A bi-objective model for vessel emergency maintenance under a condition-based maintenance strategy

SIMULATION ◽  
2017 ◽  
Vol 94 (7) ◽  
pp. 609-624 ◽  
Author(s):  
Jinlou Zhao ◽  
Liqian Yang
Keyword(s):  
Complex Problem ◽  
Spare Parts ◽  
Condition Based Maintenance ◽  
Maintenance Strategy ◽  
Nsga Ii ◽  
Maintenance Costs ◽  
Inventory Costs ◽  
Penalty Costs ◽  
Proposed Model ◽  
Objective Model

When sailing on the open seas, far from onshore dockyards, if a crucial part of the ship’s machinery fails, the ship will experience a costly event that carries a high risk of seriously affecting ship operations. If the ship receives warning of an impending defect, then it can try to sail to a dockyard and simultaneously order the spare parts needed to fix the problem. In this paper, we define this type of maintenance situation as ‘vessel emergency maintenance’. It is a complex problem, due to uncertainties with both the machinery condition development and spare parts delivery. To solve this problem, our paper proposes a bi-objective model under a condition-based maintenance strategy, with the aim of simultaneously minimizing maintenance costs and maximizing ship reliability. Maintenance costs include four things: (1) fuel consumption costs; (2) renting extra vessels; (3) shipping delay penalty costs; and (4) spare parts inventory costs. Ship reliability is represented by the reliability of the ship’s main engine, and can be described through a stochastic process. To solve this bi-objective model, we employ a non-dominated sorting genetic algorithm II (NSGA-II) to generate the Pareto optimal front of the two objectives. A numerical experiment is presented to demonstrate the applicability of the proposed model. The results indicate that the proposed model can provide emergency maintenance decision support for ship operators while they are sailing at sea.

scholarly journals Using NSGAII to Solve Bi-objective Bed Allocation Problems

2015 ◽  
Vol 1 (3) ◽  
pp. 397
Author(s):  
Jalal A. Sultan ◽  
Ban A. Mitras ◽  
Raghad M. Jasim
Keyword(s):  
Queuing Theory ◽  
Optimization Problems ◽  
Admission Rate ◽  
Nsga Ii ◽  
Multi Objective ◽  
Decision Aiding ◽  
Proposed Model ◽  
Objective Model ◽  
Bed Allocation ◽  
Suitable Framework

The Bed Allocation Problem (BAP) is NP-complete and always high dimensional. In this paper, a bi-objective decision aiding model based on queuing theory is introduced for allocation of beds in a hospital. The problem is modeled as an M/PH/n queue. The objectives include maximizing the patient admission rate human resources, in particular, maximization of the nursing work hours. The proposed model is solved by using Non-dominated Sorting Genetic Algorithm-II (NSGA-II), which is a very effective algorithm for solving multi-objective optimization problems and finding optimal Pareto front. The paper describes an application of the model, dealing with a public hospital in Iraq. The results related that multi-objective model was presented suitable framework for bed allocation and optimum use.

scholarly journals Determining decoupling points in a supply chain networks using NSGA II algorithm

2017 ◽  
Vol 10 (2) ◽  
pp. 352 ◽  
Author(s):  
Mina Ebrahimiarjestan ◽  
Guoxin Wang
Keyword(s):  
Heuristic Method ◽  
Production Costs ◽  
Pareto Optimal ◽  
Delivery Time ◽  
Nsga Ii ◽  
Supply Chain Networks ◽  
Proposed Model ◽  
Product Delivery ◽  
Objective Model ◽  
Decision Making Model

Purpose: In the model, we used the concepts of Lee and Amaral (2002) and Tang and Zhou (2009) and offer a multi-criteria decision-making model that identify the decoupling points to aim to minimize production costs, minimize the product delivery time to customer and maximize their satisfaction.Design/methodology/approach: We encounter with a triple-objective model that meta-heuristic method (NSGA II) is used to solve the model and to identify the Pareto optimal points. The max (min) method was used.Findings: Our results of using NSGA II to find Pareto optimal solutions demonstrate good performance of NSGA II to extract Pareto solutions in proposed model that considers determining of decoupling point in a supply network.Originality/value: So far, several approaches to model the future have been proposed, of course, each of them modeled a part of this concept. This concept has been considered more general in the model that defined in follow.  In this model, we face with a multi-criteria decision problem that includes minimization of the production costs and product delivery time to customers as well as customer consistency maximization.

scholarly journals MULTI-OBJECTIVE GREEN MIXED VEHICLE ROUTING PROBLEM UNDER ROUGH ENVIRONMENT

Transport ◽  
2021 ◽  
Vol 0 (0) ◽  
pp. 1-13
Author(s):  
Joydeep Dutta ◽  
Partha Sarathi Barma ◽  
Anupam Mukherjee ◽  
Samarjit Kar ◽  
Tanmay De ◽  
...  
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Point Of View ◽  
Third Party ◽  
Third Party Logistics ◽  
Nsga Ii ◽  
Routing Problem ◽  
Multi Objective ◽  
Proposed Model ◽  
Objective Model

This paper proposes a multi-objective Green Vehicle Routing Problem (G-VRP) considering two types of vehicles likely company-owned vehicle and third-party logistics in the imprecise environment. Focusing only on one objective, especially the distance in the VRP is not always right in the sustainability point of view. Here we present a bi-objective model for the G-VRP that can address the issue of the emission of GreenHouse Gases (GHGs). We also consider the demand as a rough variable. This paper uses the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to solve the proposed model. Finally, it uses Multicriteria Optimization and Compromise Solution (abbreviation in Serbian – VIKOR) method to determine the best alternative from the Pareto front.

scholarly journals Economical Speed for Optimizing the Travel Time and Energy Consumption in Train Scheduling using a Fuzzy Multi-Objective Model

2021 ◽  
Author(s):  
Ahmad Reza Jafarian-Moghaddam
Keyword(s):  
Energy Consumption ◽  
Travel Time ◽  
Pollutant Emissions ◽  
Scheduling Problems ◽  
Train Scheduling ◽  
Multi Objective ◽  
Consumption Ratio ◽  
Proposed Model ◽  
Objective Model ◽  
Time And Energy

AbstractSpeed is one of the most influential variables in both energy consumption and train scheduling problems. Increasing speed guarantees punctuality, thereby improving railroad capacity and railway stakeholders’ satisfaction and revenues. However, a rise in speed leads to more energy consumption, costs, and thus, more pollutant emissions. Therefore, determining an economic speed, which requires a trade-off between the user’s expectations and the capabilities of the railway system in providing tractive forces to overcome the running resistance due to rail route and moving conditions, is a critical challenge in railway studies. This paper proposes a new fuzzy multi-objective model, which, by integrating micro and macro levels and determining the economical speed for trains in block sections, can optimize train travel time and energy consumption. Implementing the proposed model in a real case with different scenarios for train scheduling reveals that this model can enhance the total travel time by 19% without changing the energy consumption ratio. The proposed model has little need for input from experts’ opinions to determine the rates and parameters.

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