scholarly journals Fleet Planning Decision-Making: Two-Stage Optimization with Slot Purchase

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Lay Eng Teoh ◽  
Hooi Ling Khoo
Keyword(s):  
Programming Model ◽  
Planning Horizon ◽  
Profit Margin ◽  
Planning Problem ◽  
Illustrative Case ◽  
Two Stage ◽  
Fleet Planning ◽  
Novel Approach ◽  
Planning Decision ◽  
Service Frequency

Essentially, strategic fleet planning is vital for airlines to yield a higher profit margin while providing a desired service frequency to meet stochastic demand. In contrast to most studies that did not consider slot purchase which would affect the service frequency determination of airlines, this paper proposes a novel approach to solve the fleet planning problem subject to various operational constraints. A two-stage fleet planning model is formulated in which the first stage selects the individual operating route that requires slot purchase for network expansions while the second stage, in the form of probabilistic dynamic programming model, determines the quantity and type of aircraft (with the corresponding service frequency) to meet the demand profitably. By analyzing an illustrative case study (with 38 international routes), the results show that the incorporation of slot purchase in fleet planning is beneficial to airlines in achieving economic and social sustainability. The developed model is practically viable for airlines not only to provide a better service quality (via a higher service frequency) to meet more demand but also to obtain a higher revenue and profit margin, by making an optimal slot purchase and fleet planning decision throughout the long-term planning horizon.

OPTIMAL MANPOWER PLANNING DECISION WITH SINGLE EMPLOYEE TYPE CONSIDERING MINIMAL EMPLOYMENT PERIOD CONSTRAINT

2010 ◽  
Vol 27 (03) ◽  
pp. 411-436 ◽  
Author(s):  
YONGJIAN LI ◽  
JIAN CHEN ◽  
XIAOQIANG CAI ◽  
BENGSHENG TU
Keyword(s):  
Programming Model ◽  
Planning Horizon ◽  
Manpower Planning ◽  
Planning Problem ◽  
Setup Cost ◽  
Related Cost ◽  
Planning Decision ◽  
Dynamic Programming Model ◽  
Future Demands ◽  
Dismissal Costs

In this paper, we investigate a manpower planning problem with single employee type over a long planning horizon. The dynamic demand for manpower must be fulfilled by allocating enough number of employees and each employee has a minimal employment period. A cost objective is concerned where the costs for every employee include salary, recruitment and dismissal costs, in particular, setup cost when recruitment activity occurs in one period. In this paper we first formulate the problem as a dynamic programming model. Then we derive properties of the problem and propose an algorithm to minimize the total manpower-related cost over the entire planning horizon. Next we discuss the influence on the optimal states of changing some parameters and provide the sensitivity analysis of the optimal states concerning the future demands. Finally, we report computational results to illustrate the correctness of the approach and to analyze the influence on the optimal states of changing parameters or changing future demands.

scholarly journals MODELLING THE IMPACTS OF UNCERTAIN CARBON TAX POLICY ON MARITIME FLEET MIX STRATEGY AND CARBON MITIGATION

Transport ◽  
2018 ◽  
Vol 33 (3) ◽  
pp. 707-717
Author(s):  
Mo Zhu ◽  
Michael Chen ◽  
Murat Kristal
Keyword(s):  
Tax Policy ◽  
Stochastic Dynamics ◽  
Programming Model ◽  
Carbon Tax ◽  
Dynamic Environment ◽  
Liner Shipping ◽  
Scenario Tree ◽  
Planning Problem ◽  
Policy Uncertainty ◽  
Fleet Planning

The maritime transport industry continues to draw international attention on significant Greenhouse Gas emissions. The introduction of emissions taxes aims to control and reduce emissions. The uncertainty of carbon tax policy affects shipping companies’ fleet planning and increases costs. We formulate the fleet planning problem under carbon tax policy uncertainty a multi-stage stochastic integer-programming model for the liner shipping companies. We develop a scenario tree to represent the structure of the carbon tax stochastic dynamics, and seek the optimal planning, which is adaptive to the policy uncertainty. Non-anticipativity constraint is applied to ensure the feasibility of the decisions in the dynamic environment. For the sake of comparison, the Perfect Information (PI) model is introduced as well. Based on a liner shipping application of our model, we find that under the policy uncertainty, companies charter more ships when exposed to high carbon tax risk, and spend more on fleet operation; meanwhile the CO2 emission volume will be reduced.

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.

scholarly journals A new mathematical model for determining optimal workforce planning of pilots in an airline company

2021 ◽  
Author(s):  
İbrahim Zeki Akyurt ◽  
Yusuf Kuvvetli ◽  
Muhammet Deveci ◽  
Harish Garg ◽  
Mert Yuzsever
Keyword(s):  
Mathematical Model ◽  
Workforce Planning ◽  
Resource Planning ◽  
Planning Horizon ◽  
Mixed Integer ◽  
Planning Problem ◽  
Real World Data ◽  
Planning Decision ◽  
Airline Company

AbstractThis study aims to model a workforce-planning problem of pilot roles which include captain and first officer in an airline company and to make an efficient plan having maximal utilization of minimum workforce requirements. To tackle this problem, a mixed integer programming based a new mathematical model is proposed. The model considers different conditions such as employing pilots with different skill types, resignations, retirements, holidays of pilots, transitions between different skills regarding needs of the demands during the planning horizon. The application of the proposed approach is investigated using a case study with real-world data from an airline company in Turkey. The results show that a company can use transitions instead of new employment and this is a more suitable medium-term production and human resource planning decision.

scholarly journals A Multiperiod Vehicle Lease Planning for Urban Freight Consolidation Network

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Woosuk Yang ◽  
Taesu Cheong ◽  
Sang Hwa Song
Keyword(s):  
Programming Model ◽  
Planning Horizon ◽  
Planning Problem ◽  
Transportation Services ◽  
Urban Freight ◽  
Urban Freight Transport ◽  
Freight Consolidation ◽  
Lagrangian Heuristic ◽  
Time Restriction

This paper considers a multiperiod vehicle lease planning problem for urban freight consolidation centers (UFCCs) in the urban freight transport network where short-term-leased and long-term-leased vehicles are hired together. The objective is to allocate the two kinds of leased vehicles optimally for direct transportation services from the associated origin node to the associated UFCC or from the associated UFCC to the associated destinations so as to satisfy a given set of period-to-period freight demands over a given planning horizon at total minimum vehicle allocation cost subject to demand-dependent transportation time restriction. The problem is formulated as an integer programming model and proven to be NP-hard in a strong sense. Thus, a Lagrangian heuristic is proposed to find a good solution efficiently. Numerical experiments show that the proposed algorithm finds good lower and upper bounds within reasonable time.

scholarly journals Application of stochastic programming to electricity generation planning in South Africa

ORiON ◽  
2019 ◽  
Vol 35 (2) ◽  
pp. 88-125
Author(s):  
M Bashe ◽  
M Shuma-Iwisi ◽  
MA Van Wyk
Keyword(s):  
South Africa ◽  
Stochastic Programming ◽  
Electricity Generation ◽  
Programming Model ◽  
Programming Models ◽  
Planning Problem ◽  
Two Stage ◽  
Generation Planning ◽  
Stochastic Programming Model ◽  
Minimum Costs

A two-stage stochastic programming model is used to solve the electricity generation planning problem in South Africa for the period 2013 to 2050, in an attempt to minimise expected cost. Costs considered are capital and running costs. Unknown future electricity demand is the source of uncertainty represented by four scenarios with equal probabilities. The results show that the main contributors for new capacity are coal, wind, hydro and gas/diesel. The minimum costs obtained by solving the two-stage stochastic programming models range from R2 201 billion to R3 094 billion.

scholarly journals Solving two-stage stochastic route-planning problem in milliseconds via end-to-end deep learning

2021 ◽  
Author(s):  
Jie Zheng ◽  
Ling Wang ◽  
Shengyao Wang ◽  
Yile Liang ◽  
Jize Pan
Keyword(s):  
Deep Learning ◽  
Programming Model ◽  
Search Algorithm ◽  
Rapid Development ◽  
Optimization Technique ◽  
Route Planning ◽  
Food Delivery ◽  
Planning Problem ◽  
Two Stage ◽  
End To End

AbstractWith the rapid development of e-economy, ordering via online food delivery platforms has become prevalent in recent years. Nevertheless, the platforms are facing lots of challenges such as time-limitation and uncertainty. This paper addresses a complex stochastic online route-planning problem (SORPP) which is mathematically formulated as a two-stage stochastic programming model. To meet the immediacy requirement of online fashion, an end-to-end deep learning model is designed which is composed of an encoder and a decoder. To embed different problem-specific features, different network layers are adopted in the encoder; to extract the implicit relationship, the probability mass functions of stochastic food preparation time is processed by a convolution neural network layer; to provide global information, the location map and rider features are handled by the factorization-machine (FM) and deep FM layers, respectively; to screen out valuable information, the order features are embedded by attention layers. In the decoder, the permutation sequence is predicted by long-short term memory cells with attention and masking mechanism. To learn the policy for finding optimal permutation under complex constraints of the SORPP, the model is trained in a supervised learning way with the labels obtained by iterated greedy search algorithm. Extensive experiments are conducted based on real-world data sets. The comparative results show that the proposed model is more efficient than meta-heuristics and is able to yield higher quality solutions than heuristics. This work provides an intelligent optimization technique for complex online food delivery system.

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