the newcomer. Strictly spoken the results support our hypotheses although the differences between the groups with generalists and specialists are negligible. What really mattered was the possibility for a newcomer to fit in.Task Allocation, Group Processes, Psychological Theory, Small Groups, Self-Organisation

Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents

Evolution strategies (ES) are a family of black-box optimization algorithms able to train deep neural networks roughly as well as Q-learning and policy gradient methods on challenging deep reinforcement learning (RL) problems, but are much faster (e.g. hours vs. days) because they parallelize better. However, many RL problems require directed exploration because they have reward functions that are sparse or deceptive (i.e. contain local optima), and it is unknown how to encourage such exploration with ES. Here we show that algorithms that have been invented to promote directed exploration in small-scale evolved neural networks via populations of exploring agents, specifically novelty search (NS) and quality diversity (QD) algorithms, can be hybridized with ES to improve its performance on sparse or deceptive deep RL tasks, while retaining scalability. Our experiments confirm that the resultant new algorithms, NS-ES and two QD algorithms, NSR-ES and NSRA-ES, avoid local optima encountered by ES to achieve higher performance on Atari and simulated robots learning to walk around a deceptive trap. This paper thus introduces a family of fast, scalable algorithms for reinforcement learning that are capable of directed exploration. It also adds this new family of exploration algorithms to the RL toolbox and raises the interesting possibility that analogous algorithms with multiple simultaneous paths of exploration might also combine well with existing RL algorithms outside ES

파라미터 학습 통한 데이터 잡음 및 간섭극복 연구

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 전기·컴퓨터공학부, 2021. 2. 정교민.인공신경망 모델에 다량의 데이터를 학습시키는 방식은 컴퓨터 비전 및 자연어 처리 분야의 문제들을 해결하는데 새로운 패러다임으로 자리매김하였다. 기존 사람의 직관으로 모델을 설정하는 방식과 비교하여 높은 성능을 달성할 수 있었으나, 학습데이터의 양과 품질에 따라서 그 성능이 크게 좌우된다. 이렇게 인공 신경망을 효과적으로 훈련하려면 많은 양의 데이터를 모으는 것과 데이터의 품질을 저하시키는 요인을 파악하는 것이 중요하다. 본 연구에서는 라벨링된 데이터의 품질을 결정하는 주요 요인으로 알려져 있는 잡음(Noise)과 간섭(Interference)을 극복할 수 있는 기법을 제시한다. 연구자들은 일반적으로 웹기반의 크라우드 소싱시스템을 사용하여 다양한 사람들로부터 답변을 수집하여 데이터그룹을 구성한다\cite{simonyan2014very}. 그러나 사람들의 답변으로 얻는 데이터는 작업 지침에 대한 오해, 책임 부족 및 고유한 오류로 인해서 데이터 입력(Input)과 출력(Target)사이에 잡음이 포함된다. 본 연구에서는 이렇게 크라우드 소싱을 통해 라벨링된 데이터에 존재하는 잡음을 극복하기 위한 추론 알고리즘을 제안한다. 두번째로, 모델의 학습성능을 저하시키는 요인인 데이터간의 간섭을 다룬다. 잡음이 제거되어 정제된 입력과 출력을 라벨링된 데이터 샘플이라고 하면, 학습시에 샘플들 사이의 관계를 생각할 수 있다. 사람 수준의 인공지능에 도달하기 위해서는 하나의 모델이 하나의 문제만을 해결하는 것이 아니라 시간상 순차적으로 직면하는 여러 문제를 동시에 해결할 수 있어야 한다. 이러한 상황에서, 샘플들 사이에 간섭이 발생할 수 있고, 학계에서는 연속학습(Continual Learning)에서의 "Catastrophic Forgetting"또는 "Semantic Drift"으로 정의하고 있다. 본 연구에서는 이러한 간섭을 효과적으로 극복하기 위한 방법에 대한 연구를 다룬다. 앞서 언급한 데이터 잡음을 극복하기 위해서 첫 번째 장에서는 크라우드 소싱 시스템의 이산 객관식 및 실수 벡터 회귀 작업에 대한 새로운 추론 알고리즘을 각각 제안한다. 제안 된 알고리즘은 크라우드 소싱 모델을 그래프 모델(Graphical Model)로서 상정하고, 테스크와 답변을 주는 사람들간의 두 가지 유형의 메시지를 반복적으로 주고 받음으로써 각 작업의 정답과 각 작업자의 신뢰성을 추정 할 수 있다. 또한 이들의 평균 성능은 확률적 군중 모델을 이용하여 분석하고 입증한다. 이러한 성능에러 한계는 작업당 할당되는 사람들의 수와 작업자의 평균 신뢰성의해 결정된다. 사람들의 평균 신뢰도가 일정 수준을 넘어서면, 제안된 알고리즘의 평균 성능은 모든 작업자의 신뢰성을 알고있는 오라클 추정기 (이론적인 한계)에 수렴한다. 실제 데이터 세트와 합성 데이터 세트 모두에 대한 광범위한 실험을 통해, 제안된 알고리즘의 실제 성능이 이전의 state-of-the-art 알고리즘들 보다 우수하다는 것을 입증한다. 논문의 두 번째 장에서는 연속학습상황에서 데이터샘플사이에 발생하는 간섭을 해결하기 위해, 항상성기반의 메타 학습 구조 (Homeostatic Meta Model)를 제안한다. 구체적으로, 이전 테스크 중요한 학습 변수를 찾고 정규화에 선별적으로 적용하는 방법을 사용하는데, 제안된 모델은 이러한 정규화의 강도를 자동으로 제어한다. 이러한 기법은 새로운 학습을 진행할 때 이전에 획득한 지식을 최소한으로 잃어버리도록 인공신경망의 학습을 유도한다. 다양한 유형의 연속 학습 과제에서 제안된 방법을 검증하는데, 실험적으로 제안된 방법이 학습의 간섭완화 측면에서 기존 방법보다 우수하다는 점을 보인다.또한 기존 시냅스 가소성 기반 방법들에 비해 상대적으로 변화에 강인하다.제안된 모델에 의해 생성된 정규화의 강도 값은 시냅스에서 항상성 의 음의 피드백 메커니즘과 유사하게, 특정 범위 내에서 능동적으로 제어된다.Data-driven approaches based on neural networks have emerged as new paradigm to solve problems in computer vision and natural language processing fields. These approaches achieve better performance compared to existing human-design approaches (heuristic), however, these performance gains solely relies on a large amount of high quality labeled data. Accordingly, it is important to collect a large amount of data and improve the quality of data by analyzing degrading factors in order to well-train a model. In this dissertation, I propose iterative algorithms to relieve noise of labeled data in crowdsourcing system and meta architecture to alleviate interference among them in continual learning scenarios respectively. Researchers generally collect data using crowdsourcing system which utilizes human evaluations. However, human annotators' decisions may vary significantly due to misconceptions of task instructions, the lack of responsibility, and inherent noise. To relieve the noise in responses from crowd annotators, I propose novel inference algorithms for discrete multiple choice and real-valued vector regression tasks. Web-based crowdsourcing platforms are widely used for collecting large amount of labeled data. Due to low-paid workers and inherent noise, the quality of acquired data could be easily degraded. The proposed algorithms can overcome the noise by estimating the true answer of each task and a reliability of each worker updating two types of messages iteratively. For performance guarantee, the performances of the algorithms are theoretically proved under probabilistic crowd model. Interestingly, their performance bounds depend on the number of queries per task and the average quality of workers. Under a certain condition, each average performance becomes close to an oracle estimator which knows the reliability of every worker (theoretical upper bound). Through extensive experiments with both real-world and synthetic datasets, the practical performance of algorithms are verified. In fact, they are superior to other state-of-the-art algorithms. Second, when a model learns a sequence of tasks one by one (continual learning), previously learned knowledge may conflict with new knowledge. It is well-known phenomenon called "Catastrophic Forgetting" or "Semantic Drift". In this dissertation, we call the phenomena "Interference" since it occurs between two knowledge from labeled data separated in time. It is essential to control the amount of noise and interference for neural network to be well-trained. In the second part of dissertation, to solve the Interference among labeled data from consecutive tasks in continual learning scenario, a homeostasis-inspired meta learning architecture (HM) is proposed. The HM automatically controls the intensity of regularization (IoR) by capturing important parameters from the previous tasks and the current learning direction. By adjusting IoR, a learner can balance the amount of interference and degrees of freedom for its current learning. Experimental results are provided on various types of continual learning tasks. Those results show that the proposed method notably outperforms the conventional methods in terms of average accuracy and amount of the interference. In experiments, I verify that HM is relatively stable and robust compared to the existing Synaptic Plasticity based methods. Interestingly, the IoR generated by HM appears to be proactively controlled within a certain range, which resembles a negative feedback mechanism of homeostasis in synapses.Contents Abstract Contents List of Tables List of Figures 1 INTRODUCTION 1 2 Reliable multiple-choice iterative algorithm for crowdsourcing systems 6 2.1 Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2.1 Task Allocation . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2.2 Multiple Iterative Algorithm . . . . . . . . . . . . . . . . . . 8 2.2.3 Task Allocation for General Setting . . . . . . . . . . . . . . 10 2.3 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.4 Analysis of algorithms . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.4.1 Quality of workers . . . . . . . . . . . . . . . . . . . . . . . 16 2.4.2 Bound on the Average Error Probability . . . . . . . . . . . . 18 2.4.3 Proof of the Theorem 1 . . . . . . . . . . . . . . . . . . . . . 20 2.4.4 Proof of Sub-Gaussianity . . . . . . . . . . . . . . . . . . . . 22 2.5 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 iii2.6 Related Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3 Reliable Aggregation Method for Vector Regression in Crowdsourcing 38 3.1 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.2 Inference Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.2.1 Task Message . . . . . . . . . . . . . . . . . . . . . . . . . . 40 3.2.2 Worker Message . . . . . . . . . . . . . . . . . . . . . . . . 40 3.3 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 42 3.3.1 Real crowdsourcing data . . . . . . . . . . . . . . . . . . . . 43 3.4 Performance Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.4.1 Dirichlet crowd model . . . . . . . . . . . . . . . . . . . . . 48 3.4.2 Error Bound . . . . . . . . . . . . . . . . . . . . . . . . . . 49 3.4.3 Optimality of Oracle Estimator . . . . . . . . . . . . . . . . . 51 3.4.4 Performance Proofs . . . . . . . . . . . . . . . . . . . . . . . 52 3.5 Related Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4 Homeostasis-Inspired Meta Continual Learning 60 4.1 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 4.1.1 Continual Learning . . . . . . . . . . . . . . . . . . . . . . . 60 4.1.2 Meta Learning . . . . . . . . . . . . . . . . . . . . . . . . . 62 4.2 Homeostatic Meta-Model . . . . . . . . . . . . . . . . . . . . . . . . 63 4.3 Preliminary Experiments and Findings . . . . . . . . . . . . . . . . . 66 4.3.1 Block-wise Permutation . . . . . . . . . . . . . . . . . . . . 67 4.3.2 Performance Metrics . . . . . . . . . . . . . . . . . . . . . . 68 4.4 Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 4.4.1 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 4.4.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 4.4.3 Overall Performance . . . . . . . . . . . . . . . . . . . . . . 70 4.5 Related Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 iv4.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 5 Conclusion 78 Abstract (In Korean) 89Docto

Worker scheduling with induced learning in a semi-on-line setting

Scheduling is a widely researched area with many interesting fields. The presented research deals with a maintenance area in which preventative maintenance and emergency jobs enter the system. Each job has varying processing time and must be scheduled. Through learning the operators are able to expand their knowledge which enables them to accomplish more tasks in a limited time. Two MINLP models have been presented, one for preventative maintenance jobs alone, and another including emergency jobs. The emergency model is semi-on-line as the arrival time is unknown. A corresponding heuristic method has also been developed to decrease the computational time of the MINLP models. The models and heuristic were tested in several areas to determine their flexibility. It has been demonstrated that the inclusion of learning has greatly improved the efficiency of the workers and of the system

Allocation of Workers Utilizing Models with Learning, Forgetting, and Various Work Structures

Much of the literature on cross-training and worker assignment problems focus on simulating production systems under cross-training methods. Many have found that for specific systems some methods of allocating workers are better performing than others in terms of overall productivity and ability to deal with change. This has lead researchers to create mathematical programming models with a goal of finding optimal levels of cross-training by changing worker allocations. Learning and forgetting curves have been a key method to improve the solutions produced by the optimization models, but learning curves are often nonlinear causing increased solving times. Because of this, most works have been restricted to modeling small, simple production systems. This thesis studies the expansion of worker allocation models with human learning and forgetting to include variable work structures, thus allowing the models to be used to address a larger set of problems than previously possible. A worker assignment model with flexible inventory constraints capable of representing different production structures is constructed to demonstrate the expansion. Utilizing a reformulation technique to counteract the increased solve times of learning curve incorporation, the scale of the production systems modeled in this work is larger than in similar works and closer to the scale of systems seen in industry. Production systems with multiple products and corresponding due dates are modeled to better represent the production environment in industry. Investigative tests including a 2^4 factorial experiment are included to understand the performance of the model. The output of the optimization model is a schedule of worker assignments for the planning horizon over all of the tasks in the modeled system. Production managers could apply the schedule to their existing lines or run what-if scenarios on line structure to better understand how alternative structures may affect worker training and line productivity over the planning horizon