Task Scheduling for Subsea Flexible Pipes Decommissioning

2021 ◽  
Author(s):  
Robert da Silva Bressan ◽  
Danilo Artigas
Keyword(s):  
Optimal Solution ◽  
Real Field ◽  
Optimal Order ◽  
Beam Search ◽  
Ranking Functions ◽  
Large Space ◽  
Order Restrictions ◽  
Feedback Arc Set ◽  
Flexible Pipes ◽  
Estimation Function

Abstract Subsea flexible pipelines removal is subject to order restrictions, mostly caused by crossings. It is proposed to create a computational algorithm to design an optimal order of vessel intervention over a field. A real field was studied, and, from it, the mathematical base model was created upon graph theory, with great correlation with the minimum feedback arc set problem. Vessel movements were discretized and reduced to removal, reposition, and cut, leading to a state search. A-star algorithm was implemented to guide the search for the solution. Then, the complete algorithm was built, tested in a minimal environment, and finally applied to the real instance. To improve performance, a beam search filtering was envisioned, using seven ranking functions. Constructed model is suspected to be NP-hard, by correlation to minimum feedback arc set problem, leading to a large space search. Instances containing under 100 crossings were solved optimally, without needing any assistance. After implementing the heuristics and beam search, solution time was lowered by about 20 times, demonstrating the effectiveness of the technique. Also, ranking functions for pipe repositioning based on crossing count led to better results than crossing density. For cutting, an approximation based on feedback arc set was used. GreedyFAS was employed and gave satisfactory results. Bigger instances containing around 3000 crossings could not be solved optimally in a reasonable time, even with the heuristics. Improvements in A-star estimation function and bound the solution branches might lead to an optimal solution for these larger instances. Model proposed simplifies the operational order decisions and helps build the scheduling of operations. As it is based on state search, other aspects in logistics, vessel capacities and steps in decommissioning processes may be added, adjusting the neighboring weights and branching, keeping the same core.

A Novel Ranking Approach to Solving Fully LR-Intuitionistic Fuzzy Transportation Problems

2018 ◽  
Vol 15 (01) ◽  
pp. 95-112 ◽  
Author(s):  
Abhishekh ◽  
A. K. Nishad
Keyword(s):  
Transportation Problem ◽  
Fuzzy Numbers ◽  
Optimal Solution ◽  
Solution Procedure ◽  
Ranking Functions ◽  
Transportation Problems ◽  
Intuitionistic Fuzzy Numbers ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Environment ◽  
Fuzzy Transportation Problem

To the extent of our knowledge, there is no method in fuzzy environment to solving the fully LR-intuitionistic fuzzy transportation problems (LR-IFTPs) in which all the parameters are represented by LR-intuitionistic fuzzy numbers (LR-IFNs). In this paper, a novel ranking function is proposed to finding an optimal solution of fully LR-intuitionistic fuzzy transportation problem by using the distance minimizer of two LR-IFNs. It is shown that the proposed ranking method for LR-intuitionistic fuzzy numbers satisfies the general axioms of ranking functions. Further, we have applied ranking approach to solve an LR-intuitionistic fuzzy transportation problem in which all the parameters (supply, cost and demand) are transformed into LR-intuitionistic fuzzy numbers. The proposed method is illustrated with a numerical example to show the solution procedure and to demonstrate the efficiency of the proposed method by comparison with some existing ranking methods available in the literature.

scholarly journals Replenishment Model with Entropic Order Quantity for Deteriorating Items under Inflation

2021 ◽  
pp. 34-47
Author(s):  
P. K. Tripathy ◽  
Anima Bag
Keyword(s):  
Cost Minimization ◽  
Optimal Solution ◽  
Deteriorating Items ◽  
Optimal Order ◽  
Selling Price ◽  
Order Quantity ◽  
Total Cost ◽  
Purchase Cost ◽  
Holding Cost ◽  
Special Approach

The purpose of the current paper is to determine an optimal order quantity so as to minimize the total cost of the inventory system of a business enterprise. The model is developed for deteriorating items with stock and selling price dependent demand under inflation without permitting shortage. Optimal solution is achieved by cost minimization strategy considering replenishment cost, purchase cost, holding cost and deterioration cost with a special approach to entropy cost for bulk size purchasing units. The effectiveness of the proposed model has been avowed through empirical investigation. Sensitivity analysis has been accomplished to deduce managerial insights. Findings suggest that an increased inflationary effect results in increment in the system total cost. The paper can be extended by allowing shortage. The model can be utilized in the business firms dealing with bulk purchasing units of electric equipments, semiconductor devices, photographic films and many more.

scholarly journals An Inventory Model based on Imperfect Production with Shortage Backorder

2020 ◽  
Vol 9 (2) ◽  
pp. 89-92
Keyword(s):  
Raw Materials ◽  
Optimal Solution ◽  
Inventory Model ◽  
Production Costs ◽  
Business Strategies ◽  
Optimal Order ◽  
Model Based ◽  
Proposed Model ◽  
Imperfect Items ◽  
Marketing Systems

In this paper, we discussed about the imperfect items. In practice items may get damaged due to production or transportation conditions. Each lot receives some imperfect items. This model also considers the effects of business strategies such as optimal order size of raw materials, production rates and unit production costs, and idle time in different areas on the cooperation of marketing systems. The model can be used in industries such as textiles and footwear, chemicals, food. We develop an inventory model based on imperfect products and shortages. We consider demand is constant and continuous. Purpose of this study is not only to find the retailer`s optimal replenishment policies but also to minimize the total average cost. Finally, a numerical example is presented to illustrate the proposed model and sensitivity analysis of the optimal solution concerning parameters is carried out using the Mathematica 10.0 software.

scholarly journals APPLICATIONS OF TRAVELING SALESMAN PROBLEM ON THE OPTIMAL SIGHTSEEING ORDERS OF MACAO WORLD HERITAGE SITES WITH REAL TIME OR DISTANCE VALUES BETWEEN EVERY PAIR OF SITES

2021 ◽  
Vol 5 (5) ◽  
pp. 41-53
Author(s):  
Kin Neng Tong ◽  
Iat In Fong ◽  
In Iat Li ◽  
Chi Him Anthony Cheng ◽  
Soi Chak Choi ◽  
...  
Keyword(s):  
Public Transportation ◽  
Optimal Solution ◽  
Computation Time ◽  
World Heritage ◽  
World Heritage Site ◽  
Brute Force ◽  
Optimal Route ◽  
Large Space ◽  
World Heritage Sites ◽  
Order Of Magnitude

The optimal route of sightseeing orders for visiting every Macao World Heritage Site at exactly once was calculated with Simulated Annealing and Metropolis Algorithm (SAMA) after considering real required time or traveling distance between pairs of sites by either driving a car, taking a bus, or on foot. We found out that, with the optimal tour path, it took roughly 78 minutes for driving a car, 115 minutes on foot, while 117 minutes for taking a bus. On the other hand, the optimal total distance for driving a car would be 13.918 km while for pedestrians to walk, 7.844 km. These results probably mean that there is large space for the improvement on public transportation in this city. Comparison of computation time demanded between the brute- force enumeration of all possible paths and SAMA was also presented, together with animation of the processes for the algorithm to find out the optimal route. It is expected that computation time is astronomically increasing for the brute-force enumeration with more number of sites, while it only takes SAMA much less order of magnitude in time to calculate the optimal solution for larger number of sites. Several optimal options of routes were also provided in each transportation method. However, it is possible that in some types of transportation there could be only one optimal route having no circular or mirrored duplicates.

Inventory Management in Multi Echelon Supply Chain using Sample Average Approximation

2009 ◽  
Vol 39 (1) ◽  
pp. 45-51
Author(s):  
Oksana Soshko
Keyword(s):  
Supply Chain ◽  
Inventory Management ◽  
Optimal Solution ◽  
Sample Average Approximation ◽  
Optimal Order ◽  
Decision Variable ◽  
Target Level ◽  
Solution Quality ◽  
Sample Average ◽  
Average Approximation

Inventory Management in Multi Echelon Supply Chain using Sample Average ApproximationAn optimization model of multiechelon supply chain is presented in this paper. The decisions to be made are the amount of beer to be ordered in every echelon of supply chain in each echelon over the time horizon of one year. Since demand of the end customer is stochastic and presented by means of scenarios, the problem is solved by using sample average approximation method. This method uses only a subset of the scenarios, randomly sampled according to the distribution over scenarios, to represent the full scenario space. An important theoretical justification for this method is that as the number of scenarios sampled increases, the solution to the approximate problem converges to an optimal solution in the expected sense. The computational results are presented for two cases. First target level is chosen as a decision variable and then order size is chosen as a decision variable of the problem. The target level strategy is based on making inventory for each echelon; in its turn order strategy is based on determination of optimal order quantity, which is independent from scenarios. However target level strategy provides high service at low cost, but it offers less reality under uncertain demand than order strategy. Practical experiments on finding the optimal SAA parameters are presented in the paper and as well as the analysis of their impact on solution quality.

EOQ Model with Stock-Level Dependent Demand and Different Holding Cost Functions

2017 ◽  
Vol 8 (4) ◽  
pp. 59-75 ◽  
Author(s):  
H.S. Shukla ◽  
R.P. Tripathi ◽  
Neha Sang
Keyword(s):  
Order Approximation ◽  
Optimal Solution ◽  
Optimal Order ◽  
Order Quantity ◽  
Inventory Cost ◽  
Stock Level ◽  
Numerical Examples ◽  
Eoq Model ◽  
Holding Cost ◽  
Dependent Demand

This paper presents EOQ (Economic Order Quantity) model with stock- level dependent demand and different types of holding cost function. We show that the total relevant inventory cost per unit time is convex with respect to cycle time. Mathematical models are established to determine optimal order quantity and total relevant inventory cost. Numerical examples are provided for two different models i.e. (i): Instantaneous replenishment with inventory dependent holding cost and (ii) Instantaneous replenishment with quadratic time dependent carrying cost. Numerical examples are provided to illustrate the proposed model. Sensitivity analysis of the optimal solution with respect to the parameters of the system is carried out. The second order approximation is used for finding closed form optimal solution. Mathematica 5.2 software is used to find numerical results.

scholarly journals The Convergence of Gallego’s Iterative Method for Distribution-Free Inventory Models

Mathematics ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 484 ◽  
Author(s):  
Ting-Chen Hu ◽  
Kuo-Chen Hung ◽  
Kuo-Lung Yang
Keyword(s):  
Iterative Method ◽  
Optimal Solution ◽  
Threshold Value ◽  
Optimal Order ◽  
Order Quantity ◽  
Inventory Models ◽  
Worst Case ◽  
Distribution Free ◽  
Second Moments ◽  
Optimal Order Quantity

For inventory models with unknown distribution demand, during shortages, researchers used the first and the second moments to derive an upper bound for the worst case, that is the min-max distribution-free procedure for inventory models. They applied an iterative method to generate a sequence to obtain the optimal order quantity. A researcher developed a three-sequence proof for the convergence of the order quantity sequence. We directly provide proof for the original order quantity sequence. Under our proof, we can construct an increasing sequence and a decreasing sequence that both converge to the optimal order quantity such that we can obtain the optimal solution within the predesigned threshold value.

scholarly journals A Deep Learning Approach for MIMO-NOMA Downlink Signal Detection

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2526 ◽  
Author(s):  
Chuan Lin ◽  
Qing Chang ◽  
Xianxu Li
Keyword(s):  
Deep Learning ◽  
Signal Detection ◽  
Power Allocation ◽  
Interference Cancellation ◽  
Communication Systems ◽  
High Efficiency ◽  
Optimal Solution ◽  
Optimal Order ◽  
Learning Method ◽  
Allocation Factor

As a key candidate technique for fifth-generation (5G) mobile communication systems, non-orthogonal multiple access (NOMA) has attracted considerable attention in the field of wireless communication. Successive interference cancellation (SIC) is the main NOMA detection method applied at receivers for both uplink and downlink NOMA transmissions. However, SIC is limited by the receiver complex and error propagation problems. Toward this end, we explore a high-performance, high-efficiency tool—deep learning (DL). In this paper, we propose a learning method that automatically analyzes the channel state information (CSI) of the communication system and detects the original transmit sequences. In contrast to existing SIC schemes, which must search for the optimal order of the channel gain and remove the signal with higher power allocation factor while detecting a signal with a lower power allocation factor, the proposed deep learning method can combine the channel estimation process with recovery of the desired signal suffering from channel distortion and multiuser signal superposition. Extensive performance simulations were conducted for the proposed MIMO-NOMA-DL system, and the results were compared with those of the conventional SIC method. According to our simulation results, the deep learning method can successfully address channel impairment and achieve good detection performance. In contrast to implementing well-designed detection algorithms, MIMO-NOMA-DL searches for the optimal solution via a neural network (NN). Consequently, deep learning is a powerful and effective tool for NOMA signal detection.

scholarly journals Optimal Order Strategy in Uncertain Demands with Free Shipping Option

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Qing-Chun Meng ◽  
Teng Zhang ◽  
Ming Li ◽  
Xiao-Xia Rong
Keyword(s):  
Game Theory ◽  
Cooperative Game Theory ◽  
Uncertain Data ◽  
Optimal Solution ◽  
Optimal Order ◽  
Random Data ◽  
The Core ◽  
Distributional Information ◽  
Linear Decision Rule ◽  
Free Shipping

Free shipping with conditions has become one of the most effective marketing tools; more and more companies especially e-business companies prefer to offer free shipping to buyers whenever their orders exceed the minimum quantity specified by them. But in practice, the demands of buyers are uncertain, which are affected by weather, season, and many other factors. Firstly, we model the centralization ordering problem of retailers who face stochastic demands when suppliers offer free shipping, in which limited distributional information such as known mean, support, and some deviation measures of the random data is needed only. Then, based on the linear decision rule mainly for stochastic programming, we analyze the optimal order strategies of retailers and discuss the approximate solution. Further, we present the core allocation between all retailers via dual and cooperative game theory. The existence of core shows that each retailer is pleased to cooperate with others in the centralization problem. Finally, a numerical example is implemented to discuss how uncertain data and parameters affect the optimal solution.

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