Assignment Cost Research Articles

Unravelling the assignment problem under intuitionistic triangular fuzzy environment by the novel heuristic Dhouib-Matrix-AP1

The Assignment Problem (AP) can be stated as n activities to be assigned to n resources in such a way that the overall cost of assignment is minimized and each activity is assigned to one and only one resource. In real-life, the parameters of the AP are presented as uncertain numbers due to the lack of knowledge, experiences or any other (internal or external) factor. In this paper, the AP is considered under intuitionistic triangular fuzzy number and solved by the novel constructive heuristic Dhouib-Matrix-AP1 (DM-AP1) with a time complexity of O(n). Actually, this paper presents the first enhancement of the novel heuristic DM-AP1 to solve the AP under intuitionistic triangular fuzzy environment. DM-AP1 is composed of three simple steps: computing the total cost, selecting the highest value and finding the minimal element. These steps are repeated in n iterations with the use of a standard deviation statistical metric. Two case studies of AP under intuitionistic triangular fuzzy set are taken from the literature and a step-by-step application of the novel DM-AP1 heuristic is presented for more clarification.

A Closer Look at the Joint Training of Object Detection and Re-identification in Multi-Object Tracking.

Unifying object detection and re-identification (ReID) into a single network enables faster multi-object tracking (MOT), while this multi-task setting poses challenges for training. In this work, we dissect the joint training of detection and ReID from two dimensions: label assignment and loss function. We find previous works generally overlook them and directly borrow the practices from object detection, inevitably causing inferior performance. Specifically, we identify a qualified label assignment for MOT should: 1) have the assignment cost aware of ReID cost, not just detection cost; 2) provide sufficient positive samples for robust feature learning while avoiding ambiguous positives (i.e., the positives shared by different ground-truth objects). To achieve the above goals, we first propose Identity-aware Label Assignment, which jointly considers the assignment cost of detection and ReID to select positive samples for each instance without ambiguities. Moreover, we advance a novel Discriminative Focal Loss that integrates ReID predictions with Focal Loss to focus the training on the discriminative samples. Finally, we upgrade the strong baseline FairMOT with our techniques and achieve up to 7.0 MOTA / 54.1% IDs improvements on MOT16/17/20 benchmarks under favorable inference speed, which verifies our tailored label assignment and loss function for MOT are superior to those inherited from object detection.

ACTIVITY-BASED COSTING: A PRACTICAL MODEL OF COST CALCULATION IN PSYCHIATRIC HOSPITALS.

The aim: This paper is an analytical and descriptive study of the use of the Activity Based Costing (ABC) system in mental healthcare institutions for the allo¬cation of overhead costs of auxiliary units that provide non-medical services. The realistic estimates obtained using this costing system will help improve the allocation of resources, especially in the conditions of increased number of patients, and to control overhead costs, ensuring the reduction of non-productive expenses, increasing the efficiency and effectiveness of the mental health and psychosocial support institution. Materials and methods: The study describes and uses ABC as a system and method for calculating inpatient care costs in 2022. The cost of medical services will be calculated as the sum of direct and overhead costs. Overhead costs into inpatient care consist of medical support units overhead costs and non-medical support units overhead costs. Overhead costs in non-medical support units were allocated to the cost of services within the cost centers:hospital departments that specialize in the treatment of specific mental health conditions. Expert opinion regarding the alignment of the cost allocation base with the factors identified for the allocation of each group of overhead costs in the mental health and psychosocial support facility was taken into account . Results: Calculations showed the impracticality of using a single tariff for psychiatric care medical services for adults and children in inpatient conditions, since there are cost centers, namely, clinical departments of health care institutions, which consume a larger amount of resources. In particular, these are the departments providing the treatment of psychotic disorders caused by taking psychoactive substances, as well as schizophrenic, organic disorders, including symptomatic, mental disorders). Accordingly, the tariff for such medical services should be higher. Conclusions: ABC is applicable in mental health care institutions and can help to efficiently cost processes, and thereby overcome a key challenge: to overesti¬mation or underestimation of costs into a medical services. Cost assignment through ABC generated more accurate cost estimates, which suggests that model of cost calculation ABC is more able to address complexity in mental health care institutions. ABC should be gradually incorporated into an estimated approach to costing leads, to progressively bring reliable cost-accounting practices to mental health care institutions.

A Spatial Decision Support System for Prioritizing Repair Interventions on Power Networks

The business continuity of services provided by Critical Infrastructures is vital in order to ensure the security, the economy and the public’s health of a Nation. Delays and bad recovery strategies after disasters or failures can lead to impairing impacts in terms of injury to people, environmental pollution and loss of time, money and resources. In such a context, the adoption of a spatial Decision Support System (DSS) might play a crucial role in order to help operators to adopt the best recovery strategy in the shortest possible time frame. Current approaches do not consider the problem of assigning an intervention location to a maintenance crew and do not account for the effective time needed for emergency intervention. In this paper we develop a novel spatial multi-criteria DSS methodology for prioritizing repair interventions on power networks. The multi criteria strategy is solved by the adoption of Incomplete Analytic Hierarchy Process (AHP) which computes holistic assignment costs as the result of the combination of multiple and possibly conflicting metrics of cost.Then, we use the holistic costs as the basis for a task assignment phase that is based on the Hungarian algorithm. The proposed strategy has been implemented as a module in the Decision Support System, namely Critical Infrastructure Protection Risk Analysis and Forecast (CIPCast), whose outputs are represented on a web-based Geographic Information System (GIS) platform. The effectiveness of the proposed multi-criteria strategy has been validated via a real case study on the Rome City electrical distribution network.

Collection of economic data using UB-04s: Is it worth the effort? Evidence from two clinical trials.

Cost collection using UB-04 forms for economic evaluation is challenging, as UB-04 collection is time and effort intensive and compliance is imperfect. Alternative data sources could overcome those challenges. The objective of this study is to determine the usefulness of UB-04 data in estimating hospital costs compared to clinical case report form (CRF) data. Health care utilization costs were compared from financial information in UB-04s and from an assignment process using CRF data, from the WRAP-IT (23 infections) and the Micra IDE trials (61 adverse events and 108 implants). Charge-based costs were calculated by multiplying charges in UB-04s and hospital-specific Cost-to-Charge ratios from the Centers for Medicare and Medicaid Services cost reports. The cost assignment process used clinical information to find comparable encounters in real world data and assigned an average cost. Paired difference tests evaluated whether both methods yield similar results. The mean difference in total infection related costs between methods in the WRAP-IT trial was $152 +/-$22,565. In the Micra IDE trial, the mean difference in total adverse event related costs between methods was -$355 +/-$8,298 while the mean difference in total implant related costs between methods was $-3,488 +/-$13,859. Wilcoxon tests and generalized linear models could not reject the difference in costs between methods in the first two cases. Cost assignment methods achieve results similar to costs obtained through UB-04s, without the additional investment in time and effort. The use of UB-04 information for services that are not mature in a health care system may present unexpected challenges, necessitating a tradeoff with other methods of cost assignment.

Analysis of temporal trends in robotic surgery costs using the outpatient inguinal hernia repair model

BackgroundRobotic technology is increasingly utilized despite increased costs compared with laparoscopic procedures. As the robot is a fixed, indirect cost, we hypothesized increased volume of robotic procedures will decrease operative costs per patient. The model of same-day, unilateral, primary inguinal hernia surgery in males was chosen. MethodsThe Florida Agency for Health Care Administration database was queried for inguinal hernia repairs from 2015 to 2020. Inflation adjusted total and operative costs per patient were collected. Cost-over-time and change in cost-over-time were calculated for open, laparoscopic, and robotic cases. Linear regression using cost as the dependent variable generated predictive parameters. ResultsIn the study, 36,393 cases (19,364 open, 12,322 laparoscopic, 4,707 robotic) among 86 hospitals were included. In addition, 18 hospitals were “high volume,” defined as total robotic inguinal hernia volume of >100 (range, 107-368) during the study period, and included 8,604 cases (3,915 open, 1,786 laparoscopic, 2,903 robotic). Compared with laparoscopic, total robotic cost and cost over time were 1.22- (P < .001) and 1.5-fold higher (P < .002). The change in cost-over-time was increased significantly in robotic cases: 358, 420, 548, 691, and 1,542 cost-over-time for 2015 to 2020, respectively. Positive contributors to total hospital robotic costs were total robotic inguinal hernia volume (17.3), total laparoscopic inguinal hernia volume (12.6), and number of hospital beds (1.9). Total open inguinal hernia volume was a negative contributor (–10). ConclusionWe conclude, in the short term, robotic surgical costs are not behaving as traditional fixed costs in outpatient, unilateral inguinal hernia surgeries. Hospital methodology for cost assignment and increased robotic fixed costs such as purchase of additional instruments may explain these results.

One’s Fixing Method for a Distinct Symmetric Fuzzy Assignment Model

This paper hinges upon the subject of an (n × n) assignment problem and the distinct symmetric fuzzy assignment problem byassigning n machines to n jobs. One’s orientation algorithm is developed for solving the assignment problems based on the position of one’s chosen in every row as well as every column to perform allocations and obtain the assignment cost at every (n − 1) reduced matrix. We also extended the two different symmetric concept to the problem to find the optimum solution based on symmetrical data and also used the ranking concept in our fuzzy assignment problem. In this proposed algorithm, the one’s position is associated with the successor of one in each iteration toobtain the optimal schedule and assignment cost. The comparative analysis is properly considered and discussed. The proposed technique is elaborated with the help of numerical computations and it gives clarity to the idea of this concept.

Dynamic Task Allocation Method for Heterogenous Multiagent System in Uncertain Scenarios of Agricultural Field Operation

This study focuses on the problem of dynamic task allocation for a heterogeneous multiagent system (MAS) in uncertain scenarios and its application in agricultural field operation. Previous studies lacked robustness or efficiency for uncertain environments especially in agricultural field, such as agent removal, agent inclusion, changes of agent capabilities, and task changes. We present herein a novel concept of the potential field of capability influence (PFCI), and based on which, we can estimate potential overloaded tasks. This provides an opportunity to improve the allocation of the remaining tasks. Then, we propose a heuristic-based clustering auction (HBCA) method by introducing PFCI into the auction mechanism to achieve an effective and efficient task allocation. The whole method consists of the three following phases based on the auction mechanism: 1) auctioneer preprocessing phase that adopts a capability priority strategy to select the preferable agent; 2) bidder preprocessing phase that introduces the PFCI to obtain a list of clustered tasks heuristically; and 3) adaptive auction phase that applies the auction mechanism to achieve an appropriate match between agents and tasks. Numerical simulations with different uncertain field operation scenarios validate the effectiveness of the proposed method. The results show that the proposed HBCA can dramatically reduce the total assignment cost and outperform state-of-the-art methods, especially in the case of obvious variances in agent capabilities.

Due-Window Assignment Methods and Scheduling with Generalized Positional-Dependent Weights

We focus on a single-machine scheduling problem with common and slack due-window assignment methods. Both the job sequence and due-windows are decision variables to be determined by the decision maker. We consider the following performance criterion: the total weighted number of early and late jobs plus the total weighted of earliness, tardiness and due-window assignment cost, where the weights depend on the position in which a job is scheduled. Some properties are established, and it is shown that the problem can be solved in [Formula: see text] time, where [Formula: see text] is the number of jobs. The extensions of the model are to cases of general position-dependent processing times and time-dependent processing times.

From STRs to SNPs via ddRAD-seq: Geographic assignment of confiscated tortoises at reduced costs.

Assigning individuals to their source populations is crucial for conservation research, especially for endangered species threatened by illegal trade and translocations. Genetic assignment can be achieved with different types of molecular markers, but technical advantages and cost saving are recently promoting the shift from short tandem repeats (STRs) to single nucleotide polymorphisms (SNPs). Here, we designed, developed, and tested a small panel of SNPs for cost‐effective geographic assignment of individuals with unknown origin of the endangered Mediterranean tortoise Testudo hermanni. We started by performing a ddRAD‐seq experiment on 70 wild individuals of T. hermanni from 38 locations. Results obtained using 3182 SNPs are comparable to those previously obtained using STR markers in terms of genetic structure and power to identify the macro‐area of origin. However, our SNPs revealed further insights into the substructure in Western populations, especially in Southern Italy. A small panel of highly informative SNPs was then selected and tested by genotyping 190 individuals using the KASP genotyping chemistry. All the samples from wild populations of known geographic origin were genetically re‐assigned with high accuracy to the original population. This reduced SNPs panel represents an efficient molecular tool that enables individuals to be genotyped at low cost (less than €15 per sample) for geographical assignment and identification of hybrids. This information is crucial for the management in‐situ of confiscated animals and their possible re‐allocation in the wild. Our methodological pipeline can easily be extended to other species.

EmbAssi: embedding assignment costs for similarity search in large graph databases

The graph edit distance is an intuitive measure to quantify the dissimilarity of graphs, but its computation is mathsf {NP}-hard and challenging in practice. We introduce methods for answering nearest neighbor and range queries regarding this distance efficiently for large databases with up to millions of graphs. We build on the filter-verification paradigm, where lower and upper bounds are used to reduce the number of exact computations of the graph edit distance. Highly effective bounds for this involve solving a linear assignment problem for each graph in the database, which is prohibitive in massive datasets. Index-based approaches typically provide only weak bounds leading to high computational costs verification. In this work, we derive novel lower bounds for efficient filtering from restricted assignment problems, where the cost function is a tree metric. This special case allows embedding the costs of optimal assignments isometrically into ell _1 space, rendering efficient indexing possible. We propose several lower bounds of the graph edit distance obtained from tree metrics reflecting the edit costs, which are combined for effective filtering. Our method termed EmbAssi can be integrated into existing filter-verification pipelines as a fast and effective pre-filtering step. Empirically we show that for many real-world graphs our lower bounds are already close to the exact graph edit distance, while our index construction and search scales to very large databases.

Non-additive image steganographic framework based on variational inference in Markov Random Fields

The majority of image steganography methods lie within the additive scheme that assumes the pixel modifications are independent. Whereas, adapting the embedding modifications based on the mutual embedding impact of neighboring pixels reduce changes in the higher-order statistics of the cover imposed by embedding. However, non-additive schemes are more challenging due to the lack of practical embedding codes that minimize an arbitrary distortion function. In this paper, we propose a general non-additive image steganographic framework based on Markov Random Fields (MRF) which generalizes a few existing research in many aspects and offers an elegant way to model the mutual dependencies between neighborhood modifications in terms of pairwise cliques. The proposed model satisfies spatial coherence by penalizing the desynchronized embedding changes, which orients the changes towards the direction of the majority of changes in the neighbors. Mean Field (MF) inference is used to iteratively estimate the marginal probability at each pixel based on its neighbors so that the original interactions are information projected to the resulting marginals. MF replaces the values of variables with expectations so the result is compatible with practical embedding methods.The proposed framework can be applied to any additive scheme; the initial cost assignment is done by an additive method and MRF modeling defines a non-additive distortion related to statistical detectability that encourages the adjacent changes to synchronize. We study our framework in both symmetric and asymmetric schemes. In the symmetric scheme, a parallel MF is used as an adaptive filter to encourage synchronized embedding changes. The framework can upgrade to an asymmetric scheme that reduces the detectability by conditioning embedding based on the message using clamping exerted in a structural MF. Therefore, our framework is compatible with and generalizes existing non-additive methods, and as shown in experimental results, outperforms the state-of-the-art in both schemes.

HEVC video information hiding scheme based on adaptive double-layer embedding strategy

High Efficiency Video Coding (HEVC) is well-known as an internationally popular video coding standard, and HEVC-based steganography has received increasing attention. In this paper, a new adaptive HEVC video information hiding method based on Prediction Unit (PU) partition mode and double-layer embedding strategy is proposed. Double-layer embedding is a method to complete the first-layer embedding using the mapping rules of PU partition mode, and to perform the second-layer embedding after the first-layer embedding. The cost assignment function designed in this paper can accurately evaluate the second-layer data embedding distortion. The frame position, motion properties and block size of PU are taken into consideration for the second-layer data embedding, and the syndrome-trellis codes (STCs) are used to minimize the embedding distortion. Experimental results show that the proposed adaptive double-layer embedding algorithm has better embedding efficiency and less embedding distortion in most cases.

Scalable Feature Matching Across Large Data Collections

This article is concerned with matching feature vectors in a one-to-one fashion across large collections of datasets. Formulating this task as a multidimensional assignment problem with decomposable costs (MDADC), we develop fast algorithms with time complexity roughly linear in the number n of datasets and space complexity a small fraction of the data size. These remarkable properties hinge on using the squared Euclidean distance as dissimilarity function, which can reduce matching problems between pairs of datasets to n problems and enable calculating assignment costs on the fly. To our knowledge, no other method applicable to the MDADC possesses these linear scaling and low-storage properties necessary to large-scale applications. In numerical experiments, the novel algorithms outperform competing methods and show excellent computational and optimization performances. An application of feature matching to a large neuroimaging database is presented. The algorithms of this article are implemented in the R package matchFeat available at github.com/ddegras/matchFeat. Supplementary materials for this article are available online.

Bipartite Matching

The Kuhn-Munkres (KM) algorithm is a classical combinatorial optimization algorithm that is widely used for minimum cost bipartite matching in many real-world applications, such as transportation. For example, a ride-hailing service may use it to find the optimal assignment of drivers to passengers to minimize the overall wait time. Typically, given two bipartite sets, this process involves computing the edge costs between all bipartite pairs and finding an optimal matching. However, existing works overlook the impact of edge cost computation on the overall running time. In reality, edge computation often significantly outweighs the computation of the optimal assignment itself, as in the case of assigning drivers to passengers which involves computation of expensive graph shortest paths. Following on from this, we also observe common real-world settings exhibit a useful property that allows us to incrementally compute edge costs only as required using an inexpensive lower-bound heuristic. This technique significantly reduces the overall cost of assignment compared to the original KM algorithm, as we demonstrate experimentally on multiple real-world data sets and workloads. Moreover, our algorithm is not limited to this domain and is potentially applicable in other settings where lower-bounding heuristics are available.

Solving the reliability-oriented generalized assignment problem by Lagrangian relaxation and Alternating Direction Method of Multipliers

The well-known generalized assignment problem has many real-world applications. The assignment costs between agents and tasks affected by several factors could be unstable and uncertain. In this paper, we assume that the means and variances of assignment costs are known in advance. The idea is that the decision-maker aims to minimize the total assignment costs not only on average, but also to keep their variability as small as possible. Then, a reliability-oriented model with a nonlinear and concave objective function is formulated, and two decomposition-based methods are systematically developed for this challenging problem. The task allocation constraints are dualized and the Lagrangian relaxed problem is broken into many reliability-oriented knapsack subproblems. By the Lagrangian substitution technique, each subproblem is further decomposed into a standard knapsack problem and a simple univariate concave minimization problem. A lower bound is constructed and multipliers are optimized in dual problems by the subgradient method. Feasible solutions can be generated from the results of these reliability-oriented subproblems by Lagrangian heuristic. To further improve the convergence and solution quality, an Alternating Direction Method of Multipliers (ADMM) based decomposition approach is proposed. The augmented Lagrangian relaxed problem is split into a sequence of subproblems by the block coordinate descent method. To cope with the quadratic terms and the concave terms in these subproblems, linearization and Lagrangian substitution techniques are applied. Feasible solutions are produced from these subproblems, and solution quality can be evaluated by the lower bound provided by the Lagrangian relaxed problem. Numerical experiments are conducted on the test cases transformed from the standard benchmark instances, and the ADMM-based method has superior convergence and solution quality.

A Distributed Service-Matching Coverage Via Heterogeneous Agents

We propose a distributed deployment solution for a group of networked agents that should provide a service for a large set of targets, which densely populate a finite area. The agents are heterogeneous in the sense that their quality of service (QoS), modeled as spatial Gaussian distribution, is different. To provide the best service, the objective is to deploy the agents such that their collective QoS distribution is as close as possible to the density distribution of the targets in the sense of the Kullback-Leibler divergence (KLD) measure. We propose a distributed consensus-based expectation-maximization (EM) algorithm to estimate the target density distribution, modeled as a Gaussian mixture model (GMM). Different than the existing algorithms, our proposed distributed EM algorithm enables every agent in the network to obtain an estimate of the GMM model of the distribution of the targets even if only a subset of agents can measure the targets locally. The GMM not only gives an estimate of the targets’ distribution but also clusters the targets to a set of subgroups, each of which is represented by one of the GMM’s Gaussian bases. We use the KLD measure to evaluate the similarity between the QoS distribution of each agent and each Gaussian basis/cluster. A distributed assignment problem is then formulated and solved as a discrete optimal mass transport problem that allocates each agent to a target cluster by taking the KLD as the assignment cost. We demonstrate our results by a sensor deployment for event detection where the sensor’s QoS is modeled as an anisotropic Gaussian distribution.

Cost Assignment at Production Enterprises

Cost Assignment at Production Enterprises

Exergoeconomics as a Cost-Accounting Method in Thermal Grids with the Presence of Renewable Energy Producers

Thermal grids are efficient, reliable, and sustainable technologies for satisfying the thermal demands of buildings. The capability to operate at a low temperature allows not only for the integration of heat produced by renewable energy sources but also for the storage of surplus electricity from the grid via “power to heat” technologies. Besides, in the future, heat consumers are expected to behave increasingly as “prosumers”, supplying in some periods heat produced by renewable energy plants on site. In this scenario, it is important to propose a method for the cost allocation among producers connected to the grid. In this regard, this paper proposes Exergoeconomics as a possible tool for rational cost assignment. To show the capabilities of the method, some operating scenarios are compared for a cluster of five buildings of the tertiary sector interconnected by a thermal grid. Based on exergoeconomic indicators, such as the exergy and exergoeconomic unit costs, insights into the cost formation process of the heat consumed by users are provided. Sensitivity analyses of heat unit cost to design and operating variables are also performed. Results show that in the presence of distributed producers, the heat unit cost could be approximately 33% lower than in the case of centralized production, due to the lower amount of irreversibility generated. Capital investment accounts for 20–28% of the heat unit cost.

A New Coarse Gating Strategy Driven Multidimensional Assignment for Two-Stage MHT of Bearings-Only Multisensor-Multitarget Tracking

The problem of two-dimensional bearings-only multisensor-multitarget tracking is addressed in this work. For this type of target tracking problem, the multidimensional assignment (MDA) is crucial for identifying measurements originating from the same targets. However, the computation of the assignment cost of all possible associations is extremely high. To reduce the computational complexity of MDA, a new coarse gating strategy is proposed. This is realized by comparing the Mahalanobis distance between the current estimate and initial estimate in an iterative process for the maximum likelihood estimation of the target position with a certain threshold to eliminate potential infeasible associations. When the Mahalanobis distance is less than the threshold, the iteration will exit in advance so as to avoid the expensive computational costs caused by invalid iteration. Furthermore, the proposed strategy is combined with the two-stage multiple hypothesis tracking framework for bearings-only multisensor-multitarget tracking. Numerical experimental results verify its effectiveness.