Two-stage Clustering Method for Discovering People's Perceptions: A Case Study of the COVID-19 Vaccine from Twitter

The variation of the traveling salesman problem (TSP) with multiple salesmen (m-TSP) has been studied for many years resulting in diverse solution methods, both exact and heuristic. However, the high difficulty level on finding optimal (or acceptable) solutions has opposed the many efforts of doing so. The proposed method regards a two stage procedure which implies a modified version of the p-Median Problem (PMP) alongside the TSP, making a partition of the nodes into subsets that will be assigned to each salesman, solving it with Branch & Cut (B&C), in the first stage. This is followed by the routing, applying an Ant Colony Optimization (ACO) metaheuristic algorithm to solve a TSP for each subset of nodes. A case study was reviewed, detailing the positioning of five vehicles in strategic places in the Mexican Republic.

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Two-Stage Robust Optimization for the Orienteering Problem with Stochastic Weights

Complexity ◽

10.1155/2020/5649821 ◽

2020 ◽

Vol 2020 ◽

pp. 1-15

Author(s):

Ke Shang ◽

Felix T. S. Chan ◽

Stephen Karungaru ◽

Kenji Terada ◽

Zuren Feng ◽

...

Keyword(s):

Mathematical Programming ◽

Robust Optimization ◽

Comparative Studies ◽

Complex Model ◽

Orienteering Problem ◽

Two Stage ◽

Second Stage ◽

Robust Model ◽

Uncertainty Set

In this paper, the two-stage orienteering problem with stochastic weights is studied, where the first-stage problem is to plan a path under the uncertain environment and the second-stage problem is a recourse action to make sure that the length constraint is satisfied after the uncertainty is realized. First, we explain the recourse model proposed by Evers et al. (2014) and point out that this model is very complex. Then, we introduce a new recourse model which is much simpler with less variables and less constraints. Based on these two recourse models, we introduce two different two-stage robust models for the orienteering problem with stochastic weights. We theoretically prove that the two-stage robust models are equivalent to their corresponding static robust models under the box uncertainty set, which indicates that the two-stage robust models can be solved by using common mathematical programming solvers (e.g., IBM CPLEX optimizer). Furthermore, we prove that the two two-stage robust models are equivalent to each other even though they are based on different recourse models, which indicates that we can use a much simpler model instead of a complex model for practical use. A case study is presented by comparing the two-stage robust models with a one-stage robust model for the orienteering problem with stochastic weights. The numerical results of the comparative studies show the effectiveness and superiority of the proposed two-stage robust models for dealing with the two-stage orienteering problem with stochastic weights.

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Constrained clustering method for class-based storage location assignment in warehouse

Industrial Management & Data Systems ◽

10.1108/imds-09-2015-0361 ◽

2016 ◽

Vol 116 (4) ◽

pp. 667-689 ◽

Cited By ~ 6

Author(s):

Chao-Lung Yang ◽

Thi Phuong Quyen Nguyen

Keyword(s):

Principal Component ◽

Constrained Clustering ◽

Clustering Method ◽

Content Type ◽

Assignment Model ◽

Retrieval Efficiency ◽

Storage Location ◽

Component Loadings ◽

Location Assignment

Purpose – Class-based storage has been studied extensively and proved to be an efficient storage policy. However, few literature addressed how to cluster stuck items for class-based storage. The purpose of this paper is to develop a constrained clustering method integrated with principal component analysis (PCA) to meet the need of clustering stored items with the consideration of practical storage constraints. Design/methodology/approach – In order to consider item characteristic and the associated storage restrictions, the must-link and cannot-link constraints were constructed to meet the storage requirement. The cube-per-order index (COI) which has been used for location assignment in class-based warehouse was analyzed by PCA. The proposed constrained clustering method utilizes the principal component loadings as item sub-group features to identify COI distribution of item sub-groups. The clustering results are then used for allocating storage by using the heuristic assignment model based on COI. Findings – The clustering result showed that the proposed method was able to provide better compactness among item clusters. The simulated result also shows the new location assignment by the proposed method was able to improve the retrieval efficiency by 33 percent. Practical implications – While number of items in warehouse is tremendously large, the human intervention on revealing storage constraints is going to be impossible. The developed method can be easily fit in to solve the problem no matter what the size of the data is. Originality/value – The case study demonstrated an example of practical location assignment problem with constraints. This paper also sheds a light on developing a data clustering method which can be directly applied on solving the practical data analysis issues.

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Analysis of Tugboat Activities using AIS Data for the Tianjin Port

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198120916734 ◽

2020 ◽

Vol 2674 (5) ◽

pp. 498-509

Author(s):

Shukai Chen ◽

Feng Wang ◽

Xiaoyang Wei ◽

Zhijia Tan ◽

Hua Wang

Keyword(s):

Spatial Distribution ◽

Spatial Analysis ◽

Service Time ◽

Automatic Identification ◽

Identification System ◽

Crucial Importance ◽

Two Stage ◽

Temporal And Spatial ◽

Shed Light

The tugboat is the vessel that helps to maneuver large ships for berthing and un-berthing operations. To achieve efficient tugboat operations, investigating the features of tugboat activities is of crucial importance. This study aims to use automatic identification system (AIS) data to identify the maneuver services and analyze the characteristics of tugboat activities. A two-stage algorithm is developed to extract the time, locations, and involved tugboats for berthing and un-berthing operations from AIS data. The AIS data from Tianjin port, China, are used in the case study to demonstrate the effectiveness of the proposed method and analyze the pattern of tugboat activities. First, some important features of tugboat jobs are presented, such as the daily number of jobs and the spatial distribution of jobs. Then, a temporal and spatial analysis is conducted to investigate tugboat assignment, service time, tugboat utilization, and locations of berthing and un-berthing operations. The obtained results and implications could shed light on the deployment of tugboat berths, tugboat scheduling, and evaluation of tugboat fleet operation.

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