Allan N. Zhang

A Simple and Fast Hypervolume Indicator-Based Multiobjective Evolutionary Algorithm

To find diversified solutions converging to true Pareto fronts (PFs), hypervolume (HV) indicator-based algorithms have been established as effective approaches in multiobjective evolutionary algorithms (MOEAs). However, the bottleneck of HV indicator-based MOEAs is the high time complexity for measuring the exact HV contributions of different solutions. To cope with this problem, in this paper, a simple and fast hypervolume indicator-based MOEA (FV-MOEA) is proposed to quickly update the exact HV contributions of different solutions. The core idea of FV-MOEA is that the HV contribution of a solution is only associated with partial solutions rather than the whole solution set. Thus, the time cost of FV-MOEA can be greatly reduced by deleting irrelevant solutions. Experimental studies on 44 benchmark multiobjective optimization problems with 2-5 objectives in platform jMetal demonstrate that FV-MOEA not only reports higher hypervolumes than the five classical MOEAs (nondominated sorting genetic algorithm II (NSGAII), strength Pareto evolutionary algorithm 2 (SPEA2), multiobjective evolutionary algorithm based on decomposition (MOEA/D), indicator-based evolutionary algorithm, and S-metric selection based evolutionary multiobjective optimization algorithm (SMS-EMOA)), but also obtains significant speedup compared to other HV indicator-based MOEAs.

Supply Chain Visibility: A decision making perspective

The objective of this paper is to analyze supply chain visibility (SCV) which is ill-defined and to provide a comprehensive understanding of SCV from a collaborative decision making perspective. A literature review has been conducted to understand the meanings of the extensively used jargon of supply chain visibility. The characteristics of SCV have been conceptually analyzed and a comprehensive definition for it has been proposed to clear the confusion of its usages. SCV is a unique expression specific to the field of supply chain management and logistics. Currently, there are various definitions of which tackled different issues from different perspectives. A common definition/understanding of SCV can be induced after treating these definitions from a collaborative decision making perspective. A comprehensive understanding of SCV is beneficial to supply chain professionals to clearly communicate about SCV which is a critical issue for many companies keen to operate in an end-to-end environment. The definition of SCV presented in this paper is comprehensive and novel. It helps model and assess SCV for better supply chain decision making.

Multi-objective Heterogeneous Capacitated Vehicle Routing Problem with Time Windows and Simultaneous Pickup and Delivery for Urban Last Mile Logistics

The Urban Last Mile Logistics (LML) is known to be the most expensive, least efficient and most polluting section of the supply chain. To that extent, a multi-objective heterogeneous capacitated vehicle routing problem with time windows and simultaneous pickup and delivery (MoHCVRPTWSPD) is formulated and solved to cater to this section of the supply chain. The proposed model is solved through two proposed methods that are based on exact methods. A small benchmark was adopted from the current literature to test the proposed methods and computational results are reported. Based on the computational results, a number of insights into the MoHCVRP-TWSPD problem are provided.

Disruption recovery modeling in supply chain risk management

It is well known that disruptions can significantly affect the performance of a companys supply chain especially in highly volatile markets. It is therefore imperative to have appropriate mechanisms/tools to mitigate the effects of disruptions. We developed the concept for a disruption recovery-modelling approach that provides more accurate supply forecasts during supply chain disruptions (i.e. smaller variance), which are of prime importance to supply chain risk management. Specifically, we show that a combination of model forecasts performs no worse than the individual component models applied in this paper. In addition, the projections of the models updated through a Bayesian framework generate supply forecasts with smaller variances.

Forecast UPC-level FMCG demand, Part II: Hierarchical reconciliation

In a big data enabled environment, manufacturers and distributors may have access to previously unobserved retailer-level demand related information. This additional information can be considered in demand forecasting to produce more accurate forecasts, and thus enable better stock-outs management. In Part II of this two-part paper, we explore the hierarchical nature of fast moving consumer goods (FMCG) demand (represented by sales) time series and produce one week ahead rolling forecasts on universal product code (UPC) level (or distributor level, as per our definition below). We show that the hierarchical forecasting framework has significant accuracy improvement over the conventional univariate forecasting methods. The main reason of the observed improvements is due to the price and promotion information available at the retailer level, which is assumed to be unknown to the distributor. To reconcile forecasts according to the hierarchy, only the forecast values at retailer level are needed, the business strategies of individual retailers remain proprietary. A freely available dataset is considered to encourage further exploration. Data exploratory analysis and visualization tools are discussed in Part I of the paper.

A Framework for Mining RFID Data From Schedule-Based Systems

A schedule-based system is a system that operates on or contains within a schedule of events and breaks at particular time intervals. Entities within the system show presence or absence in these events by entering or exiting the locations of the events. Given radio frequency identification (RFID) data from a schedule-based system, what can we learn about the system (the events and entities) through data mining? Which data mining methods can be applied so that one can obtain rich actionable insights regarding the system and the domain? The research goal of this paper is to answer these posed research questions, through the development of a framework that systematically produces actionable insights for a given schedule-based system. We show that through integrating appropriate data mining methodologies as a unified framework, one can obtain many insights from even a very simple RFID dataset, which contains only very few fields. The developed framework is general, and is applicable to any schedule-based system, as long as it operates under certain basic assumptions. The types of insights are also general, and are formulated in this paper in the most abstract way. The applicability of the developed framework is illustrated through a case study, where real world data from a schedule-based system is analyzed using the introduced framework. Insights obtained include the profiling of entities and events, the interactions between entity and events, and the relations between events.

Optimizing performance of sentiment analysis through design of experiments

Traditional manual design of analytical processes is challenging as it requires a general analyst to have good grasping of numerous algorithms and the interaction effects between each technique and the data across multiple domains. Especially in an increasingly high data variety/multi-domain environment today, this design process can be very laborious/challenging. In this paper, we describe a design optimization approach using design of experiments to determine a suitable design in a standardized text classification process with high classification performance. We focus on sentiment analysis as a use case for this approach, as standard analytical methods in each phase of the sentiment analysis process have been established; from data pre-processing, feature selection and classification. In our proposed approach, we present an automatic and domain-free technique of using design of experiments to this design process, with the sentiment classification evaluation metrics as the performance criteria for optimization. In addition, we show that several interpretable analyses can be made to better understand the complex interaction effects of various analytical techniques with the data, which then can guide a general analyst to select more appropriate process design parameters for better text classification performance.

Spatial data dimension reduction using quadtree: A case study on satellite-derived solar radiation

Satellite data is discrete in both space and time; it can be considered as temporal snapshots (time series) of lattice processes. As the raw datasets are often too large to host publicly, processed datasets with a coarse spatial resolution are often hosted as an alternative. Nevertheless, with a regular grid, the inhomogeneous variability in the lattice processes cannot be captured effectively. In this paper, a quadtree-based spatial data dimension reduction algorithm is demonstrated. Based on the stratum variance, this algorithm iteratively divides lattice data into strata of fours. In this way, the number of strata in an area can be correlated to the variability of that area. A satellite-derived surface solar radiation (SSR) dataset is used for the case study. Using parallel computing, the quadtree algorithm is applied on each temporal snapshot of SSR in the dataset. The processed data is then saved in a list structure. Finally, a solar resource assessment application, namely, optimizing the orientation of a photovoltaic array, is considered to demonstrate the effectiveness and efficiency of the dimension-reduced dataset.

Forecast UPC-level FMCG demand, Part III: Grouped reconciliation

Coordination across a supply chain creates win-win situation for all players in that supply chain; we address the benefits, in terms of forecast accuracy, of reconciling demand forecasts across a supply chain. In Part III of this three-part paper, we continue our discussion on optimal reconciliation of forecasts. Two contributions are made in this paper: 1) the grouped reconciliation technique is used to address the forecast inconsistency in situations when more than one hierarchy can be defined in a supply chain, and 2) minimum trace (MinT) estimator is used to further improve the reconciliation accuracy on top of the weighted least square (WLS) approach, which was used in the earlier parts of this three-part paper. Following the earlier works, the same set of fast moving consumer goods data is used here. The current results are compared to the previous ones. It is shown that the MinT reconciliation technique outperforms the WLS approach, which has been previously identified as the best reconciliation technique for the data from the bottled juice category in the Dominicks Finer Food dataset.

Towards adaptive weight vectors for multiobjective evolutionary algorithm based on decomposition

The decomposition method in multiobjective evolutionary algorithms (MOEA/D) is an effective approach to evolve solutions along predefined weight vectors for solving multiobjective optimization problems (MOPs). However, obtaining evenly distributed weight vectors for different types of MOPs is a challenge problem especially when the true Pareto fronts (PFs) are unknown before a MOEA/D starts. In this paper, a new MOEA/D with a fast hypervolume archive (called FV-MOEA/D) is proposed to adaptively adjust the weight vectors for various shapes of PFs. The core idea of FV-MOEA/D is to periodically adjust weight vectors based on solutions in the proposed archive, in which convergence and diversity are maintained by maximizing hypervolume. Experimental studies on 58 benchmark MOPs in jMetal demonstrate that the proposed FV-MOEA/D not only reached higher hypervolumes when compare to five classical MOEAs i.e., NSGAII, SPEA2, IBEA, FV-MOEA and MOEA/D, but also obtained well distributed weight vectors on PFs with different geometrical characteristics.