scholarly journals Fast and exact audit scheduling optimization

2021 ◽  
Vol 3 (10) ◽  
Author(s):  
Jan Motl ◽  
Pavel Kordík
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Input Data ◽  
Scheduling Optimization ◽  
Schedule Optimization ◽  
The Cost

AbstractThis article is concerned with the cost and time-effective scheduling of financial auditors with integer linear programming. The schedule optimization considers 13 different constraints, staff scarcity, frequent alterations of the input data with the need to minimize the changes in the generated schedule, and scaling issues. The delivered implementation reduced the time to the first schedule from 3 man-days to 1 h and the schedule update time from 1 man-day to 4 min.

scholarly journals Minimising negative externalities cost using 0-1 mixed integer linear programming model in e-commerce environment

2017 ◽  
Vol 11 ◽  
Author(s):  
Akyene Tetteh ◽  
Sarah Dsane-Nsor
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Programming Model ◽  
Petroleum Products ◽  
Mixed Integer ◽  
The Internet ◽  
Negative Externalities ◽  
The Government ◽  
The Cost

Background: Although the Internet boosts business profitability, without certain activities like efficient transportation, scheduling, products ordered via the Internet may reach their destination very late. The environmental problems (vehicle part disposal, carbon monoxide [CO], nitrogen oxide [NOx] and hydrocarbons [HC]) associated with transportation are mostly not accounted for by industries.Objectives: The main objective of this article is to minimising negative externalities cost in e-commerce environments.Method: The 0-1 mixed integer linear programming (0-1 MILP) model was used to model the problem statement. The result was further analysed using the externality percentage impact factor (EPIF).Results: The simulation results suggest that (1) The mode of ordering refined petroleum products does not impact on the cost of distribution, (2) an increase in private cost is directly proportional to the externality cost, (3) externality cost is largely controlled by the government and number of vehicles used in the distribution and this is in no way influenced by the mode of request (i.e. Internet or otherwise) and (4) externality cost may be reduce by using more ecofriendly fuel system.

scholarly journals Integer linear programming outperforms simulated annealing for solving conservation planning problems

2019 ◽  
Author(s):  
Richard Schuster ◽  
Jeffrey O. Hanson ◽  
Matt Strimas-Mackey ◽  
Joseph R. Bennett
Keyword(s):  
Linear Programming ◽  
Simulated Annealing ◽  
Integer Linear Programming ◽  
Conservation Planning ◽  
Practical Advantage ◽  
Processing Times ◽  
Conservation Plans ◽  
The Cost ◽  
Planning Problems

AbstractThe resources available for conserving biodiversity are limited, and so protected areas need to be established in places that will achieve objectives for minimal cost. Two of the main algorithms for solving systematic conservation planning problems are Simulated Annealing (SA) and Integer linear programming (ILP). Using a case study in British Columbia, Canada, we compare the cost-effectiveness and processing times of SA versus ILP using both commercial and open-source algorithms. Plans for expanding protected area systems based on ILP algorithms were 12 to 30% cheaper than plans using SA. The best ILP solver we examined was on average 1071 times faster than the SA algorithm tested. The performance advantages of ILP solvers were also observed when we aimed for spatially compact solutions by including a boundary penalty. One practical advantage of using ILP over SA is that the analysis does not require calibration, saving even more time. Given the performance of ILP solvers, they can be used to generate conservation plans in real-time during stakeholder meetings and can facilitate rapid sensitivity analysis, and contribute to a more transparent, inclusive, and defensible decision-making process.

GoSeed: Optimal Seeding Plan for Deduplicated Storage

2021 ◽  
Vol 17 (3) ◽  
pp. 1-28
Author(s):  
Aviv Nachman ◽  
Sarai Sheinvald ◽  
Ariel Kolikant ◽  
Gala Yadgar
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Target Volume ◽  
Storage Management ◽  
Data Migration ◽  
Acceleration Methods ◽  
Easy Problem ◽  
Storage Volume ◽  
Problem Instances ◽  
The Cost

Deduplication decreases the physical occupancy of files in a storage volume by removing duplicate copies of data chunks, but creates data-sharing dependencies that complicate standard storage management tasks. Specifically, data migration plans must consider the dependencies between files that are remapped to new volumes and files that are not. Thus far, only greedy approaches have been suggested for constructing such plans, and it is unclear how they compare to one another and how much they can be improved. We set to bridge this gap for seeding —migration in which the target volume is initially empty. We prove that even this basic instance of data migration is NP-hard in the presence of deduplication. We then present GoSeed, a formulation of seeding as an integer linear programming (ILP) problem, and three acceleration methods for applying it to real-sized storage volumes. Our experimental evaluation shows that, while the greedy approaches perform well on “easy” problem instances, the cost of their solution can be significantly higher than that of GoSeed’s solution on “hard” instances, for which they are sometimes unable to find a solution at all.

scholarly journals A mixed-integer linear programming model for harvesting, loading and transporting sugarcane. A case study in Peru

DYNA ◽  
2016 ◽  
Vol 83 (195) ◽  
pp. 173-179 ◽  
Author(s):  
Marcela María Morales-Chávez ◽  
José A. Soto-Mejía ◽  
William Ariel Sarache
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Programming Model ◽  
Raw Material ◽  
Linear Programming Model ◽  
Mixed Integer ◽  
Production Plant ◽  
Integer Linear Programming Model ◽  
The Cost

Due to opportunities for economic and social development in the biofuels market, improvement to the supply chain has become a relevant matter. In agro-industrial supply chains, procurement costs are highly relevant. Since sugarcane is a high performance raw material for ethanol production, this paper proposes a Mixed-Integer Linear Programming Model for cost optimization for harvesting, loading and transportation operations. The model determines the quantity of machines and workers to meet the biofuel plant requirements. Costs of resources for harvesting and loading as well as transportation costs from the land parcel to the production plant are minimized. Also, the model calculates the cost of penalties for shortages (unmet demand) and the cost of equipment idle time. The implementation of the model in a Peruvian biofuels company, showed a cost reduction of around 11 % when compared to the current costs.

scholarly journals Repetitive construction processes scheduling using mixed-integer linear programming

2015 ◽  
Vol 14 (2) ◽  
pp. 055-061 ◽  
Author(s):  
Piotr Jaśkowski
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Construction Projects ◽  
Mixed Integer ◽  
Repetitive Processes ◽  
Project Duration ◽  
Schedule Optimization ◽  
Multistory Buildings ◽  
Procurement System

Many construction projects contain several identical or similar units, such as floors in multistory buildings, houses in housing developments, sections of pipelines or highways. Repetitive processes arise from the subdivision of a generalized construction process into specific activities associated with particular units. In many cases it is possible to perform individual processes (repeated in each units) in alternative ways (modes). Regardless of the construction project procurement system, duration and cost are the key factors determining project’s economic efficiency and fulfillment of the owner’s needs and requirements. Minimizing project duration and cost are the most important criteria for schedule optimization. Processes that repeat from unit to unit are realized by specialized crews. Uninterrupted resource utilization becomes an extremely important issue for scheduling repetitive processes to minimize employment costs. In this paper, the problem of selecting appropriate modes and minimizing the total project cost and with a constraint on project duration is presented with respect to the continuity of the crews’ work. The paper uses the mixed integer linear programming to model this problem and uses a case study to illustrate it.

Designing a framework of power procurement for a power distribution utility: a case study

2019 ◽  
Vol 13 (4) ◽  
pp. 1063-1087
Author(s):  
Debadyuti Das ◽  
Virander Kumar ◽  
Amit Kumar Bardhan ◽  
Rahul Kumar
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Power Distribution ◽  
Mixed Integer Linear Programming ◽  
Sensitivity Analyses ◽  
Mixed Integer ◽  
Daily Cost ◽  
Content Type ◽  
Power Exchange ◽  
The Cost

Purpose The study aims to find out an appropriate volume of power to be procured through long-term power purchase agreements (PPAs), the volume to be sourced from the power exchange through day-ahead and term-ahead options and also a suitable volume to be sold at different points of time within a day, which would finally lead to the optimum cost of power procurement. Design/methodology/approach The study has considered a Delhi-based power distribution utility and has collected all relevant data from its archival sources. A stochastic optimization model has been developed to capture the problem of power procurement faced by the distribution utility, which is modelled as a mixed integer linear programming problem. Sensitivity analyses were carried out on the important parameters including hourly demand of power, unit variable cost of power available through PPAs, maximum back-down percentage allowed under PPAs, etc., to investigate their impact on daily cost of power under PPAs, daily cost of power under day-ahead and term-ahead options, daily sales revenue and also the net total daily cost of power procurement. Findings The findings include the appropriate volume of power procured from different suppliers through PPAs and from the power exchange under day-ahead and term-ahead options and also the surplus volume of power sold under the day-ahead arrangement. It has also computed the total cost of power purchased under PPAs, the cost of power purchased from the power exchange under day-ahead and term-ahead options and also the revenue generated out of the sale of surplus power under the day-ahead arrangement. In addition, it has also presented the results of sensitivity analyses, which provide rich managerial insights. Originality/value The paper makes two significant contributions to the existing body of power procurement literature. First, the stochastic mixed-integer linear programming model helps decision makers in determining the right volume of power to be purchased from different sources. Second, based on the findings of the procurement model, a power procurement framework is developed considering the dimensions of uncertainty in power supply and the cost of power procurement. This power procurement framework would aid managers in making procurement decisions under different scenarios.

scholarly journals Comparison of Integer Linear Programming and Dynamic Programming Approaches for ATM Cash Replenishment Optimization Problem

2020 ◽  
Vol 11 (3) ◽  
pp. 120-132
Author(s):  
Fazilet Özer ◽  
Ismail Hakki Toroslu ◽  
Pinar Karagoz
Keyword(s):  
Dynamic Programming ◽  
Linear Programming ◽  
Integer Linear Programming ◽  
Optimization Problem ◽  
Optimal Solution ◽  
Real Data ◽  
The Other ◽  
Other Hand ◽  
Automated Teller Machine ◽  
The Cost

With the automated teller machine (ATM) cash replenishment problem, banks aim to reduce the number of out-of-cash ATMs and duration of out-of-cash status. On the other hand, they want to reduce the cost of cash replenishment, as well. The problem conventionally involves forecasting ATM cash withdrawals, and then cash replenishment optimization based on the forecast. The authors assume that reliable forecasts are already obtained for the amount of cash needed in ATMs. The focus of the article is cash replenishment optimization. After introducing linear programming-based solutions, the authors propose a solution based on dynamic programming. Experiments conducted on real data reveal that the proposed approach can find the optimal solution more efficiently than linear programming.

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