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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]

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]

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.

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]

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]

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.

Dimensioning of telecommunications networks requires the allocation of the ows (bandwidth) to given trac demands for the source-destination pairs of nodes. Unit ow allocated to the given demand is associated with revenue that may vary for dierent 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 ows 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.

Simultaneous expansion of the electrical and thermal energies collected with conventional expansion options is scrutinised. A robust, bio‐inspired evolutionary optimisation method is proposed, to handle the complex expansion planning of a system consisting of both electrical and thermal forms of energy. Rewiring, network reconfiguration, installation of new lines and also new electrical and thermal generation units are considered as the traditional alternatives in expansion planning. To solve the problem, overall generation requirements of a network are assigned along the planning horizon. The allocation problem is formulated as a mixed‐integer non‐linear programming problem that minimises the overall system cost owing to generation capacity among the grid nodes and the newly added or upgraded lines. The performance of the original shuffled frog leaping (SFL) optimisation algorithm is advanced to overcome the complexity of the proposed expansion planning problem. Two modification steps were added to the original SFL technique to enable the proposed modified SFL algorithm to extricate from local minima. The two modification phases pledge a fast convergence rate by achieving a rapid adaptive algorithm, besides a better diversification which is the key to extricate from local minima. The efficacy and robustness of the proposed methodology are verified by applying the method to two modified standard test systems. [ABSTRACT FROM AUTHOR]