scholarly journals Containerships Sailing Speed and Fleet Deployment Optimization under a Time-Based Differentiated Freight Rate Strategy

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Hualong Yang ◽  
Yuwei Xing
Keyword(s):  
Optimization Problem ◽  
Optimal Solution ◽  
Freight Rate ◽  
Liner Shipping ◽  
Mixed Integer ◽  
Time Value ◽  
Proposed Model ◽  
Discretization Algorithm ◽  
Deployment Optimization ◽  
Fleet Deployment

This paper investigates the problem of containership sailing speed and fleet deployment optimization in an intercontinental liner shipping network. Under the consideration of the time value of container cargo, three kinds of impact of sailing speed changes on long legs of each liner route are analysed, and a time-based freight rate strategy is proposed. Then, the optimization problem is formulated as a mixed-integer nonlinear programming. Its goal is to maximize the total profits of a container liner shipping. To find the optimal solution to the model and improve the efficiency of model solution, a discretization algorithm is proposed. Numerical results verify the applicability of the proposed model and the efficiency of the algorithm. In addition, the time-based freight rate strategy is able to achieve more profit compared to a fixed freight rate strategy.

Network Design for Shipping Service of Large-Scale Intermodal Liners

2012 ◽  
Vol 2269 (1) ◽  
pp. 42-50 ◽  
Author(s):  
Qiang Meng ◽  
Shuaian Wang ◽  
Zhiyuan Liu
Keyword(s):  
Network Design ◽  
Large Scale ◽  
Programming Model ◽  
Transportation Network ◽  
Liner Shipping ◽  
Maritime Transportation ◽  
Mixed Integer ◽  
Hub And Spoke ◽  
Shipping Company ◽  
Proposed Model

A model was developed for network design of a shipping service for large-scale intermodal liners that captured essential practical issues, including consistency with current services, slot purchasing, inland and maritime transportation, multiple-type containers, and origin-to-destination transit time. The model used a liner shipping hub-and-spoke network to facilitate laden container routing from one port to another. Laden container routing in the inland transportation network was combined with the maritime network by defining a set of candidate export and import ports. Empty container flow is described on the basis of path flow and leg flow in the inland and maritime networks, respectively. The problem of network design for shipping service of an intermodal liner was formulated as a mixed-integer linear programming model. The proposed model was used to design the shipping services for a global liner shipping company.

scholarly journals Integrated Optimization on Assortment Packing and Collaborative Shipping for Fashion Clothing

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Qianying Wang ◽  
Yiping Jiang ◽  
Yang Liu
Keyword(s):  
Numerical Experiments ◽  
Optimization Problem ◽  
Programming Model ◽  
Social Resources ◽  
Mixed Integer ◽  
Integrated Optimization ◽  
Proposed Model ◽  
Integer Nonlinear Programming ◽  
Nonlinear Programming Model ◽  
Effectiveness And Efficiency

With the diversification of customer’s demand and the shortage of social resources, meeting diverse requirements of customers and reducing logistics costs have attracted great attention in logistics area. In this paper, we address an integrated optimization problem that combines fashion clothing assortment packing with collaborative shipping simultaneously. We formulate this problem as a mixed integer nonlinear programming model (MINLP) and then convert the proposed model into a simplified model. We use LINGO 11.0 to solve the transformed model. Numerical experiments have been conducted to verify the effectiveness and efficiency of the proposed model, and the numerical results show that the proposed model is beneficial to the fashion clothing assortment packing and collaborative shipping planning.

Liner shipping cycle cost modelling, fleet deployment optimization and what-if analysis

2011 ◽  
Vol 13 (3) ◽  
pp. 278-297 ◽  
Author(s):  
Panayotis G Zacharioudakis ◽  
Stylianos Iordanis ◽  
Dimitrios V Lyridis ◽  
Harilaos N Psaraftis
Keyword(s):  
Liner Shipping ◽  
Cost Modelling ◽  
Deployment Optimization ◽  
Fleet Deployment

scholarly journals Optimal Operation of the Campus Microgrid considering the Resource Uncertainty and Demand Response Schemes

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Hafiz Abd ul Muqeet ◽  
Hafiz Mudassir Munir ◽  
Aftab Ahmad ◽  
Intisar Ali Sajjad ◽  
Guang-Jun Jiang ◽  
...  
Keyword(s):  
Power Systems ◽  
Demand Response ◽  
Solar Irradiance ◽  
Distribution System ◽  
Energy Demand ◽  
Mathematical Problem ◽  
Optimal Solution ◽  
Optimal Operation ◽  
Mixed Integer ◽  
Proposed Model

Present power systems face problems such as rising energy charges and greenhouse gas (GHG) releases. These problems may be assuaged by participating distributed generators (DGs) and demand response (DR) policies in the distribution system (DS). The main focus of this paper is to propose an energy management system (EMS) approach for campus microgrid (µG). For this purpose, a Pakistani university has been investigated and an optimal solution has been proposed. Conventionally, it contains electricity from the national grid only as a supply to fulfil the energy demand. Under the proposed setup, it contains campus owned nondispatchable DGs such as solar photovoltaic (PV) panels and microturbines (MTs) as dispatchable sources. To overcome the random nature of solar irradiance, station battery has been integrated as energy storage. The subsequent nonlinear mathematical problem has been scheduled by mixed-integer nonlinear programming (MINLP) in MATLAB for saving energy cost and battery aging cost. The framework has been validated under deterministic and stochastic environments. Among random parameters, solar irradiance and load have been taken into consideration. Case studies have been carried out considering the demand response strategies to analyze the proposed model. The obtained results show that optimal management and scheduling of storage in the presence of DGs mutually benefit by minimizing consumption cost (customer) and grid load (utility) which show the efficacy of the proposed model. The results obtained are compared to the existing literature and a significant cost reduction is found.

scholarly journals Closed-Loop Supply Chain Design with Sustainability Aspects and Network Resilience under Uncertainty: Modelling and Application

2021 ◽  
Vol 2021 ◽  
pp. 1-23
Author(s):  
Komeyl Baghizadeh ◽  
Julia Pahl ◽  
Guiping Hu
Keyword(s):  
Supply Chain ◽  
Closed Loop ◽  
Optimal Solution ◽  
Supply Chain Design ◽  
Mixed Integer ◽  
Distribution Centers ◽  
Closed Loop Supply Chain ◽  
Proposed Model ◽  
Network Flexibility ◽  
Uncertainty Parameters

In this study, we present a multiobjective mixed-integer nonlinear programming (MINLP) model to design a closed-loop supply chain (CLSC) from production stage to distribution as well as recycling for reproduction. The given network includes production centers, potential points for establishing of distribution centers, retrieval centers, collecting and recycling centers, and the demand points. The presented model seeks to find optimal locations for distribution centers, second-hand product collection centers, and recycling centers under the uncertainty situation alongside the factory’s fixed points. The purpose of the presented model is to minimize overall network costs including processing, establishing, and transportation of products and return flows as well as environmental impacts while maximizing social scales and network flexibility according to the presence of uncertainty parameters in the problem. To solve the proposed model with fuzzy uncertainty, first, the improved epsilon (ε)-constraints approach is used to transform a multiobjective to a single-objective problem. Afterward, the Lagrangian relaxation approach is applied to effectively solve the problem. A real-world case study is used to evaluate the performance of the proposed model. Finally, sensitivity analysis is performed to study the effects of important parameters on the optimal solution.

scholarly journals An Analytic Center Cutting Plane Method to Determine Complete Positivity of a Matrix

2021 ◽  
Author(s):  
Riley Badenbroek ◽  
Etienne de Klerk
Keyword(s):  
Optimization Problem ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Cutting Plane ◽  
Mixed Integer ◽  
Cutting Plane Method ◽  
Analytic Center ◽  
Completely Positive ◽  
Plane Method ◽  
Copositive Cone

We propose an analytic center cutting plane method to determine whether a matrix is completely positive and return a cut that separates it from the completely positive cone if not. This was stated as an open (computational) problem by Berman et al. [Berman A, Dur M, Shaked-Monderer N (2015) Open problems in the theory of completely positive and copositive matrices. Electronic J. Linear Algebra 29(1):46–58]. Our method optimizes over the intersection of a ball and the copositive cone, where membership is determined by solving a mixed-integer linear program suggested by Xia et al. [Xia W, Vera JC, Zuluaga LF (2020) Globally solving nonconvex quadratic programs via linear integer programming techniques. INFORMS J. Comput. 32(1):40–56]. Thus, our algorithm can, more generally, be used to solve any copositive optimization problem, provided one knows the radius of a ball containing an optimal solution. Numerical experiments show that the number of oracle calls (matrix copositivity checks) for our implementation scales well with the matrix size, growing roughly like [Formula: see text] for d × d matrices. The method is implemented in Julia and available at https://github.com/rileybadenbroek/CopositiveAnalyticCenter.jl . Summary of Contribution: Completely positive matrices play an important role in operations research. They allow many NP-hard problems to be formulated as optimization problems over a proper cone, which enables them to benefit from the duality theory of convex programming. We propose an analytic center cutting plane method to determine whether a matrix is completely positive by solving an optimization problem over the copositive cone. In fact, we can use our method to solve any copositive optimization problem, provided we know the radius of a ball containing an optimal solution. We emphasize numerical performance and stability in developing this method. A software implementation in Julia is provided.

scholarly journals Simultaneous Optimization of the Liner Shipping Route and Ship Schedule Designs with Time Windows

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xi Jiang ◽  
Haijun Mao ◽  
Hao Zhang
Keyword(s):  
Time Window ◽  
Programming Model ◽  
Time Windows ◽  
Operating Cost ◽  
Optimal Solution ◽  
Liner Shipping ◽  
Simultaneous Optimization ◽  
Mixed Integer ◽  
Time Window Constraints ◽  
Call Sequence

This paper proposes to address the problem of the simultaneous optimization of the liner shipping route and ship schedule designs by incorporating port time windows. A mathematical programming model was developed to minimize the carrier’s total operating cost by simultaneously optimizing the port call sequence, ship arrival time per port of call, and sailing speed per shipping leg under port time window constraints. In view of its structure, the nonlinear nonconvex optimization model is further transformed into a mixed-integer linear programming model that can be efficiently solved by extant solvers to provide a global optimal solution. The results of the numerical experiments performed using a real-world case study indicated that the proposed model performs significantly better than the models that handle the design problems separately. The results also showed that different time windows will affect the optimal port call sequence. Moreover, port time windows, bunker price, and port efficiency all affect the total operating cost of the designed shipping route.

Variable-Capacity Operations with Modular Transits for Shared-Use Corridors

2020 ◽  
Vol 2674 (9) ◽  
pp. 230-244
Author(s):  
Xiaowei Shi ◽  
Zhiwei Chen ◽  
Mingyang Pei ◽  
Xiaopeng Li
Keyword(s):  
Programming Model ◽  
Optimal Solution ◽  
Operating Costs ◽  
Mixed Integer ◽  
Transit Systems ◽  
Persistent Problem ◽  
Proposed Model ◽  
Variable Capacity ◽  
New Perspective ◽  
In Transit

Since passenger demand in urban transit systems is asymmetrically distributed across different periods in a day and different geographic locations across the cities, the tradeoff between vehicle operating costs and service quality has been a persistent problem in transit operational design. The emerging modular vehicle technology offers us a new perspective to solve this problem. Based on this concept, we propose a variable-capacity operation approach with modular transits for shared-use corridors, in which both dispatch headway and vehicle capacity are decision variables. This problem is rigorously formulated as a mixed integer linear programming model that aims to minimize the overall system cost, including passenger waiting time costs and vehicle operating costs. Because the proposed model is linear, the state-of-the-art commercial solvers (e.g., Gurobi) can be used to obtain the optimal solution of the investigated problem. With numerical experiments, we demonstrate the feasibility of the mathematical model, verify the effectiveness of the proposed model in reducing overall system costs in transit systems, as well as the robustness of the proposed model with different parameter settings.

An Improved Boltzmann Machine for Strategic Investment Planning in Power System Environment

2017 ◽  
Vol 6 (2) ◽  
pp. 259 ◽  
Author(s):  
S. H. M. Tahar ◽  
S. B. Yaakob ◽  
A. Ahmed
Keyword(s):  
Power System ◽  
Optimal Solution ◽  
Mixed Integer ◽  
Computational Time ◽  
Second Phase ◽  
Boltzmann Machine ◽  
Investment Planning ◽  
Strategic Investment ◽  
System Environment ◽  
Proposed Model

The objective of this research is to propose an effective method to determine an optimal solution for strategic investment planning in power system environment. The proposed method will be formulated by using mean-variance analysis approach in the form of mixed-integer quadratic programming problem. Its target is to minimize the risk and maximize the expected return. The proposed method consists of two phase neural networks combining Hopfield network at the first phase and Boltzmann machine in the second phase resulting the fast computational time. The originality of the proposed model is it will delete the unit of the second phase, which is not selected in first phase in its execution. Then, the second phase is restructured using the selected units. Due to this feature, the proposed model will improve times and the accuracy of obtained solution. The significance of output from this project is the improvement of computational time and the accurate solution will be obtained. This model might help the decision makers to choose the optimal solution with variety options provided from this proposed method. Therefore, the performance of strategic investment planning in power system engineering certainly enhanced.

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