Bicriterion Optimization of an M/G/1 Queue with A Removable Server

1996 ◽  
Vol 10 (1) ◽  
pp. 57-73 ◽  
Author(s):  
Eugene A. Feinberg ◽  
Dong J. Kim
Keyword(s):  
Operating Cost ◽  
Operating Costs ◽  
Pareto Optimal ◽  
Removable Server ◽  
Optimal Policies ◽  
Average Operating ◽  
Bicriterion Optimization ◽  
Number Of Customers

This paper studies bicriterion optimization of an M/G/1 queue with a server that can be switched on and off. One criterion is an average number of customers in the system, and another criterion is an average operating cost per unit time. Operating costs consist of switching and running costs. We describe the structure of Pareto optimal policies for a bicriterion problem and solve problems of optimization of one of these criteria under a constraint for another one.

scholarly journals Cost Economies for an Airline: An Analysis of Airlines’ Operating Costs

2019 ◽  
Vol 12 (3) ◽  
pp. 1856-1859
Author(s):  
Harendra Nishantha Kariyawasam
Keyword(s):  
Economies Of Scale ◽  
Low Cost ◽  
Operating Cost ◽  
Operating Costs ◽  
Asia Pacific ◽  
Profit Margins ◽  
Cost Structures ◽  
Low Cost Carriers ◽  
Average Operating ◽  
The Cost

This study focuses on analyzing the variables affecting the average operating cost per aircraft movement. Since airlines around the world are operated on thin profit margins and with increasing competition from Low Cost Carriers it will be important for an airline to get a complete understanding about their operating cost structure. The aim of this study is to suggest an airline of actions to reduce their operating cost and will differentiate the cost structures of Low Cost Carriers and Full Service Carriers. This study was conducted for 20 airlines which were operating in Asia Pacific region. Published financial and statistical data were used for analysis and a parametric approach was used. The results of this study do not suggest economies of scale for the airline, which is to have higher number of aircraft to reduce cost.

scholarly journals Optimal placement design for picking and packing in distribution centers

2021 ◽  
Vol 13 (4) ◽  
pp. 168781402110106
Author(s):  
John Rios ◽  
Rodrigo Linfati ◽  
Daniel Morillo-Torres ◽  
Iván Derpich ◽  
Gustavo Gatica
Keyword(s):  
Optimal Allocation ◽  
Programming Model ◽  
Operating Cost ◽  
Nonlinear Integer Programming ◽  
Operating Costs ◽  
Optimal Placement ◽  
Distribution Centers ◽  
Suitable Space ◽  
Efficient Distribution

An efficient distribution center (DC) is one that receives, stores, picks and packs products into new logistics units and then dispatches them to points of sale at the minimal operating cost. The picking and packing processes represent the highest operating cost of a DC, and both require a suitable space for their operation. An effective coordination between these zones prevents bottlenecks and has a direct impact on the DC’s operational results. In the existing literature, there are no studies that optimize the distribution of the picking and packing areas simultaneously while also reducing operating costs. This article proposes an integer nonlinear integer programming model that minimizes order preparation costs. It does so by predicting customer demand based on historical data and defining the ideal area for picking and packing activities. The model is validated through a real case study of seven clients and fifteen products. It achieves a [Formula: see text] reduction in operating costs when the optimal allocation of the picking and packing areas is made.

Model-Based Multi-Objective Reinforcement Learning by a Reward Occurrence Probability Vector

2020 ◽  
pp. 269-295
Author(s):  
Tomohiro Yamaguchi ◽  
Shota Nagahama ◽  
Yoshihiro Ichikawa ◽  
Yoshimichi Honma ◽  
Keiki Takadama
Keyword(s):  
Reinforcement Learning ◽  
Experimental Results ◽  
Previous Model ◽  
Pareto Optimal ◽  
Occurrence Probability ◽  
Contrast Model ◽  
Multi Objective ◽  
Model Based ◽  
Model Free ◽  
Optimal Policies

This chapter describes solving multi-objective reinforcement learning (MORL) problems where there are multiple conflicting objectives with unknown weights. Previous model-free MORL methods take large number of calculations to collect a Pareto optimal set for each V/Q-value vector. In contrast, model-based MORL can reduce such a calculation cost than model-free MORLs. However, previous model-based MORL method is for only deterministic environments. To solve them, this chapter proposes a novel model-based MORL method by a reward occurrence probability (ROP) vector with unknown weights. The experimental results are reported under the stochastic learning environments with up to 10 states, 3 actions, and 3 reward rules. The experimental results show that the proposed method collects all Pareto optimal policies, and it took about 214 seconds (10 states, 3 actions, 3 rewards) for total learning time. In future research directions, the ways to speed up methods and how to use non-optimal policies are discussed.

Forest Harvesting Equipment Ownership and Operating Costs in 1984

1986 ◽  
Vol 10 (1) ◽  
pp. 10-15 ◽  
Author(s):  
Dennis A. Werblow ◽  
Frederick W. Cubbage
Keyword(s):  
Fuel Consumption ◽  
Operating Cost ◽  
Forest Harvesting ◽  
Cost Data ◽  
Operating Costs ◽  
Fixed Costs ◽  
Historical Records ◽  
The South ◽  
Consumption Data ◽  
Harvesting Systems

Abstract Forest harvesting equipment purchase costs in 1984 were determined by a survey of equipment dealers and manufacturers operating in the South. Based on delivered purchase prices, fixed costs for equipment ownership were calculated using machine rate formulas. Equipment operating costs were estimated based on general guidelines, fuel consumption data, and historical records. The fixed and operating cost data can be used when considering equipment investments and analyzing actual or potential harvesting systems.

The Discussion on Countermeasures of Perfecting the Operating Cost Control and Strengthening the Hospital Financial Management

2020 ◽  
Vol 9 (4) ◽  
pp. 174
Author(s):  
Youhong Liu
Keyword(s):  
Financial Management ◽  
Medical Service ◽  
Cost Control ◽  
Operating Cost ◽  
Medical Industry ◽  
Operating Costs ◽  
Effective Control ◽  
New Era ◽  
Technical Requirements ◽  
Hospital Financial Management

At present, the medical industry has developed into a sunrise industry in the new era. With the continuous improvement of the level of medical services and technical requirements and the establishment and improvement of related medical service institutions, the competition between the medical industries is further intensified. As a complex, hospitals must achieve cost control and financial management in order to achieve ideal operating benefits. At present, there are still many problems in the financial management and cost control of related hospitals. For this, it is necessary to grasp the problem and take effective measures to cope with it, promoting the effective control of hospital operating costs, and achieving efficient financial management goals.

A note on bias optimality in controlled queueing systems

2000 ◽  
Vol 37 (1) ◽  
pp. 300-305 ◽  
Author(s):  
Mark E. Lewis ◽  
Martin L. Puterman
Keyword(s):  
Optimal Policy ◽  
Queueing Systems ◽  
Control Limit ◽  
Lower Control Limit ◽  
Optimal Policies ◽  
Holding Cost ◽  
Bias Optimality ◽  
Number Of Customers

The use of bias optimality to distinguish among gain optimal policies was recently studied by Haviv and Puterman [1] and extended in Lewis et al. [2]. In [1], upon arrival to an M/M/1 queue, customers offer the gatekeeper a reward R. If accepted, the gatekeeper immediately receives the reward, but is charged a holding cost, c(s), depending on the number of customers in the system. The gatekeeper, whose objective is to ‘maximize’ rewards, must decide whether to admit the customer. If the customer is accepted, the customer joins the queue and awaits service. Haviv and Puterman [1] showed there can be only two Markovian, stationary, deterministic gain optimal policies and that only the policy which uses the larger control limit is bias optimal. This showed the usefulness of bias optimality to distinguish between gain optimal policies. In the same paper, they conjectured that if the gatekeeper receives the reward upon completion of a job instead of upon entry, the bias optimal policy will be the lower control limit. This note confirms that conjecture.

scholarly journals ECONOMIC AND OPERATIONAL ANALYSIS OF MECHANIZED FOREST IMPLEMENTATION

2020 ◽  
Vol 44 ◽  
Author(s):  
Nilton Cesar Fiedler ◽  
Alexandre Arantes de Campos ◽  
Marcos Vinicius Winckler Caldeira ◽  
Julião Soares de Souza Lima ◽  
Antônio Henrique Cordeiro Ramalho ◽  
...  
Keyword(s):  
Operating Cost ◽  
High Energy ◽  
Northern Region ◽  
Production Costs ◽  
Operating Costs ◽  
Operating Cycle ◽  
Cycle Times ◽  
Waste Removal ◽  
The Cost ◽  
Efficiency And Productivity

ABSTRACT Mechanization in forestry implantation demands high energy, time, and high operational and production costs. Thus, studies related to the influence of variables on the efficiency of these activities are essential to reduce costs and optimize operations. The objective of this study was to evaluate the operational and cost performance of mechanized forest implantation operations in Eucalyptus sp. Data were collected from eucalyptus plantations located in the northern region of the state of Espírito Santo, Brazil. The analysis of operational performance determined the distribution of operating times, mechanical availability, degree of utilization, operational efficiency, and productivity of the machines. The cost analysis estimated the operating costs in forestry implantation activities. The forest planting operations were: waste removal, subsoiling, digging with fertilization, planting, chemical weeding, and covering fertilization. According to the results, planting (39.20%) and waste removal (15.99%) represented the longest operating cycle times, the shortest production times (51.48% and 53.64%), and finally the longest maintenance times (32.95% and 29%). Chemical weeding and subsoiling showed the lowest maintenance times (4.64% and 3.47%). The cover fertilization was the operation that presented the highest productivity (2.99 ha he-1), and the removal of residues had the lowest (0.97 ha he-1). The highest costs per effective hour (R$13.57 he-1) and lowest production costs (R$81.59 ha-1) occurred at planting. Subsoiling had the highest production cost (R$112.80 ha-1). The lowest operating cost was obtained in the fertilizing operation. Operating costs had the greatest weight in labor, fuel, and maintenance and repairs.

Sign in / Sign up

Export Citation Format

Share Document