Optimization of job shop scheduling with material handling by automated guided vehicle

Job Shop Scheduling with Material Handling has attracted increasing attention in both industry and academia, especially with the inception of Industry 4.0 and smart manufacturing. A smart manufacturing system calls for efficient and effective production planning. On a typical modern shop floor, jobs of various types follow certain processing routes through machines or work centers, and automated guided vehicles (AGVs) are utilized to handle the jobs. In this research, the optimization of a shop floor with AGV is carried out, and we also consider the planning scenario under variable processing time of jobs. The goal is to minimize the shop floor production makespan or other specific criteria correlated with makespan, by scheduling the operations of job processing and routing the AGVs. This dissertation includes three research studies that will constitute my doctoral work. In the first study, we discuss a simplified case in which the scheduling problem is reformulated into a vehicle dispatching (assignment) problem. A few AGV dispatching strategies are proposed based on the deterministic optimization of network assignment problems. The AGV dispatching strategies take future transportation requests into consideration and optimally configure transportation resources such that material handling can be more efficient than those adopting classic AGV assignment rules in which only the current request is considered. The strategies are demonstrated and validated with a case study based on a shop floor in literature and compared to classic AGV assignment rules. The results show that AGV dispatching with adoption of the proposed strategy has better performance on some specific criterions like minimizing job waiting time. In the second study, an efficient heuristic algorithm for classic Job Shop Scheduling with Material Handling is proposed. Typically, the job shop scheduling problem and material handling problem are studied separately due to the complexity of both problems. However, considering these two types of decisions in the same model offers benefits since the decisions are related to each other. In this research, we aim to study the scheduling of job operations together with the AGV routing/scheduling, and a formulation as well as solution techniques are proposed. The proposed heuristic algorithm starts from an optimal job shop scheduling solution without limiting the size of AGV fleet, and iteratively reduces the number of available vehicles until the fleet size is equal to the original requirements. The computational experiments suggest that compared to existing solution techniques in literature, the proposed algorithm can achieve comparable solution quality on makespan with much higher computational efficiency. In the third study, we take the variability of processing time into consideration in optimizing job shop scheduling with material handling. Variability caused by random effects and deterioration is discussed, and a series of models are developed to accommodate random and deteriorating processing time respectively. With random processing time, the model is formulated as a Stochastic Programming Job Shop Scheduling with Material Handling model, and with deteriorating processing time the model can be nonlinear under specific deteriorating functions. Based on a widely adopted dataset in existing literature, the stochastic programming model were solved with Pyomo, and models with deterioration were linearized and solved with CPLEX. By considering variable processing time, the JSSMH models can better adapt to real production scenarios

Investor Target Prices

We argue that investors have target prices as anchors for the stocks that they own; once a stock exceeds target prices, investors are satisfied and more likely to sell the stock. This increased selling can generate a price drift after good news. Consistent with our argument, using analyst-target-price forecasts as a proxy, we provide evidence that the fraction of satisfied investors generates the post-earnings-announcement drift, and stocks with a high fraction of satisfied investors experience stronger selling around announcements. This pattern is stronger for stocks with low institutional ownership and high uncertainty.</p

Essays on information asymmetry in financial market

I study how asymmetric information affects the financial market in three papers. In the first paper, I study the joint determination of optimal contracts and equilibrium asset prices in an economy with multiple principal-agent pairs. Principals design optimal contracts that provide incentives for agents to acquire costly information. With agency problems, the agents’ compensation depends on the accuracy of their forecasts for asset prices and payoffs. Complementarities in information acquisition delegation arise as follows. As more principals hire agents to acquire information, asset prices become less noisy. Consequently, agents are more willing to acquire information because they can forecast asset prices more accurately, thus mitigating agency problems and encouraging other principals to hire agents. This mechanism can explain many interesting phenomena in markets, including multiple equilibria, herding, home bias and idiosyncratic volatility comovement. In the second paper (co-authored with Yao Zeng from Harvard University), we investigate how firms’ cross learning amplifies industry-wide investment waves. Firms’ investment opportunities have idiosyncratic shocks as well as a common shock, and firms’ asset prices aggregate speculators’ private information about these two shocks. In investing, each firm learns from other firms’ prices to make better inference about the common shock. Thus, a spiral between firms’ higher investment sensitivity to the common shock and speculators’ higher weighting on the common shock emerges. This leads to systematic risks in investment waves: higher investment and price comovements as well as their higher comovements with the common shock. Moreover, each firm’s cross learning creates a new pecuniary externalities on other firms, because it makes other firms’ prices less informative on their idiosyncratic shocks through speculators’ endogenous over-weighting on the common shock. In the third model, we study the effect of introducing an options market on investors’ incentive to collect private information in a rational expectation equilibrium model. We show that an options market has two effects on information acquisition: a negative effect, as options act as substitutes for information, and a positive effect, as informed investors have less need for options and can earn profits from selling them. When the population of informed investors is high because of the low information acquisition cost, the supply for options is larger than the demand, leading to low option prices. Low option prices in turn induce investors to use options instead of information to reduce risk, while informed investors have little opportunity to earn profits from selling options to cover their information acquisition cost. Introducing an options market thus decreases investors’ incentive to acquire information, and the prices of the underlying assets become less informative, leading to lower prices and higher volatilities. A dynamic extension of this analysis shows that introducing an options market increases the price reactions to earnings announcements. However, when the information acquisition cost is high, the opposite effects arise. Further analysis shows that our results are robust for more general derivatives. These results provide a potentially unified theory to reconcile the conflicting empirical findings on the options listing of individual stocks in both the U.S. market and international markets

The booms and busts of beta arbitrage

Low-beta stocks deliver high average returns and low risk relative to high-beta stocks, an opportunity for professional investors to “arbitrage” away. We argue that beta-arbitrage activity generates booms and busts in the strategy’s abnormal trading profits. In times of low arbitrage activity, the beta-arbitrage strategy exhibits delayed correction, taking up to three years for abnormal returns to be realized. In contrast, when arbitrage activity is high, prices overshoot and then revert in the long run. We document a novel positive-feedback channel operating through firm leverage that facilitates these boom-and-bust cycles

Why don't most mutual funds short sell?

An intriguing observation in the US mutual fund industry is that most equity funds do not short sell, even though virtually all regulatory restrictions on short selling had been lifted by 1997. We shed light on this puzzle by conducting the first systematic analysis of long-short equity funds' portfolio compositions, fund performance, and capital flows. Our results reveal that: 1) long-short mutual funds hold substantially more cash than their long-only peers and have a market beta significantly below one; 2) long-short funds generate a large positive alpha of 5% a year in risky holdings but slightly underperform their long-only peers in total returns; and 3) long-short funds face much higher flow-performance sensitivities and are more prone to use cash to absorb capital flows. We discuss several possible explanations for these findings

Offsetting disagreement and security prices

We propose that investor beliefs frequently “cross” in the sense that an investor may like company A but dislike company B, whereas another investor may like company B but dislike company A. Such belief-crossing makes it almost impossible to construct a portfolio that is composed solely of every investor’s most favored companies. This causes the level of excitement for portfolios to be generally lower than the levels of excitement that individual companies generate among their most fervent supporters. Coupled with short-sale constraints, wherein prices are set by the most optimistic investors, this causes portfolios to trade at discounts. Utilizing several settings whereby the value of a portfolio and the values of the underlying components can be evaluated separately (e.g., closed-end funds), we present evidence supporting our proposition that, in financial markets, the “whole” is often less than the “sum of its parts.

Reforming Physics Exams Using Openly Accessible Large Isomorphic Problem Banks created with the assistance of Generative AI: an Explorative Study

This paper explores using large isomorphic problem banks to overcome many challenges of traditional exams in large STEM classes, especially the threat of content sharing websites and generative AI to the security of exam items. We first introduce an efficient procedure for creating large numbers of isomorphic physics problems, assisted by the large language model GPT-3 and several other open-source tools. We then propose that if exam items are randomly drawn from large enough problem banks, then giving students open access to problem banks prior to the exam will not dramatically impact students' performance on the exam or lead to wide-spread rote-memorization of solutions. We tested this hypothesis on two mid-term physics exams, comparing students' performance on problems drawn from open isomorphic problem banks to similar transfer problems that were not accessible to students prior to the exam. We found that on both exams, both open bank and transfer problems had the highest difficulty. The differences in percent correct were between 5% to 10%, which is comparable to the differences between different isomorphic versions of the same problem type. Item response theory analysis found that both types of problem have high discrimination (>1.5) with no significant differences. Student performance on open-bank and transfer problems are highly correlated with each other, and the correlations are stronger than average correlations between problems on the exam. Exploratory factor analysis also found that open-bank and transfer problems load on the same factor, and even formed their own factor on the second exam. Those observations all suggest that giving students open access to large isomorphic problem banks only had a small impact on students' performance on the exam but could have significant potential in reforming traditional classroom exams

Real2Sim2Real Transfer for Control of Cable-driven Robots via a Differentiable Physics Engine

Tensegrity robots, composed of rigid rods and flexible cables, exhibit high strength-to-weight ratios and extreme deformations, enabling them to navigate unstructured terrain and even survive harsh impacts. However, they are hard to control due to their high dimensionality, complex dynamics, and coupled architecture. Physics-based simulation is one avenue for developing locomotion policies that can then be transferred to real robots, but modeling tensegrity robots is a complex task, so simulations experience a substantial sim2real gap. To address this issue, this paper describes a Real2Sim2Real strategy for tensegrity robots. This strategy is based on a differential physics engine that can be trained given limited data from a real robot (i.e. offline measurements and one random trajectory) and achieve a high enough accuracy to discover transferable locomotion policies. Beyond the overall pipeline, key contributions of this work include computing non-zero gradients at contact points, a loss function, and a trajectory segmentation technique that avoid conflicts in gradient evaluation during training. The proposed pipeline is demonstrated and evaluated on a real 3-bar tensegrity robot.Comment: Submitted to ICRA202