Multiple Allocation Research Articles

Enhancing supply chain resilience under disruption: analysis of the farmed data by Monte Carlo simulation

Motivated by the critical need for supply chains to withstand disruptions, this paper investigates the application of three core resilience strategies and their combinations at the network design level. We propose an investigation framework that utilises Monte Carlo simulation to generate disruption rates of varying severity. This allows for the mathematical optimisation of eight different models, each representing a combination of the three core resilience strategies, based on the simulated disruptions. The framework also incorporates farmed structured data analysis to examine the performance and features of each model. Considering disruptions that can affect both suppliers and factories, a detailed comparison is performed between the strategies. Numerical comparisons reveal that the model incorporating all resilience strategies (Multiple Allocation Network with Backup Suppliers and Factories, or MANBSF) outperforms others, potentially achieving zero shortages even under significant disruptions. This comprehensive model allows for multiple allocations of facilities and incorporates backup suppliers and factories. The analysis further suggests that network resilience increases with size, density, and the number of complete paths.

Mixtures of Probit Regression Models with Overlapping Clusters

Studies with binary outcomes on a heterogeneous population are quite common. Typically, the heterogeneity is modelled through varying effect coefficients within some binary regression setting combined with a clustering procedure. Most of the existing methods assign statistical units to distinct and non-overlapping clusters. However, there are scenarios where units exhibit a more complex organization and the clusters can be thought as partially overlapping. In this case, the standard approach does not work. In this paper, we define a mixture of regression models that allows overlapping clusters. This approach involves an overlap function that maps the regression coefficients, either at the unit or response level, of the parent clusters into the coefficients of the multiple allocation clusters. In order to deal with this intrinsic heterogeneity, regression analyses have to be stratified for different groups of observations or clusters. We present a computationally efficient Monte Carlo Markov Chain (MCMC) scheme for the case of a mixture of probit regressions. A simulation study shows the overall performance of the method. We conclude with two illustrative examples of modelling voting behavior, involving United States (US) Supreme Court justices over a number of topics and members of the United Kingdom (UK) parliament over divisions related to Brexit. These applications provide insights on the usefulness of the method in real applications. The method described can be extended to the case of a generic mixture of multivariate generalized linear models under overlapping clusters.

Discovering Novel Social Preferences Using Simple Artificial Neural Networks

How do we make decisions about allocating scarce resources to others when we face multiple alternatives? While people usually evaluate multiple allocations jointly or non-independently, distributional preferences have been analyzed through behavioral models that assume independent evaluation. Here, we explore distributional preferences that can be identified when we analyze allocation decisions with a flexible model class that can factor in joint evaluation. In Study 1, we recruited 3,006 Japanese crowd workers and provided them with 13,041 theoretically designed resource allocation problems. Using artificial neural networks, we discovered two types of allocation problems among 13,041 problems where the choices made by the participants could only be predicted with a model that factored in joint evaluation of two options. For example, one of these problems showed that the participants became sensitive to differences in their self-reward between two allocations which pitted the participants against disadvantageous equity, which could be naturally supported by our intuition but could not have been discovered unless joint evaluation was considered. In preregistered Study 2, we recruited 185 Japanese participants to conduct a conceptual replication of the machine-discovered distributional preferences. We had participants evaluate two allocation options on the same (i.e., jointly) or different (i.e., separately) screens, confirming that the distributional preferences discovered in Study 1 (i.e., joint-evaluation situation) were more often observed in the former joint-screen situation. Our findings showcase the usefulness of a prediction-oriented machine learning approach to the exploration of novel behavioral theories in social decision-making.

Correlation between clinical and neuropathological subtypes of progressive supranuclear palsy

IntroductionProgressive supranuclear palsy (PSP) is characterized by pathology prominently in the basal ganglia, the tegmentum of the brainstem, and the frontal cortex. However, pathology varies according to clinical features. This study aimed to statistically verify the correspondence between the clinical and pathological subtypes of PSP. MethodsWe identified patients with a pathological diagnosis of PSP and classified the eight clinical subtypes of the Movement Disorders Society criteria for the clinical diagnosis of PSP (MDS-PSP criteria) into the Richardson, Akinesia, and Cognitive groups. We used anti-phosphorylated tau antibody immunostaining to semi-quantitatively evaluate neurofibrillary tangles (NFTs) and coiled bodies/threads (CB/Ths) in the globus pallidus, subthalamic nucleus, and midbrain tegmentum. In the frontal cortex, tufted astrocytes (TAs) and CB/Ths were assessed on a 3-point scale. We compared the pathology among the three groups, recorded the phenotypes ranked the second and lower in the multiple allocation extinction rule and examined whether the pathology changed depending on applying each phenotype. ResultsThe Richardson group exhibited severe NFTs and CB/Ths in the midbrain tegmentum. The Akinesia group showed severe NFTs in the globus pallidus. The Cognitive group had severe TAs and CB/Ths in the frontal cortex. TAs and CB/Ths in the frontal cortex correspond to behavioral variant frontotemporal dementia, and supranuclear vertical oculomotor palsy. ConclusionThese clinical symptoms may reflect the distribution of tau pathologies in PSP.

Allocation and smart inverter setting of ground-mounted photovoltaic power plants for the maximization of hosting capacity in distribution networks

As the integration of solar photovoltaic (PV) power plants into distribution networks grows, quantifying the amount of PV power that distribution networks can host without harmfully impacting power quality becomes critical. This work aims to determine the best number, location, and size of PV systems to be installed on a distribution feeder, as well as the best control set-points of the PV inverters, to maximize the PV hosting capacity (HC). Therefore, a simulation-optimization framework is proposed for siting and sizing ground-mounted PV power plants equipped with smart inverters (SIs). Single (decentralized) and multiple (distributed) allocations are analyzed by considering the connection of one, two, and three PV systems. Genetic algorithm (GA) and particle swarm optimization (PSO) metaheuristics are employed to solve the optimization problem. The simulation-optimization framework is tested on a real-world feeder model from an Ecuadorian utility. Installing two PV systems with their SIs operating with the Volt-VAr control function yields maximum PV HC, which is increased by 32.1 % compared to a single PV power plant operating at a unity power factor. Moreover, a comparative analysis of the two metaheuristic algorithms reveals that the PSO method provides better results than GA.

Reliable multiple allocation hub location problem under disruptions

Reliable multiple allocation hub location problem under disruptions

Global warming potential of lithium-ion battery cell production: Determining influential primary and secondary raw material supply routes

This study assesses the global warming potential (GWP) impacts of lithium-ion battery (LIB) recycling and subsequent cell production from primary and secondary raw materials. Furthermore, the study proposes multiple allocation strategies for the GWP impacts of LIB recycling, which range from 18 to 22 kg CO2 equivalent per kilowatt-hour NMC111 and NMC811 battery packs to determine the environmental burdens of secondary raw materials. The results demonstrate that using secondary raw materials could ideally reduce NMC111 and NMC811 cell production GWP by 30 %. In practice, GWP impact reductions will vary depending on factors, such as the contributions of primary production and recycling to battery raw material supply, and the market share of the different technologies employed within both primary production and recycling.Based on estimates for the availability of recycled raw materials to the European battery value chain, the sensitivity analysis methods identified NMC hydroxide, nickel sulfate, and battery-grade graphite as the most influential recycled raw materials on NMC111 cell production GWP. These materials received importance scores of 59%, 8%, and 6% respectively. Primary supply routes for cobalt sulfate received the highest importance score, followed by the primary supply routes of lithium carbonate and nickel sulfate. For NMC811 cells, nickel sulfate (34%), NMC hydroxide (27%) and graphite (8%) were identified as the most influential recycled raw materials, with the primary supply routes of nickel sulfate, lithium hydroxide and battery-grade graphite being the most influential.

Capacitated multiple allocation hub location problems under the risk of interdiction: model formulations and solution approaches

Capacitated multiple allocation hub location problems under the risk of interdiction: model formulations and solution approaches

Target decomposition of regional CO2 emissions peaking under multiple allocation models: a case study in Jiangsu province, China

Target decomposition of regional CO2 emissions peaking under multiple allocation models: a case study in Jiangsu province, China

An efficient model for the multiple allocation hub maximal covering problem

The hub location problem is one of the challenging subjects in location theory. This problem can benefit transportation, telecommunication or delivery systems, in which hub nodes are responsible for receiving, collecting and delivering commodities. This paper discusses the multiple allocation hub maximal covering problem (MAHMCP). In this problem, the flow of an origin–destination pair is transferred via multiple hubs such that the total flow covered by located hubs is maximized. We present a new model for the MAHMCP, which has a significantly smaller number of binary variables compared to previous models. The proposed model is theoretically stronger than past models, and an empirical study using the Australia Post (AP) dataset demonstrates its effectiveness. Our experiments show that the new formulation provides high-quality solutions and fast run times for instances up to 100 nodes.

ILS-based algorithms for the profit maximizing uncapacitated hub network design problem with multiple allocation

This study addresses a hub network design problem to maximize net profit. This problem considers an incomplete hub network with multiple allocation that does not impose capacity constraints, does not allow direct connections between non-hub nodes, and accepts the demand to be partially met, being satisfied only when profitable. To tackle this problem, which is NP-hard, we propose two heuristic algorithms based on the Iterated Local Search (ILS) metaheuristic, a standard ILS algorithm, and an Enhanced ILS algorithm, which increases the perturbation level only after a few unsuccessful attempts at improvement. Both algorithms use Random Variable Neighborhood Descent in the local search. Computational experiments were performed using benchmark instances for hub location problems, and statistical analyzes of the algorithms were presented. Numerical results confirm that both algorithms yield good-quality solutions with an acceptable runtime. In particular, the proposed algorithms obtain the optimal solution for most instances with up to 150 nodes, which have known optimal solutions. Furthermore, the proposed algorithms were able to handle instances with up to 500 nodes.

A GPU-Based Genetic Algorithm for the Multiple Allocation P-Hub Median Problem

As the sizes of realistic hub location problems increase as time goes on (reaching thousands of nodes currently) this makes such problems difficult to solve in a reasonable time using conventional computers. This study aims to show that such problems may be solved in a short computing time and with high-quality solutions using the computational power of the GPU (actually available in most personal computers). So, we present a GPU-based approach for the uncapacitated multiple allocations p-hub median problems. Our method identifies the nodes that are likely to be hubs in the optimal solution and improves them via a parallel genetic algorithm. The obtained GPU implementation reached within seconds the optimal or the best solutions for all the known benchmarks we had access to and solved larger instances up to 6000 nodes so far unsolved. Compared to this study, no other article dealing with hub location problems has presented results for instances as large.

Multiple allocation hub location with service level constraints for two shipment classes

In this paper, we study a hub network design problem arising in the context of a third-party logistics (3PL) service provider, which acts as an intermediary between shippers and carriers. A 3PL service provider usually caters to different classes of shipments that require different levels of service, e.g. two-day delivery, next-day delivery etc. We, therefore, study the problem under stochastic demand from two classes of shipments, with one class receiving priority over the other in service at the hubs to maintain the different service levels required by them. To this end, we present two models for designing a capacitated hub network with a service level constraint, defined using the distribution of time spent at hubs, for each shipment class. The models seek to design the hub network at the minimum total cost, which includes the total fixed cost of equipping open hubs with sufficient processing capacity and the variable transportation costs. The network of hubs, given their locations, is thus modeled as spatially distributed priority queues. The resulting model is challenging to solve, for which we propose a cutting plane-based exact solution method.

Solving Hub Location Problems With Profits Using Variable Neighborhood Search

This paper proposes variable neighborhood search (VNS) heuristics to solve hub network design problems with profits, which are uncapacitated hub location problems with incomplete hub networks. These problems seek to locate hub facilities, design hub networks, and assign spokes to hubs to maximize total profits. Six problems consisting of multiple allocation, single allocation, and r-allocation strategies, with optional direct connections, are solved. Unlike hub location problems that minimize costs satisfying all service demands, the operators can choose to satisfy a subset of travel demands to maximize profits. Although exact methods and heuristics are both commonly used for solving hub location problems, the problems with profits are mainly solved by the former. Therefore, VNS-based heuristics are proposed to solve six variants of hub location problems. The proposed heuristics have the same shaking procedure to escape local optima, while neighborhood structures in the improvement procedure depend on the allocation strategies. To evaluate the heuristics, this study also designs enhanced Benders decomposition methods which are exact algorithms. Computational experiments on existing benchmark datasets reveal an extraordinary performance of the heuristics. For the instances that can be solved by exact methods, the heuristics solve over 90% to optimality while being one to three orders of magnitude faster than the commercial solver CPLEX and Benders decomposition. Given the outstanding accuracy, with significantly reduced computational cost, the study contributes to the usage of heuristics for hub location problems with profits, especially for larger-scale networks, where exact methods cannot be executed because of limited computational resources.

An Adaptive Multiobjective Genetic Algorithm with Multi-Strategy Fusion for Resource Allocation in Elastic Multi-Core Fiber Networks

Core switching on different links in optical networks enables network operators to allocate network resources more flexibly, so as to reduce the network request blocking ratio under limited resources. Facing a differentiated network environment and diversified user demands, network operators need to optimize multiple objectives that are independent and diversionary of each other, and to provide multiple resource allocation schemes whose objective values do not dominate each other. For the static routing, spectrum, and core assignment (RSCA) problem in elastic optical networks with multi-core fiber (MCF-EONs), there is no literature that simultaneously considers core switching and multiobjective optimization algorithms. This paper improves the existing models and algorithms to adapt to the RSCA problem. In this paper, the RSCA problem is formulated as an integer linear programming model to minimize both network request blocking and crosstalk ratios simultaneously by considering core switching and inter-core crosstalk. To solve the model efficiently, we, therefore, design a joint routing and core coding scheme supporting core switching and propose a multiobjective evolutionary algorithm based on decomposition with adaptation and multi-strategy fusion (MOEA/D-AMSF), which integrates the new mechanisms of hybrid initial population generation, adaptive crossover, and double-layer and multi-point mutation in different iteration stages. These new mechanisms accelerate algorithm convergence and enhance solution diversity. Simulation results show that the proposed algorithm can obtain more dominated and diverse solutions compared with the existing multiobjective algorithm without considering core switching.

Designing a new model for the hub location-allocation problem with considering tardiness time and cost uncertainty

ABSTRACT This paper has addressed the new model for the hub location-allocation problem that can evaluate the impact of cost and time of tardiness in delivering goods to the destination, find the optimal number of hubs and vehicles with different capacities, and congestion in hubs. In addition, this study investigates the problem from two perspectives: scheduled time and real-time. Also, it compares the problem in circumstances with tardiness and solves the problem with two objective functions by minimizing the total costs, handling time, and delay time caused by congestion in the hubs. Moreover, the uncertainty of transportation costs and hub establishment are considered. For this purpose, the Monte-Carlo simulation is used. Due to the models being non-convex and non-derivative, they are NP-hard. So, the models are solved with the particle swarm optimization (PSO) algorithm. The models are tested on Australian Post (AP) data for single and multiple allocations. Also, we presented a model that can illustrate the effect of tardiness on the results and service time. The results show multiple allocation than single ones needs the smaller hubs and cheaper vehicles for transportation due to access the nodes to several hubs.

Capacitated Air/Rail Hub Location Problem With Uncertainty: A Model, Efficient Solution Algorithm, and Case Study

Well-designed multi-modal transportation networks are crucial for our connected world. For instance, the excessive construction of railway tracks in China, at speeds up to 350 km/h, makes it necessary to consider the interaction of rail with air transportation for network design. In this study, we propose a model for an air/rail multi-modal, multiple allocation hub location problem with uncertainty on travel demands. Our model is unique in that it integrates features from the existing literature on multi-modal hub location problem (including hub-level capacities, link capacities, direct links, travel cost and time, transit costs and uncertainty), which have not been considered simultaneously, given its high computational complexity. We formulate this model with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(n^{4})$ </tex-math></inline-formula> variables and show that the implementation of a Benders decomposition algorithm is inherently hard, because of the cubic number of variables in the master problem. Furthermore, we derive an iterative network design algorithm and additional improvement strategies: MMHUBBI which resolves a restricted problem by the solver CPLEX and MMHUBBI-DIRECT which re-designs the transportation network by a heuristic. Our evaluation on real-world dataset for Chinese domestic transportation shows that MMHUBBI provides a significant speed-up on all instances, compared to using CPLEX, while obtaining near-optimal solutions. MMHUBBI-DIRECT further reduces the runtime/memory usage but provides solutions with worse quality. We believe that our study contributes towards the design of more realistic multi-modal hub location problems.

Intelligent Resource Allocation in Industrial IoT using Reinforcement Learning with Hybrid Meta-Heuristic Algorithm

The exponential growth of networking technology has resulted in a massively expanded computer ecosystem. With implications in the Industrial Internet of Things (IIoT) and virtual reality, mobile networks operate and provide a multi-aspect strategy for multiple resource allocation paradigms and service-oriented possibilities in the computing sectors. The Mobile Edge Computing (MEC) model combines a virtual source with edge communication among execution. Thus, this study is to develop and implement a revolutionary resource allocation technique in the IIoT by combining optimal Reinforcement Learning (RL) with a hybrid meta-heuristic algorithm. The three basic levels in the proposed paradigm are "Data input layer," "Data management layer," and "Data analytics layer". The data management layer is responsible for the data collected from edge devices and external devices. The proposed model's goal at the data management layer is to provide an intelligent work allocation mechanism. The Harris Hawks-Spider Monkey Optimization (HH-SMO) method combines Harris Hawks Optimization (HHO) and Spider Monkey Optimization (SMO) to find the best job allocation. From the statistical analysis, the mean of HH-SMO-RL is 15.88%, 14.91%, 14.79%, and 5.99% superior to SMO-RL, HHO-RL, JA-RL, and DHOA-RL respectively, which has shown the resource allocation in IIoT using optimized RL respectively.

Stochastic hub location problems with Bernoulli demands

This paper introduces the hub location problem with Bernoulli demands (HLPBD). The problem is a variant of capacitated stochastic hub location problems in which demands for transportation service are assumed to be Bernoulli random variables. We address the problem under single and multiple allocation settings, and for each case, a two-stage stochastic programming model is presented. Further, assuming a homogeneous demand probability, a closed-form expression for the recourse function is presented, and deterministic equivalent formulations are developed for the original stochastic problems. To solve large instances, exact solution algorithms based on Benders decomposition and Lagrangian relaxation are proposed. An extensive set of computational experiments on three well-known data sets is conducted to test the efficiency of the proposed models and solution algorithms, and also to evaluate the effect of different input parameters on the optimal solutions.

Competitive multiple allocation hub location problem considering primary and backup routes

Competitive multiple allocation hub location problem considering primary and backup routes