scholarly journals Effect of Railway Track Segmentation Method on the Optimal Solution of Tamping Planning Problem

2021 ◽  
Vol 7 (12) ◽  
pp. 1998-2010
Author(s):  
Mohammad Daddow ◽  
Xinglin Zhou ◽  
Hasan A.H. Naji ◽  
Mo'men Ayasrah
Keyword(s):  
Programming Model ◽  
Optimal Solution ◽  
Railway Track ◽  
Mixed Integer ◽  
Maintenance Cost ◽  
Planning Problem ◽  
Segmentation Method ◽  
Railway Network ◽  
Railway Infrastructure ◽  
Optimal Maintenance

The safety and continuality of the railway network are guaranteed by carrying out a lot of maintenance interventions on the railway track. One of the most important of these actions is tamping, where railway infrastructure managers focus on optimizing tamping activities in ballasted tracks to reduce the maintenance cost. To this end, this article presents a mixed integer linear programming model of the Tamping Planning Problem (TPP) and investigates the effect of track segmentation method on the optimal solution by three scenarios. It uses an opportunistic maintenance technique to plan tamping actions. This technique clusters many tamping works through a time period to reduce the track possession cost as much as possible. CPLEX 12.6.3 is used in order to solve the TPP instances exactly. The results show that the total number of machine preparations increases by increasing the number of track segments. It is also found that the total costs increase by 6.1% and 9.4% during scenarios 2 and 3, respectively. Moreover, it is better to consider the whole railway track as a single segment (as in scenarios 1) that consists of a set of sections during the tamping planning in order to obtain the optimal maintenance cost. Doi: 10.28991/cej-2021-03091774 Full Text: PDF

scholarly journals A Refueling Scheme Optimization Model for the Voyage Charter with Fuel Price Fluctuation and Ship Deployment Consideration

2017 ◽  
Vol 5 (3) ◽  
pp. 267-278 ◽  
Author(s):  
Peng Jia ◽  
Weilun Zhang ◽  
E Wenhao ◽  
Xueshan Sun
Keyword(s):  
Programming Model ◽  
Optimal Solution ◽  
Arma Model ◽  
Maritime Transportation ◽  
Mixed Integer ◽  
Fuel Price ◽  
Shipping Company ◽  
Significant Difference ◽  
Nonlinear Programming Model ◽  
Operation Cycle

Abstract Due to the long operation cycle of maritime transportation and frequent fluctuations of the bunker fuel price, the refueling expenditure of a chartered ship at different time or ports of call make significant difference. From the perspective of shipping company, an optimal set of refueling schemes for a ship fleet operating on different voyage charter routes is an important decision. To address this issue, this paper presents an approach to optimize the refueling scheme and the ship deployment simultaneously with considering the trend of fuel price fluctuations. Firstly, an ARMA model is applied to forecast a time serials of the fuel prices. Then a mixed-integer nonlinear programming model is proposed to maximize total operating profit of the shipping company. Finally, a case study on a charter company with three bulk carriers and three voyage charter routes is conducted. The results show that the optimal solution saves the cost of 437,900 USD compared with the traditional refueling scheme, and verify the rationality and validity of the model.

scholarly journals Optimal Placement of Social Digital Twins in Edge IoT Networks

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6181
Author(s):  
Olga Chukhno ◽  
Nadezhda Chukhno ◽  
Giuseppe Araniti ◽  
Claudia Campolo ◽  
Antonio Iera ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Social Relationships ◽  
Data Exchange ◽  
Service Discovery ◽  
Programming Model ◽  
Optimal Solution ◽  
Optimal Placement ◽  
Mixed Integer ◽  
Digital Twins ◽  
The Social

In next-generation Internet of Things (IoT) deployments, every object such as a wearable device, a smartphone, a vehicle, and even a sensor or an actuator will be provided with a digital counterpart (twin) with the aim of augmenting the physical object’s capabilities and acting on its behalf when interacting with third parties. Moreover, such objects can be able to interact and autonomously establish social relationships according to the Social Internet of Things (SIoT) paradigm. In such a context, the goal of this work is to provide an optimal solution for the social-aware placement of IoT digital twins (DTs) at the network edge, with the twofold aim of reducing the latency (i) between physical devices and corresponding DTs for efficient data exchange, and (ii) among DTs of friend devices to speed-up the service discovery and chaining procedures across the SIoT network. To this aim, we formulate the problem as a mixed-integer linear programming model taking into account limited computing resources in the edge cloud and social relationships among IoT devices.

Mixed-Integer Programming Model for Fixture Layout Optimization

1999 ◽  
Vol 121 (4) ◽  
pp. 701-708 ◽  
Author(s):  
Q. A. Sayeed ◽  
E. C. De Meter
Keyword(s):  
Integer Programming ◽  
Mixed Integer Programming ◽  
Programming Model ◽  
Optimal Solution ◽  
Geometric Error ◽  
Mixed Integer ◽  
Integer Programming Model ◽  
Mixed Integer Programming Model ◽  
Fea Model ◽  
Candidate Regions

Workpiece deformation during machining is a significant source of machined feature geometric error. This paper presents a linear, mixed integer programming model for determining the optimal locations of locator buttons, supports, and their opposing clamps for minimizing the affect of static workpiece deformation on machined feature geometric error. This model operates on discretized candidate regions as opposed to continuous candidate regions. In addition it utilizes a condensed FEA model of the workpiece in order to minimize model size and computation expense. This model has two advantages over existing nonlinear programming (NLP) formulations. The first is its ability to solve problems in which fixture elements can be placed over multiple regions. The second is that a global optimal solution to this model can be obtained using commercially available software.

scholarly journals Location and optimal sizing of photovoltaic sources in an isolated mini-grid

TecnoLógicas ◽  
2019 ◽  
Vol 22 (44) ◽  
pp. 61-80 ◽  
Author(s):  
Juliana Jiménez ◽  
John E. Cardona ◽  
Sandra X. Carvajal
Keyword(s):  
Electric Power ◽  
Power Plants ◽  
Programming Model ◽  
Irradiation Time ◽  
Optimal Solution ◽  
Solar Irradiation ◽  
Mixed Integer ◽  
Voltage Profile ◽  
Power Losses ◽  
Photovoltaic Generators

This article introduces a new mixed integer linear programming model that guarantees the optimal solution to the location and sizing problem of distributed photovoltaic generators in an isolated mini-grid. The solar radiation curves of each node in the mini-grids were considered, and the main objective was to minimize electric power losses in the operation of the system. The model is non-linear in nature because some restrictions are not linear. However, this article proposes the use of linearization techniques to obtain a linear model with a global optimal solution, which can be achieved through commercial solvers; CPLEX in this case. The proposed model was tested in an isolated 14-bus mini-grid, based on real data of topology, demand and generation adapted to a balanced operation. This model shows, as a result, the optimal location of photovoltaic generators and their optimal capacity produced by the maximum active power delivered at the maximum solar irradiation time of the region. It is also evident that the hybrid operation between small hydroelectric power plants and photovoltaic generation improves the network voltage profile and the electric power losses without the use power storage systems.

scholarly journals The Airlift Planning Problem

2019 ◽  
Vol 53 (3) ◽  
pp. 773-795
Author(s):  
Dimitris Bertsimas ◽  
Allison Chang ◽  
Velibor V. Mišić ◽  
Nishanth Mundru
Keyword(s):  
Large Scale ◽  
Programming Model ◽  
Simulated Data ◽  
Mixed Integer ◽  
Planning Problem ◽  
Weight Restrictions ◽  
Total Delay ◽  
Data Set ◽  
Scale Optimization ◽  
Airlift Planning

The U.S. Transportation Command (USTRANSCOM) is responsible for planning and executing the transportation of U.S. military personnel and cargo by air, land, and sea. The airlift planning problem faced by the air component of USTRANSCOM is to decide how requirements (passengers and cargo) will be assigned to the available aircraft fleet and the sequence of pickups and drop-offs that each aircraft will perform to ensure that the requirements are delivered with minimal delay and with maximum utilization of the available aircraft. This problem is of significant interest to USTRANSCOM because of the highly time-sensitive nature of the requirements that are typically designated for delivery by airlift, as well as the very high cost of airlift operations. At the same time, the airlift planning problem is extremely difficult to solve because of the combinatorial nature of the problem and the numerous constraints present in the problem (such as weight restrictions and crew rest requirements). In this paper, we propose an approach for solving the airlift planning problem faced by USTRANSCOM based on modern, large-scale optimization. Our approach relies on solving a large-scale mixed-integer programming model that disentangles the assignment decision (which aircraft will pickup and deliver which requirement) from the sequencing decision (in what order the aircraft will pickup and deliver its assigned requirements), using a combination of heuristics and column generation. Through computational experiments with both a simulated data set and a planning data set provided by USTRANSCOM, we show that our approach leads to high-quality solutions for realistic instances (e.g., 100 aircraft and 100 requirements) within operationally feasible time frames. Compared with a baseline approach that emulates current practice at USTRANSCOM, our approach leads to reductions in total delay and aircraft time of 8%–12% in simulated data instances and 16%–40% in USTRANSCOM’s planning instances.

scholarly journals Application of Two-Phase Fuzzy Optimization Approach to Multiproduct Multistage Integrated Production Planning with Linguistic Preference under Uncertainty

2015 ◽  
Vol 2015 ◽  
pp. 1-20 ◽  
Author(s):  
Shan Lu ◽  
Hongye Su ◽  
Lian Xiao ◽  
Li Zhu
Keyword(s):  
Production Planning ◽  
Programming Model ◽  
Fuzzy Optimization ◽  
Service Level ◽  
Mixed Integer ◽  
Planning Problem ◽  
Optimization Approach ◽  
Two Phase ◽  
Steel Rolling ◽  
Solution Approach

This paper tackles the challenges for a production planning problem with linguistic preference on the objectives in an uncertain multiproduct multistage manufacturing environment. The uncertain sources are modelled by fuzzy sets and involve those induced by both the epistemic factors of process and external factors from customers and suppliers. A fuzzy multiobjective mixed integer programming model with different objective priorities is proposed to address the problem which attempts to simultaneously minimize the relevant operations cost and maximize the average safety stock holding level and the average service level. The epistemic and external uncertainty is simultaneously considered and formulated as flexible constraints. By defining the priority levels, a two-phase fuzzy optimization approach is used to manage the preference extent and convert the original model into an auxiliary crisp one. Then a novel interactive solution approach is proposed to solve this problem. An industrial case originating from a steel rolling plant is applied to implement the proposed approach. The numerical results demonstrate the efficiency and feasibility to handle the linguistic preference and provide a compromised solution in an uncertain environment.

Research on the Modeling of Tugboat Assignment Problem in Container Terminal

2012 ◽  
Vol 433-440 ◽  
pp. 1957-1961 ◽  
Author(s):  
Su Wang ◽  
Iko Kaku ◽  
Guo Yue Chen ◽  
Min Zhu
Keyword(s):  
Assignment Problem ◽  
Numerical Experiments ◽  
Solution Method ◽  
Programming Model ◽  
Container Terminal ◽  
Optimal Solution ◽  
Turnaround Time ◽  
Mixed Integer ◽  
Service Capacity ◽  
Decision Making Problem

Tugboat is one kind of important equipment in container terminal to help ships for docking or leaving the berth. Tugboat assignment operation is one of the most important decision making problem because it has an important effect on the turnaround time of ships. In this paper, a mixed-integer programming model combined with scheduling rule is formulated for the Tugboat Assignment Problem (TAP). Then a solution method is provided to obtain the optimal solution of TAP problem. Finally, numerical experiments are executed to illustrate the utility of the model and to analyze the effects of the number and service capacity of tugboats on the turnaround time of ships.

An optimization model for annual harvest planning

2004 ◽  
Vol 34 (8) ◽  
pp. 1747-1754 ◽  
Author(s):  
Jenny Karlsson ◽  
Mikael Rönnqvist ◽  
Johan Bergström
Keyword(s):  
Programming Model ◽  
Mixed Integer ◽  
Maintenance Cost ◽  
Road Maintenance ◽  
Continuous Variables ◽  
Home Base ◽  
The Road ◽  
Wood Processing ◽  
Production Capacities ◽  
And Storage

The problem we consider is annual harvesting planning from the perspective of Swedish forest companies. The main decisions deal with which areas to harvest during an annual period so that the wood-processing facilities receive the required amount of assortments. Each area has a specific size and composition of assortments, and the choice of harvesting areas affects the production level of different assortments. We need to decide which harvest team to use for each area, considering that each team has different skills, home base, and production capacities. Also, the weather and road conditions vary during the year. Some roads cannot be used during certain time periods and others should be avoided. The road maintenance cost varies during the year. Also, some areas cannot be harvested during certain periods. Overall decisions about transportation and storage are also included. In this paper, we develop a mixed integer programming model for the problem. There are binary variables associated with harvesting, allocation of teams, and road-opening decisions. The other decisions are represented by continuous variables. We solve this problem directly with CPLEX 8.1 within a practical solution time limit. Computational results from a major Swedish forest company are presented.

scholarly journals Production Planning Problem of a Two-Level Supply Chain with Production-Time-Dependent Products

2021 ◽  
Vol 11 (20) ◽  
pp. 9687
Author(s):  
Jun-Hee Han ◽  
Ju-Yong Lee ◽  
Bongjoo Jeong
Keyword(s):  
Supply Chain ◽  
Production Planning ◽  
Programming Model ◽  
Planning Horizon ◽  
Mixed Integer ◽  
Planning Problem ◽  
Manufacturing Plant ◽  
Production Lines ◽  
Multiple Production ◽  
Production Planning Problem

This study considers a production planning problem with a two-level supply chain consisting of multiple suppliers and a manufacturing plant. Each supplier that consists of multiple production lines can produce several types of semi-finished products, and the manufacturing plant produces the finished products using the semi-finished products from the suppliers to meet dynamic demands. In the suppliers, different types of semi-finished products can be produced in the same batch, and products in the same batch can only be started simultaneously (at the same time) even if they complete at different times. The purpose of this study is to determine the selection of suppliers and their production lines for the production of semi-finished products for each period of a given planning horizon, and the objective is to minimize total costs associated with the supply chain during the whole planning horizon. To solve this problem, we suggest a mixed integer programming model and a heuristic algorithm. To verify performance of the algorithm, a series of tests are conducted on a number of instances, and the results are presented.

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