Simple Programming Model Research Articles

Optimization of chemotherapy regimens using mathematical programming

Cancer is a leading cause of death and a cost burden on healthcare systems worldwide. The mainstay of treatment is chemotherapy which is most often administered empirically. Optimizing the frequency of drug administration would benefit patients by avoiding overtreatment and reducing costs. In this work, the optimization of chemotherapy regimens using mathematical programming techniques is demonstrated by developing a simple mathematical programming model for the administration of a fictitious drug. The question to be answered by the solution of the model is how often the drug should be administered so that the tumor size does not exceed a predefined size and the treatment cost reaches a minimum value. The proposed mathematical programming model is computer-implemented using a well-established mathematical programming system, thus keeping the cost and effort of obtaining the optimization results low. An example is used to demonstrate the superiority of the proposed optimization approach over the mainstay approach.

Optimizing consumption and emission in gas fuel consuming power applyinf DEA model

In this paper, energy consumption and carbon dioxide emission of the Iran gas generators is studied using Data Envelopment Analysis (DEA) and Goal Programming (GP) to show the importance of CO2 emission in Iran like most other countries and also conserve fuel as key role of today life and industry. Ramanathan studied energy consumption and carbon dioxide emission as well, from 17 countries of the Middle East and North Africa (MENA) with four indicators for the year 1996. In this paper a simple goal programming model to minimizing carbon dioxide emission and gas consumption is revealed for the year 2004 by employing three main indicators. First electricity generated by generators, then carbon dioxide emission and at last natural gas consumption as fuel. Albeit there are other factors like generators condition, environmental situation and etc, that could be considered for further studies. The analysis concludes the most and least efficient units and revealed an optimizing model which its optimized results are comparable to international standards for CO2 emission and fuel consumption.

A win-win situation for environment and economy: Analysis of maximizing benefits in municipal sludge treatment plants

The huge cost (economic cost and environmental cost) of treating municipal sludge has caused enormous pressure on China. How to balance economic cost and environmental benefits in China's sludge treatment industry is an important issue that the government urgently needs to address. This article selects 9 types of municipal sludge treatment plants (MSTP) that can represent different treatment methods and scales in China, and attempts to help all provinces in China find the optimal solution for maximizing sludge treatment benefits (economic benefits and environmental benefits) under different constraints (economic constraints and environmental constraints). Firstly, this paper analyzes the economic and environmental costs and return of 9 types of MSTP. Secondly, we use the unguided Super-SBM model to combine the costs and return of 9 types of MSTP to measure the benefits of each plant. It has been found that under ideal conditions, medium Incineration treatment (IT) and large building material (BM) utilization are the most beneficial options for sludge treatment in China. Finally, we use a simple linear programming model to establish a constrained linear programming model for the environment and economy, substituting provinces that are divided into four categories under different constraint conditions into the model, and finding the optimal ratio for each type of province to build MSTP, in order to maximize benefits. Research has found that after adding constraints, the future development trend of municipal sludge treatment in China will mainly focus on small BM utilization and medium LA. This research result has universal applicability. As long as the constraints of this article are met, any country or province can adjust the construction proportion of municipal sludge treatment plants according to the research results of this article.

Configuration and Placement of Serverless Applications Using Statistical Learning

In the last decade, serverless computing emerged as a new compelling paradigm for the deployment of cloud applications and services. It represents an evolution of cloud computing with a simplified programming model, that aims to abstract away most operational concerns. Running serverless applications requires users to configure multiple parameters, such as memory, CPU, cloud provider, etc. While relatively simpler, configuring such parameters correctly while minimizing cost and meeting delay constraints is not trivial. In this paper, we present, a framework that uses Bayesian Optimization to find the optimal resource configuration and placement for functions in a serverless application. uses statistical learning techniques to intelligently collect samples and predict the cost and execution time of a serverless function across unseen configuration values. Our framework uses the predicted cost and execution time on available locations to select the “best” configuration parameters and placement for running a serverless application while satisfying customer objectives. We evaluate on AWS Lambda with real-world applications consisting of multiple functions (both linear chains and service graphs), where we successfully found optimal/near-optimal configurations. We also evaluate over a wide range of simulated distributed cloud environments that confirm the efficacy of our approach.

Multi‐objective sunflower optimization: A new hypercubic meta‐heuristic for constrained engineering problems

AbstractIn order to solve challenging engineering problems, the state‐of‐the‐art in multi‐objective optimization shows a trend toward using meta‐heuristics and a posteriori decision‐making methods. This encourages the search for algorithms better able to find Pareto fronts with more convergence, coverage, and lower computational cost. This work shows the creation and validation of the Multi‐objective Sunflower Optimization (MOSFO), a hypercubic and constrained multi‐objective meta‐heuristic inspired by the phototropic life cycle of sunflowers around the sun. Having a much simpler programming model than most evolutionary algorithms, MOSFO was validated using the most difficult set of test functions in the literature (CEC 2009) and applied to ten constrained multi‐objective optimization problems (CEC 2021). The proposed algorithm was compared with ten other powerful algorithms: MOGWO, MOPSO, NSGA‐II, MOEA/D, NSGA‐III, CCMO, ARMOEA, ToP, TiGE 2, and AnD. Inverted General Distance, Spacing, Maximum Spread, and Hyper volume were used as comparison metrics to evaluate the convergence and coverage capabilities of the algorithms. MOSFO had the best average IGD value in 8 of the 10 test functions when compared with the other algorithms. In terms of MS, MOSFO had the highest average value of MS for 7 of the test functions. In summary, MOSFO showed substantial convergence and coverage capabilities and proved to be very competitive among the algorithms used, which were carefully selected to be popular and recent. The method is even more promising for problems with three or more objectives.

A new two-stage constraint programming approach for open shop scheduling problem with machine blocking

In this paper, a variant of the open shop scheduling problem is considered in which the intermediate storage is forbidden among two adjacent production stages (zero buffer or machine blocking constraint). The performance measure is to minimise the maximal completion time of the jobs (makespan). Since this is an NP-hard problem, a two-stage constraint programming approach is proposed as a new exact method. Computational experiments were carried out on 222 literature problem instances in order to test the performance of the proposed algorithm. The relative deviation is adopted as the performance criteria. Computational results point to the ability of the proposed method to solve large-sized instances in comparison with the developed mixed-integer linear programming model and a simple constraint programming model, both with user cuts. In all set of instances, the proposed two-stage method performed better than benchmarking methods and integer programming models, with average relative deviation regarding objective values as lower as 12%. In addition, the results point to a competitive efficiency in computational times of the proposed method with less than 200 s in the most instances to obtain the optimal solution, in comparison to competitive metaheuristics from literature of the problem, for the tested test instances.

A parametric neutrosophic model for the solid transportation problem

PurposeThis research attempts to present a solid transportation problem (STP) mechanism in uncertain and indeterminate contexts, allowing decision makers to select their acceptance, indeterminacy and untruth levels.Design/methodology/approachDue to the lack of reliable information, changeable economic circumstances, uncontrolled factors and especially variable conditions of available resources to adapt to the real situations, the authors are faced with a kind of uncertainty and indeterminacy in constraints and the nature of the parameters of STP. Therefore, an approach based on neutrosophic logic is offered to make it more applicable to real-world circumstances. In this study, the triangular neutrosophic numbers (TNNs) have been utilized to represent demand, transportation capacity, accessibility and cost. Then, the neutrosophic STP was converted into an interval programming problem with the help of the variation degree concept. Then, two simple linear programming models were extracted to obtain the lower and upper bounds of the optimal solution.FindingsThe results reveal that the new model is not complicated but more flexible and more relevant to real-world issues. In addition, it is evident that the suggested algorithm is effective and allows decision makers to specify their acceptance, indeterminacy and falsehood thresholds.Originality/valueUnder the transportation literature, there are several solutions for TP and STP in crisp, fuzzy set (FS) and intuitionistic fuzzy set (IFS) conditions. However, the STP has never been explored in connection with neutrosophic sets to the best of the authors’ knowledge. So, this work tries to fill this gap by coming up with a new way to solve this model using NSs.

Managing Advance Admission Requests for Obstetric Care

This paper is devoted to the management of advance admission requests for obstetric care. Pregnant women in China select one hospital and request admission for both antenatal and postnatal care after nine weeks of pregnancy. Schedulers must make the admission decision instantly based on the availability of the most critical resource, that is, hospital beds for postnatal care. The random delay between admission requests and postnatal care has created a distinct advance admission control problem. To address this issue, we propose a basic model that assumes a unit bed requirement for one day. Each admission generates a unit of revenue and each unit of overcapacity use incurs an overcapacity cost. With the objective of maximizing the expected net revenue, we establish an optimal policy for unlimited requests, that is, an expected arrival time quota (EATQ) policy that accepts a fixed quota of advance admission requests with the same expected date of confinement. We then propose an extended model for general capacity requirements. Using the Poisson approximation, we establish the optimality of the EATQ policy, which is shown to be solvable by a simple linear programming model. We compare the numerical results from the different policies and conduct a sensitivity analysis. The EATQ policy is demonstrated to be the best option in all test instances and notably outperforms the current admission rules used in hospitals, which usually accept admission requests according to some empirical monthly quota of the expected delivery month. The Poisson approximation is shown to be effective for determining the optimal EATQ policy for both stationary and nonstationary arrivals. Summary of Contribution: First, this paper investigates the advance admission control problem for obstetric care. Pregnant women in China choose one hospital and request admission for both antenatal and postnatal care after nine weeks of pregnancy but the most critical resource is hospitalization beds needed for postnatal care. The random delay between admission request and postnatal care makes the problem unique and challenging to solve. It belongs to the scope of computing and operations research. Second, this paper formulates a dynamic programming model, analyzes the structural properties of the optimal control policy, and finally proposes a mathematical programming model to determine the optimal quota. Numerical experiments show the validity of the proposed approach. It covers the research contents of theories on dynamic stochastic control, mathematic programming model, and experiments. Moreover, this paper is motivated by the practical problem (advance admission control) in obstetric units of Shanghai. Using these optimality properties, solution approaches, and numerical results, this paper provides guidance on how to manage advance obstetric admission requests.

수량할인을 포함하는 여정기반의 항공사 수익경영 문제

Revenue Management problems have been studied widely and actively in airlines for many decades, especially focusing on a seat assignment problem, a booking control problem and a dynamic pricing problem. Traditionally, most of seat revenue management problems in airlines have focused on the seat assignment and booking control for one-way flight. However, most booking requests for air travel are occurred a round-trip basis. In the traditional model, the round trip itinerary was divided into two one-way itineraries independently and the seat assignment problem based on the each itinerary demand has been studied individually. The traditional RM approach is no longer suitable for managing seat assignments and booking controls on round trip itineraries. Furthermore, it is common to travel in small groups rather than individuals, and airlines may consider offering discount fares (quantity discount fares) for such groups which can be found in many retail shopping and recognized to induce more demand. In this paper, we deal with the itinerary based revenue management problem focusing on the seat assignment and booking control with quantity discount fares. Our problem has not been studied effectively in the previous researches. We will suggest an optimization model and a greedy-type heuristic to determine the seat assignment for our complex problem. Based on the seat assignment solution, we will also suggest an effective booking control method to decide the acceptance or the rejection for a seat request. Even though the seat assignment model can be modeled as a probabilistic Integer programming model, we show it can be transformed to the simple Integer programming model by applying Linear Approximation technique. For itinerary-based booking control, we also suggest an effective mixed nesting control method. As a result of the simulation, we can find that our methods make more revenue gains (2.2%～5.1%) compared to the modified traditional ones. Expecially, when the demand is relatively high, the revenue gain is larger. In this study, we suggest some effective approach to solve the problem of seat assignment and booking allocation for itinerary-based demand with small groups, which has not been considered effectively so far. To enhance the applicability of our model in practice, it needs to develop more comprehensive models including diversions, no-shows and behavioral factors in air travelers.

Mathematical models for the minimization of open stacks problem

AbstractIn this paper, we address the minimization of open stacks problem (MOSP). This problem often appears during production planning of manufacturing industries, such as in the cutting of objects to comply with space constraints around the cutting machine in the glass, furniture, and metallurgical industries. During the processing of the cutting patterns, all the copies of a demanded item are stored in a stack usually placed near the cutting machine. One stack for each type of demanded item, that is, different items do not share the same stack. In this sense, the MOSP consists of finding an optimal sequence of a given set of cutting patterns, while minimizing the maximum number of simultaneously open stacks. To effectively model and solve the problem, we present a novel integer linear programming (ILP) formulation based on a graph representation of the problem. We derive an ILP formulation from the modeling approach of Faggioli and Bentivoglio for the MOSP. Then we develop a simple constraint programming model based on interval variables and renewable resources. We performed computational experiments to evaluate the proposed approaches in comparison with other ILP formulations from the literature. Using a general‐purpose solver, the proposed approaches perform well in terms of solution quality and computational time in comparison to the benchmark models for small and moderate‐sized problem instances.

ScaleG: A Distributed Disk-based System for Vertex-centric Graph Processing

Designing distributed graph systems has drawn a lot of research interests due to the strong expressiveness of the graph model and rapidly increasing graph volume. Most of them require the graph data and all intermediate messages to reside in main memory, which may sacrifice the scalability. Even though several disk-based systems have been studied to remedy such issue, several challenges still exist in achieving both high computational efficiency and low network communication under the limitation of memory usage. In this paper, we design a novel disk-based distributed graph system, called ScaleG. The system provides a series of user-friendly programming interfaces. Unlike previous systems, the programmer in ScaleG does not need to concern any logic regarding the communication between vertices like sending messages and combining messages. In addition to a simple and clear programming model, we propose several techniques to reduce both disk I/Os in each machine and message I/Os via the network. We manage all messages in memory and bound all messages by the number of vertices. We also carefully design the data structure to support partial computation and automatic vertex activation. We conduct extensive experiments on six big graphs to show the high efficiency of our system.

A framework of hiring strategy for manpower hiring in a hyper-local food delivery organization

PurposeThe present work attempts to determine an appropriate number of different categories of Delivery Persons for a Hyper-local Food Delivery Organization for different intervals within a day and across days within a week which would provide a satisfactory level of service to the target customers and at the same time would become cost-efficient.Design/methodology/approachCurrently the firm estimates the required number of Delivery Persons for “lunch peak” and “dinner peak” of the next week's weekdays and weekend based on the maximum number of orders occurring during the same period of both weekdays and weekend in the current week. The proposed approach involves determining the projected demand in every four-hourly interval of both week-days and weekend in the next week. Subsequently, the study has developed a simple integer programming model for determining the optimum number of Delivery Persons based on the projected demand data.FindingsThe existing approach followed by the firm indicates that the Delivery Persons remain unutilized during periods of low demand. The proposed model demonstrated savings to the tune of 21.4% in manpower cost without any erosion in the service level.Originality/valueThe study has made three tangible contributions. First, the development of a simple methodology for estimating the demand of next period allows the Managers to utilize dynamic demand data. Second, the development of a simple integer programming model helps managers determine an appropriate number of Delivery Persons in different intervals in both weekdays and weekend. Third, the development of a framework of hiring strategy aids managers in adopting a particular hiring strategy under a particular context keeping in mind the magnitude of demand for food, demand for delivery service and the cost of providing the service.

Revenue Sharing in Airline Alliance Networks

This paper takes an axiomatic approach to the revenue sharing problem for an airline alliance network. We propose a simple sharing rule that allocates the revenue of each flight equally among the carriers of the flight. We show that it is the only rule satisfying the axioms of separability, the null airline property and equal treatment of equals. We show that the rule coincides with the Shapley value of the game associated with the problem. We provide two extensions of the rule, allowing it to depend on the lengths or the capacities of the flight legs. We also consider the maximum revenue problem for the airline alliance. We propose a simple integer linear programming model. We examine its Owen set. Lastly, we provide an algorithm to compute both the optimal solution and the revenue sharing solution given by the simple sharing rule for the maximum revenue problem.

Distributed Interactive Visualization Using GPU-Optimized Spark.

With the advent of advances in imaging and computing technologies, large-scale data acquisition and processing have become commonplace in many science and engineering disciplines. Conventional workflows for large-scale data processing usually rely on in-house or commercial software that are designed for domain-specific computing tasks. Recent advances in MapReduce, which was originally developed for batch processing textual data via a simplified programming model of the map and reduce functions, have expanded its applications to more general tasks in big-data processing, such as scientific computing, and biomedical image processing. However, as shown in previous work, volume rendering and visualization using MapReduce is still considered challenging and impractical owing to the disk-based, batch-processing nature of its computing model. In this article, contrary to this common belief, we show that the MapReduce computing model can be effectively used for interactive visualization. Our proposed system is a novel extension of Spark, one of the most popular open-source MapReduce frameworks, which offers GPU-accelerated MapReduce computing. To minimize CPU-GPU communication and overcome slow, disk-based shuffle performance, the proposed system supports GPU in-memory caching and MPI-based direct communication between compute nodes. To allow for GPU-accelerated in-situ visualization using raster graphics in Spark, we leveraged the CUDA-OpenGL interoperability, resulting in faster processing speeds by several orders of magnitude compared to conventional MapReduce systems. We demonstrate the performance of our system via several volume processing and visualization tasks, such as direct volume rendering, iso-surface extraction, and numerical simulations with in-situ visualization.

Managing sensor data streams in a smart home application

A challenge in developing an ambient activity recognition system for use in elder care is finding a balance between the sophistication of the system and a cost structure that fits within the budgets of public and private sector healthcare organisations. Much activity recognition research in the context of elder care is based on dense networks of sensors and advanced methods, such as supervised machine learning algorithms. This paper presents the data processing aspects of an activity recognition system based on a simpler, knowledge-based unsupervised approach, designed for a sparse network of sensors. By structuring sensor data management as a streaming system, we provide a simple programming model for the application logic, which facilitates building a fault-tolerant system with the potential for distributed data management within the sensor network. The system, evaluated by a public sector healthcare organisation, constitutes an example of a system that is useful and has a sustainable cost structure.

Necessary and possible interaction between criteria in a 2-additive Choquet integral model

This paper deals with the interpretation of the 2-additive Choquet integral model in the context of Multiple Criteria Decision Making. When the set of alternatives is discrete, using classical interaction indices proposed in the literature may lead to interpretations that are not robust. Indeed, the sign of these indices may depend upon the arbitrary choice of a numerical representation within the set of all possible numerical representations. We tackle this problem in two ways. First, in the context of binary alternatives, we characterize the preference relations for which the problem does not occur. Outside the framework of binary alternatives, we propose a simple linear programming model allowing one to test for robust conclusions concerning the sign of interaction indices. We illustrate our results on a real world example in the domain of health.

A Lightweight Dynamic Enforcement of Privacy Protection for Android

Inter-process communication (IPC) provides a message passing mechanism for information exchange between applications. It has been long believed that IPCs can be abused by malware writers to launch collusive information leak using two or more applications. Much work on privacy protection focuses on the simple information leak caused by the individual applications and lacks effective approaches to preventing the collusive information leak caused by IPCs between multiple processes. In this paper, we propose a hybrid approach to prevent the collusive information leak based on information flow control. Our approach combines static information flow analysis and dynamic runtime checking together. Information leak caused by individual processes is prevented through static information flow control, and dynamic checking is done at runtime to prevent the collusive information leak. Such a combination may effectively reduce the runtime overhead of pure dynamic checking, and reduce false-alarms in pure static analysis. We develop this approach based on an abstract and simplified programming model, and formalize a novel definition of the leak-freedom property as our target security property. A simulation-based proof technique is used to prove that our approach is able to guarantee leak-freedom. All proofs are mechanized in Coq.

Foreword to the special issue of the workshop on high performance computing systems (XVIII Simpósio em Sistemas Computacionais de Alto Desempenho, WSCAD 2017)

Foreword to the special issue of the workshop on high performance computing systems (XVIII Simpósio em Sistemas Computacionais de Alto Desempenho, WSCAD 2017)

FluidDyn: A Python Open-Source Framework for Research and Teaching in Fluid Dynamics by Simulations, Experiments and Data Processing

FluidDyn is a project to foster open-science and open-source in the fluid dynamics community. It is thought of as a research project to channel open-source dynamics, methods and tools to do science. We propose a set of Python packages forming a framework to study fluid dynamics with different methods, in particular laboratory experiments (package fluidlab), simulations (packages fluidfft, fluidsim and fluidfoam) and data processing (package fluidimage). In the present article, we give an overview of the specialized packages of the project and then focus on the base package called fluiddyn, which contains common code used in the specialized packages. Packages fluidfft and fluidsim are described with greater detail in two companion papers, Mohanan et al. (2018a,b). With the project FluidDyn, we demonstrate that specialized scientific code can be written with methods and good practices of the open-source community. The Mercurial repositories are available in Bitbucket (https://bitbucket.org/fluiddyn/). All codes are documented using Sphinx and Read the Docs, and tested with continuous integration run on Bitbucket, Pipelines and Travis. To improve the reuse potential, the codes are as modular as possible, leveraging the simple object-oriented programming model of Python. All codes are also written to be highly efficient, using C++, Cython and Pythran to speedup the performance of critical functions.

PRODUCTIVITY ESTIMATION OF SERVERLESS COMPUTING

Cloud computing has enabled organizations to focus less on their IT infrastructure and more on their core products and ser-vices. In fact, Cloud is no longer viewed as an alternative to hosting infrastructure. Serverless computing is a technology, also known as function-as-a-service, that gives the cloud provider complete management over the container function run on as necessary to serve requests. As a result, the architectures remove the need for continuously running systems and serve as event driven computing. Serv-erless computing presents new opportunities to architects and developers of Cloud-oriented solutions. Primarily, it provides a simpli-fied programming model for distributed Cloud-based systems development, with the infrastructure abstracted away. It is no longer the concern of the developer to manage load balancers, provisioning and resource allocation (although system implementers need to be aware of such things). This reduced focus on operational concerns should allow greater attention to be paid to delivering value, functionality and an ability to adapt rapidly to changes. Such issues as deployment, monitoring, quality of service and fault tolerance are moved into the hands of the Cloud provider and still need to be actively considered and managed. Serverless computing is still in its infancy and while the model matures further, tools will be created to allow developers and architects to create patterns and pro-cesses to fully exploit the advantages of the Serverless model. This paper explores the performance profile of a Serverless ecosystem under low latency and high availability. The results of application and performance tests for image recognition by using neural net-works are presented. The proposed implementation uses open source libraries and tools: TensorFlow for the study of machine learn-ing and LabelImg for data preparation. A correlation between the amount of experimental training data and recognition accuracy is studied and shown. For experiments, the software package was developed using the Python scripting programming language and .Net technology. The developed software showed excellent accuracy of recognition using regular computer with low-cost hardware. Inter-action of the client side with the “server” is carried out using HTTP-requests in any browser with low-speed network connection.