Allocation Rules Research Articles

Against Jamming Attack in Wireless Communication Networks: A Reinforcement Learning Approach

When wireless communication networks encounter jamming attacks, they experience spectrum resource occupation and data communication failures. In order to address this issue, an anti-jamming algorithm based on distributed multi-agent reinforcement learning is proposed. Each terminal observes the spectrum state of the environment and takes it as an input. The algorithm then employs Q-learning, along with the primary and backup channel allocation rules, to finalize the selection of the communication channel. The proposed algorithm designs primary and backup channel allocation rules for sweep jamming and smart jamming strategies. It can predict the behavior of jammers while reducing decision conflicts among terminals. The simulation results demonstrate that, in comparison to existing methods, the proposed algorithm not only enhances data transmission success rates across multiple scenarios but also exhibits superior operational efficiency when confronted with jamming attacks. Overall, the anti-jamming performance of the proposed algorithm outperforms the comparison methods.

Evaluation of a delayed liver transplantation strategy for patients with HCC receiving bridging therapy: the DELTA-HCC study

To maximize utility and prevent premature liver transplantation (LT), a delayed LT strategy (DS) was adopted in France in 2015 in patients listed for any single HCC treated with resection or thermal-ablation during waiting phase, postponing LT until recurrence. The purpose of this study was to evaluate DS to make sure that it did not hamper pre and post-LT outcomes in DS patients. Patients listed for HCC in France between 2015 and 2018 were studied. After data extraction from the national LT database, 2,025 patients were identified and classified according to 6 groups: single tumor entering DS, single tumor not entering DS, multiple tumors, no curative treatment, untreatable HCC or T1 tumors. 18-months Kaplan-Meier estimates of drop-out for death, too sick to be transplanted or tumor progression before LT, 5-year post-LT HCC recurrence and post LT-survival rates were compared. Median waiting-time in DS group was 910 days. Pre-LT drop-out probability was significantly lower in DS compare to other groups (13% vs 19%, p=0.0043) and significantly higher in the T1 group (25.4%, p=0.05). Post-LT HCC-recurrence rate in multiples nodules group was significantly higher (19.6%, p= 0.019) and post-LT 5-year survival did not differ among groups with 74% in DS group (p=0.22). The DELTA HCC study shows that DS does not negatively impact neither pre- nor post-LT patients 'outcomes, and has the potential to redistribute organs to patients in more urgent need of LT. It can reasonably be proposed and pursued. The unexpected high risk of drop out in T1 patients seems related to the MELD-based driving rules underserving this subgroup, calling for revision of allocation rules. To maximize utility and prevent premature liver transplantation (LT), a delayed LT strategy (DS) was adopted in France in 2015. It consists in postponing LT until recurrence in patients listed for any single HCC curatively treated by surgical resection or thermal ablation. The DELTA HCC study was conducted to evaluate this nationwide strategy. It shows in a non-US, European LT program that DS:- does not negatively impact pre- nor post-LT patients 'outcome,- concerns up to 20% of LT candidates-has therefore the potential to redistribute organs to patients in more urgent need of LT. Such a delayed strategy can reasonably be pursued and extended to other LT programs. Of note, an unexpected high risk of drop out in T1 patients, seemingly related to MELD-based offering rules which underserve these patients, calls for further scrutinization and revision of allocation rules in this subgroup.

Optimal allocation strategies in platform trials with continuous endpoints.

Platform trials are randomized clinical trials that allow simultaneous comparison of multiple interventions, usually against a common control. Arms to test experimental interventions may enter and leave the platform over time. This implies that the number of experimental intervention arms in the trial may change as the trial progresses. Determining optimal allocation rates to allocate patients to the treatment and control arms in platform trials is challenging because the optimal allocation depends on the number of arms in the platform and the latter typically varies over time. In addition, the optimal allocation depends on the analysis strategy used and the optimality criteria considered. In this article, we derive optimal treatment allocation rates for platform trials with shared controls, assuming that a stratified estimation and a testing procedure based on a regression model are used to adjust for time trends. We consider both, analysis using concurrent controls only as well as analysis methods using concurrent and non-concurrent controls and assume that the total sample size is fixed. The objective function to be minimized is the maximum of the variances of the effect estimators. We show that the optimal solution depends on the entry time of the arms in the trial and, in general, does not correspond to the square root of allocation rule used in classical multi-arm trials. We illustrate the optimal allocation and evaluate the power and type 1 error rate compared to trials using one-to-one and square root of allocations by means of a case study.

Migration and Knowledge Diffusion: The Effect of Returning Refugees on Export Performance in the Former Yugoslavia

Abstract During the early 1990s, Germany offered temporary protection to 700,000 Yugoslavian refugees fleeing war. By 2000, many had been repatriated. We exploit this natural experiment to investigate the role of returning migrants in boosting export performance upon their return. Using confidential German administrative data, we find that industries with 10% more returning refugees exhibit larger exports between the pre- and postwar periods by 1% to 1.6%. We use exogenous allocation rules for asylum seekers within Germany as an instrument to deal with endogeneity concerns. We show evidence pointing to productivity shifts as the main mechanism behind our results. Consistently, we find our results are driven by refugees in occupations more likely to transfer knowledge, technologies, and best practices.

Validation of a new global irrigation scheme in the land surface model ORCHIDEE v2.2

Abstract. Irrigation activities are important for sustaining food production and account for 70 % of total global water withdrawals. In addition, due to increased evapotranspiration (ET) and changes in the leaf area index (LAI), these activities have an impact on hydrology and climate. In this paper, we present a new irrigation scheme within the land surface model ORCHIDEE (ORganising Carbon and Hydrology in Dynamic EcosystEms)). It restrains actual irrigation according to available freshwater by including a simple environmental limit and using allocation rules that depend on local infrastructure. We perform a simple sensitivity analysis and parameter tuning to set the parameter values and match the observed irrigation amounts against reported values, assuming uniform parameter values over land. Our scheme matches irrigation withdrawals amounts at global scale, but we identify some areas in India, China, and the USA (some of the most intensively irrigated regions worldwide), where irrigation is underestimated. In all irrigated areas, the scheme reduces the negative bias of ET. It also exacerbates the positive bias of the leaf area index (LAI), except for the very intensively irrigated areas, where irrigation reduces a negative LAI bias. The increase in the ET decreases river discharge values, in some cases significantly, although this does not necessarily lead to a better representation of discharge dynamics. Irrigation, however, does not have a large impact on the simulated total water storage anomalies (TWSAs) and its trends. This may be partly explained by the absence of nonrenewable groundwater use, and its inclusion could increase irrigation estimates in arid and semiarid regions by increasing the supply. Correlation of irrigation biases with landscape descriptors suggests that the inclusion of irrigated rice and dam management could improve the irrigation estimates as well. Regardless of this complexity, our results show that the new irrigation scheme helps simulate acceptable land surface conditions and fluxes in irrigated areas, which is important to explore the joint evolution of climate, water resources, and irrigation activities.

The German Transplant Registry - An Analysis of Legacy Data 2006-2016

In 2018, medical transplant data from three institutions were merged to create a German transplant registry. Since June 2021, access to data of the registry has been available. It was planned to analyze the registry data in order to compare special allocation rules with regular allocation for heart, liver, lung, and kidney transplantation. Our approach led to a quality analysis of the registry. Upon request, legacy data (2006-2016) of the registry was provided, divided into 61 elements. From these elements, the user had to compile the required dataset. Data checks were performed for completeness, correct allocation of information, and consistency among different sources. Software used for these tasks included R, SQL, and Excel. The initial elements ("waiting list" elements) of the four types of transplantations contained data from a total of 80,259 originally listed patients. However, these patients were only partially present in other elements resulting in complete datasets reflecting waiting time in only 23%, 30%, 50%, and 96%, and for post-transplantation outcomes in 14%, 11%, 38%, and 13% (heart, liver, lung, and kidney transplantation, respectively). The linking of urgency information with clinical data was successful in only a small proportion, with only 6% for heart transplantation. Incorrect and thus implausible allocations in the case of special allocation rules indicated incorrect entries in the registry. Data from different data providers were inconsistent. The incompleteness and incorrect data allocation raise doubts about the reliability of scientific studies based on the transplant registry. The complex structure also hinders the compilation of a reliable dataset, which is uncommon internationally. New data (acquisition since 2017) has only been available since December 2023. The transplant registry urgently needs restructuring. Competent clinical data management, involving transplant medical expertise, and continuous quality controls are essential in this process.

Overbidding and underbidding in package allocation problems

AbstractWe study the problem of allocating packages of different objects to a group of bidders. A rule is overbidding-proof if no bidder has incentives to bid above his actual valuations. We prove that if an efficient rule is overbidding-proof, then each winning bidder pays a price between his winning bid and what he would pay in a Vickrey auction for the same package. In counterpart, the set of rules that satisfy underbidding-proofness always charge a price below the corresponding Vickrey price. A new characterization of the Vickrey allocation rule is provided with a weak form of strategy-proofness. The Vickrey rule is the only rule that satisfies efficiency, individual rationality, overbidding-proofness and underbidding-proofness. Our results are also valid on the domains of monotonic valuations and of single-minded bidders. Finally a family of overbidding rules is introduced that price the assigned packages at a fixed average of the Vickrey price and the pay-as-bid price.

How regional attitudes towards immigration shape the chance to obtain asylum: Evidence from Germany

Abstract Asylum recognition rates in advanced democracies differ not only across states but also vary within them, translating into fluctuating individual chances to obtain protection. Existing studies on the determinants of these regional inequities typically rely on aggregate data. Utilizing a German refugee survey and leveraging a quasi-natural experiment arising from state-based allocation rules tied to national dispersal policies, we test two explanations for the perplexing regional differences. Drawing on principal–agent models of administrative decision-making, we test whether asylum decision-makers consciously or unconsciously comply with regional political preferences between 2015 and 2017 in Germany, one of the major European destination countries for refugee migration. We furthermore explore whether such biased decision-making amplifies in times of organizational stress as suggested by the statistical discrimination theory. Using mixed-effects logistic regressions, our analyses confirm a lower approval probability in regions with more immigration-averse residents or governments. We cannot confirm, however, that this association is mediated by high workloads or large knowledge gaps. Our results thus suggest that regional political biases affect the individual chance of asylum-seekers to obtain protection irrespective of temporal administrative conditions.

Towards a consulting model for a good urban generation of social housing districts in terms of equipment

<div>Social housing in Morocco is housing built with state assistance. It is subject to precise rules of construction, management and allocation and governed by specifications defining requirements in a manual manner. This work which is done manually can produce errors either at the level of calculations or at the level of equipment proposal. The objective of this article is to computerize this area as there is a lack of tools and platforms. To solve this problem, we have created an advisory platform for good urban planning for residents which will help engineers. Based on mathematical formulas. This tool will allow architects and urban planners to design autonomous populations in terms of equipment.</div>

Performance of deep reinforcement learning algorithms in two-echelon inventory control systems

This study conducts a comprehensive analysis of deep reinforcement learning (DRL) algorithms applied to supply chain inventory management (SCIM), which consists of a sequential decision-making problem focussed on determining the optimal quantity of products to produce and ship across multiple capacitated local warehouses over a specific time horizon. In detail, we formulate the problem as a Markov decision process for a divergent two-echelon inventory control system characterised by stochastic and seasonal demand, also presenting a balanced allocation rule designed to prevent backorders in the first echelon. Through numerical experiments, we evaluate the performance of state-of-the-art DRL algorithms and static inventory policies in terms of both cost minimisation and training time while varying the number of local warehouses and product types and the length of replenishment lead times. Our results reveal that the Proximal Policy Optimization algorithm consistently outperforms other algorithms across all experiments, proving to be a robust method for tackling the SCIM problem. Furthermore, the (s, Q)-policy stands as a solid alternative, offering a compromise in performance and computational efficiency. Lastly, this study presents an open-source software library that provides a customisable simulation environment for addressing the SCIM problem, utilising a wide range of DRL algorithms and static inventory policies.

An impact energy erosion model with an energy allocation rule for the discrete element method

Surface erosion caused by solid particle impact frequently occurs in engineering applications involving particulate flow. With the tremendous progress in computational technology, discrete element method (DEM)-based numerical methods have become a powerful means of studying surface erosion. However, the applicability of applied erosion models remains one of the critical challenges in DEM modelling to obtain satisfactory accuracy over a wide range of materials and impact conditions. In this study, an impact energy erosion model (IEEM) that evaluates erosive wear using accumulated impact energy during the impact process is developed. In IEEM, both cutting and deformation wear are considered. A calibration method of the specific energy that combines DEM simulations and abrasive jet tests is established. To quantitatively predict the erosive wear of the target surface eroded by different particles, an energy allocation rule based on the hardness ratio of the target surface to particles is proposed. Its rationality is thoroughly evaluated before embarking on DEM simulations. Subsequently, the accuracy of DEM simulations incorporated with IEEM and the energy allocation rule is comprehensively verified by comparing them with experimental data on ductile, brittle and semi-brittle materials. The results confirm that DEM simulations can reasonably predict the dependence of erosive wear on impact velocities, impact angles and erodent particle hardness, regardless of the material involved. The calibrated specific energy reflects the properties of the material and further determines the wear mechanism. Finally, the influence of critical DEM parameters on erosion prediction is studied. Generally, DEM incorporated with IEEM and the energy allocation rule provides a quantitative erosion prediction framework applicable to different materials and impact conditions. In the future, this framework has the potential to be applied to the erosion prediction of more systems.

Effort complementarity and role assignments in group contests

AbstractThis study characterizes role assignments in maximizing a group's winning probability under the influence of the complementarity of group members' efforts in a group contest, in contrast to prize and multiple resource allocations. We use a constant elasticity of substitution effort aggregator function to parameterize the complementarity. While the prize and resource allocation rules depend on the complementarity, the assignment rule does not when multiple roles are assignable to a single group member: All roles are assigned only to the most productive group member. However, when only a single role per group member is assignable, the assignment rule depends on the complementarity: Roles from greater to less importance are assigned to group members in descending order of their productivity under strong complementarity; only the most important role is assigned to the most productive group member and the others have no effect under weak complementarity.

Optimization of Dualistic Reservoir System Two-Dimensional Rule Curve with Three Allocation Rules

A two-dimensional operation chart is commonly used to manage the operation of a dual-reservoir system, where the water storage in each reservoir is accurately considered in the water-supply decision. The dual reservoir chart should be combined with one allocation rule to better represent water supply distribution between reservoirs. In this study, the 2D rule curve was coupled with three allocation rules: variable allocation ratios, fixed allocation ratios, and compensation regulation, to identify the efficiency of using these rules with the 2D rule curve in operating the dual reservoirs. Mosul-Dukan dual reservoirs in Iraq were implemented as a study area using monthly data extended from 2001 to 2020. The Shuffled Complex Evolution Algorithm was used to optimize the water allocation ratios. The results revealed that the variable allocation ratios were superior to the other two rules in terms of water deficit, in which the total water shortage of the variable allocation ratios rule was 56590 Mm3. The total shortage was less than that obtained by the fixed allocation ratio and compensation regulation rules by 0.9% and 56%, respectively. Finally, the variable allocation ratio was more suitable for application with a 2D reservoir rule curve than the two remaining rules (fixed allocation ratio and compensation regulation rules). The variable allocation ratios sustainably manage reservoirs in the regions that suffer from water scarcity and represent the most vulnerable to the impact of climate change. Doi: 10.28991/CEJ-2024-010-02-04 Full Text: PDF

Completion Time Optimization in UAV-Relaying-Assisted MEC Networks With Moving Users

As one of the most popular consumer electronics, Unmanned Aerial Vehicle (UAV) has the potential to assist User Equipment (UE) in the Internet of Everything. Mobile edge computing (MEC) is also an emerging technique that can provide sufficient computing resources to IoE users. In this paper, we focused on the UAV-assisted MEC problem with mobile UEs, where the UAV serves as an MEC server to assist UEs in computing and acts as a relay to deliver tasks to a ground access point (AP). The objective is to minimize the average time for completing all tasks in the network while jointly optimizing resource allocation such as communication bandwidth, CPU frequency, task division ratio, and UAV’s three-dimensional location deployment. First, we proposed an online MEC network adjustment scheme. Then, we decomposed the formulated non-convex problem into three low-complexity subproblems. Last, we proposed a successive convex approximation-based joint optimization algorithm to solve them. In addition, we presented heuristic conclusions in task allocation rules, UAV flight trajectory prediction, and UAV hovering altitude selection, which can further reduce task completion time and deepen the understanding of the working mode of mobile edge servers. Simulation results show that the algorithm can significantly reduce the completion time.

Environmental Context and Organizational Aspiration Determination

AbstractThis research develops an attention‐based, environment‐inclusive model of organizational aspiration determination. The behavioural view embraces that organizations determine aspirations based on three reference points: past aspiration, past performance, and social reference group performance. We build hypotheses to explain how environmental munificence, dynamism, and complexity shape organizational attention allocation among these three reference points. Using data on US publicly traded firms (2006–16), we find that organizations, when determining sales aspirations, allocate (1) more attention to past aspiration and social reference group performance but less attention to past performance in more munificent environments; (2) more attention to past performance and social reference group performance but less attention to past aspiration in more dynamic environments; and (3) more attention to past performance but less attention to past aspiration and social reference group performance in more complex environments. Overall, we contribute to aspiration research by explicitly theorizing a previously understudied contingency, using direct aspiration measures from a wide range of industries, and providing evidence that organizations’ attention allocation rules are regulated by the external environment when determining aspirations.

Heuristic for Railway Crew Scheduling With Connectivity of Schedules

This paper addresses the crew scheduling for long-distance passenger train services. A heuristic with bin packing features is developed to generate repeatable crew schedules that satisfy the operational and crew allocation rules. By ensuring the connectivity of crew duties that can be repeated over periodic train schedules, a better estimate of the crew requirement in a region is also obtained. Further, the heuristic ensures a fair division of the total workload and creates long duty cycles, which also makes the process of cyclic rostering easier. The paper also presents an exact approach for crew scheduling using a combination of constraint programming and set covering formulations. The exact approach is not computationally viable for practical scale problem instances, but the heuristic generates good quality solutions (often very close to optimal) even on large data sets. We illustrate the approach on data from the Mumbai Division in Indian Railways and the computational results show that there is potential to reduce the total number of crew duties in the region by around 12%. The heuristic approach provides an efficient way to generate improved crew schedules every time there is a change in the train timetable.

Allocation of transportation capacity for complex natural gas pipeline network under fair opening

China has formed a national natural gas pipeline network, and reasonable and accurate pipeline capacity allocation scheme has important significance for improving the utilization rate of pipeline network, improving the income of pipeline network operators and ensuring the gas consumption of downstream users, which poses greater challenges to the formulation of pipeline capacity allocation rules and mechanisms. This paper takes the natural gas pipeline network system as the research object, and studies the rules and mechanisms of pipeline capacity allocation. A capacity allocation model of natural gas pipeline network is proposed with the net present value of pipeline network operator's income as the goal, considering the gas consumption characteristics of downstream users, the dynamic change of gas source node capacity, pipeline hydraulic characteristics, pipeline flow direction, compressor and other constraints. Based on the simulation of the actual pipeline network by the proposed model, seven allocation rules and one mechanism are proposed. Specifically, the user priority of non-interruptible type, the pipeline path adjustable type and the gas source node adjustable type is higher than the corresponding comparison type. In the pipeline capacity allocation mechanism, the amount compensation mechanism for pipeline capacity transactions is as follows: When the pipeline capacity transaction occurs between users, the transaction information is submitted to the pipeline network operator. The pipeline network operator proposes the corresponding amount compensation according to the transaction information, and the transaction parties negotiate the proportion of the amount compensation and submit it to the pipeline network operator. In addition, it is recommended that pipeline network operators vigorously develop LNG receiving stations and downstream interruptible and compressible users in the future.

Evaluating the impact of items and cooperation in inventory models with exemptable ordering costs

In this paper we introduce and analyse, from a game theoretical perspective, several multi-agent or multi-item continuous review inventory models in which the buyers are exempted from ordering costs if the price of their orders is greater than or equal to a certain amount. For all models we obtain the optimal ordering policy. We first analyse a simple model with one firm and one item. Then, we study a model with one firm and several items, for which we design a procedure based on cooperative game theory to evaluate the impact of each item on the total cost. Then, we deal with a model with several firms and one item for each firm, for which we characterise a rule to allocate the total cost among the firms in a coalitionally stable way. Finally, we discuss a model with several firms and several items, for which we characterise a rule to allocate the total cost among the firms in a coalitionally stable way and to evaluate the impact of each item on the cost that would be payable to each firm when using the allocation rule. All the concepts and results of this article are illustrated using data from a case study.

Mathematical optimization modelling for group counterfactual explanations

Counterfactual Analysis has shown to be a powerful tool in the burgeoning field of Explainable Artificial Intelligence. In Supervised Classification, this means associating with each record a so-called counterfactual explanation: an instance that is close to the record and whose probability of being classified in the opposite class by a given classifier is high. While the literature focuses on the problem of finding one counterfactual for one record, in this paper we take a stakeholder perspective, and we address the more general setting in which a group of counterfactual explanations is sought for a group of instances. We introduce some mathematical optimization models as illustration of each possible allocation rule between counterfactuals and instances, and we identify a number of research challenges for the Operations Research community.

Sophisticated banking contracts and fragility when withdrawal information is public

I study whether self‐fulfilling bank runs can occur when banks use sophisticated contracts and withdrawal decisions are public information. In a finite‐agent version of Diamond and Dybvig (1983) with correlated types, I first present an example in which a bank run perfect Bayesian equilibrium exists. However, its existence relies on off‐path beliefs that are unreasonable in terms of forward induction. To discipline beliefs, I use forward induction equilibrium (Cho (1987)) as the solution concept. I show that, whenever the allocation rule is strictly incentive compatible, the truth‐telling strategy is the unique forward induction equilibrium in the withdrawal game, and no bank run occurs. Therefore, with forward induction, sophisticated contracts can prevent bank runs when there is public information about withdrawal decisions.