Jiuh-Biing Sheu

A REVERSE LOGISTICS COST MINIMIZATION MODEL FOR THE TREATMENT OF HAZARDOUS WASTES

This study presents a cost-minimization model for a multi-time-step, multi-type hazardous-waste reverse logistics system. A discrete-time linear analytical model is formulated that minimizes total reverse logistics operating costs subject to constraints that take into account such internal and external factors as business operating strategies and governmental regulations. Application cases are presented to demonstrate the feasibility of the proposed approach. By using the proposed model coupled with operational strategies, it is shown that the total reverse logistics costs for the applications cases can be reduced by more than 49%. � 2002 Elsevier Science Ltd. All rights reserved.

STOCHASTIC MODELING AND REAL-TIME PREDICTION OF VEHICULAR LANE-CHANGING BEHAVIOR

Time-varying lane-changing fractions and queue lengths are important lane traffic characteristics which may exhibit significant changes in the presence of a lane-blocking incident. This paper describes a stochastic system modeling approach to estimate time-varying lane-changing fractions and queue lengths for real-time incident management on surface streets. A discrete-time nonlinear stochastic model, which consists of recursive equations, measurement equations, and boundary constraints, is proposed to characterize inter-lane and intra-lane traffic state variables during incidents. To estimate lane-changing fractions and other state variables of the model, a recursive estimation algorithm is developed which primarily involves an extended Kalman filter, truncation, normalization, and a queue-updating procedure. Lane traffic counts are the sole input data used in this method. These data can be readily collected from conventional point detectors. The proposed model was calibrated using video-based data, then tested using simulated data from the TRAF-NETSIM simulation model, Version 5.0, as well as real video-based data sets. Preliminary test results indicate the feasibility of employing the proposed approach to estimate time-varying mandatory lane-changing fractions as well as queue lengths during incidents. The estimated lane-changing fractions and queue lengths can be used not only in better understanding the phenomena of incident-related inter-lane and intra-lane traffic characteristics, but also in developing real-time incident management technologies. Moreover, it is hoped that the results of this study might contribute to future research in related areas such as incident traffic prediction, incident-responsive traffic control and management, and automatic road congestion warning systems for further use in advanced transportation management and information systems.

A coordinated reverse logistics system for regional management of multi-source hazardous wastes

Abstract This paper presents a coordinated reverse logistics (CRL) management system for the treatment of multi-source hazardous wastes in a given region, in this case, a specific high-technology manufacturing zone. A linear multi-objective analytical model is formulated that systematically minimizes both the total reverse logistics operating costs and corresponding risks. In addition to inter-organizational logistics operating factors, environmental concerns are considered and formulated as corresponding risk-related constraints. Using the proposed model, results of numerical studies indicate that when the aspect of risk-induced penalties is not considered, the operational costs of regional hazardous-waste management can be efficiently reduced by 58%, compared to the existing operational costs at the study site. In addition, it is also observed that the corresponding weight associated with the risk-induced objective function embedded in the proposed model seems to have a significant effect on the CRL costs.

A hybrid neuro-fuzzy analytical approach to mode choice of global logistics management

This paper presents a hybrid neuro-fuzzy methodology to identify appropriate global logistics (GL) operational modes used for global supply chain management. The proposed methodological framework includes three main developmental phases: (1) establishment of a GL strategic hierarchy, (2) formulation of GL-mode identification rules, and (3) development of a GL-mode choice model. By integrating advanced multi-criteria decision-making (MCDM) techniques including fuzzy analytical hierarchy process (Fuzzy-AHP), Fuzzy-MCDM, and the technique for order preference by similarity to an ideal solution (TOPSIS), six types of global logistics and operational modes coupled with corresponding fuzzy-based multi-criteria decision-making rules are specified in the second phase. Using the specified fuzzy decision-making rules as the input database, an adaptive neuro-fuzzy inference system (ANFIS) is then developed in the third phase to identify proper GL modes for the implementation of global supply chain management. A numerical study with a questionnaire survey database aimed at the information technology (IT) industries of Taiwan is conducted to illustrate the applicability of the proposed method.

A fuzzy-based customer classification method for demand-responsive logistical distribution operations

In some cases, customer classification is important for the development of advanced logistical distribution strategies in response to the growing complexity in business logistical markets. This paper presents a new approach that can be employed to cluster customers before executing fleet routing in logistical operations. The proposed approach is developed on the basis of fuzzy clustering techniques, and involves three sequential mechanisms including: (1) binary transformation, (2) generation of a fuzzy correlation matrix, and (3) customer clustering. Such a customer clustering method should be performed prior to vehicle dispatching and routing in the process of goods distribution. The proposed methodology clusters customers on the basis of their demand attributes, rather than the static geographic property which is considered extensively in most published vehicle routing algorithms. In addition to methodology development, a case study was conducted to demonstrate the potential advantages of the proposed fuzzy clustering based method. It is expected that this study can stimulate more research on time-based logistics control and management.

A fuzzy clustering-based approach to automatic freeway incident detection and characterization

Automatic incident detection and characterization is urgently required in the development of advanced technologies used for reducing non-recurrent traffic congestion on freeways. This paper presents a new method which is constructed primarily on the basis of the fuzzy clustering theories to identify automatically freeway incidents. The proposed approach is capable of distinguishing the time-varying patterns of incident-induced traffic states from the patterns of incident-free traffic states, and characterizing incidents with respect to the onset and end time steps of incidents, incident location, the temporal and spatial change patterns of incident-related traffic variables in response to the impacts of incidents on freeway traffic flows in real time. Lane traffic count and density are the two major types of input data, which can be readily collected from point detectors. Based on the spatial and temporal relationships of the collected raw traffic data, several time-varying state variables are defined, and then evaluated quantitatively and qualitatively to determine the decision variables used for real-time incident characterization. Utilizing the specified decision variables, the proposed fuzzy clustering-based algorithm executes recurrently three major procedures: (1) identification of traffic flow conditions, (2) recognition of incident occurrence, and (3) incident characterization. In this study, data used for model tests are generated from the CORSIM traffic simulator. Our preliminary test results indicate that the proposed approach is promising, and, in expectation, can be integrated with any published real-time incident detection technologies. Importantly, this study may contribute significantly to the applications of fuzzy clustering techniques, and stimulate more related research.

A sequential detection approach to real-time freeway incident detection and characterization

Abstract In this paper, a new methodology is presented for real-time detection and characterization of freeway incidents. The proposed technology is capable of detecting freeway incidents in real time as well as characterizing incidents in terms of time-varying lane-changing fractions and queue lengths in blocked lanes, the lanes blocked due to incidents, and duration of incident, etc. The architecture of the proposed incident detection approach consists of three sequential procedures: (1) symptom identification for identification of anomalous changes in traffic characteristics probably caused by incidents, (2) signal processing for stochastic estimation of incident-related lane traffic characteristics, and (3) pattern recognition for incident detection. Lane traffic count and occupancy are two major types of input data, which can be readily collected from point detectors. The primary techniques utilized to develop the proposed method include: (1) discrete-time, nonlinear, stochastic system modeling used in the signal processing procedure, and (2) modified sequential probability ratio tests employed in the pattern recognition procedure. Off-line tests were conducted to substantiate the performance of the proposed incident detection algorithm based on simulated data generated employing the calibrated INTRAS simulation model and on real incident data collected on the I-880 freeway in Oakland, California. The test results indicate the feasibility of achieving real-time incident detection and characterization utilizing the proposed method.

A stochastic modeling approach to dynamic prediction of section-wide inter-lane and intra-lane traffic variables using point detector data

Real-time section-wide lane traffic variables such as density and lane-changing are vital to traffic control and management in urban areas. They can be used as decision variables to determine traffic control and management strategies in real time as well as characterize road traffic congestion for further use in advanced traveler information systems. Therefore, developing techniques which provide real-time information regarding section-wide inter-lane and intra-lane traffic variables is an increasingly important task in the area of advanced transportation management and information systems. This paper presents a stochastic system modeling approach to extracting real-time information of section-wide inter-lane as well as intra-lane traffic (e.g. lane-changing fractions, lane densities, etc.) utilizing lane traffic counts detected from point detectors. The proposed methodology consists of three principle elements: (1) specification of system states, (2) system modeling, and (3) recursive estimation. Preliminary test results indicated that the proposed methodology is promising for estimating real-time section-wide inter-lane as well as intra-lane traffic variables based merely on point detector data. The inter-lane and intra-lane traffic information generated by the proposed method can be further used in developing related technologies such as road traffic congestion detection, automatic incident detection, prediction of driver route choices, variable message signs and in-car navigation devices. ©

A multi-layer demand-responsive logistics control methodology for alleviating the bullwhip effect of supply chains

This paper presents a multi-layer demand-responsive logistics control strategy for alleviating, effectively and efficiently, the bullwhip effect of a supply chain. Utilizing stochastic optimal control methodology, the proposed method estimates the time-varying demand-oriented logistics system states, which originate directly and indirectly downstream to the targeted member of a supply chain, and associate these estimated demands with estimates of different time-varying weights under the goal of systematically optimizing the logistical performance of chain members. In addition, an experimental design is conducted where the proposed method is evaluated with the two specified criteria. Numerical results indicate that the proposed method permits alleviating, to a great extent, the bullwhip effect in comparison with the existing logistics management strategies. Furthermore, the methodology presented in this study is expected to help address issues regarding the uncertainty and complexity of the distortion of demand-related information existing broadly among supply chain members for an efficient supply chain coordination.

Relief supply collaboration for emergency logistics responses to large-scale disasters

This paper proposes a novel relief supply collaboration approach to address the issue of post-disaster relief supply–demand imbalance in emergency logistics (EL) operations. This proposed approach involves two levels of recursive functions: (1) a two-stage relief supplier clustering mechanism for time-varying multi-source relief supplier selection and (2) the use of stochastic dynamic programming model to determine a multi-source relief supply that minimises the impact of relief supply–demand imbalance during EL response. The distinctive features of this proposed approach are to identify the potential relief suppliers and to minimise the imbalanced supply–demand impact under relief supply collaboration. Scenario design and model tests are conducted to demonstrate that relief supply collaboration with grouped relief suppliers has a significant benefit of alleviating the impact of imbalanced relief supply–demand, relative to collaboration with ungrouped ones.