Simultaneous Assignment Research Articles

Toxicity Adaptive Lists Design: A Practical Design for Phase I Drug Combination Trials in Oncology.

We introduce a novel algorithmic approach to design phase I trials for oncology drug combinations. Our proposed Toxicity Adaptive Lists Design (TALE) is straightforward to implement, requiring the prespecification of a small number of parameters that define rules governing dose escalation, de-escalation, or reassessment of previously explored dose levels. These rules effectively regulate dose exploration and control the number of toxicities. A key feature of TALE is the possibility of simultaneous assignment of multiple-dose combinations that are deemed safe by previously accrued data. A numerical study shows that TALE shares comparable operative characteristics, in terms of identification of the maximum tolerated dose (MTD), to alternative approaches such as the Bayesian optimal interval design, the COPULA, the product of independent beta probabilities escalation, and the continual reassessment method for partial ordering designs while reducing the risk of overdosing patients. The proposed TALE design provides a favorable balance between maintaining patient safety and accurately identifying the MTD. To facilitate the use of TALE, we provide a user-friendly R Shiny application and an R package for computing relevant operating characteristics, such as the risk of assigning highly toxic dose combinations.

Координация деятельности органов публичной власти как субъектов системы обеспечения безопасности дорожного движения

safety for. The problem of simultaneous assignment of coordination functions in the sphere of road safety to the Government Commission and the Ministry of Internal Affairs of Russia is raised. The gap of federal legislation in terms of determining the coordination of local government activities in this area is noted. The mechanisms of coordination are analyzed on the example of coordination of legal acts, documents on the organization of road traffic, implementation of regional projects and implementation of state control (supervision) over the activities of executive bodies of public authority.

A Novel Fulfillment-Focused Simultaneous Assignment Method for Large-Scale Order Picking Optimization Problem in RMFS

A Novel Fulfillment-Focused Simultaneous Assignment Method for Large-Scale Order Picking Optimization Problem in RMFS

A decomposition approach for integrated locomotive scheduling and driver assignment in rail freight transport

In this work, we consider the integrated problem of locomotive scheduling and driver assignment in rail freight companies. Our aim is to compute an optimal simultaneous assignment of locomotives and drivers to the trains listed in a given order-book. Mathematically, this leads to the combination of a set-packing problem with compatibility constraints and a multi-commodity-flow problem. We develop a binary-programming formulation to model the given task and improve it by performing a clique-based tightening of the original set-packing inequalities. The objective function of this model makes sure that as many trains as possible are running. To handle the computational complexity of the problem, we introduce a novel decomposition approach which decomposes the problem into a master locomotive scheduling problem and a subproblem for driver assignment. It exploits the fact that the master problem is empirically much easier to solve than the subproblem. For any fixed solution of the master problem, we can use the subproblem to either confirm feasibility of the master solution or to derive valid inequalities from various constraint classes to cut the infeasible master solution off and reiterate. To further improve solution times, we also develop a presolve heuristic. We demonstrate the potential of the presented method by solving a large-scale real-world problem instance provided by our industry partner DB Cargo Polska S.A., as well as a set of derived realistic instances.

Optimal Power Allocation in Wireless Communication Networks Using Stochastic Control

Wireless communication networks operate over time-varying channels that depend on channel gains, power allocation algorithms, and inter-channel interference. In this paper, We present techniques for computing the optimal control inputs, which lead to the attainment of the intended Signal to Interference plus Noise ratio (SINR) at different receivers. This optimization problem can be efficiently resolved using non-linear programming tools and permits the simultaneous assignment of multiple desired SINRs at different instances. We establish the conditions necessary to ensure the feasibility of the optimal control problem.

A memetic algorithm with fuzzy-based population control for the joint order batching and picker routing problem

The joint order batching and picker routing problem (JOBPRP) is a combinatorial optimization problem that occurs during in the order-picking operation of warehouse management. It consists of simultaneous assignment of customer orders to batches and routing of picking operations in the batches such that the total travel distance is minimized. In this paper, we develop a memetic algorithm with fuzzy-based population control (MA-FPC) to solve the JOBPRP. MA-FPC takes advantage of the hierarchical nature of the problem to design a batch exchange crossover (BEX) operator and a two-level local improvement procedure. BEX transfers complete batches from parent solutions to offsprings. Local improvement systematically employs a combined heuristic and exact routing approach to solve the picker routing problem. Furthermore, fuzzy-based population control is used to dynamically regulate population diversity during the search, which removes individuals from or adds individuals to the population. In numerical experiments, MA-FPC significantly outperforms state-of-the-art point-based and population-based algorithms developed for the order batching problem and JOBPRP with respect to solution quality in reasonable running times. Our algorithm improves the previous best-known solutions for 57 of the 64 benchmark instances.

SEPARATING MANY LOCALISATION CARDINALS ON THE GENERALISED BAIRE SPACE

Abstract Given a cofinal cardinal function $h\in {}^{\kappa }\kappa $ for $\kappa $ inaccessible, we consider the dominating h-localisation number, that is, the least cardinality of a dominating set of h-slaloms such that every $\kappa $ -real is localised by a slalom in the dominating set. It was proved in [3] that the dominating localisation numbers can be consistently different for two functions h (the identity function and the power function). We will construct a $\kappa ^+$ -sized family of functions h and their corresponding localisation numbers, and use a ${\leq }\kappa $ -supported product of a cofinality-preserving forcing to prove that any simultaneous assignment of these localisation numbers to cardinals above $\kappa $ is consistent. This answers an open question from [3].

Risk Related to AGV Systems—Open-Access Literature Review

AGV systems are considered a necessary element of the recently created cyber-physical systems whose task is to support material flows in intelligent factories. The implementation of AGV systems in cyber-physical systems generates new types of threats, and the level of risk of adverse events related to the operation of these systems is changing. This article aims to present the results of an open-access literature review, the purpose of which was to identify issues addressed in studies of the analysis and risk management of AGV systems, to group publications according to the proposed topical framework, and to identify gaps in the current research. We used the PRISMA concept to review the literature. After completing all stages of the procedure, 153 publications on risk in AGV systems were selected for analysis. To ensure the high quality of the analysis, access to the full texts of the selected documents was necessary. For this reason, we have limited the collection to open-access publications. The results of the analysis allowed us to distinguish 12 primary categories and 26 additional subcategories within the literature. These categories are characterized by different numbers of publications and cover different AGV systems operation areas. The simultaneous assignment of documents to several subcategories indicates the need for a holistic view of issues related to the risk of AGV operation in operating environments with varying degrees of automation. An essential scientific contribution of our analysis is the identification of a research gap. The analyzed publications lacked studies on methods of risk identification and assessment that take into account the specificity of AGV operation, especially in the cyber-physical systems of Logistics 4.0.

A Two-Stage Hybrid Heuristic Algorithm for Simultaneous Order and Rack Assignment Problems

The problem of order and rack assignment to picking stations (ORAPS) is a key joint optimization problem in the order picking process of the robotic mobile fulfillment system (RMFS). Given a set of customer orders and a set of mobile racks, the goal of this problem is to simultaneously assign orders and racks to multiple picking stations so that the racks can provide products to meet the demand of orders by the minimal number of visits to all picking stations. In this article, we build a mathematical model for the ORAPS, which considers all orders assignment with the allocable capacity interval of the picking station and the rack product capacity limitation. To solve the ORAPS problem, we propose a two-stage hybrid heuristic algorithm (TS-HHA) consisting of the reducing stage and the assigning stage. In the reducing stage, a scheme of dynamic programming (DP) is introduced to find a critical rack set, which can focus attention on the most promising racks and increase the speed of problem-solving. In the assigning stage, we propose an optimization strategy that combines a constructive heuristic algorithm and adaptive neighborhood search (CH-ANS). It can generate a high-quality simultaneous assignment scheme and further improve its quality effectively. The computational results show that our proposed algorithm performs better than its competitors on both simulation and practical instances of the ORAPS problem. Note to Practitioners—This article investigates the order and rack assignment to picking stations (ORAPS) problem in the robotic mobile fulfillment system (RMFS) originated from a renowned Chinese logistics platform. We build a more comprehensive and reasonable mathematical programming model for this ORAPS problem and propose a two-stage hybrid heuristic algorithm (TS-HHA) strategy, which can simultaneously assign orders and racks to multiple picking stations so that all customer orders can be processed. The experimental results show that our TS-HHA performs at least 35% better than its competitors on all simulation and practical instances set. We believe that this research work can serve as a kind of generic framework for practical ORAPS problems and be very helpful for operating the RMFS efficiently.

Integrated scheduling of machines and automated guided vehicles (AGVs) in flexible job shop environment using genetic algorithms

In this research integrated scheduling of machines and automated guided vehicles (AGVs) in a flexible job shop environment is addressed. The scheduling literature generally ignores the transportation of jobs between the machines and when considered typically assumes an unlimited number of AGVs. In order to comply with Industry 4.0 requirements, today’s manufacturing systems make use of AGVs to transport jobs between the machines. The addressed problem involves simultaneous assignment of operations to one of the alternative machines, determining the sequence of operations on each machine and assignment of transportation operations between machines to an available AGV. We present a Microsoft Excel® spreadsheet-based solution for the problem. Evolver®, a proprietary GA is used for the optimization. The GA routine works as an add-in to the spreadsheet environment. The flexible job shop model is developed in Microsoft Excel® spreadsheet. The assignment of AGV is independent of the GA routine and is done by the spreadsheet model while the GA finds the assignment of operations to the machines and then finds the best sequence of operations on each machine. Computational analysis demonstrates that the proposed method can effectively and efficiently solve a wide range of problems with reasonable accuracy. Benchmark problems from the literature are used to highlight the effectiveness and efficiency of the proposed implementation. In most of the cases the proposed implementation can find the best-known solution found by previous studies.

Simultaneous Assignment and Structure Determination of Proteins From Sparsely Labeled NMR Datasets

Sparsely labeled NMR samples provide opportunities to study larger biomolecular assemblies than is traditionally done by NMR. This requires new computational tools that can handle the sparsity and ambiguity in the NMR datasets. The MELD (modeling employing limited data) Bayesian approach was assessed to be the best performing in predicting structures from sparsely labeled NMR data in the 13th edition of the Critical Assessment of Structure Prediction (CASP) event—and limitations of the methodology were also noted. In this report, we evaluate the nature and difficulty in modeling unassigned sparsely labeled NMR datasets and report on an improved methodological pipeline leading to higher-accuracy predictions. We benchmark our methodology against the NMR datasets provided by CASP 13.

A multi-objective optimization approach for exploring the cost and makespan trade-off in additive manufacturing

Additive manufacturing (AM) suggests promising manufacturing technologies, which complement traditional manufacturing in multiple areas, such as biomedical, aerospace, defense, and automotive industries. This paper addresses the production planning problem in multi-machine AM systems. We consider all relevant physical and technological parameters of the machines and the produced parts, for using direct metal laser sintering (DMLS) technology. In DMLS technology, each machine produces jobs, where each job consists of several parts arranged horizontally on the build tray. Starting a new job requires a setup operation. We address the simultaneous assignment of parts to jobs and jobs to the machines, while considering the cost and makespan objectives. A unified mixed-integer linear-programming (MILP) formulation that can minimize the above objectives separately and simultaneously is suggested, along with analytical bounds and valid inequalities. Experimentation demonstrates the effectiveness of the proposed formulation with single objectives versus similar formulations from the literature. An efficient frontier approach is applied to the multi-objective problem while generating a diverse set of exact non-dominated solutions. The trade-off between the objectives is analyzed via experimentation. Results show that when identical machines are used, the trade-off is relatively small, and hence the decision-maker can use any of the single objectives. However, when non-identical machines are used, it is important to consider both objectives simultaneously. Moreover, the trade-off increases with the number of machines and heterogeneity of the system, with respect to the size and settings of the machines.

Simultaneous Interleaver Assignment Requests Handling in Inverse Tree Interleaver Deployment Scenario for Multi-user 5G Communications

Interleaver centric systems have a significant role to play in fifth generation (5G) real-time communication. Flip Left–Right approach based Inverse Tree Interleavers (FLRITIs) are among one of the recently developed interleavers for these interleaver centric systems. During the real-time communication, interleaver assignment requests from multiple users often arrive at next generation NodeB (gNB) simultaneously. Therefore, a mechanism is essentially needed to handle these simultaneous interleaver assignment requests at the gNB. In this paper, an efficient mechanism to handle the simultaneous interleaver i.e. FLRITI assignment requests originated from different wireless transmit receive units is proposed. This mechanism helps the network in taking a correct logical decision based on pre-configured policy/policies while assigning correct sequence number and hence, the correct FLRITI to each 5G user from the inverse tree structure. The method is equally effective in all types of time-critical and non-time critical 5G communication scenarios e.g. Ultra reliable low latency communication 5G new radio, enhanced mobile broadband etc.

Active control of linear vibrating systems for antiresonance assignment with regional pole placement

This paper proposes a novel method for antiresonance assignment and regional pole placement in linear time-invariant vibrating systems, by means of state feedback control. The method also handles asymmetric systems and unstable ones too. Additionally, it works with both point and cross-receptances and handles the simultaneous assignment of more antiresonances in the same receptance.The method relies on two stages. In the first stage, the desired pairs of closed-loop zeros of a prescribed receptance are exactly assigned. In the second stage, all the closed-loop system poles are placed within the desired region of the complex plane. This feature allows the controller to impose the system stability and to feature the desired dynamic properties through a regional pole placement. Since the gain correction computed in the second stage is obtained as a solution of the homogeneous system related to the zero-assignment problem, it does not cause any spillover on the assigned zeros. The first step exploits the receptance method for gain computing, while the second step uses the first-order model formulation to exploit all the benefits of the Linear Matrix Inequality theory, by formulating a bilinear matrix problem solved as a semidefinite optimization aimed at reducing the control effort.The chief original contribution of the proposed method is that it embeds an a-priori imposition of both the closed-loop stability and the pole clustering in the desired regions, by overcoming the limitations of most of the methods appeared in the literature. The method effectiveness is demonstrated through five meaningful test cases.

A new mathematical formulation for a potash-mine shift scheduling problem with a simultaneous assignment of machines and workers

In this paper, we introduce a mixed-integer linear program for a shift scheduling problem in a German potash mine. In particular, we consider a short-term (work shift) production scheduling problem, where drill-and-blast mining operations have to be assigned to machines and workers simultaneously. Since we deal with several sequence-dependent setup, changeover, and removal times, TSP-variables are used in the mathematical program to determine the processing-sequence of the operations on each worker and each machine, respectively. In addition, several mining-specific requirements are taken into account to obtain a solution that can be put into practice. Computational experiments are conducted on problem instances of realistic size derived from real-world data. The results show that our new mixed-integer linear formulation outperforms both existing solution procedures for the problem at hand.

Simultaneous assignment of resonances and antiresonances in vibrating systems through inverse dynamic structural modification

This paper proposes a method for the concurrent assignment of resonance and antiresonance frequencies in cross-receptances of undamped vibrating systems, by exploiting inverse dynamic structural modification. The technique also allows partially assigning the mode shapes of interest, which in turn makes it possible to perform pole-zero flipping in cross receptances. This is useful to shape receptances in the frequency range of interest. The method uses the inverse eigenvalue problem of the actual system and the adjunct one, by formulating it through the system matrices, to overcome the numerical issues of using the characteristic polynomials. Besides the theoretical formulation and the numerical application of the method, experimental validation is proposed through a laboratory cantilever beam, that shows the method effectiveness and ease of implementation.

Securing Delay-Sensitive CR-IoT Networking Under Jamming Attacks: Parallel Transmission and Batching Perspective

Cognitive radio (CR) is envisioned as an enabling technology for the Internet-of-Things (IoT) networking by providing spectrum opportunities to the huge number of communicating IoT devices (the so-called CR-IoT networking). Due to the nature of its wireless operating media, CR-IoT networks are vulnerable to jamming attacks. In CR-IoT networks with delay-sensitive applications, such attacks can incur longer packet transmission delay, resulting in received obsolete packets and service unavailability. Therefore, an important challenge in this domain is how to perform a secured channel assignment that can provide soft guarantees on the timeline of the packet delivery while satisfying a predefined Quality-of-Service (QoS) requirements. In this article, we propose a security-aware batch-based MAC protocol for delay-sensitive CR-IoT networks that attempts at improving the network performance by exploiting the parallel transmission capabilities of the CR-IoT devices while being jamming aware. Our protocol performs simultaneous assignment decisions for contending CR-IoT devices. Specifically, we formulate the channel assignment problem that attempts at maximizing the number of served CR-IoT users subject to user-rate demand, number of transceivers per CR-IoT device, and delay (packet invalidity) constraints as an optimization problem. This problem is shown to be a binary linear programming problem, which is, in general, NP-hard. Hence, we use a sequential fixing linear programming technique, which provides a near-optimal solution. To implement our channel assignment in a distributed manner, we adopt an access window (AW)-based access protocol that allows the users to exchange their control information and conduct the spectrum assignment. Compared to reference protocols, simulation results show that our protocol significantly improves spectrum efficiency and CR-IoT network performance.

Active nonreciprocal metamaterial using an eigen-structure assignment control strategy.

A class of active nonreciprocal metamaterials (ANMMs) is developed to control the flow and distribution of energy along periodic dynamical systems. Such a development constitutes a radical departure from the currently available approaches where the non-reciprocities are generated either by utilizing various sources of passive nonlinearities, gyroscopic circulators, spatiotemporal modulation, or active control of nonlinear systems. The proposed ANMM cell consists of a one-dimensional acoustic duct provided with linear active control capabilities. The controller is designed by simultaneous assignment of both the eigenvalues and eigenvectors, i.e., the entire eigen-structure, of the closed-loop system. Conventionally, the placement of the eigenvalues has been considered to improve the damping and system's response. However, in this study, the emphasis is placed also on tailoring the eigenvectors in order to enable the spatial control and redistribution of the wave propagation energy flow along the acoustic duct in such a manner that it can introduce non-reciprocity as well as control its direction and reversal. During this entire process, the system remains behaving in a linear fashion. Numerical examples are presented to demonstrate the basic features and non-reciprocal behavior, as well as the energy flow characteristics.

Assessing the impact of patient prioritization on operating room schedules

This paper proposes an integrated approach to merge patient prioritization and patient scheduling to improve access to services in an elective (i.e., non-urgent) context. In particular, we assume that patients are included on a waiting list for a given surgery, and that every patient on the list has received a “utility score”, which is a proxy for the relative urgency with regards to the other patients on the list. A mathematical model is formulated to solve the patient scheduling problem, i.e., the simultaneous assignment of surgery sessions to surgeons and patients to surgeons, in such a way that the total utility is maximized along with other practical requirements. The model has been applied to a testbed of randomly generated instances, inspired by the context of the Urology Department at a University Hospital in Quebec City. Experiments have been conducted to analyze both the short- and medium-term behaviors of the proposed approach. The numerical results confirm that the use of an objective function designed to maximize utility does not deteriorate the efficiency of the resulting schedules in terms of the number of surgeries performed. They also show that, as expected, higher utility patients are scheduled first, and their waiting time before surgery are shorter than those of lower utility. However, this approach may lead to longer, and even unacceptable waiting times for low utility patients. To mitigate such an undesirable effect, a dynamic utility updating approach is proposed to progressively increase the utility of patients according to their time spent on the waiting list. This approach seems to adequately balance the advantages of scheduling patients based on their utility and the risk of causing too much delay for low priority patients.

Fuzzy clustering and fuzzy c-means partition cluster analysis and validation studies on a subset of citescore dataset

A hard partition clustering algorithm assigns equally distant points to one of the clusters, where each datum has the probability to appear in simultaneous assignment to further clusters. The fuzzy cluster analysis assigns membership coefficients of data points which are equidistant between two clusters so the information directs have a place toward in excess of one cluster in the meantime. For a subset of CiteScore dataset, fuzzy clustering (fanny) and fuzzy c-means (fcm) algorithms were implemented to study the data points that lie equally distant from each other. Before analysis, clusterability of the dataset was evaluated with Hopkins statistic which resulted in 0.4371, a value < 0.5, indicating that the data is highly clusterable. The optimal clusters were determined using NbClust package, where it is evidenced that 9 various indices proposed 3 cluster solutions as best clusters. Further, appropriate value of fuzziness parameter <em>m</em> was evaluated to determine the distribution of membership values with variation in <em>m</em> from 1 to 2. Coefficient of variation (CV), also known as relative variability was evaluated to study the spread of data. The time complexity of fuzzy clustering (fanny) and fuzzy c-means algorithms were evaluated by keeping data points constant and varying number of clusters.