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This paper provides a mathematical optimization strategy for optimal municipal solid waste management in the context of the COVID-19 epidemic. This strategy integrates two approaches: optimization and machine learning models. First, the optimization model determines the optimal supply chain for the municipal waste management system. Then, machine learning prediction models estimate the required parameters over time, which helps generate future projections for the proposed strategy. The optimization model was coded in the General Algebraic Modeling System, while the prediction model was coded in the Python programming environment. A case study of New York City was addressed to evaluate the proposed strategy, which includes extensive socioeconomic data sets to train the machine learning model. We found the predicted waste collection over time based on the socioeconomic data. The results show trade-offs between the economic (profit) and environmental (waste sent to landfill) objectives for future scenarios, which can be helpful for possible pandemic scenarios in the following years. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) charging swapping stations (CSSs), as well as multi‐functional integrated charging and swapping facilities (CSFs), have become important to reduce the impact of e‐mobility on the electric power distribution system. This paper presents a coordinated planning optimization strategy for CSSs/CSFs and active distribution networks (AND) that includes distributed generation. The approach is based on the application of a specifically developed spatial‐temporal load forecasting method of both plug‐in EVs (PEVs) and swapping EVs (SEVs). The approach is formulated as a mathematical programming optimization model that provides the location and sizing of new CSSs, the best active distribution network topology, the required distributed generation, and substation capacities. The developed model is solved using CPLEX, and its characteristics and performances are evaluated through a realistic case study. [ABSTRACT FROM AUTHOR]

This paper performs a comprehensive analysis of the wind energy potential of onshore regions in Greece with emphasis on quantifying the volume risk and the spatial covariance structure. Optimization techniques are employed to derive efficient wind capacity allocation plans (also known as generation portfolios) incorporating different yield aspirations. The generation profile of minimum variance and other optimal portfolios along the efficient frontier are subject to rigorous evaluation using a fusion of descriptive and statistical methods. In particular, principal component analysis is employed to estimate factor models and investigate the spatiotemporal properties of wind power generation, providing valuable insights into the persistence of volume risk. The overarching goal of the study is to employ a set of statistical and mathematical programming tools guiding investors, aggregators and policy makers in their selection of wind energy generating assets. The findings of this research challenge the effectiveness of current policies and industry practices, offering a new perspective on wind energy harvesting with a focus on the management of volume risk. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to propose a new model called the stochastic green capacitated p-median problem with a simulation-based optimisation approach. An integer linear programming mathematical model is built considering the total emission produced by vehicles and the uncertain parameters including the travel cost for a vehicle to travel from a particular facility to a customer and the amount of CO2 emissions produced. We also develop a simulation-based optimisation algorithm for solving the problem. Design/methodology/approach: The authors proposed new algorithms to solve the problem. The proposed algorithm is a hybridisation of Monte Carlo simulation and a Variable Neighbourhood Search matheuristic. The proposed model and method are evaluated using instances that are available in the literature. Findings: Based on the results produced by the computational experiments, the developed approach can obtain interesting results. The obtained results display that the proposed method can solve the problems within a short computational time and the solutions produced have good quality (small deviations). Originality/value: To the best of our knowledge, there is no paper in the previous literature investigating the simulation-based optimisation for the stochastic green capacitated p-median problem. There are two main contributions in this paper. First, to build a new model for the capacitated p-median problem taking into account the environmental impact. Second, to design a simulation-based optimisation approach to solve the stochastic green capacitated p-median problem incorporating VNS-based matheuristic and Monte Carlo simulation. [ABSTRACT FROM AUTHOR]

The symmetric wireless network has been expected to be a revolutionary technology for mobile communications. Due to the limited resources of the microbase stations in the wireless network, the way to jointly optimize resource allocation, traffic throughput, latency, and other key performances is a hot research issue. In this paper, we introduce a joint optimization algorithm for improving the performance and balancing the traffic load of the wireless network. For the optimal traffic routing scheme, we transfer the problem to a mixed mathematical programming model. The model contains multiple traffic constraints and a single joint objective; the objective of the joint optimization are data transmission latency, energy consumption of wireless microbase stations, and throughput of links. Moreover, in order to approximately solve the optimization problem, we propose an efficient heuristic traffic transmission and migration scheme with load balancing, called an adaptive data traffic control scheme. The main idea of the proposed scheme is to split the traffic of overloaded microbase stations and links in the symmetric wireless network, so as to achieve load balancing and reduce the energy consumption of microbase stations. At last, the evaluations and simulations verify the proposed algorithm can efficiently optimize the energy allocation of microbase stations, and the network lifetime is increased to 210 rounds. Meanwhile, the network latency is reduced to 2–3 ms, and the network throughput is increased to 1000 Mb in our simulation environment. The constructed traffic control system for the traffic engineering-based wireless network in this paper can serve the intelligent system in the future. [ABSTRACT FROM AUTHOR]

Economic dispatch with valve-point effect (EDVPE) considered is presented as a more accurate model of the real problem compared to the conventional economic dispatch model. It is basically a non-convex, non-differentiable, and multi-modal optimization model with many local minima. Part I of the paper focuses on the local minimum analysis of the EDVPE. The analysis indicates that a local minimum consists of the singular points, the small convex regions, and the output of a slack unit that is dispatched to balance the load demand. Two types of local minima are identified and the second type could be ignored. To verify the rationality of the analyses, a traverse search has been performed to solve the EDVPE with and without considering the transmission loss on different test systems. All the simulation results support the analysis given in the paper. To effectively solve the EDVPE on a large-scale power system, based on the analysis presented in this paper, a new method, dimensional steepest decline method, is proposed in Part II of the paper. [ABSTRACT FROM PUBLISHER]

With the development of modern science and technology, the field of UAV has also entered the era of high-tech exploration. Among them, the task planning, allocation, path exploration, and algorithm optimization of heterogeneous multi UAV technology are our main concerns. Based on the above situation, this paper proposes a heterogeneous multi UAV task planning technology based on ant colony algorithm powered BP neural network. The planning, research, and design are mainly carried out according to the actual situation of the UAV flight test, and the mathematical programming model is established according to the UAV load degree and maximum flight distance as constraints. This paper focuses on the contribution of the ant colony optimization algorithm to benefit maximization and task minimization. The experimental results show that the BP neural network optimized by the ant colony algorithm can improve the number of iterations and training time. Compared with some comparative algorithms, its performance is better. [ABSTRACT FROM AUTHOR]

The design of the optimum wastewater interception network is presented in our research using LINGO Program. Minimising freshwater consumption and the total cost of freshwater usage, treatment of discharged wastewater and interception units are the objective functions in the optimisation model. In our research, we presented two models; the first model can use twelve sources and twelve sinks, each source can enter five techniques of treatment, and each technique has five probabilities to decrease the mass load of contaminants. The second model draws the wastewater interception network after transferring the results of the first model. This paper achieves the minimisation of freshwater consumption in the Kotchener Drain by obtaining the environmental impact of wastewater discharge. Three interception techniques are applied to decrease the chemical oxygen demand (COD) and biological oxygen demand (BOD) of the Segaaya source. Simultaneous linear equations are proposed to get the mass load which is removed with the cost of removing contaminant COD and BOD for other sources. A mathematical approach was applied to seek the two-objective functions, minimise the freshwater consumption as the first objective function and minimise the cost of fresh water, interception units and wastewater discharge in wastewater distribution as the second objective function in Kotchener Drain. Our technique is easy to apply in drains, fertilisers, and chemical plants. [ABSTRACT FROM AUTHOR]

The authors introduce an algorithm that uses both a decomposition-based approach, as well as a constraint programming solver, allowing for the inclusion of extra constraints found in real-life instances. The proposed algorithm is tested on a real dataset and outperforms well-known recommendation algorithms by at least 13.6% in all categories, showing an even greater performance among active users. It is recognized that many real-world problems can be interpreted and formulated as optimization problems. In conclusion, the resulting mixture of methods, algorithms and applications for the treatment of complex optimization problems presented in this Special Issue, either through mathematical tools or metaheuristic algorithms, is expected to contribute to the development of research in this area. [Extracted from the article]

Timetable optimization techniques offer opportunity for saving energy and hence reducing operational costs for high-speed rail services. The existing energy-saving timetable optimization is mainly concentrated on the train running state adjustment and the running time redistribution between two stations. Not only the adjustment space of timetables is limited, but also it is hard for the train to reach the optimized running state in reality, and it is difficult to get feasible timetable with running time redistribution between two stations for energy-saving. This paper presents a high-speed railway energy-saving timetable based on stop schedule optimization. Under the constraints of safety interval and stop rate, with the objective of minimizing the increasing energy consumption of train stops and the shortest travel time of trains, the high-speed railway energy-saving timetable optimization model is established. The fuzzy mathematics programming method is used to design an efficient algorithm. The proposed model and algorithm are demonstrated in the actual operation data of Beijing-Shanghai high-speed railway. The results show that the total operating energy consumption of the train is reduced by 3.7%, and the total travel time of the train is reduced by 11 minutes. [ABSTRACT FROM AUTHOR]

In the process of cement equipment manufacturing, the demand of rectangle pieces of steel structure is very large. The traditional manual nesting, which is simply cutting by hand-making according to the arrangement of the number and size, causes the low efficiency and material wasting. To solve the problem above, this paper proposes an optimizing model for nesting problem of rectangle pieces. Firstly, with the aim of the maximum utilization ratio of the sheet, the optimization mathematical model for nesting problem of rectangle pieces is established. The lowest horizontal line searching algorithm is described in detail. Secondly, the mathematical model is solved to get the optimal solution by the combination of genetic algorithm and the lowest horizontal line searching algorithm. In the solution process, this paper presents the methods of gene encoding and decoding, definition of fitness function, the design of genetic operators and the design of algorithm operating parameters. Finally, we use one sheet as an example to illustrate the proposed model and algorithm process. Experimental results have shown that the proposed approach is able to achieve rectangle pieces nesting with the maximum material utilization ratio. [ABSTRACT FROM AUTHOR]

In the present paper, a multiobjective linear programming problem under uncertainty, particularly when parameters are given in interval forms, is investigated. In this case, it is assumed that objective coefficients and constraints parameters have arrived in interval numbers. Considering a suitable order relation for interval numbers, a solution procedure for dealing with such a problem is developed. A numerical example is provided to illustrate the efficiency of the solution procedure. [ABSTRACT FROM AUTHOR]

In the emerging modern technology of additive manufacturing, the need for optimization can be found in literature in many places. Additive manufacturing (AM) is making an object layer by layer directly from digital data. Previous works of literature have classified additive manufacturing processes into seven types. However, there is a lack of comprehensive review describing the optimization challenges and opportunities in the material extrusion process (polymer technology) and also the need for FDM polymer materials application in impeller making. In this review paper, a specific optimization method called multicriteria decision-making (MCDM) from the mathematical programming technique used in additive manufacturing polymer technology (AMPT) is discussed. The other topics such as different types of optimization techniques, applications of different MCDM tools and their applications in different fields including AM, and the optimization challenges and opportunities in AMPT particularly impeller application are discussed. [ABSTRACT FROM AUTHOR]

Process integration, which started its development in the early 1970s, is an emerging branch of study for conserving various resources. Process integration studies the interdependencies among various process units at the system level and the development and use of tools for holistically designing process networks with generic optimization of resources and sustainable development. The problems addressed in process integration are often referred to as resource conservation or source–sink allocation problems. Most of these problems are solved with precise input parameters. However, due to a wide range of known and unknown factors, these input parameters are uncertain in practical applications. To make the designed network more reliable, these uncertainties should be incorporated at the targeting stage of the problem. Over the years, researchers have used various approaches for managing resource conservation networks under uncertainty. This review examines the different mathematical optimization approaches adopted for handling uncertainties associated with the resource conservation networks along with their practical applications in recent years. The paper primarily examines the four most common approaches used to address uncertainties in process integration: sensitivity analysis, chance-constrained programming, fuzzy optimization, and interval programming. Recent advances in handling uncertainties within the framework of process integration, covering both mathematical programming and Pinch analysis, are also discussed. The review ends with a discussion on the significance and contributions of recent approaches. Some of the important future research directions are also identified to be addressed using process integration and Pinch analysis. [ABSTRACT FROM AUTHOR]

Optimization is an important and decisive task in science. Many optimization problems in science are naturally too complicated and difficult to be modeled and solved by the conventional optimization methods such as mathematical programming problem solvers. Meta-heuristic algorithms that are inspired by nature have started a new era in computing theory to solve the optimization problems. The paper seeks to find an optimization algorithm that learns the expected quality of different places gradually and adapts its exploration-exploitation dilemma to the location of an individual. Using birds' classical conditioning learning behavior, in this paper, a new particle swarm optimization algorithm has been introduced where particles can learn to perform a natural conditioning behavior towards an unconditioned stimulus. Particles are divided into multiple categories in the problem space and if any of them finds the diversity of its category to be low, it will try to go towards its best personal experience. But if the diversity among the particles of its category is high, it will try to be inclined to the global optimum of its category. We have also used the idea of birds' sensitivity to the space in which they fly and we have tried to move the particles more quickly in improper spaces so that they would depart these spaces as fast as possible. On the contrary, we reduced the particles' speed in valuable spaces in order to let them explore those places more. In the initial population, the algorithm has used the instinctive behavior of birds to provide a population based on the particles' merits. The proposed method has been implemented in MATLAB and the results have been divided into several subpopulations or parts. The proposed method has been compared to the state-of-the-art methods. It has been shown that the proposed method is a consistent algorithm for solving the static optimization problems. [ABSTRACT FROM AUTHOR]

In the context of Industry 4.0, which encompasses advanced technologies and interconnected systems, the integration of optimization techniques assumes a crucial role in addressing resource management challenges across various sectors, including manufacturing systems. In bulk ports, which are an integral part of the manufacturing and logistics chain, one of the most critical resource management challenges is the scheduling of automated or semi-automated reclaimers. These machines are responsible for reclaiming dry bulk materials for loading onto vessels using ship-loaders. The efficient operation of reclaimers directly impacts the overall efficiency of the port. However, the current works for the reclaimer scheduling problem (RSP) overlook the necessity of maintenance activities and assume continuous machine availability. Nonetheless, the inclusion of preventive maintenance activities is critical to ensuring optimal scheduling decisions that minimize production disruptions and downtime. To address these challenges, this paper proposes a novel approach that considers periodic preventive maintenance activities of the reclaimers. We consider two cases to tackle this problem. The first case involves the optimization of material handling within a system of two stockpads and one reclaimer machine. For this case, two novel Mixed Integer Programming (MIP) formulations are developed to drive efficient scheduling decisions and maximize productivity. Furthermore, for the second case representing a more complex system comprising three stockpads and two reclaimers, a novel MIP formulation is devised. The effectiveness of the proposed mathematical formulations is rigorously tested through the use of randomly generated instances based on a real-world case. [ABSTRACT FROM AUTHOR]

Forests are among the most sensitive systems in nature. This is attributed to the fact that, forests are directly affected by fluctuations in price of fossil fuels. Wood products and especially forest fuel products are accessible by anyone, without any prior processing. As forest fuel is a subsidy for fossil fuels (oil) for heating purposes, households turn to forest fuel especially in countries that are heavily impacted by economic recession. The over-exploitation of this natural resource leads the forest to abnormal situation and eventually to deforestation. The exhaust of the natural resource capital has negative impact not only on the local economy, where fuelwood market contributes especially in mountainous regions, but also on the environmental stability of ecosystems. In this paper, two multi-period Linear Programming models are proposed for management of coppice forests. The aim of these models is to maximize the Net Present Value, which is constructed as a function of the revenue from trading fuelwood (price times the logged quantities) minus the transportation cost from the forest to merchants. Two aspects have been investigated in this paper; sustainability and maximum yield. The sustainability aspect is guaranteed by imposing constraints for equalization of non-logged areas at the end of the planning horizon. With maximum yield aspect, the maximization of the logged quantities (and therefore the maximization of the objective function) is guaranteed. The model is solved for various scenarios regarding transportation cost. The applicability of the model is demonstrated through a real-world case study of an even coppice forest in Achladochori–Aggistro–Sidirokastro. The proposed model is easy to be implemented, since it uses only the initial conditions of the forest (area) and can be applied to even and uneven aged forests. [ABSTRACT FROM AUTHOR]

Multiple sources localization based on time difference of arrival (TDOA) measurements is investigated in this paper. Different from the traditional methods, a novel and practical multisource localization algorithm is proposed by adopting a priori information of relative distance among emitting sources. Since the maximum likelihood (ML) cost function for multisource estimation is highly nonconvex, the semidefinite relaxation (SDR) is utilized to reformulate the ML cost function. A robust estimator is obtained, which can be solved by semidefinite programming (SDP). Moreover, the constrained Cramér-Rao bound is also derived as a benchmark by considering the range constraints between sources. Simulation results verify the superior performance of the proposed algorithm over the traditional methods. [ABSTRACT FROM AUTHOR]

Order-value optimization problems and low order-value optimization problems are new subclasses of optimization problems which arise from decision-making problems under uncertainty and robust estimation problems. In this paper, We present KKT necessary and sufficient conditions for low order-value optimization problems under convexity hypothesis in this paper. A smooth reformulation of low order-value optimization problems are presented whose local solutions satisfy the KKT necessary conditions. we prove that order-value optimization problems is NP-hard in the strong sense even when constraints are polytope. Order-value optimization problems and low order-value optimization problems are NP-hard even when their presentation functions are linear and constraints are polytope. Some special cases that could be solved in polynomial time are proposed. [ABSTRACT FROM AUTHOR]

Purpose: The time required by the algorithms for general layouts to solve the large-scale two-dimensional cutting problems may become unaffordable. So this paper presents an exact algorithm to solve above problems. Design/methodology/approach: The algorithm uses the dynamic programming algorithm to generate the optimal homogenous strips, solves the knapsack problem to determine the optimal layout of the homogenous strip in the composite strip and the composite strip in the segment, and optimally selects the enumerated segments to compose the three-stage layout. Findings: The algorithm not only meets the shearing and punching process need, but also achieves good results within reasonable time. Originality/value: The algorithm is tested through 43 large-scale benchmark problems. The number of optimal solutions is 39 for this paper's algorithm; the rate of the rest 4 problem's solution value and the optimal solution is 99. 9%, and the average consumed time is only 2. 18seconds. This paper's pattern is used to simplify the cutting process. Compared with the classic three-stage, the two-segment and the T-shape algorithms, the solutions of the algorithm are better than that of the above three algorithms. Experimental results show that the algorithm to solve a large-scale piece packing quickly and efficiency. [ABSTRACT FROM AUTHOR]

Caching at mobile devices and leveraging device-to-device (D2D) communication are two promising approaches to support massive content delivery over wireless networks. The analysis of cache-enabled wireless networks is usually carried out by assuming that devices are uniformly distributed, however, in social networks, mobile devices are intrinsically grouped into disjoint clusters. In this regards, this paper proposes a spatiotemporal mathematical model that tracks the service requests arrivals and account for the clustered devices geometry. Two kinds of devices are assumed, particularly, content clients and content providers. Content providers are assumed to have a surplus memory which is exploited to proactively cache contents from a known library, following a random probabilistic caching scheme. Content clients can retrieve a requested content from the nearest content provider in their proximity (cluster), or, as a last resort, the base station (BS). The developed spatiotemporal model is leveraged to formulate a joint optimization problem of the content caching and spectrum partitioning in order to minimize the average service delay. Due to the high complexity of the optimization problem, the caching and spectrum partitioning problems are decoupled and solved iteratively using the block coordinate descent (BCD) optimization technique. To this end, an optimal and suboptimal solutions are obtained for the bandwidth partitioning and probabilistic caching subproblems, respectively. Numerical results highlight the superiority of the proposed scheme over conventional caching schemes under equal and optimized bandwidth allocations. Particularly, it is shown that the average service delay is reduced by nearly 100% and 350%, compared to the Zipf and uniform caching schemes under equal bandwidth allocations, respectively. [ABSTRACT FROM AUTHOR]

An optimal planning model of the sectional switch and tie line for the distribution network is proposed considering the tradeoff between the system reliability and economy. First, the fault incidence matrices are established for the distribution network, and the explicit analytical expression of the system reliability index is realized by the algebraic operation of the fault incidence matrices and fault parameter vectors. Then, the 0–1 integer quadratically constrained optimization model is established for the sectional switch configuration considering the capacity constraint of the tie line. Finally, the sectional switches and tie lines are optimized jointly to ensure the economic investment and maximize the system reliability. The distribution network in Taiwan Power Company is used to verify the applicability of the proposed optimization method. Compared with the intelligent optimization algorithm, the mathematical programming method in this paper can always find the global optimal switch configuration quickly. In addition, the influence of the tie line capacity constraints on the sectional switch configuration is also considered, which can provide practical reference for the power network planners in the optimization design of the distribution network. [ABSTRACT FROM AUTHOR]

In this paper, we have taken under considerations two auxiliary variables used as stratification variables and single study variable. For obtaining the optimum strata boundaries, we have solved the non linear equations using dynamic programming that resulted in more efficient results rather than obtained on using single study variable and single auxiliary variable. The proposed method is illustrated by using different distributions followed by stratification variables. [ABSTRACT FROM AUTHOR]

This paper studies the energy efficiency optimization problem for coordinated multipoint (CoMP)-enabled and backhaul-constrained ultra-dense small-cell networks (UDNs). Energy efficiency is an eternal topic for future wireless communication networks; however, taking actual bottleneck of the backhaul link and the coordinated network architecture into consideration, it is difficult to find an effective way to improve the energy efficiency of the network. Aiming at this problem, we propose to combine cell association, subchannel allocation, backhaul resource allocation, and sleep/on of the cells together to develop an optimization algorithm for energy efficiency in UDN and then solve the formulated energy efficiency optimization problem by means of improved modified particle swarm optimization (IMPSO) and linear programming in mathematics. Simulation results indicate that nearly 13 % energy cost saving and 21 % energy efficiency improvement can be obtained by combining IMPSO with linear programming, and the backhaul link data rate can be improved by 30 % as the number of small cells increases. From the results, it can be found that by combining IMPSO with linear programming, the proposed algorithm can improve the network energy efficiency effectively at the expense of limited complexity. [ABSTRACT FROM AUTHOR]

Goal programming provides an efficient technique to deal with decision making problems with multiple conflicting objectives. This paper joins the streams of research on goal programming by providing a so-called uncertain random goal programming to model the multi-objective optimization problem involving uncertain random variables. Several equivalent deterministic forms are derived on the condition that the set of parameters consists of uncertain variables and random variables. Finally, an example is given to illustrate the application of the approach. [ABSTRACT FROM AUTHOR]

In this paper, we consider a variational-type inequality and study its weak sharp solutions in terms of a dual gap function, under certain assumptions and convex notion of a functional. Moreover, we aim to constitute the relationship between the minimum principle sufficiency property and weak sharp solutions of the considered variational-type inequality. A numerical result is also constructed to give a better insight for the main derived result. [ABSTRACT FROM AUTHOR]

Given an edge weighted tree T(V, E), rooted at a designated base vertex r∈V, and a color from a set of colors C={1,…,k} assigned to every vertex v∈V, All Colors Shortest Path problem on trees (ACSP-t) seeks the shortest, possibly non-simple, path starting from r in T such that at least one node from every distinct color in C is visited. We show that ACSP-t is NP-hard, and also prove that it does not have a constant factor approximation. We give an integer linear programming formulation of ACSP-t. Based on a linear programming relaxation of this formulation, an iterative rounding heuristic is proposed. The paper also explores genetic algorithm and tabu search to develop alternative heuristic solutions for ACSP-t. The performance of all the proposed heuristics are evaluated experimentally for a wide range of trees that are generated parametrically. [ABSTRACT FROM AUTHOR]

Duality theory plays an important role in optimization theory. It has been extensively used for many theoretical and computational problems in mathematical programming. In this paper duality results are established for first and second order Wolfe and Mond-Weir type symmetric dual programs over general polyhedral cones in complex spaces. Corresponding duality relations for nondifferentiable case are also stated. This work will also remove inconsistencies in the earlier work from the literature. [ABSTRACT FROM AUTHOR]

Optimization of set-valued mappings has become an important subbranch of optimization. In set optimization, one of the main tools to compare sets is given by set relations, which are binary relations among sets. The main two set relations known in the literature are the upper and lower set relation, which are somewhat counterparts of each other and are used to model robust and optimistic solutions in uncertain (vector) programming, respectively. In this paper, we consider the convex case and propose a new set relation which is able to act as a weighting function between these two important set relations and therefore balances out possible gaps that can occur in modeling set optimization problems. [ABSTRACT FROM AUTHOR]

Multi-objective optimization in development and operational planning of power systems becomes crucially important for system designers or operators. In addition, appropriate procedures increasingly involve comparison of scenarios, each with variables that have to be neither dimensionally nor qualitatively comparable. Selecting the best solution is usually performed posteriorly by comparing the results of different scenarios. In order to eliminate unnecessary calculations during the optimization, especially in the case of large number of scenarios, it is useful to eliminate the scenarios with bad indicators. This process can significantly reduce the computational time. It results in a timely elimination of all inferior scenarios, thus simplifying the decision-making. This paper analyses the methods and the efficiency of comparison of different scenarios being simultaneously processed using NSGA-II and MOPSO optimization procedures. The proposed procedures are discussed in the example of selecting the type, the size and the location of FACTS in transmission networks. Different scenarios consider the common types of these devices, wherein the number of devices depends on the system. The paper proposes the reduction of the optimization procedures. Having no significant influence on the final set of optimal solutions, the proposed modifications are significant and applicable. [ABSTRACT FROM AUTHOR]

This research investigates the effectiveness of loops and paths as embedded in a modified pinch package (Aspen Energy Analyzer), that comprises a blend of traditional pinch technique with mathematical programming, in simultaneous optimization of total annual cost (TAC) in heat exchanger network synthesis (HENS). It uses composite curves, grand composites curves, supertargeting and looping system just as in pinch, as well as linear programming and mixed integer linear programming (MILP) in the design of HENs. The tool was adopted in solving some literature problems that had earlier been solved using pinch and mathematical programming techniques. The results obtained compared well with those of various authors that used different techniques as shown in the tables of cost comparison in this paper. The loop and path optimization technique adopted in this research obtained the least TAC in four out of five problems solved in this paper. This shows that loop and path optimization as available in a software-combining pinch and mathematical programming can be more effective than various other methods that have been adopted in the literature. It further demonstrated that no particular technique can return the lowest TAC for all HENS problems. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to present an optimization model for planning the distribution of materials in earthmoving operations, considering possible interferences between cut-and-fill sections such as rivers, vegetation, topographical features, or expropriations. The earth allocation problem incorporating interferences was modeled as a linear programming problem, aiming to minimize the total earthmoving cost while considering the constraints related to volume balance, construction project duration, and time for the release of traffic. The proposed linear programming model was run by an integrated system, using Excel for data analysis and IBM CPLEX as the optimizer. The mathematical model was evaluated by a sensitivity analysis and validated by a real-world project of a dam access road in the state of Ceará, Brazil. The unit costs and productivity rates used in the fictional example and in the real-world application followed the referential cost system created by Ceará's Secretariat of Infrastructure (SEINFRA-CE). The proposed optimization model achieved reasonable processing times for all tested applications, presenting itself as a viable and efficient option for planning earthmoving operations. Furthermore, the linear programming approach provided a 2.12% cost reduction for the real-world case study, when comparing the optimized solution and original budget. This study explored the problem of earth allocation with interferences using a linear programming approach, while avoiding complex modeling issues found in recent literature. As a result, this paper proposes a user-friendly optimization system that can be easily utilized by construction companies and departments. [ABSTRACT FROM AUTHOR]

Generation capacity expansion planning (GCEP) is the process of deciding on a set of optimal new investments in generation capacity to adequately supply future loads, while satisfying technical and reliability constraints. This paper shows the application of stochastic mixed-integer programming (SMIP) to account for hydrological uncertainty in GCEP for the Chilean Central Interconnected System, using a two-stage SMIP multi-period model with investments and optimal power flow (OPF). The substantial computational challenges posed by GCEP imply compromising between the detail of the stochastic hydrological variables and the detail of the OPF. We selected a subset of hydrological scenarios to represent the historical hydro variability using moment-based scenario reduction techniques. The tradeoff between modeling accuracy and computational complexity was explored both regarding the simplification of the MIP problem and the differences in the variables of interest. Using a simplified OPF model, we found the difference of using a subset of hydro scenarios to be small when compared with using a full representation of the stochastic variable. Overall, SMIP with scenario reduction provided optimal capacity expansion plans whose investment plus expected operational costs were between 1.3% and 1.9% cheaper than using a deterministic approach and proved to be more robust to hydro variability. [ABSTRACT FROM PUBLISHER]

This paper presents a mixed-integer linear mathematical programming model for the integrated planning problem of the part quality inspection and preventive maintenance activities in serial multi-stage manufacturing system. The model concurrently determines the right time and place for performing the above-mentioned activities while the production stages are linearly deteriorating. These two decisions are made while the model is to minimize the total cost including the production, maintenance, inspection, scrap, replacement, and the penalty of shipped defective items to customer(s). A numerical example and a real case study are investigated to validate and verify the proposed model. The results show that the determination of inspection locations along a manufacturing line in different periods of time regarding the impact of preventive maintenance activities on defective production probability results in a more efficient manufacturing system. [ABSTRACT FROM AUTHOR]

In multi-parametric programming an optimization problem is solved as a function of certain parameters, where the parameters are commonly considered to be bounded and continuous. In this paper, we use the case of strictly convex multi-parametric quadratic programming (mp-QP) problems with affine constraints to investigate problems where these conditions are not met. Based on the combinatorial solution approach for mp-QP problems featuring bounded and continuous parameters, we show that (i) for unbounded parameters, it is possible to obtain the multi-parametric solution if there exists one realization of the parameters for which the optimization problem can be solved and (ii) for binary parameters, we present the equivalent mixed-integer formulations for the application of the combinatorial algorithm. These advances are combined into a new, generalized version of the combinatorial algorithm for mp-QP problems, which enables the solution of problems featuring both unbounded and binary parameters. This novel approach is applied to mixed-integer bilevel optimization problems and the parametric solution of the dual of a convex problem. [ABSTRACT FROM AUTHOR]

Manufacturing process optimization is an ever-actual goal. Within this goal, machining parameters optimization is a very important task. Machining parameters strongly influence the manufacturing costs, process productivity and piece quality. Literature presents a series of optimization methods. The results supplied by these methods are comparable and it is difficult to establish which method is the best. For machining parameters optimization, this paper proposes a novel, simple and efficient method, without additional costs related to new software packages. This approach is based on linear mathematical programming. The optimization mathematical models are, however, nonlinear. Therefore, mathematical model linearization is required. The major and difficult problem is the linearization of the objective function. This represents the key element of the proposed optimization method. In this respect, the paper proposes an original mathematical procedure for calculating the part of the objective function that refers to the analytical integration of the tool life into the model. This calculus procedure was transposed into an original software tool. For demonstrating the validity of the method, a comparison is presented among the results obtained by certain optimization techniques. It results that the proposed method is simple and as good as those presented by the literature. [ABSTRACT FROM AUTHOR]

The aim of this paper is to introduce some generalized contractions and prove certain new fixed point results for self-mappings satisfying these contractions in the setting of F -metric space. As an application of our results, we investigate the problem of dynamic programming related to the multistage process which formulates the problems of computer programming and mathematical optimization. We also provide an example to support the validity of our main results. [ABSTRACT FROM AUTHOR]

Despite the effectiveness of the decomposition-based multi-objective evolutional algorithm (MOEA/D-M2M) in solving continuous multi-objective optimization problems (MOPs), its performance in addressing 0/1 multi-objective knapsack problems (MOKPs) has not been fully explored. In this paper, we use MOEA/D-M2M with an improved greedy repair strategy to solve MOKPs. It first decomposes an MOKP into a number of simple optimization subproblems and solves them in a collaborative way. Each subproblem has its own subpopulation, and then an improved greedy strategy is introduced to improve the performance of the proposed algorithm on MOKPs. Therein, a weight vector chosen randomly from a corresponding subpopulation is utilized to repair infeasible individuals or improve feasible individuals to have a better fitness, which improves the convergence of the population. Experimental studies on a set of test instances indicate that the MOEA/D-M2M with the improved greedy strategy is superior to MOGLS and MOEA/D in terms of finding better approximations to the Pareto front. [ABSTRACT FROM AUTHOR]

In recent years sampling approaches have been used more widely than optimization algorithms to find parameters of conceptual rainfall–runoff models, but the difficulty of calibration of such models remains in dispute. The problem of finding a set of optimal parameters for conceptual rainfall–runoff models is interpreted differently in various studies, ranging from simple to relatively complex and difficult. In many papers, it is claimed that novel calibration approaches, so-called metaheuristics, outperform the older ones when applied to this task, but contradictory opinions are also plentiful. The present study aims at calibration of two simple lumped conceptual hydrological models, HBV and GR4J, by means of a large number of metaheuristic algorithms. The tests are performed on four catchments located in regions with relatively similar climatic conditions, but on different continents. The comparison shows that, although parameters found may somehow differ, the performance criteria achieved with simple lumped models calibrated by various metaheuristics are very similar and differences are insignificant from the hydrological point of view. However, occasionally some algorithms find slightly better solutions than those found by the vast majority of methods. This means that the problem of calibration of simple lumped HBV or GR4J models may be deceptive from the optimization perspective, as the vast majority of algorithms that follow a common evolutionary principle of survival of the fittest lead to sub-optimal solutions. [ABSTRACT FROM PUBLISHER]

This paper introduces a novel technique that enables access by a cognitive secondary user (SU) to a spectrum occupied by an incumbent primary user (PU) that employs Type-I hybrid automatic retransmission request (ARQ). The technique allows the SU to perform selective retransmissions of SU data packets, whose transmission previously failed. The temporal redundancy introduced by the PU ARQ protocol and by the selective retransmission process of the SU can be exploited by the SU receiver to perform interference cancellation (IC) over multiple transmission slots, thus creating a “clean” channel for the decoding of the concurrent SU or PU packets. The chain decoding (CD) technique is initiated by a successful decoding operation of an SU or a PU packet and proceeds by an iterative application of IC as previously buffered packets become decodable and their interference can be removed, thus making it possible to recover the concurrent data packets, and so on, until no more packets are decodable. Based on this scheme, an optimal policy is designed that maximizes the SU throughput under a constraint on the average long-term PU performance. The optimality of the CD protocol is proved, which determines which packet the SU should send at any given time, based on four basic rules. Moreover, a decoupling principle is proved, which establishes the optimality of decoupling the secondary access strategy from the CD protocol. Specifically, first, the SU access policy, optimized via dynamic programming, specifies whether the SU should access the channel or remain idle, based on a compact state representation of the protocol, and second, the CD protocol embeds four basic rules that are used to select the packet transmitted by the SU. It is shown numerically that CD outperforms by up to 35% other schemes considered in the literature, which do not employ retransmissions at the SU pair and thus do not exploit the full potentiality of IC. [ABSTRACT FROM PUBLISHER]

Two important characteristics encountered in many real-world scheduling problems are heterogeneous processors and a certain degree of uncertainty about the processing times of jobs. In this paper we address both, and study for the first time a scheduling problem that combines the classical unrelated machine scheduling model with stochastic processing times of jobs. By means of a novel time-indexed linear programming relaxation, we show how to compute in polynomial time a scheduling policy with provable performance guarantee for the stochastic version of the unrelated parallel machine scheduling problem with the weighted sum of completion times objective. Our performance guarantee depends on the squared coefficient of variation of the processing times and we show that this dependence is tight. Currently best-known bounds for deterministic scheduling problems are contained as special cases. [ABSTRACT FROM AUTHOR]

This paper introduces a class of contest models in which each player decides when to stop a privately observed Brownian motion with drift and incurs costs depending on his stopping time. The player who stops his process at the highest value wins a prize. We prove existence and uniqueness of a Nash equilibrium outcome and derive the equilibrium distribution in closed form. As the variance tends to zero, the equilibrium outcome converges to the symmetric equilibrium of an all-pay auction. For two players and constant costs, each player's equilibrium profit decreases if the drift increases, the variance decreases, or the costs decrease. [ABSTRACT FROM AUTHOR]

Optimization solvers include many options that allow users to control algorithmic aspects that may have a considerable impact on solver performance. Tuning solver options is often necessary to reduce execution time and improve solution quality. Previous studies of solver tuning techniques have focused on mixed-integer linear programming and local nonlinear programming solvers. In this paper, we investigate the potential of tuning a global optimization solver for nonlinear and mixed-integer nonlinear programming problems. In particular, derivative-free optimization (DFO) algorithms are used to find optimal values for options of the global optimization solver BARON. A set of 126 problems from the GLOBALLib and MINLPLib collections are utilized in a computational study from which we conclude that tuning options can improve the default performance of BARON for individual problems and an entire library. Additionally, we present a systematic comparison of 27 DFO solvers in terms of their ability to improve the performance of the global solver. We find that several DFO implementations are much better than others in terms of finding optimal tuning parameters. [ABSTRACT FROM AUTHOR]

This paper deals with a robust multiobjective optimization problem involving nonsmooth/nonconvex real-valued functions. Under an appropriate constraint qualification, we establish necessary optimality conditions for weakly robust efficient solutions of the considered problem. These optimality conditions are presented in terms of Karush-Kuhn-Tucker multipliers and convexificators of the related functions. Examples illustrating our findings are also given. [ABSTRACT FROM AUTHOR]

This paper presents a multi-objective mathematical programming (MMP) model to day-ahead market clearing of joint energy and reserves auctions by integrating a thyristor-controlled series compensator (TCSC) device with the optimization problem developed under normal and contingency cases. The proposed market clearing framework includes minimization of energy and reserves offer cost, congestion rent, TCSC cost, expected interruption cost, line overload, voltage deviation, and loadability limit. A new index called congestion efficiency index is proposed for the best placement of TCSC under network contingency conditions. Traditional MMP methods such as direction scalarization and ε-constraint methods scalarize the objective vector into a single objective. Those cases are time-consuming and require a number of runs equal to the number of desired efficient solutions. In this paper, the non-dominated sorting genetic algorithm II (NSGA-II), which is integrated with an adaptive neuro-fuzzy inference system (ANFIS), is proposed to find the solution of the optimal schedule of the units energy and reserves by which the parameters of NSGA-II (probabilities of crossover and mutation) are dynamically set, according to a training process. The proposed methodology is developed on the IEEE 30-bus test system, and the results are compared with fuzzy-based and ordinary NSGA-II methods. These comparisons confirm the efficiency of the developed method. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this paper, we study an optimal portfolio, consumption-leisure and retirement choice problem for an infinitely lived economic agent with a CES utility function. Using the dynamic programming method, we obtain the value function and optimal investment, consumption, leisure, and retirement strategies in analytic form. Numerically we observe that the threshold retirement wealth level is an increasing function with respect to the elasticity of substitution. [ABSTRACT FROM AUTHOR]

In the present paper, an efficient computational method for the solution of bang–bang optimal control problems is investigated. The method is based on control parametrization and belongs to the direct methods for numerical solution of optimal control problems. In this method, control functions are considered to be piecewise constant with values and switching points taken as decision variables. Thereby, the problem is converted into a mathematical programming problem which can be solved by well-developed parameter optimization algorithms. The main advantages of the present method are that: (i) it obtains good results and the switching points can be captured accurately; and (ii) an incorrectly guessed number of switching points can be detected by the results of the method. These are illustrated through three examples and the efficiency of the method is reported. [ABSTRACT FROM AUTHOR]