Chi-Guhn Lee

A generic approach to measuring the machine flexibility of manufacturing systems

Despite extensive studies on the flexibility of manufacturing systems over the last two decades, a unified measurement approach has not been developed. To this end, we integrate two domains of machine flexibility models from the literature: operational capability-based machine flexibility and time and cost-based machine flexibility, and propose a generic model to measure machine flexibility with consideration of uncertainties in the system. Furthermore, in our approach we include part characteristics such as processing time and processing cost, the number of operations that a machine can perform, and uncertainties in demand and machine-part assignment. The resulting framework to measure machine flexibility is a two-stage model: a super efficiency Data Envelopment Analysis Model and a flexibility model. The results show that the marginal system machine flexibility does not always increase as the number of operations that a machine can perform increases, and the system machine flexibility depends on the demand uncertainty.

Model and algorithm of fuzzy joint replenishment problem under credibility measure on fuzzy goal

The joint replenishment problem (JRP) has received considerable attention and all of the work on JRP is under explicit environment. In fact, the decision makers often have to face vague operational conditions. In this paper, a novel JRP model with fuzzy minor replenishment cost and fuzzy inventory holding cost is developed. More concisely, this model is a fuzzy dependent-chance programming (DCP) model. Subsequently, the technique of the traditional fuzzy simulation (FS) approach and differential evolution algorithm (DE) are integrated to design a hybrid intelligent algorithm named FSDE-I to solve this practical fuzzy JRP. Thirdly, another intelligent algorithm named FSDE-II using an improved FS approach is proposed to estimate the credibility more precisely. Finally, FSDE-I and FSDE-II are illustrated with numerical examples and the results show the effectiveness of FSDE-II.

Stochastic DEA with ordinal data applied to a multi-attribute pricing problem

Multiple attribute pricing problems are highly challenging due to the dynamic and uncertain features in the associated market. In this paper, we address the condominium multiple attribute pricing problem using data envelopment analysis (DEA). In this study, we simultaneously consider stochastic variables, non-discretionary variables, and ordinal data, and present a new type of DEA model. Based on our proposed DEA, an effective performance measurement tool is developed to provide a basis for understanding the condominium pricing problem, to direct and monitor the implementation of pricing strategy, and to provide information regarding the results of pricing efforts for units sold as well as insights for future building design. A case study is executed on a leading Canadian condominium developer.

Supply Uncertainty and Diversification: A Review

We review inventory models that use multiple sourcing (diversification) to deal with upstream (supply) uncertainty. To provide a structured review, we identify three sources of supply uncertainty as follows: supply timing, supply quantity (or quality), and purchase price. Then, we summarize the main results that exist in the literature. Finally, based on our observations, we provide directions for future research.

Evaluation of inventory policies with unidirectional substitutions

We propose evaluation approaches to multi-item base-stock inventory policies where unidirectional substitutions are allowed. The problems in the paper are in the context of spare parts management and we identify two substitution cases: substitution upon demand arrivals and substitution upon order deliveries. This leads us to three unidirectional substitution policies, for each of which we develop Markovian models. As the number of part types increases, computational effort required to solve the Markovian models increases rapidly. To reduce computation burden, an approximation approach based on the decomposition of multi-dimensional state transition is used for systems with two or more spare part types. Numerical studies show unidirectional substitution improves various system performance measures such as the average inventory level, the average backlogged demand, and the fill rate. The proposed decomposition approach reduces the computation required to compute the performance measures and the approximation errors seems to be quite small.

Pairs trading: optimal thresholds and profitability

Since its birth in the 1980s, pairs trading have been popular as a statistical arbitrage strategy among major investment banks and hedge funds. Despite the high average annualized excess return, wh...

Pricing swing options with regime switching

Gasoline price is highly volatile and exhibits Markov regime-switching process. In the electricity and the natural gas markets, “swing” options, which can provide some protection against day-to-day price fluctuations, are used to incorporate flexibility in delivering acquired energy. We propose a framework for pricing swing options for an underlying variable that follows a regime-switching process. We study the proposed framework in the gasoline industry for pricing swing options under price uncertainty by extracting the gasoline market information, estimating the parameters of the regime-switching process, and then presenting different numerical examples.

Acceleration Operators in the Value Iteration Algorithms for Markov Decision Processes

We study the general approach to accelerating the convergence of the most widely used solution method of Markov decision processes (MDPs) with the total expected discounted reward. Inspired by the monotone behavior of the contraction mappings in the feasible set of the linear programming problem equivalent to the MDP, we establish a class of operators that can be used in combination with a contraction mapping operator in the standard value iteration algorithm and its variants. We then propose two such operators, which can be easily implemented as part of the value iteration algorithm and its variants. Numerical studies show that the computational savings can be significant especially when the discount factor approaches one and the transition probability matrix becomes dense, in which the standard value iteration algorithm and its variants suffer from slow convergence.

Using existing workstations as transfer stations in tandem AGV systems

Abstract Tandem AGV systems, which were introduced by Bozer and Srinivasan, are based on dividing all the stations in a system into non-overlapping, single-vehicle zones with transfer stations connecting adjacent zones. Although tandem AGV systems offer flexibility and simplicity, the transfer stations, that is, the additional pickup/deposit (P/D) points and the conveyors connecting the P/D points, generally increase the cost and floor space requirements of the system. Furthermore, the transfer conveyors may interfere with other material handling functions and make it more difficult to reconfigure the zones. This paper considers the elimination of conveyros by using an existing station as a transfer point. Depending on how the vehicles access the station, however, such an approach may lead to blocking between vehicles. An analytical model is developed and an iterative algorithm is presented to evaluate the performance of two vehicles under possible blocking scenarios at the transfer station. A numerical example illustrates the accuracy of the analytical model. The proposed algorithm converges under the most realistic blocking scenario.

Multistate Bayesian Control Chart Over a Finite Horizon

We study a multistate partially observable process control model with a general state transition structure. The process is initially in control and subject to Markovian deterioration that can bring it to out-of-control states. The process may continue making transitions among the out-of-control states, or even back to the in-control state until it reaches an absorbing state. We assume that at least one out-of-control state is absorbing. The objective is to minimize the expected total cost over a finite horizon. By transforming the standard Cartesian belief space into the spherical coordinate system, we show that the optimal policy has a simple control-limit structure. We also examine two specialized models. The first is the phase-type transition time model, in which we develop an algorithm whose complexity is not affected by the number of phases. The second is a model with multiple absorbing out-of-control states, by which we show that certain out-of-control states may incur less total cost than the in-control state, a phenomenon never occurs in the two-state models. We conclude that there are fundamental differences between multistate models and two-state models, and that the spherical coordinate transformation offers significant analytical and computational benefits.