Simultaneous Planning of Liner Ship Speed Optimization, Fleet Deployment, Scheduling and Cargo Allocation with Container Transshipment

Due to a substantial growth in the world waterborne trade volumes and drastic changes in the global climate accounted for CO2 emissions, the shipping companies need to escalate their operational and energy efficiency. Therefore, a multi-objective mixed-integer non-linear programming (MINLP) model is proposed in this study to simultaneously determine the optimal service schedule, number of vessels in a fleet serving each route, vessel speed between two ports of call, and flow of cargo considering transshipment operations for each pair of origin-destination. This MINLP model presents a trade-off between economic and environmental aspects considering total shipping time and overall shipping cost as the two conflicting objectives. The shipping cost comprises of CO2 emission, fuel consumption and several operational costs where fuel consumption is determined using speed and load. Two efficient evolutionary algorithms: Nondominated Sorting Genetic Algorithm II (NSGA-II) and Online Clustering-based Evolutionary Algorithm (OCEA) are applied to attain the near-optimal solution of the proposed problem. Furthermore, six problem instances of different sizes are solved using these algorithms to validate the proposed model.Comment: 28 pages, 10 figure

Benefits of retailer-supplier partnership initiatives under time-varying demand:a comparative analytical study

This paper aims to help supply chain managers to determine the value of retailer-supplier partnership initiatives beyond information sharing (IS) according to their specific business environment under time-varying demand conditions. For this purpose, we use integer linear programming models to quantify the benefits that can be accrued by a retailer, a supplier and system as a whole from shift in inventory ownership and shift in decision-making power with that of IS. The results of a detailed numerical study pertaining to static time horizon reveal that the shift in inventory ownership provides system-wide cost benefits in specific settings. Particularly, when it induces the retailer to order larger quantities and the supplier also prefers such orders due to significantly high setup and shipment costs. We observe that the relative benefits of shift in decision-making power are always higher than the shift in inventory ownership under all the conditions. The value of the shift in decision-making power is greater than IS particularly when the variability of underlying demand is low and time-dependent variation in production cost is high. However, when the shipment cost is negligible and order issuing efficiency of the supplier is low, the cost benefits of shift in decision-making power beyond IS are not significant

A big data MapReduce framework for fault diagnosis in cloud-based manufacturing

This research develops a MapReduce framework for automatic pattern recognition based on fault diagnosis by solving data imbalance problem in a cloud-based manufacturing (CBM). Fault diagnosis in a CBM system significantly contributes to reduce the product testing cost and enhances manufacturing quality. One of the major challenges facing the big data analytics in cloud-based manufacturing is handling of datasets, which are highly imbalanced in nature due to poor classification result when machine learning techniques are applied on such datasets. The framework proposed in this research uses a hybrid approach to deal with big dataset for smarter decisions. Furthermore, we compare the performance of radial basis function based Support Vector Machine classifier with standard techniques. Our findings suggest that the most important task in cloud-based manufacturing, is to predict the effect of data errors on quality due to highly imbalance unstructured dataset. The proposed framework is an original contribution to the body of literature, where our proposed MapReduce framework has been used for fault detection by managing data imbalance problem appropriately and relating it to firm’s profit function. The experimental results are validated using a case study of steel plate manufacturing fault diagnosis, with crucial performance matrices such as accuracy, specificity and sensitivity. A comparative study shows that the methods used in the proposed framework outperform the traditional ones

A computational algebraic geometry approach to enumerate Malcev magma algebras over finite fields

The set of Mn(K) of n-dimensional Malcev magma algebras over a finite field can be identified with algebraic sets defined by zero-dimensional radical ideals for which the computation of their reduced Gröbner bases makes feasible their enumeration and distribution into isomorphism and isotopism classes. Based on this computation and the classification of Lie algebras over finite fields given by De Graaf and Strade, we determine the mentioned distribution for Malcev magma algebras of dimension $n\leq 4$. We also prove that every 3-dimensional Malcev algebra is isotopic to a Lie magma algebra. For n=4, this assertion only holds when the characteristic of the base field is distinct of two

An Integer Programming Model for Locating Vehicle Emissions Testing Stations

Connecticut and other states not in compliance with federal air quality standards are required to implement a motor vehicle inspection program to test vehicles for pollutants---hydrocarbons and carbon monoxide. The problem is to determine the number, size, and locations of stations given constraints on the maximum travel distance from each town to its nearest station and the average waiting time at a station. In this paper we use simulation to find the maximum allowable arrival rates (in vehicles per hour) of stations of different sizes and formulate the station location problem as a set covering model. We generate a range of solutions through sensitivity analysis, varying both the average waiting time and maximum distance constraints. Comparing the current configuration of stations in Connecticut to our integer programming solutions we find that the integer programming approach reduces the objective function by at least $3 million. The current configuration has more stations than the IP solutions but they are not as well distributed.optimal location, public facility location, integer programming location models, emissions testing stations location models

The influence of soft tissue biotype on the marginal bone changes around dental implants: A 1-year prospective clinico-radiological study

Background: The peri-implant mucosa undergoes surgical and bacterial assaults in various stages of implant therapy, however, the literature on changes occurring in the peri-implant mucosa is minimal. This study was thus conducted to evaluate the change in the peri-implant mucosal thickness and its effect on the marginal bone levels around dental implants treated in a conventional two-stage implant therapy. Materials and Methods: A total of 36 implants were placed in 22 subjects. Two subjects dropped out. Thirty-three implants in 20 subjects were then evaluated. Initial mucosal thickness, marginal bone levels on radiographs, pain, and exudation were evaluated. All these parameters were recorded at the time of implant placement, at the time of cementation of final restoration, 6 months and 12 months post cementation/restoration. Results: The peri-implant mucosal thickness reduced from implant placement to second stage and till restorations and was statistically significant, in both the thick and thin biotypes, however, at 12 months there was a rebound of the tissue thickness, which was more in the thick biotype (P < 0.05). At 1-year follow-up, there was a reduction in the marginal bone levels, which was more in the thick biotype as compared to the thin biotype (P < 0.05). Conclusion: The mucosa at implant sites undergoes a reduction in thickness from the time of implant placement till the placement of final restorations. The placement of the final restorations and then end of active therapy leads to a rebound of the tissue thickness. Sites with thicker tissues preoperatively have a lesser bone loss and better rebound as compared to thinner tissues

Palatal approach of anterior superior alveolar injection technique may not be potentially useful in periodontal procedures

Background: The palatal approach of anterior superior alveolar (P-ASA) using WAND injection was reported to effectively provide a profound bilateral maxillary anesthesia of the soft tissue of anterior one-third of the palate and facial gingivae extending from canine to canine which lasted for more than an hour thus making it ideal for scaling root planing and minor periodontal procedures in the anterior maxilla. Our study suggests that the conventional P-ASA injection is of very short duration and the extent of anesthesia was not profound and consistent. This has not been reported earlier in the literature. Materials and Methods: Thirty-five cases (20 males and 15 females), who underwent scaling, root planing and minor periodontal surgical procedures such as abscess drainage, gingivectomy, and frenectomy in the maxillary anterior region in the age range of 19–45 years was assessed for the efficacy of the P-ASA injection. After the administration of the P-ASA injection, the subjective and the objective symptoms were used to evaluate the extent and duration of the anesthesia at 10, 15, and 20 min. Results: This study suggests that the conventional P-ASA injection technique does not provide anesthesia for more than 20 min. Wilcoxon matched pairs test was used to compare the effect of anesthesia at the different time intervals and the results were found to be statistically significant (P < 0.05). Conclusions: The conventional P-ASA injection technique is of very short duration and does not demonstrate effectiveness in periodontal surgery of the anterior maxilla