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In the dynamic expansion of urban population, there arises a pressing need to establish well-defined parameters for parking spaces. The provision of parking plays a pivotal role in both residential complexes and commercial establishments. Ill-conceived roadside parking areas can result in severe traffic congestion, and at times, even lead to accidents. Different car sizes need different parking lot sizes. Therefore, given these factors, adaptable parking solutions have become imperative. Within the framework of the proposed research, the parking spaces in question are envisaged as rectangles, and a mathematical model has been devised within a fuzzy environment. Numerical examples are taken to illustrate the mathematical model. LINGO software is used to solve the mathematical model and MATLAB is used to define the fuzzy variable. At the outset the results will reveal the importance of making the length of the parking space in fuzzy environment. [ABSTRACT FROM AUTHOR]

With the surge in car ownership, the end-of-life vehicles recycling market has shown enormous development potential. As the reverse logistics network for recycling end-of-life vehicles suffers from high operating costs and low recycling rates, there is an urgent need to upgrade the actual recycling measures for end-of-life vehicles to an operable level. This article first uses the OGM (1, N) model to predict the number of end-of-life vehicles in the coming years. At the same time, a reverse logistics network model with the second-hand car market as the recycling centre was constructed with the goal of minimizing the total cost of the end-of-life vehicles reverse logistics network. The network model was simulated using mixed integer programming (MILP), and the optimal solution was solved through LINGO 12 programming. Through an example analysis of Shanghai, it is found that the market of end-of-life vehicles will embrace growth, and it is verified that the optimized reverse logistics network can effectively reduce the operation cost and logistics cost of recycling centres, and can effectively improve the actual recycling rate of end-of-life vehicles. Finally, the optimized site selection results are obtained, and a specific traffic distribution scheme is proposed, which is crucial for promoting cars that meet scrap standards to be recycled through formal channels and reducing logistics costs for recycling enterprises. [ABSTRACT FROM AUTHOR]

Confronted with budgetary constraints and complex sustainment systems in challenging environments, optimization of supply chains has become a necessity. The paper investigates a large body of literature related to integrated and decomposed supply chain problems' modeling, optimization software and mathematical programming, including analytical and heuristic solution methods. The main purpose of this study is to contribute with a review of global optimization models of forward/reverse crisis relief supply chains within tactical and operational levels before developing an Integrated Three-Echelon Multi-Period Multi-Commodity Inventory-Production-Distribution Problem formulation. It aims to minimize the total cost of inventory, production, transportation and shortage penalties from support facilities to satisfy deterministic non-stationary demand of finished and recovered repaired commodities over T periods. Besides functions integration and constraints, no such elaborated decision model has also considered before, simultaneously direct shipments in heterogeneous vehicles with non-stationary demand, hostility attrition, and the loading and safety constraints of heterogeneous commodities clustering and vehicle-commodity compatibility. This work shows how existing modeling approaches are valuable and inspiring to understand and adapt to complex new real-world supply chains with the unique proposed formulation and anticipated upgrades. It receives growing attention worldwide from business, humanitarian and military decision-makers, as well as from automotive industries, to build efficient supply chain strategies, how vital economically, socially and environmentally. This mathematical model leads to an NP-hard mixed integer linear programming (MILP) problem with too many variables and constraints where some promising solution methods are discussed. What we have investigated and modeled so far appeal to a further contribution of a sophisticated solution approach, decomposing it into sub-problems, more tractable through Lagrangian relaxation for lower bounds, or branch-and-price based column generation method. [ABSTRACT FROM AUTHOR]

Copyright of China Forest Products Industry is the property of China Forest Products Industry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In recent years, there has been a growing significance of research on humanitarian logistics for both researchers and practitioners. This research is crucial for aiding relief operations. While there has been extensive study of mathematical models for disaster operations management in the preparedness and response phases, the recovery phase models still need more attention. One of the significant challenges during the recovery phase is the spread of contagious diseases in the affected area, which necessitates the timely and cost-effective transportation of both contagious and non-contagious populations while preventing further casualties and disease spread. The paper proposes a multi-objective solid transportation model with different conveyance types for the relocation process to address these challenges. The proposed multi-objective model seeks to minimize two essential objectives: the cost and time required for relocation, and includes factors such as transportation, penalties, accommodations, medical expenses, halts, refueling, and maintenance. To account for the unpredictability and vagueness of input data in post-disaster scenarios, the proposed model incorporates fuzzy inputs and introduces a novel defuzzification technique that is validated by comparing it with an existing methodology. The research employs optimization techniques using the LINGO optimizing solver and presents a case study and particular cases that provide valuable management insights for improving decision support systems. Among the optimization techniques, namely the Neutrosophic compromise approach, Goal programming, Fuzzy goal programming, and Global criterion method, the optimal solution is obtained using the Neutrosophic compromise approach. The cost and time objective values obtained using the Neutrosophic compromise approach are 2034725 and 3923, respectively. [ABSTRACT FROM AUTHOR]

The purpose of this study was to optimize the distribution route of commercial rice for general logistics companies in Kendari City using the branch and bound method with the help of lingo software. This research was conducted at the Perum Bulog, Southeast Sulawesi Regional Office. Determination of the location of the research is done intentionally (purposive sampling) which considers that Perum Bulog in Kendari has regular customers, distributes its products, and requires optimization of route determination to optimize distribution costs. Variables in this study: (a) location and several customer requests; (b) Bulog's DC location and capacity; (c) the number and capacity of vehicles for the distribution of Bulog's commercial rice; and (d) the distance between Bulog's DC and customers and the distance between Bulog's commercial rice customers using Google Maps in the form of secondary data. Data analysis in research uses: (a) nearest neighbor method, done by starting from the Distribution Center Warehouse/DC Bulog Sultra as the starting point of distribution and then looking for customer points in Kendari City; (and (b) the branch and bound method assisted by the syntax script of the Lingo program. The study results show that the optimal route that can be used in the distribution of Bulog's commercial rice in Kendari City is from DC in Mandonga to all customers in various regions in Kendari City. Thenreturned to DC in Mandonga, where this route experienced a decrease in vehicle mileage from 424.83km to 349.59km. The branch and bound (BnB) method showed that it optimize distribution routes so that the distance traveled by vehicles on each route cluster is reduced.

In this study, we compare the performance of optimization software to solve the bi-objective sectorization problem. The used solution method is based on an approach that has not been used before in the literature on sectorization, in which, the bi-objective model is transformed into single-objective ones, whose results are regarded as ideal points for the objective functions in the bi-objective model. Anti-ideal points are also searched similarly. Then, using the ideal and anti-ideal points, the bi-objective model is redefined as a single-objective one and solved. The difficulties of solving the models, which are basically non-linear, are discussed. Furthermore, the models are linearized, in which case how the number of variables and constraints changes is discussed. Mathematical models are implemented in Python's Pulp library, Lingo, IBM ILOG CPLEX Optimization Studio, and GAMS software, and the obtained results are presented. Furthermore, metaheuristics available in Python's Pymoo library are utilized to solve the models' single- and bi-objective versions. In the experimental results section, benchmarks of different sizes are derived for the problem, and the results are presented. It is observed that the solvers do not perform satisfactorily in solving models; of all of them, GAMS achieves the best results. The utilized metaheuristics from the Pymoo library gain feasible results in reasonable times. In the conclusion section, suggestions are given for solving similar problems. Furthermore, this article summarizes the managerial applications of the sectorization problems.

Municipal solid waste (MSW) management is a critical issue in the Kingdom of Saudi Arabia (KSA). Management of solid waste in crowded conditions, especially in main cities such as the Al-Taif Province. This paper deals with that case study and focuses on how to solve material handling problems by applying conventional methods and computer ability. One of the costliest operations in the civil sector is transportation for material handling. City mayors considered transportation and materials handling as a subclass of the linear programming technique, thus linear programming problems take into consideration that the main objective is to handle materials from the city centre site to the identified dumping area. There are many methods and tools used to solve the transportation problem (TP) to find out the maximum or minimum in order to reduce cost or maximize profit. The methods related to manual solutions and others used many different computer applications. Manual solutions include the northwest corner method, the minimum cost method, the row minimum cost method, the column minimum cost method, and Vogel's approximation method, and computer applications such as LINGO, Lingo, and solver function using Microsoft Excel. This case study focuses on applying the Microsoft Excel function to find the minimum cost of the transportation system in waste materials to reduce the transportation cost from the city centre to the final destination (dumping area) to be used. The results achieved with the model aided in obtaining optimal solutions and finally, the optimal solutions were checked.

The side effects of climate change have affected most aspects of human life; Therefore, the study of the various effects of climate change is more important than the identification of the climate change itself. Using the 40-year discharge (1978-2017) of the using Kamish River in the WEAP model. The reservoir of the Harsin Dam has been simulated the LINGO model, optimization was performed with the aim of minimizing the shortage of the downstream needs and the effects of climatic scenarios. By applying the RCP climate scenarios, the results indicated a decrease in the inflow of the Harsin Reservoir; however, the output of the two climate models HADGEM2-ao and FGOALS-G2 increased the maximum temperature by 5 and at least by 3 degrees Celsius in future periods. According to the RCP8.5 scenario, the volume of runoff predicted in the periods 2020-2059 and 2060-2099, is less than the runoff predicted under the RCP2.6 scenarios. Application of the runoff prediction by the precipitation-runoff model under the RCP2.6 and RCP8.5 scenarios in the WEAP model caused the reliability index of downstream demands to fall below 80%. The highest value of reliability index by applying the climate scenarios using the HADGEM2-ao model under the RCP2.6 scenario is related to the industry demand, which in the simulation mode is equal to 77.08%, while this index in the optimal model is 88.42%. The lowest value of the reliability index in the WEAP model was estimated at 32.19%; it attained 52.71% using the LINGO model.

Copyright of Rudarsko-Geolosko-Naftni Zbornik is the property of Faculty of Mining, Geology & Petroleum Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper examines the Goal Programming (GP) approach in food production planning in order to further enhance and find better solutions. The objective of this paper is to determine the optimum level of frozen food production for small and medium enterprise (SME). Azali Frozen Food, a small and medium enterprise located in Penang was selected as it can produce a range of frozen foods throughout the country. The problem is handled through Lexicographic Goal Programming. The results are compared to the available data that was given and other findings were reviewed. The findings of this paper are expected to assist community small and medium enterprise and other decision makers involved in production planning. The developed method will also be of use for those who are interested in the model of goal programming to solve complex planning issues involving uncertain parameters.

This paper examines the environmental impact produced by multi-vehicle transportation on a sustainable supply chain (SC) network. The relevance of green principles is gaining momentum day by day, which has forced the governments to introduce carbon emission schemes for the transportation associated with the firms. Various countries around the globe are introducing carbon-pricing schemes, in which a carbon tax is imposed based on the amount of anthropogenic emissions. A firm, which sets environmental standards for the emission associated with its operational activities, should design a transportation network based on the trade-off between its economic efficiency and the carbon emission. In this paper, the main focus is to design a sustainable supply chain network. A mixed-integer-non-linear-programming (MINLP) model is formulated to minimize the overall cost incurred in a multi-vehicle, multi-product sustainable transportation network. The meta-heuristic approach i.e., Hybrid Chemical Reaction Optimization Algorithm with Tabu search (CRO-TS) and LINGO solver have been used to solve the proposed model. This analysis can guide the government to encourage the logistics service providers to capitalize on anthropogenic gas emission systems and simultaneously design the tax policy on carbon emission. [ABSTRACT FROM AUTHOR]

Kendang djembe is a percussion instrument played by striking with the fingers and palms. The body of the kendang djembe is generally made of wood and shaped like a cup or mug, carved either by machine or traditionally only by hand. This problem affects the production costs, the employee working house, and the profit of kendang djembe. Customer orders and requirements determine the production process for kendang djembe. It leads to fluctuations in market demand, affecting production costs and the working hours of kendang djembe employees. As a result, employees work overtime when orders rush in, resulting in poor product finishes such as crude engraving and painting. This research aims to minimize the production cost of kendang djembe, maximize the employee working hours, and maximize the profit by using the goal programming method. Goal programming is applied to decide the number of kendang djembe, the minimum production cost, and the time for each kendang djembe. The result of this research is that the company has to make 237 units of kendang djembe paintings, 1266 kendang djembe carvings, 870 kendang djembe painting carvings, and 91 kendang djembe deep carvings. The company spent a production cost of Rp 399,413,400, with the employee working 1846.36 hours, and obtained a maximum profit of Rp 126,526,600. This research helps the company to avoid unprofitable options in the production process of kendang djembe. [ABSTRACT FROM AUTHOR]

Recreational mathematics is the term frequently used to refer to applications of mathematics for fun. It is used to refer to games and puzzles and thus has a much broader appeal to students and the general public than would mathematics. The purpose of this paper is to illustrate how a classic mathematical puzzle, written by Sam Loyd, which appeared in Sam Loyd's Cyclopedia of 5000 Puzzles Tricks and Conundrums with Answers can be utilized in the pedagogy of engagement with and understanding of critical thinking and development of mathematical skills. [ABSTRACT FROM AUTHOR]

Groundwater-monitoring network is a set of boreholes (wells) that is used to monitor the water table fluctuation and to detect groundwater contamination. In this research, the maximal covering location problem (MCLP) method is employed to discretize the area, and the LINGO modeling program is used to optimize the number of boreholes. Tabriz oil refinery at the northwest of Iran with high pollution potential that imposes a serious threat to the beneath multilayered aquifer was chosen to evaluate the feasibility of these techniques in field scale. The location and content of storage tanks, leakage history, groundwater flow direction, contaminated well location and the facilities leakage potential are considered as the weighting factors to calculate the number and location of the optimal wells. As a result of optimization, the initial estimated number of boreholes by the MCLP model for the study area is reduced from 349 to 184. A high density of optimal boreholes is allocated to refining zone and oil storing yard, especially near tanks containing dangerous substances due to their toxicity and potential for contaminating water. A vulnerability zoning map prepared using the analytical hierarchy process method indicates a suitable conformation between locations of the boreholes and the vulnerable areas. HIGHLIGHTS A multicriteria method is introduced to design and optimize the aquifer-monitoring network.; A reliable optimized monitoring network produces by the combination of MCLP and LINGO methods.; The initial number of 349 monitoring wells is optimized to 184 boreholes in an oil refinery.; A vulnerability zoning map prepared by the AHP method shows a suitable conformity with the density of boreholes.;

This paper proposes a green closed-loop supply chain network (GCSN) for optimizing closed-loop supply chains. The GCSN focuses on the application of the recycling express box in logistics circulation, accelerates the standardization of logistics operations and the use of express packaging in e-commerce companies, and promotes the reduction and greening of recycling express box in the e-commerce industry. The GCSN is represented as a mathematical formulation and implemented using LINGO. Greening, environmental protection, and wisdom are the general trends for promoting the growth of the e-commerce industry. Meanwhile, the price of raw materials has increased owing to a shortage of resources, which emphasizes the need for e-commerce enterprises to develop green packaging. Therefore, this study considers the shared circular packaging launched by e-commerce enterprises as the research object, and integrates the problem of facility positioning and path planning in the logistics system. The conclusion summarizes the significance of this study. [ABSTRACT FROM AUTHOR]

The paper presents different clustering approaches in legal judgments from the Special Civil Court located at the Federal University of Santa Catarina (JEC/UFSC). The subject is Consumer Law, specifically cases in which consumers claim moral and material compensation from airlines for service failures. To identify patterns from the dataset, we apply four types of clustering algorithms: Hierarchical and Lingo (soft clustering), K-means and Affinity Propagation (hard clustering). We evaluate the results based on the following criteria: (1) entropy and purity; (2) algorithm's ability in providing labels; (3) legal expert's evaluation; and (4) experimental complexity. The results demonstrate that the most advantageous approach is Hierarchical Clustering, since it has the best entropy and purity numbers, as well as the least difficulty for the expert to analyze the clusters, and the least experimental complexity. The main contribution of the paper is to show the advantages and disadvantages of each approach, especially to identify labels in unstructured and non-indexed legal texts. [ABSTRACT FROM AUTHOR]

A major fast-food restaurant chain, such as McDonald's, must perform well to maintain its credibility with customers and dominance over other competitors. A fair and balanced shift schedule of workers must be generated to ensure that the workers provide the best service and production for the restaurant. Consequently, this study proposed a fair and efficient workforce schedule at a McDonald's restaurant in Kelantan, Malaysia. Furthermore, the goal programming method and the LINGO software are used in this study to develop the best schedule for the workers over a 28-day period. Five hard constraints and three soft constraints are identified. The primary goal of this study, which demanded the same total workload for each worker, was met. However, the other two goals are not fully achieved but have little impact on the workers due to the 18-hour operation and rotation of schedules among workers. Finally, the generated schedule pattern has been shown to provide a better schedule in terms of having the same total number of shifts for each worker and giving each worker the same total number of off days. Â

A number of recent or novel metaheuristic algorithms have been explored in the subject of optimization; although now not all of these algorithms are as efficient as their creators claim, a few have been confirmed to be a pretty efficient and useful tool for addressing complex optimization problems. On the other aspect, LINGO is a software tool used for linear, nonlinear, optimization problems. This paper's objective is to study how well the LINGO optimizer performs as contrasted to metaheuristic optimization strategies for over current relay coordination. The operation of directional over current relay is demonstrated with the applicability of LINGO optimizer and metaheuristic strategies. In this study, two distinct case studies, which include a mixed overhead line‐underground cable and a DER primarily based IEC microgrid benchmark, are tested for comparative evaluation among the LINGO optimizer and metaheuristic strategies. During the first stage of case study I, the crow search algorithm (CSA), a novel metaheuristic technique, was proposed, and its results are contrasted with the most popular conventional techniques: Particle swarm optimization (PSO) and water cycle algorithm (WCA). It reveals that implementing the proposed CSA algorithms improved over all time setting concerning the most common traditional techniques such as PSO and WCA. And, after implementing the LINGO optimizer, it is proven that compared with other metaheuristic optimization techniques, the second one proposed LINGO optimizer provides advanced consequences. In a similar manner, both proposed methods were applied and determined their performances in case study II and once more LINGO proved its strength over the metaheuristic approach. [ABSTRACT FROM AUTHOR]

The aim of this paper is to obtain all magic squares of size four, by using an Optimization Model where, in addition to the traditional restrictions (sums for rows, columns and diagonals), other restrictions related to four 2 x 2 submatrices, into which the square can be divided, are taken into account. It is shown that these additional restrictions are included in the traditional ones; therefore, all squares of size four could be classified as supermagic. The problem is represented by means of a Linear Programming Model in Integers with multiple solutions; which, when solved with the help of LINGO software, allowed to find the 880 squares that meet such restrictions. The way squares are presented makes it easy to locate them from characteristics referred to the position of number one. The different combinations for number one in any row for which these squares do not exist, are ordered.

The present study focuses on optimizing the hydropower generation capacity of a multi‐purpose reservoir system considering various constraints, including reservoir water mass balance, minimization of sediment, maximum and minimum reservoir water storage, maximum and minimum reservoir water releases, reservoir water drawdown levels, reservoir overflows, maximum flood zone space, and maximum and minimum storage constraints under 50%, 70% and 90% dependable inflow conditions The focus is the Maithon multi‐purpose reservoir system located at Maithon, a distance of 40.2 km from Dhanbad, the coal capital of India, in the State of Jharkhand. The study results indicate the derived policy is capable of producing maximum annual hydropower of 146,519.48 MWh/year for a 50% dependable inflow condition, 105,512.02 MWh/year for a 70% dependable inflow condition and 58,924.38 MWh/year for a 90% dependable inflow condition, compared to the existing hydropower generation of 129,428.4 MWh/year (average of last 10 years). A trade‐off between reservoir release water releases, mostly for meeting irrigation water needs and maximizing energy generation, was determined for use by various stakeholders and managers of reservoir operations. [ABSTRACT FROM AUTHOR]

The line notations of chemical structures are more compact than those of graphs and connection tables, so they can be useful for storing and transferring a large number of molecular structures. The simplified molecular input line system (SMILES) representation is the most extensively used, as it is much easier to utilise and comprehend than others, and it can be generated automatically from connection tables. A SMILES represents and encodes the molecule structure. It has been used by an existing method, LINGO, to calculate the molecular similarities and predict the structure-related properties. The LINGO method decomposes a canonical SMILES into a set of substrings of four characters referred to as LINGOs. The purpose of LINGO method is to measure the similarity between a pair of molecules by comparing the LINGOs that occur in each molecule. This paper aims to introduce an alternative version of the LINGO method using LINGOs of different lengths, called LINGO-DL. LINGO-DL is based on the fragmentation of canonical SMILES into substrings of three different lengths rather than one in LINGO method. Retrospective virtual screening experiments with MDDR, DUD, and MUV datasets show that the LINGO-DL outperforms the LINGO method, especially when the active molecules being sought have a high degree of structural heterogeneity. [ABSTRACT FROM AUTHOR]

Increasing rivalry for-profit or non-profit is pushing companies to devote more and more attention to pleasing consumers with excellent quality customer services. This study aims to develop a model to analyse customer behaviour in a retail store and provide accurate inference for decision making. Another critical objective for this research work is the adaptation of the faceted form of neuro-response, which is substituted by the Adaptive Fuzzy Logistic Regression Model (AFLRM). AFLRM has resulting benefits over Neuro-surface and Mean Demand Heuristic methods. A sample of 100 customers who visited or walked in the retails was used as a sample. Other than neuroresponse surfaces (NRSM) and The Mean Demand Heuristic models (MDSM), the present study has accustomed a generalized form known as Adaptive Fuzzy Linear Regression Model (AFLRM) to deliver the benchmark for former models and give the highest level of accuracy for future behaviour of a customer. LINGO based Markovian analysis has also been used with the above model to understand the behaviour of the system under study. The significance of service and product attributes is implicitly derived via the fuzzy regression model for customer satisfaction measurement. It is observed that the critical gap between the quality of product and services and Customer Satisfaction is Product/Service Satisfaction, Motivation and Buying Experience, and Credibility and Security. The authors' finding indicates that the effort of listening to the customer's voice should be more critical. Result analysis based on computational results concerning the questionnaire for measuring the customer behaviour and the system validates the model under study. Appropriate, useful with reliable action plans for every critical product and service aspect can be developed by applying the adaptive regression methodology to control the quality of service or managing the customer satisfaction, thereby providing executives with a competitive gain. Also explored the behaviour of the system, i.e., whether the customer will move to the new retail outlets or they will remain in the same state by using the LINGO based software program model. [ABSTRACT FROM AUTHOR]

The government of Sri Lanka established several economic centres in the provinces according to the budget proposals in the year 1998. The Dambulla economic centre was the first such centre that was established on the 01st of April 1999. Thereafter, a number of economic centres were established throughout the island. However, the Dambulla main hub remained the central warehouse of vegetables in the island. This paper deals with a vehicle scheduling problem related to transportation, and investigates a method whereby a solution can be arrived at to overcome the problem using linear programming (LP). Marketing Department Logistics (MDL) Ltd needs to distribute vegetables and fruits to different provinces. Its main hub is situated near the Dambulla vegetable and fruit market, and minor hubs are situated in different provinces in Sri Lanka. The main objective of this research is building a cost minimized model which creates a suitable method for delivering vegetables and fruits from the Dambulla major hub through its minor hubs to outlets in the provinces. Hence, to optimize the cost of outbound distribution, a mathematical model has been developed by using Integer Linear Programming, and by using reliable sources to collect data. Software assistance was obtained using the LINGO 06 optimizer, Java, MS Access and MS Excel tools to solve this mathematical model. This study is based on the Dambulla economic centre. This is an initial step to bring a correct protocol to arrange a transport model to distribute the vegetables and fruits from this centre in a cost-effective way. According to this study, all districts in Sri Lanka could be divided into four clusters. At the beginning of this research, we assumed that each district contains two warehouses and three vendors. This model is flexible enough to be re-scheduled at any request. It paves the way to create a larger model for solving any type of transportation planning problem. [ABSTRACT FROM AUTHOR]

Featured Application: The improved model can effectively adapt to the problem of UAV swarm reconnaissance task allocation. On the basis of ensuring the optimization of reconnaissance reward, the UAV resources should be distributed as much as possible to reasonably complete the overall combat mission. This paper is devoted to the unmanned aerial vehicle (UAV) mission allocation problem. To solve this problem in a more realistic battlefield environment, an improved mathematical model for UAV mission allocation is proposed. Being different from previous formulations, this model not only considers the difference in the importance of the target but also the constraints of the time window. In addition, an indicator of reconnaissance reward is added to this model. Each target area has a different importance, just as the strategic value of each region is different in combat. In this paper, we randomly generate the value factor for each reconnaissance area. To solve the mathematical model with different operational intentions, a dimensionality reduction process for which the reconnaissance reward is the optimization objective is presented. Finally, based on the improved model, the simulation result with Lingo is compared with that of non-dominated sorting genetic algorithm with elite strategy (NSGA-II) and genetic algorithm (GA) to verify the reliability and the effectiveness of the improved method. [ABSTRACT FROM AUTHOR]

Several methods are available when choosing an optimum method and location for wastewater treatment facilities. However, making these two decisions at the same time depends on several factors. Multi-criteria decision making (MCDM) is one of the best options when controversial criteria must be considered. The best answer should be determined using mathematical programming methods. This study was an attempt to find an optimal location for wastewater facilities and an optimal wastewater treatment system for subway stations using mathematical techniques in Lingo software. The study was carried out as an applied quantitative study of the five stations of the eastern end of Tehran subway line 2. Linear binary programming was used to follow the minimizing objectives. Financial, spatial and capacity constraints were notable in this approach. Five scenarios were defined to choose the most appropriate location and the optimum method of wastewater treatment. Given the objective function and the constraints, scenario 1 (collecting all wastewater at Sarsabz station and pumping it to the urban ecosystem) was adopted as the optimum scenario. The study showed that finding the best method and location of a wastewater treatment system is a challenge. The study’s decision-making method can yield the best scenario given the objective functions and constraints. [ABSTRACT FROM AUTHOR]

The problem of internet pricing is a problem that is often a major problem in optimization. In this study, the internet pricing scheme focuses on optimizing the use of bandwidth consumption. This research utilizes modification of cloud model in finding optimal solution in network. Cloud computing is computational model which is like network, server, storage and service that is utilizing internet connection. As ISP's Internet service provider requires appropriate pricing schemes in order to maximize revenue and provide quality of service (Quality on Service) or QoS so as to satisfy internet users or users. The model used will be completed with the help of LINGO software program to get optimal solution and accurate result. Based on the optimal solution obtained from the modification of the cloud model can be utilized ISP to maximize revenue and provide services in accordance with needs and requests. [ABSTRACT FROM AUTHOR]

Femtocells in two-tier femto-macro networks can enhance indoor coverage and improve overall network performance. Macro networks may share spectrum with overlaid femtocells so as to improve spectral efficiency. However, the deployment of femtocells also brings co-tier and cross-tier interferences, which will significantly degrade system performance. In order to solve this problem efficiently, we propose a distributed scheme to manage wireless resources in this heterogeneous networks. The feasible solution can be obtained by dividing the problem into two sub-problems. First, we propose a femtocells clustering scheme, which uses a mathematical modeling idea based on LINGO, an optimization software that can solve the joint clustering problem for the femtocell access points (FAPs). The proposed branch-and-bound algorithm and the simplex algorithm are used jointly to find the optimal solution by LINGO. The optimality of the proposed clustering algorithm is verified both theoretically and through simulations where the comparison with other algorithms is made. Second, a novel algorithm is proposed to allocate sub-channels to the femtocell users (FUEs). Compared with other related schemes, the proposed channel-allocation algorithm can reduce the interference more effectively and achieve higher data-rate fairness among FUEs. Specifically, according to the situation that the FUEs move in the room, the FUE mobility model is proposed to predict the change tendency of path loss values of the FUEs, which can guarantee the mobile service quality and improve system capacity effectively. Finally, the power of the FAPs is adjusted dynamically through setting the interference threshold to further improve the performance of the system.

This study is focused on the application of Genetic Algorithm (GA) as an effective tool for modeling the operation of a multi-purpose reservoir with specific emphasis on Jebba Hydropower Dam, Nigeria. The specific objectives are to study the reservoir operation rule; model the reservoir parameters such as inflow, elevation, turbine release, generating head, energy generation, tailrace water level and plant coefficient. Available Data for 27-year period (1984–2011) was obtained from the Dam Station for statistical analysis. MATLab software for GA was used, and for comparison and check, another similar optimization software (LINGO) was utilized. The optimal solution obtained at operating performance of 50% reservoir inflow reliability has total annual energy generation of 42,105.63MWH. GA for the total annual energy generation at operation performance of 95, 90 and 75% reservoir inflow reliability are 15,964.48 MWH, 21,009.53 MWH and 20,798.58 MWH, respectively. The application of GA will lead to a more realistic and reliable optimal value for the improvement of hydroelectric power generation and flood management, which would guide decision makers in the hydropower sector.

Alternative solution methods for the puzzle Outwitting the Weighing Machine by Sam Loyd are considered. The puzzle provides a great opportunity to study how Excel Solver and LINGO can be used to find solutions to challenging problems. [ABSTRACT FROM AUTHOR]

This paper is devoted to the unmanned aerial vehicle (UAV) mission allocation problem. To solve this problem in a more realistic battlefield environment, an improved mathematical model for UAV mission allocation is proposed. Being different from previous formulations, this model not only considers the difference in the importance of the target but also the constraints of the time window. In addition, an indicator of reconnaissance reward is added to this model. Each target area has a different importance, just as the strategic value of each region is different in combat. In this paper, we randomly generate the value factor for each reconnaissance area. To solve the mathematical model with different operational intentions, a dimensionality reduction process for which the reconnaissance reward is the optimization objective is presented. Finally, based on the improved model, the simulation result with Lingo is compared with that of non-dominated sorting genetic algorithm with elite strategy (NSGA-II) and genetic algorithm (GA) to verify the reliability and the effectiveness of the improved method.

The multi-objective traveling salesman problem (MOTSP) involves one salesman to visit a set of locations (n > 1) so that each location is visited exactly once while satisfying multiple targets of distance, time, cost, etc. To solve problem like this, two different algorithms are introduced and their optimization performances are presented in details by comparisons. On one hand, the genetic algorithm (GA) implemented on Matlab was improved in this paper, which performs well in combination with rotating-disk selection with ranking, one-point crossover and mutation after similarity contrast. On the other hand, the optimization also benefits from application of 0-1 integer programming algorithm with linear and non-linear constraints where Lingo is used. Experimental results reveal both advantages and disadvantages of these two algorithms, and it is concludes that characteristics of the problem itself will decide which algorithm is more suitable when it comes to a special case. [ABSTRACT FROM AUTHOR]

The sequential irrigation scheduling problem is one where a set of irrigation jobs have to be scheduled sequentially. In this paper, an integer program is presented to solve this problem. The objective function is a dual-goal function that avoids night irrigation and minimizes earliness or tardiness, with the former objective taking priority over the latter. A scheduled start time cost function is introduced and is user defined to reflect the behavior and preferences (and aversions) of farmers. In this paper, the scheduled start time cost function is a piecewise linear function and uses techniques from operations research. It is linearized using the disjunctive method. The integer program is implemented in a general-purpose solver and applied to a 16-job problem to demonstrate that a schedule can be prepared that avoids night irrigation and minimizes earliness and tardiness. There is a trade-off between avoiding night irrigation and earliness and tardiness compared to earlier models that minimize only earliness and tardiness. However, the increase in earliness and tardiness for the example presented is modest. [ABSTRACT FROM AUTHOR]

The paper presents a model to analyse the production process of a large international milk-processing company, with the main objective of minimizing total production costs. Process optimization is an important task that must be solved during the strategic and operating steps of the company planning. The proposed methodology visualizes the situations as a linear blending optimisation problem and the model is based on goal programming methodology. The model was implemented using an original software application that is currently used in the company as an operational decision making tool. This system is a MS Excel add-in application written in VBA (Visual Basic for Applications). The experience of the users that are currently using the software is positive and the company shows interest to continue using the application. [ABSTRACT FROM AUTHOR]

Purpose: Nowadays, governments and people pay more attention to use green products due to environmental pollution, irreplaceable energy and shortage of resources. Green products are resulted from the application of green supply chain management strategies to the organizations' performance strategies, so that we can reduce environmental pollutants and wastes and take a step towards saving energy with limited resources. Design/methodology/approach: In this paper, the effect of reducing energy consumption in green supply chain is examined by using queuing theory and transportation models. Data was generated and solved by a commercial optimization epackage. Findings: The findings indicate that suitable assignment of existing transportation fleet with specified capacity, and using queueing theory in a closed-loop network to reduce the queue length and handle congestion, can cause a reduction in energy consumption by optimizing transportation and waiting times in a green supply chain. Originality/value: Adopting investment strategy in improving the environmental performance of the supply chain, will yield in many advantages and benefits. This article investigates the effect of queuing theory on reducing waiting time and optimizing energy consumption in green supply chain. [ABSTRACT FROM AUTHOR]

Few studies have been done on the layout of ditches, and the quantitative models for the optimization of the channel layout is lacking. Since the cost of channel layout project accounts for a large proportion of the cost of land reclamation project, it is necessary to find a design in order to reduce the cost of channel project. In this paper, a new model was developed for optimal layout of canals and ditches. The theory of minimum spanning tree was used to describe the channel layout of land reclamation project, and an optimal model based on minimum spanning tree theory was established in combination with the virtual point, the alternative water intake point and the weighted definition node. The study area was located at a coal mining subsidence area in Yongqiao District, Suzhou City, Anhui Province (33°24'-33°25'N and 117°3'-117°5'E). The channel optimization layout model based on the minimum spanning tree was constructed to optimize the layout of farmland irrigation channels in the plain areas. Different from the traditional minimum spanning tree model, this model solved the optimization problem for two-level channels, and the final solution using this model was greater than or equal to a minimum spanning tree. In order to take advantage of the mature minimum- production-tree approach, the virtual point concept was created to eliminate the difference between the channel optimization layout model and the traditional minimum spanning tree model. Four cases of solving the model were discussed by convex hull theory. The LINGO program was written for solving the model. Finally, the channel optimization model based on the minimum spanning tree was applied to a research area, and then the channel layout scheme using this model was obtained with a total length of 20 523 m containing 1 lateral canal (2 811-m length) and 22 ditches (17 712-m length). According to the traditional optimum principle, there were totally 20 572 m with 2 lateral canals (6 037-m length) and 23 ditches (14 535-m length). The lateral canal was designed as a trapezoid for the land reclamation with a mouth width of 150 cm, a base width of 50 cm and a depth of 100 cm. The ditch was designed as a rectangle with a mouth width of 80 cm and a depth of 90 cm. For the lateral canal, the engineering workload of unit length was 1 m3 and for ditch engineering workload per unit length was 0.75 m3. The unit length engineering workload of the lateral canal was 1.33 times of the ditch. Based on unit length engineering workload mentioned above, the total engineering workload of the land reclamation project was 16 095 m3 by using the channel optimization layout method based on minimum spanning tree and 16 938.25 m3 by using optimum principle method, respectively. The engineering workload of the minimum spanning tree method was 5.0% less than that of the optimum principle. According to the unit price of the lateral canal engineering project 85 yuan per meter and the unit price of ditch engineering 49.58 yuan per meter, the project cost of 116 694 yuan was saved. The result shows that the channel optimization model based on the minimum spanning tree can obtain the channel layout plan with less cost, that is, the maximum control of land reclamation cost. This study can provide an effective way to cost control of land reclamation projects. [ABSTRACT FROM AUTHOR]

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The research is a comparison of solving some game theory problems by using the genetic algorithm and LINGO software package to define the best one in solving such problems. The research concluded that the least value of the match was accomplished by using genetic algorithm in contrast with solving by LINGO . The time ratio by which every player can use the available strategy to achieve the optimum value of the match was also defined .

This paper deals with issues of shaping and functioning ofcomputer programs in the modelling and solving of multimoda Itransportation network problems. A methodology of an integrateduse of a programming language for mathematical modellingis defined, as well as spreadsheets for the solving of complexmultimodal transportation network problems. The papercontains a comparison of the partial and integral methods ofsolving multimodal transportation networks. The basic hypothesisset forth in this paper is that the integral method results inbetter multimodal transportation network rationalization effects,whereas a multimodal transportation network modelbased on the integral method, once built, can be used as the basisfor all kinds of transportation problems within multimodaltransport. As opposed to linear transport problems, multimodaltransport network can assume very complex shapes. This papercontains a comparison of the partial and integral approach totransp01tation network solving. In the partial approach, astraightforward model of a transp01tation network, which canbe solved through the use of the Solver computer tool within theExcel spreadsheet inteiface, is quite sufficient. In the solving ofa multimodal transportation problem through the integralmethod, it is necessmy to apply sophisticated mathematicalmodelling programming languages which supp01t the use ofcomplex matrix functions and the processing of a vast amountof variables and limitations. The LINGO programming languageis more abstract than the Excel spreadsheet, and it requiresa certain programming knowledge. The definition andpresentation of a problem logic within Excel, in a manner whichis acceptable to computer software, is an ideal basis for modellingin the LINGO programming language, as well as a fasterand more effective implementation of the mathematical model.This paper provides proof for the fact that it is more rational tosolve the problem of multimodal transportation networks by usingthe integral, rather than the partial method.

This paper aimed to build unified pattern per customer not only represent normal behavior but also Fraud pattern that's represented previously and confirmed as fraud transactions that's facilitate studding fraudsters behavior. An enhancement for the proposed algorithm of Fraud Miner has been proposed. This enhancement involves introducing LINGO clustering Data mining algorithm by replacing Apriori algorithm used in Fraud Miner for Frequently Pattern creation and facilitate summarize customer previous behavior either within his Legal or Fraud transactions. Using this algorithm provide more chance for easily fraud detection as the fraudsters always behaving same as customer behaviors instead of study fraudster behavior the customer frequent behavior will be identified from his legal or previously confirmed transactions being fraud. A performance comparison with other algorithms has been carried out. [ABSTRACT FROM AUTHOR]

Background: Solid Waste Management (SWM) in metropolises with systematic methods and following environmental issues, is one of the most important subjects in the area of urban management. In this regard, it is regarded as a legal entity so that its activities are not overshadowed by other urban activities. In this paper, a linear mathematical programming model has been designed for integrated SWM. Using Lingo software and required data from Tehran, the proposed model has been applied for Tehran SWM system as a case study. Results: To determine the optimal status of the available system for Tehran's Solid Waste Management System (SWMS), a novel linear programming model is applied. Tehran has 22 municipal regions with 11 transfer stations and 10 processing units. By running of the model, the transfer stations and processing units are decreased to 10 and 6 units, respectively. Conclusions: The proposed model is an alternative method for improvement the SWMS by decreasing the transfer stations and processing units. [ABSTRACT FROM AUTHOR]

Supply chain network makes it possible to create an effective and helpful context for managing supply chain. This network is a series of equipments that play roles in the supply chain development. In this network exist producers of raw materials and product-making factories, centers of distributing products and customers. The aim of the network is minimizing the total cost so that customer's demands might be answered. In this paper, three-phase multi-product supply chain network model is presented. The super-innovative method of genetic algorithm is used to solve these problems since they are classified into NP-Hard problems. Encoding of this presented. The super-innovative method of genetic algorithm is used to solve these problems since they are classified into NP-Hard problems. Encoding of this genetic algorithm is based on priority-centered encoding. In this method, the network nodes are developed according to their priority. Some types of problems are posed that are solved by means of genetic algorithm and mathematic programming problem solving software (LINGO) and then the results are compared. Moreover, this algorithm is shown to give acceptable answers and is therefore suitable for solving the problems in three-phase multi-product supply chain network. [ABSTRACT FROM AUTHOR]

In this article we design a supply chain with fixed charge and dedicated facilities. The data is collected from a pump manufacturing industry. A mathematical model is created and the model is optimized using LINGO package. The result is discussed with the management and readied for implementation. [ABSTRACT FROM AUTHOR]