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Heuristic algorithms are practical, easy to implement, and work fast to provide short-term, feasible solutions for any kind of problem within economical budgets as compared to other meta-heuristic algorithms. This paper presents a novel heuristic algorithm named the Dahiya-Garg Heuristic Algorithm (DG-Alg) to find the optimal solution for constrained reliability redundancy allocation optimization problems. The cornerstone of the novel DG-Alg is its novel selection factor, which is a mathematical formula that helps the heuristic algorithm search for optimal subsystems for reliability optimization. A novel formulated selection factor in DG-Alg has increased its effectiveness and efficiency. To analyze the performance of the proposed heuristic algorithm and the other three existing heuristic algorithms, they are applied to a problem taken from a pharmaceutical manufacturing plant named Yaris Pharmaceuticals. During the application of the heuristic algorithms, it was ensured that redundancy allocation was done within stipulated cost constraints. Further, a comparative analysis of the obtained results has been done to judge the performance of the proposed heuristic algorithm. It is deduced that the proposed heuristic algorithm gives optimized and computationally efficient results in comparison to the other existing heuristic algorithms. [ABSTRACT FROM AUTHOR]

The allocation of experts to programmer teams, which meet constraints on professional competences related to programming technologies, languages and tools an IT project specifies is a hard combinatorial problem. This paper solves the problem of forming the maximum number of teams whose experts meet all the constraints within each team. It develops and compares two algorithms: a heuristic greedy and exact optimal. The greedy algorithm iteratively solves the set cover problem on a matrix of expert competences until can create the next workable team of remaining experts. The paper proves that the allocation greedy algorithm is not accurate even if the set cover algorithm is exact. We call the allocation algorithm as double greedy if the set cover algorithm is greedy. The exact algorithm we propose finds optimal solution in three steps: generating a set of all non-redundant teams, producing a graph of team’s independency, and searching for a maximum clique in the graph. The algorithm of generating the non-redundant teams traverses a search tree constructed in such a way as to guarantee the creation of all non-redundant teams and absorbing all redundant teams. The edges of the non-redundant team independency graph connect teams that have no common expert. The maximum clique search algorithm we propose accounts for the problem and graph features. Experimental results show that the exact algorithm is a reference one, and the double-greedy algorithm is very fast and can yield suboptimal solutions for large-size allocation problems.

Location and allocation of emergency response units are assumed to be interdependent in the incident management system. System costs will be excessive if delay regarding allocating decisions is ignored when locating response units. This paper presents an integrated method to solve location and allocation problem of emergency vehicles on freeways. The principle is to begin with a location phase for managing initial incidents and to progress through an allocation phase for managing the stochastic occurrence of next incidents. Previous studies used the frequency of independent incidents and ignored scenarios in which two incidents occurred within proximal regions and intervals. The proposed analytical model relaxes the structural assumptions of Poisson process (independent increments) and incorporates evolution of primary and secondary incident probabilities over time. Interdependent ERUs location-allocation problem is solved by taking stochastic information of future incidents explicitly into account with lookahead. The mathematical model overcomes several limiting assumptions of the previous models, such as no waiting time, returning rule to original depot, and fixed depot. The initial nonlinear stochastic model is linearized. The temporal locations flexible with lookahead are compared with current practice that locates units in depots based on Poisson theory. An efficient heuristic algorithm is implemented to deal with time-consuming process of a large-scale problem in real time. [ABSTRACT FROM PUBLISHER]

Currently, stroke patients are transported to the nearest stroke center, following specific protocols. Yet, these protocols do not consider many factors, including the spatial variation in population density, the stroke's severity, the time since stroke onset, and the congestion level at the receiving stroke center. We develop an analytical framework that enriches the stroke transport decision-making process by incorporating these factors. Our research contributes to the literature of stroke care systems by (i) developing the first analytical framework to determine the optimal primary hospital destination in a regional stroke network and (ii) comparing the impact of incorporating prehospital triaging on health outcomes. To this end, we develop an efficient reformulation for allocation problems with stochastic demand and multiserver system under congestion. We derive data-driven outcome prediction models embedded in mixed integer second-order cone programming formulation. Our framework is applied to two real-life cases: Montreal and Quebec City Stroke Networks. We show that adopting a triage strategy could lead to significantly improved health outcomes, where the magnitude of these improvements varies with the networks' sizes and congestion levels. In the Montreal case, our proposed policy may increase the ratio of patients for therapeutic intervention eligibility by 12.5% while improving by 69% the number of patients with more than two days of emergency department boarding delays. Our results reveal that it is important to consider the network's characteristics in making a decision for or against implementing a prehospital triage strategy. Finally, we propose a heuristic policy that provides a promising performance while also being easy to implement. This paper was accepted by Stefan Scholtes, healthcare management. [ABSTRACT FROM AUTHOR]

Utilizing Distributed Generators (DGs) and Energy Storage Systems (ESSs) enhances power system reliability, drawing significant research attention. However, these systems pose challenges, compelling scientists to explore optimization methods. Our paper presents an innovative solution, Parallel Multi-Objective Multi-Verse Optimization (PMOMVO), aimed at optimizing DGs and Battery Energy Storage Systems (BESSs) allocation. This optimization addresses voltage violations and operation costs, crucial concerns for system operators and consumers. By leveraging a parallel approach, PMOMVO significantly accelerates the optimization process. We compared its results with a base case scenario, demonstrating the superior efficiency of our parallel method. It not only enhances the optimization performance but also proves its efficacy by generating optimal solutions from the Pareto front set. This research showcases the benefits of PMOMVO, offering a faster, more efficient, and reliable way to optimize power systems, benefiting both operators and consumers. • Introduces a novel bi-level optimization approach for DG and BESS allocation and operation. • Proposing a multi-objective formulation to identify Pareto optimal solutions, offering a balance between voltage stability and cost-effectiveness. • Optimizes asset operation strategies, considering both peak and off-peak load times, enhancing resource utilization. • Proposes a parallel processing method based on modified MOMVO, significantly accelerating computations for large distribution systems problems. • Achieves a substantial improvement in the speed-up index, ensuring swift and effective DG and BESS allocation and operation in complex systems. [ABSTRACT FROM AUTHOR]

In this paper, we study the Nash implementation in an allocation problem with single-dipped preferences. We show that, with at least three agents, Maskin monotonicity is necessary and sufficient for implementation. We examine the implementability of various social choice correspondences (SCCs) in this environment, and prove that some well-known SCCs are Maskin monotonic ( but they do not satisfy no-veto power) and hence Nash implementable. [ABSTRACT FROM AUTHOR]

Wireless sensor network is an emerging technology that enables remote monitoring of large geographical regions. In this paper, we address the problem of distributing attributes over such a large-scale sensor network so that the cost of data retrieval is minimized. The proposed scheme is a data-centric storage scheme where the attributes are distributed over the network depending on the correlations between them. The problem addressed here is similar to the Allocation Problem of distributed databases. In this paper, we have defined the Allocation Problem in the context of sensor networks and have proposed a scheme for finding a good distribution of attributes to the sensor network. We also propose an architecture for query processing given such a distribution of attributes. We analytically determine the conditions under which the proposed architecture is beneficial and present simulation results to demonstrate the same. To the best of our knowledge, this is the first attempt to determine an allocation of attributes over a sensor network based on the correlations between attributes. [ABSTRACT FROM AUTHOR]

One of the remaining important problems of life cycle inventory analysis is the allocation problem. A proper solution of this problem calls for a proper understanding of the nature of the problem itself. This paper argues that the established definition of the allocation problem as the fact that one unit process produces more than one function, is not appropriate. That definition points to an important reason of the occurrence of the problem, but the situation of internal (closed-loop) recycling already indicates that there may be product systems which contain multifunction processes, but which nevertheless need not exhibit an allocation problem. The paper proceeds by examining a number of simple hypothetical cases, and proposes a precise and operational definition of the allocation problem. This enables a systematic categorization of approaches for dealing with the allocation problem. [ABSTRACT FROM AUTHOR]

Driven by the growing threat of natural disasters caused by climate change, there is an urgent need to strengthen the emergency rescue logistics network. However, insufficient research has been conducted on optimizing both pre-disaster preparation and post-disaster response, resulting in lower resilience and inefficiency of emergency logistics management. To this end, this study explores the optimization of emergency rescue resource allocation and transportation network design, considering the uncertainty and multi-period nature of natural disaster rescue. By employing a lateral transshipment strategy, a three-stage stochastic programming model is established, which aims to balance economic benefits with the need for devastations, thereby enhancing the resilience of the logistics network. Numerical experiments verify the effectiveness of the proposed model with different instances and the performance of the lateral transshipment strategy by comparing it with a two-stage stochastic programming model. Sensitivity analysis is performed on the costs of constructing a depot and the penalties for unmet needs. The analysis yielded valuable insights that can be used to enhance emergency rescue operations, supply chain network design, and logistics network design. The research outcome can benefit emergency responders and logistics professionals in optimizing their operations. [ABSTRACT FROM AUTHOR]

The request of equal accessibility must be respected to some extent when dealing with problems of designing or rebuilding of emergency service systems. Not only the disutility of the average user but also the disutility of the worst situated user must be taken into consideration. Respecting this principle is called fairness of system design. Unfairness can be mitigated to a certain extent by an appropriate fair allocation of additional facilities among the centers. In this article, two criteria of collective fairness are defined in the connection with the facility allocation problem. To solve the problems, a series of fast algorithms for solving of the allocation problem was suggested. This article extends the family of the original solving techniques based on branch-and-bound principle by newly suggested techniques, which exploit either dynamic programming principle or convexity and monotony of decreasing nonlinearities in objective functions. The resulting algorithms were tested and compared performing numerical experiments with real-sized problem instances. The new proposed algorithms outperform the original approach. The suggested methods are able to solve general min-sum and min-max problems, in which a limited number of facilities should be assigned to individual members from a finite set of providers. [ABSTRACT FROM AUTHOR]

The enzyme model (EM), inspired by biological enzyme catalysis, is a computational-functional description of information processing and distribution in modular cognitive systems. It has been argued that EM offers advantages in solving both the allocation problem and global computation and thus may play a role in upholding the massive modularity hypothesis (MMH). This paper, however, argues that EM solutions are untenable, as EM avoids the infinite regress of allocation problem only at a high cost and with several critical drawbacks. Moreover, to clarify global processes, EM needs to satisfy two necessary conditions: first to demonstrate that the EM allows cross module communication, and second to be sensitive to not only the syntax but also the semantics of representations. I argue that EM only satisfies the first condition and thus fails to hold. [ABSTRACT FROM AUTHOR]

This paper proposes an optimal allocation problem with ramified transport technologies in a spatial economy. Ramified transportation is used to model network-like branching structures attributed to the economies of scale in group transportation. A social planner aims at finding an optimal allocation plan and an associated optimal allocation path to minimize the overall cost of transporting commodity from factories to households. This problem differentiates itself from existing ramified transport literature in that the distribution of production among factories is not fixed but endogenously determined as observed in many allocation practices. It is shown that due to the transport economies of scale, each optimal allocation plan corresponds equivalently to an optimal assignment map from households to factories. This optimal assignment map provides a natural partition of both households and allocation paths. We develop methods of marginal transportation analysis and projectional analysis to study the properties of optimal assignment maps. These properties are then related to the search for an optimal assignment map in the context of state matrix. [ABSTRACT FROM AUTHOR]

This study presents a method to place, size, coordinate and adequate protection systems in distribution networks (DNs) with directly‐coupled distributed generation (DG). DG penetration increase in DNs is beneficial to the operation of these grids. However, since the majority of DN is already consolidated, with protective devices sized for certain current levels and directionality, adding DG to these systems may cause issues. This study introduces a method to determine protective devices placement, their coordination and sizing, depending on DG location. Direct‐coupled DG is considered since DG connected to DNs through power electronics limit the fault current to the network. Firstly, recloser allocation is treated in a multiobjective approach, reducing reliability indices. To solve this problem, particle swarm optimization is applied. Fuse cutouts allocation is determined by a set of rules, developed considering feeder particularities. After, DG is considered and its effects on the protection coordination are evaluated. To adapt DN to receive DG, fault current limiters are placed to avoid recloser – fuse miscoordination. Finally, recloser operation is analyzed to determine the operational philosophy to avoid misoperation in the presence of DG. Graphical and numerical results are presented for test systems to show the functionality and performance of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper we present some models and algoritms for solving some typical production planning and scheduling problems. We present the Resource-Constrained Project Scheduling Problem (RCPSP) and algorithms for the determination of maximal couplings with minimal arch length in the graph attached to an allocation problem, and for the determination of the solution of Dirichlet problem and of the potential-voltage problem which appear in a production planning. We develop a model for allocating work among potential VO partners, taking into account fixed and variable work costs and transportation costs. [ABSTRACT FROM AUTHOR]

In this paper a new model for solving a medical resources allocation problem is discussed. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes to reformulate the intrusion detection problem (IDP) as an allocation problem that allows the user to detect known attack patterns. Many methods and techniques were used for modeling the IDP, but some of them contribute little or not to resolve it. Indeed, some mathematical formulations of the IDP are NP-complete, and identified only two kinds of behaviours (normal and attack). This motivates some researchers to use genetic algorithms to solve these IDPs. The proposal developed in this paper aims to present a new mathematical formulation of the IDP that detects any known attack pattern without recourse to learning techniques like genetic algorithms. Our approach is based on the formulation of the IDP as an allocation problem, which can be rewritten in a polynomial form that makes it easy to solve. A Pascal code tool was developed to achieve some experiments. Our experiments show that our approach is very competitive with respect to some previous works. [Copyright &y& Elsevier]

Abstract: This paper presents a new algorithm that can be readily applied to solve the all-terminal network reliability allocation problems. The optimization problem solved considers the minimization of the network design cost subject to a known constraint on all-terminal reliability by assuming that the network contains a known number of functionally equivalent components (with different performance specifications) that can be used to provide redundancy. The algorithm is based on two major steps that use a probabilistic solution discovery approach and Monte Carlo simulation to generate the quasi-optimal network designs. Examples for different sizes of all-terminal networks are used throughout the paper to illustrate the approach. The results obtained for the larger networks with unknown optima show that the quality of the solutions generated by the proposed algorithm is significantly higher with respect to other approaches and that these solutions are obtained from restricted solution search space. Although developed for all-terminal reliability optimization, the algorithm can be easily applied in other resource-constrained allocation problems. [Copyright &y& Elsevier]

Heuristic algorithms are practical, easy to implement, and work fast to provide short-term, feasible solutions for any kind of problem within economical budgets as compared to other meta-heuristic algorithms. This paper presents a novel heuristic algorithm named the Dahiya-Garg Heuristic Algorithm (DG-Alg) to find the optimal solution for constrained reliability redundancy allocation optimization problems. The cornerstone of the novel DG-Alg is its novel selection factor, which is a mathematical formula that helps the heuristic algorithm search for optimal subsystems for reliability optimization. A novel formulated selection factor in DG-Alg has increased its effectiveness and efficiency. To analyze the performance of the proposed heuristic algorithm and the other three existing heuristic algorithms, they are applied to a problem taken from a pharmaceutical manufacturing plant named Yaris Pharmaceuticals. During the application of the heuristic algorithms, it was ensured that redundancy allocation was done within stipulated cost constraints. Further, a comparative analysis of the obtained results has been done to judge the performance of the proposed heuristic algorithm. It is deduced that the proposed heuristic algorithm gives optimized and computationally efficient results in comparison to the other existing heuristic algorithms.

The present study introduces a subtraction-average-based optimization algorithm (SAOA), a unique enhanced evolutionary technique for solving engineering optimization problems. The typical SAOA works by subtracting the average of searcher agents from the position of population members in the search space. To increase searching capabilities, this study proposes an improved SAO (ISAO) that incorporates a cooperative learning technique based on the leader solution. First, after considering testing on different standard mathematical benchmark functions, the proposed ISAOA is assessed in comparison to the standard SAOA. The simulation results declare that the proposed ISAOA establishes great superiority over the standard SAOA. Additionally, the proposed ISAOA is adopted to handle power system applications for Thyristor Controlled Series Capacitor (TCSC) allocation-based losses reduction in electrical power grids. The SAOA and the proposed ISAOA are employed to optimally size the TCSCs and simultaneously select their installed transmission lines. Both are compared to two recent algorithms, the Artificial Ecosystem Optimizer (AEO) and AQuila Algorithm (AQA), and two other effective and well-known algorithms, the Grey Wolf Optimizer (GWO) and Particle Swarm Optimizer (PSO). In three separate case studies, the standard IEEE-30 bus system is used for this purpose while considering varying numbers of TCSC devices that will be deployed. The suggested ISAOA's simulated implementations claim significant power loss reductions for the three analyzed situations compared to the GWO, AEO, PSO, and AQA. [ABSTRACT FROM AUTHOR]

Using approximate, rounded values implies, in a sense, that an exact numerical value may be ignored. In many cases the difference between the exact and approximate values is not important, and replacing exact numbers by their approximate values does not result in undesired consequences. Yet in certain circumstances, rounding significantly influences the solutions of given problems. This is the case, among others, when we allocate indivisible goods. It may happen that the rounding mode affects the result of allocation so much that the rounding differences cannot be neglected by the agents participating in distribution. This paper presents the classic problem of distributing mandates in representative bodies along with different rounding modes in respective solution procedures. [ABSTRACT FROM AUTHOR]

Dimensioning of telecommunications networks requires the allocation of the flows (bandwidth) to given traffic demands for the source-destination pairs of nodes. Unit flow allocated to the given demand is associated with revenue that may vary for different demands. Problem the decision-making basic algorithms to maximize the total revenue may lead to the solutions that are unacceptable, due to \starvation" or \locking" of some demand paths less attractive with respect to the total revenue. Therefore, the fair optimization approaches must be applied. In this paper, two fair optimization methods are analyzed: the method of ordered weighted average (OWA) and the reference point method (RPM). The study assumes that flows can be bifurcated thus realized in multiple path schemes. To implement optimization model the AMPL was used with general-purpose linear programming solvers. As an example of the data, the Polish backbone network was used. [ABSTRACT FROM AUTHOR]

Abstract: Bio-based products are considered to be a sustainable alternative to conventional fossil fuel-based materials. This paper studies the production of glucose from corn starch, an important feedstock for a wide range of bio-based products (e.g. ethanol, bio-based monomers), in a European corn wet mill (CWM). Following the Life Cycle Assessment (LCA) principles, non-renewable energy use (NREU) and greenhouse gas (GHG) emissions are assessed for the system cradle-to-factory gate. The allocation problem that the analysis is faced with is addressed by applying seven different approaches: namely sub-division (two variants), partitioning based on physical causalities and on economic relationships and the principles of system expansion (three variants). Depending on the approach, the NREU for glucose production ranges from 6.8 to 9.3 MJ/kg glucose dry solids (ds) and the GHG emissions from 0.7 to 1.1 kg CO2 eq./kg glucose ds. The results for NREU per tonne of glucose are robust while for GHG, large deviation from the average is found for system expansion and for the approach main process. The influence of each calculation should be kept in mind when assessing bio-based products from glucose produced in a CWM. The results provide a good basis for future analyses of bio-based products, as they are also similar to values published by other authors for the US, bearing in mind the different impacts of corn production in the two regions. [Copyright &y& Elsevier]

Abstract: Containerization transportation has been growing fast in the past few decades. International trades have been growing fast since the globalization of world economies intensified in the early 1990s. However, these international trades are typically imbalanced in terms of the numbers of import and export containers. As a result, the relocation of empty containers has become one of the important problems faced by liner shipping companies. In this paper, we consider the empty container allocation problem where we need to determine the optimal volume of empty containers at a port and to reposition empty containers between ports to meet exporters’ demand over time. We formulate this empty container allocation problem as a two-stage model: in stage one, we propose a fuzzy backorder quantity inventory decision making model for determining the optimal quantity of empty container at a port; whereas in stage two, an optimization mathematical programming network model is proposed for determining the optimal number of empty containers to be allocated between ports. The parameters such as the cost of loading container, cost of unloading container, leasing cost of empty container, cost of storing container, supplies, demands and ship capacities for empty containers are considered in this model. By taking advantages of the fuzzy decision making and the network structure, we show how a mixed fuzzy decision making and optimization programming model can be applied to solve the empty container allocation problem. The utilization of the proposed model is demonstrated with a case of trans-Pacific liner route in the real world. Six major container ports on the trans-Pacific route are considered in the case study, including the Port of Kaohsiung, the Port of Hong Kong, the Port of Keelung, the Port of Kobe, the Port of Yokohama and the Port of Los Angles. The results show that the proposed mixed fuzzy decision making and optimization programming model can be used to solve the empty container allocation problem well. [Copyright &y& Elsevier]

The way a given product should be advertised on television in order to achieve the highest rating under specific restrictions was and still is one of the problems companies using advertising face. In this paper, this problem is approached using mathematical programming. What is asked, is the best possible combination of placements of a commercial (which channel, what time, how often) with the objective of the highest rating and subject to the limitation of the capital that is available for advertising. At the same time, the applied model results in a decrease in the time needed for the programming of the television time. [Copyright &y& Elsevier]

Network dimensioning is a specific kind of the resource allocation problem. One of the tasks in the network optimization is to maximize the total flow on given pairs of nodes (so-called demands or paths between source and target). The task can be more complicated when different revenue/profit gained from each unit of traffic stream allocated on each demand is taken into account. When the total revenue is maximized the problem of starvation of less attractive paths can appear. Therefore, it is important to include some fairness criteria to preserve connections between all the demands on a given degree of quality, also for the least attractive paths. In this paper, a new bicriteria ratio optimization method which takes into account both, the revenue and the fairness is proposed. Mathematical model is built in a form of linear programming. The solutions are analyzed with some statistical measures to evaluate their quality, with respect to fairness and efficiency. In particular, the Gini’s coefficient is used for this purpose.