Operations Research Techniques Research Articles

Precise Worst-Case Blocking Time of Tasks Under Priority Inheritance Protocol

The problem of precisely computing the worst-case blocking time that tasks may experience is one of the fundamental issues of schedulability analysis of real-time applications. While exact methods have been proposed for more sophisticated protocols, the problem is indeed complex in case of the Priority Inheritance Protocol, even restricting the attention to uniprocessor systems, non-nested resource accesses, and non-self-suspending tasks. Besides a very simple method leading in general to loose upper bounds, only one algorithm of exponential complexity has been so far reported in literature to tighten such bounds. In this article, we describe a novel approach which, leveraging an operational research technique for modeling the problem, computes the same tight bounds in polynomial time. We then discuss the scenarios in which, assuming no conditional statements in the tasks’ code, the computed bounds derive from an actually impossible blocking chain, and we refine the initial model to more precisely compute the worst-case blocking times for any task set in any possible operating condition.

Editorial for the special issue: “Novel Solutions and Novel Approaches in Operational Research”

Abstract This special issue of Business Systems Research (SI of the BSR) is co-published by the Slovenian Society INFORMATIKA – Section for Operational Research (SSI-SOR) and highlights recent advances in Operations Research and Management Science (OR /MS), with a focus on linking OR /MS with other areas of quantitative and qualitative methods in a multidisciplinary framework. Eleven papers selected for this SI of the BSR present improvements and new techniques (methodology) in Operations Research (OR) and their application in various fields of economics, business, spatial science, smart mobility, higher education, human resources, environment, agriculture and social networks.

Using the maximal covering location problem to design a sustainable recycling network

Recycling networks are essential to achieve sustainability in supply chains. Moreover, the design of recycling networks commonly focuses on reducing the costs of collecting a specific amount of end-of-life products or maximizing profit. We are interested in different scenarios with a non-profit approach where given an initial budget, our goal is to design a recycling network where the economic benefits from selling products for reuse or recycle are used to increase the network capacity so we maximize the amount of collected end-of-life products. We implement classic operations research techniques to represent the decision-making process with a maximal covering facility location problem that determines the following decisions: the number and location of capacitated collection centers with a limited-service area; the flow of end-of-life products from demand centers to collection centers and then, to capacitated recycling plants; and the amount of collected products sold to the secondary market. We design a mixed-integer linear formulation for our optimization problem, which can be solved with commercial solvers in short computational times for most of the large instances considered in our experimental stage. Numerical results on a case study in Brazil show that our optimization approach is a handy tool to design a recycling network and identify recycling plants with high low-usage rates.

Machine learning in airline crew pairing to construct initial clusters for dynamic constraint aggregation

The crew pairing problem (CPP) is generally modelled as a set partitioning problem where the flights have to be partitioned in pairings. A pairing is a sequence of flight legs separated by connection time and rest periods that starts and ends at the same base. Because of the extensive list of complex rules and regulations, determining whether a sequence of flights constitutes a feasible pairing can be quite difficult by itself, making CPP one of the hardest of the airline planning problems. In this paper, we first propose to improve the prototype Baseline solver of Desaulniers et al. (2020) by adding dynamic control strategies to obtain an efficient solver for large-scale CPPs: Commercial-GENCOL-DCA. These solvers are designed to aggregate the flights covering constraints to reduce the size of the problem. Then, we use machine learning (ML) to produce clusters of flights having a high probability of being performed consecutively by the same crew. The solver combines several advanced Operations Research techniques to assemble and modify these clusters, when necessary, to produce a good solution. We show, on monthly CPPs with up to 50 000 flights, that Commercial-GENCOL-DCA with clusters produced by ML-based heuristics outperforms Baseline fed by initial clusters that are pairings of a solution obtained by rolling horizon with GENCOL. The reduction of solution cost averages between 6.8% and 8.52%, which is mainly due to the reduction in the cost of global constraints between 69.79% and 78.11%.

The green vehicle routing problem: A systematic literature review

Recent decades have seen increasing utilization of optimization packages, based on Operations Research and Mathematical Programming techniques, for effective management of the provision of goods and services in distribution systems. Large numbers of real-world applications have widely shown that the use of computerized procedures for distribution process planning produces substantial savings (generally from 5% to 20%) in global transportation costs. The impact of these savings on the global economic system is significant. The transportation process involves all stages of production and distribution systems and represents a relevant component (generally from 10% to 20%) of the final cost of goods. The green vehicle routing problem (GVRP) is an emerging research field that attracts many researchers. This survey paper aims to classify and review the literature on GVRPs from various perspectives. This paper covers publications between 2006 and 2019 including 309 papers. To this end, a systematic literature review has been implemented in order to respond to corresponding questions related to this area. This study proposes an extensive structure compromising various aspects including variants of GVRPs, objective functions, uncertainty, and solutions approach to analyze GVRPs studies from different perspectives. Some new research areas have been drawn based on problem classification, uncertainties, solution methodologies, and finally, the objective function approaches for future research directions. The results of this study show that researches on GVRPs are relatively fresh and there is still room for large improvements in several areas.

An overview of air delay: A case study of the Brazilian scenario

Delay is a key point in air transportation activity. As a performance metric, it affects common policy concerns. Delay impacts passenger satisfaction and imposes costs. The complexity that sets in for the air traffic manager is how to mitigate delay, especially in an environment with several stakeholders. The present article applied a problem-structuring method (PSM), named value-focused thinking (VFT), to structure the problem of the air traffic flow management arrival delay. The inflexibility of incorporating a flight operator's specific needs is considered one of the reasons for the limited success of air traffic flow management (ATFM) programs. PSM allows participants to clarify their dilemmas, converge on a mutually liable problem, or agree to the proposed solutions and compromise on what partially solves the issue. The problem is that most papers focus only on the applied solution for air delay mitigation. Before implementing operational research techniques, we investigated the nature and characteristics of air delay. Results showed that there were several stakeholders with distinctive requirements for their business and many of their objectives are interconnected. The use of VFT provided an objective map that can be used as a guide for future solutions.

Use of efficient task allocation algorithm for parallel real-time EMT simulation

Real-time EMT (Electromagnetic Transients) simulation relies on multi-cores computers to accelerate the simulation through parallelization. It also increases simulation accuracy by allowing a lower time step. First, the network has to be split into several tasks using a separation technique. Then, each task has to be allocated/mapped to a processor. This paper focuses on this problem which can be formulated as a TAP (Task Allocation Problem). To find optimal task allocation, operational research techniques can be used. Heuristics such as graph partitioning allow getting fast solutions. Their performances are asserted with very large networks and real-time simulator architectures, both from TSO (Transmission System Operator) grids. Exact resolution methods are used to verify solution quality. The validation of each task mapping strategy is done through a real EMT case study which involves real-time Hardware-in-the-Loop simulation.

Operations Research Problems and Data Envelopment Analysis in Agricultural Land Processing – A Review

Research Question: This paper aims at specifying the contribution of operations research (OR) methods and techniques to agricultural land processing. Motivation: Agricultural production is performed on an agricultural land, which has to be exploited in the best possible way, given the increasing human population and the limited availability of the land. Considering the importance of this issue, a large number of research studies dealing with problems in agriculture can be found in the literature, and many of these problems are solved by OR methods and techniques. However, to our knowledge, there are no review papers that deal with this specific area, so the main motivation is to provide a detailed review of selected OR methods application in the agricultural land processing area. Idea: The core idea behind this research is to perceive a real impact of OR methods and techniques implementation in the agricultural land processing. The research is based on detailed literature review for the period 2014-2019 and performed statistics involving publication by year, publication by journal and statistics involving keywords in articles. Data: The review was conducted using online repositories of the papers published in SCI and SCIe journals with impact factors in the period from 2014-2019. Tools: Analyzed papers are divided into three groups according to the OR method applied: linear optimization problems, DEA method and other OR methods (non linear, multicriteria, mixed integer programming, dynamic programming). Papers within the groups are analyzed according to the type of problems solved. Statistical analyses of all collected data were used to get a good insight into the applications of operations research problems and data envelopment analysis in agricultural land processing. Findings: The number of published papers in this specific area has a growing trend over the observed years (with some minor decrease in 2016 and 2019 in comparison with the previous year). All of the articles are related to specific application of the given methods to solving problems in the agricultural land processing, and this is the reason for many different keywords appearing in the articles. Some very important keywords such as “operations research” or “OR” does not appear in any article as a keyword. Inclusion of such common keywords may result in a faster search in repositories of all articles. Contribution: The primary contribution of this paper is a detailed review of application of linear optimization, data envelopment analysis and other OR methods in agricultural land processing in the period 2014-2019.

A Heuristic Methodology for Optimal Deployment of Radar Systems in a Constrained Area of Operation

In emerging network-centric warfare scenarios, the location of sensors in the sensor-grid plays a dominant role in determining the effectiveness of air defence against enemy air threats. Maximising the coverage area of sensors in the sensor-grid, considering operational performance parameters, terrain features and deployability is a challenging task for military operational planners and commanders. Such optimisation problems may not be amenable to classical operations research techniques, or may require enormous computational time to arrive at the results as the decision space grows non-linearly for large areas of operation. In this paper, a novel methodology that uses a heuristic technique (genetic algorithms) to compute the optimal or near-optimal deployment locations for a given set of sensors in a constrained area of operation is proposed. The proposed methodology is illustrated with a number of case studies and a decision support tool is developed as an aid to the military commanders.

Analisa Optimasi Analisa Optimasi Diameter Jaringan Pipa Distribusi Air Bersih Dengan Menggunakan Software EPANET, LINGO di Jalur Sentul City PDAM Tirta Kahuripan Kabupaten Bogor

One of the most important roles of water system supply is piping networking. Performance of water supply sistem service is determined not only by a capacity from the source, but also by water volume which must also be up to the vertices with high flow and adequate pressure as needed. Besides, the effectiveness of a pipline is measured from the cost of investment and operational cost over life of specified order. In order to achieve adequate pressure and flow conditions it must be analyzed the size carefully. Linear programming represents the completion of operations research techniques that can solve optimization problems. Linear prograsmming is used to minimize cost size of pipe. On this research, a sample informartion taken by a clean water distributin network in PDAM Tirta Khuripan, Bogor, West Java. By using the method of linear programing the result of optimization in PDAM Tirta Khuripan, Bogor, West Java diameter Optimal with Existing diameter ᶲ0.4 m range to ᶲ (0.15-0.35) (m) for pipes with Existing diameter ᶲ 0.25 m range to ᶲ (0.2-0.23) (m) and headloss loss analysis results in numbers / values of headloss that meet the standard which ranges from (0. - 4.0) (m). The draft total cost budget for distribution network optimization using LINGO software is IDR 4.291.967.000,00.

A SIMULATION MODEL FOR CUSTOMER FLOW ANALYSIS IN A COMMERCIAL BANK IN NIGERIA

In view of competitiveness and increasing search for improved services in Nigerian commercial banks, there are some organizational changes necessary to facilitate this fate. Recently, the possibility of applying operational research techniques, more specifically computer simulation, was raised to address the problem of customer queues at bank branches. No bank has been punished with a fine if there are customers waiting for more than a certain period of time in the queues to be served. The solution to the problem sought to offer customer-care to the crowd in all bank branches in Nigeria. In view of this, the present work proposes a computer simulation model to study the flow of customers in a branch of the bank. This model simulates service capacity and time in the face of various hypothetical scenarios to which a bank branch may be subject using the Rockwell Automation Arena® v15 software. The results make it possible to evaluate new policies for increasing the quality of service and compliance with easy customer-care operational service. The generated scenarios were evidence alternatives that would reduce waiting times with only a few minor alterations. Thereby allowing the service with maximum waiting time the standards required.

Application of Operation Research Techniques in Agriculture

Application of Operation Research Techniques in Agriculture

A robust augmented \u03b5-constraint method (AUGMECON-R) for finding exact solutions of multi-objective linear programming problems

Systems can be unstructured, uncertain and complex, and their optimisation often requires operational research techniques. In this study, we introduce AUGMECON-R, a robust variant of the augmented ε-constraint algorithm, for solving multi-objective linear programming problems, by drawing from the weaknesses of AUGMECON 2, one of the most widely used improvements of the ε-constraint method. These weaknesses can be summarised in the ineffective handling of the true nadir points of the objective functions and, most notably, in the significant amount of time required to apply it as more objective functions are added to a problem. We subsequently apply AUGMECON-R in comparison with its predecessor, in both a set of reference problems from the literature and a series of significantly more complex problems of four to six objective functions. Our findings suggest that the proposed method greatly outperforms its predecessor, by solving significantly less models in emphatically less time and allowing easy and timely solution of hard or practically impossible, in terms of time and processing requirements, problems of numerous objective functions. AUGMECON-R, furthermore, solves the limitation of unknown nadir points, by using very low or zero-value lower bounds without surging the time and resources required.

An integrated multi-objective optimization modelling for sustainable development goals of India

Modelling for long-term goals involving multiple factors and criteria often require incorporating decision-makers preferences to realize optimum satisfaction. Goal programming (GP) is an operational research technique that is relevant to analysing decision-making problems with multiple competing and conflicting objectives. Multi-objective goal programming approach takes advantage of striking the trade-off between the overachievement and underachievement of the decision-makers future aspirations. The concept of GP with a satisfaction function integrates the preference of the decision-makers explicitly. In this paper, we proposed a multi-objective optimization model integrating economic growth, electricity consumption, greenhouse gas emission and the number of employees across the primary, secondary and tertiary sectors of Indian economy using the concept of GP with a satisfaction function. The model validated with data from the three economic sectors, and the results provided a quantitative justification for achieving economic growth, electricity consumption, with optimal employment strength across the sectors, for the sustainable development goals of India vision 2030. Also, a strong suggestion for improvement and encouragement in the use of renewable energies such as wind and solar and reduction in fossil fuels utilization to arrest the high emission tendencies shortly was evidence by the solution.

Mathematical modeling to optimize pine lumber yield

In Brazil, only 4% of the 7.84 million hectares of planted forests is devoted to the production of lumber wood, being in Santa Catarina state most of the wood used for this purpose is of the Pinus genus. This work aims to estimate the maximum utilization of logs due to the production of wood boards in the city of Canoinhas, Santa Catarina. For that, a sawmill of the region were consulted and the dimensions of the pieces produced were verified. The dimensions and classes of logs commonly traded in the region were also raised. As a result, 82 models were created in Maxitora software in diagram format. With the cutting models the sawmill had its performance optimized with the use of techniques of operational research in a cutting problem. The whole linear programming technique was used for an estimated demand for the quantity of each of the pieces produced. The results showed that only five models are required to meet such demand, so the yield is 43.18%.

New Algorithms for Secure Outsourcing of Large-Scale Systems of Linear Equations

Cloud computing is the on-request accessibility of computer system resources, specially data storage and computing power, without direct dynamic management by the client. In the simplest terms, cloud computing means storing and accessing data and programs over the Internet instead of your computer’s hard drive. Along the improvement of cloud computing, more and more applications are migrated into the cloud. A significant element of distributed computing is pay-more only as costs arise. Distributed computing gives strong computational capacity to the general public at diminished cost that empowers clients with least computational assets to redistribute their huge calculation outstanding burdens to the cloud, and monetarily appreciate the monstrous computational force, transmission capacity, stockpiling, and even reasonable programming that can be partaken in a compensation for each utilization way Tremendous bit of leeway is the essential objective that forestalls the wide scope of registering model for clients when their secret information are expended during the figuring procedure. Critical thinking is a system to arrive at the pragmatic objective of specific instruments that tackles the issues as well as shield from pernicious practices.. In this paper, we examine secure outsourcing for large-scale systems of linear equations, which are the most popular problems in various engineering disciplines. Linear programming is an operation research technique formulates private data by the customer for LP problem as a set of matrices and vectors, to develop a set of efficient privacy-preserving problem transformation techniques, which allow customers to transform original LP problem into some arbitrary one while protecting sensitive input/output information. Identify that LP problem solving in Cloud component is efficient extra cost on cloud server. In this paper we are utilizing Homomorphic encryption system to increase the performance and time efficiency.

Application of the Hungarian Method for Assigning Workers to Ciptaland Development

Assignment of workers is something that is needed by the company. But in reality the company is difficult to place its employees in each allocation. However, with the development of existing technology, this can be minimized by calculating the cost of each work and each allocation by utilizing existing data and extraction to produce new knowledge. The application of operational research techniques using the Hungarian method and tested using a system or application as a decision tree to assign some decent workers to be placed on jobs within the company. Therefore the author took the initiative to make a thesis entitled "Application of the Hungarian Method in Assigning Ciptaland Development Workers". The assignment process can show every worker by the company. So that Ciptaland Development can be a reference in making decisions in assigning and allocating workers. In this study, an Assignment model was made to determine the allocation of workers who were fit to be placed. This method is quite efficient in the allocation of workers. In applying this hungarian method can provide solutions or considerations for companies in making decisions

The intellectual structure of the waste management field

Waste management is an important issue in the field of green logistics. It has consequently drawn the attention of the scientific community and has been extensively investigated over the past few years. Through an analysis of the existing waste management literature, we attempt in this paper to better understand past developments in this area as well as emerging trends and recent developments. Emphasis will be put mainly on Operations Research and Management Science techniques when dealing with waste management problems. To reach this target, we follow bibliometric-based methods, specifically Co-citation Analysis, Betweenness Centrality and Burst Detection combined with network visualization. After identifying the research papers published between 1990 and 2018 within the Thomson Reuters Web of Science database, a Co-citation network has been constructed. We propose an algorithm for modularity-based clustering in small networks that iteratively solves a sequence of Mixed Integer Non-linear Programming problems to maximize the modularity therefore providing a non-overlapping partition of the network. A display of the principal research areas and landmark articles that shape the intellectual structure of the waste management problems during the last 30 years is reported.

Survey-based calibration of a parking entry as a single-server mathematical queuing model: A case study

Traffic congestion is observed around shopping malls, mostly, due to the long queue at the parking entry gates. The queuing theory is an operation research technique that mathematically models the queuing systems consisting of randomly arriving costumers to receive service and then depart such as the parking entry gates. For a single-server mathematical queuing model, the inter-arrival time is usually assumed to be negative exponentially distributed (Poisson process). Mostly, the service times are assumed to be either similar to inter-arrival, M/M/1, or constant, M/D/1; neither assumptions is correct in terms of producing performance measures that conform with the reality. However, the M/D/1 model seems to perform more closely. This paper proposes an approach to apply the mathematical queuing model for such system more efficiently. Essentially, service time is calibrated to the best fitting distribution based on the data collected from major shopping malls in Alexandria and Giza in 2016 and 2017; respectively. The calibration proves that the service time is normally distributed. The application of the M/D/1 using the mean time of the calibrated normal distribution as the constant service time is then tested. The resulted performance measures show a significant conformity with the performance measures of the field data.

Methodology for Optimizing Sewage Sludge Treatment, Valorization and Disposal Methods. Application to the Marrakech (Morocco) Wastewater Treatment Plant

Wastewater treatment leads to a significant production of about 150 to 200 kg of sludge per population equivalent (P.E) per year, with a humidity level higher than 90%. The treatment and discharge of this sludge involves high costs for wastewater treatment plants (WWTPs), which can amount to up to 60% of their total operating cost. The environmental and public health aspect should also be carefully considered during evacuation as sludge may contain harmful elements. Currently, several treatment, valorization and disposal methods are possible for sludge. In order to respect the various constraints on the possibilities of discharging sludge and to minimize its cost, a reasonable approach must be followed to establish an optimal management path according to the WWTP situation. The objective of this work is to develop a methodology for optimizing sludge treatment, valorization and disposal methods for a given WWTP. This method is based on operational research technique. The methodology follows the following six steps: description of the existing situation, identification of possible treatment, valorization and disposal methods for the WWTP and development of a decision tree specific to the WWTP, mathematical formulation and assumptions, mathematical resolution and sensitivity analysis, validation of the solution and application of the solution. After the application of this methodology, a decision support tool is developed to define the optimal method(s) for sludge management in the WWTP. According to the example of the application of this methodology on sludge management of the WWTP of Marrakech (Morocco), the optimal solution, with respect to the data used and keeping the installations in place, is the application of agricultural spreading after the sludge treatments which are: thickening, anaerobic digestion, mechanical dehydration and solar drying. On a 20-year operation, an average gain of about €10 (110 DHs) per ton of dry solid (DS) incoming is generated.