Priority Rules Research Articles

Cramdown, reorganization bargaining and inefficient markets: The cases of the United States and China

AbstractThe cramdown rule (11 USC s 1,129(b)) originates from the U.S. 1978 Bankruptcy Code as an innovative mechanism. The cramdown provision is rarely used in judicial practice in the United States. However, it is a key rule to understand the parties' bargaining structure. The inactive cramdown provision yet has been widely transplanted into other jurisdictions, including China. Little literature exists to explore the economic rationale of cramdown provision mainly because of its inactiveness in practice. This research explores the economic function of the cramdown provision in an inefficient market, using the cases of United States and China. This research observes that the cramdown provisions can only effectively function with the essential conditions and elements, such as the Best Interests of the Creditors Test, good faith, feasibility, fairness and equitability as well as the absolute priority rule. Therefore, the partial transplantation of the cramdown provision might not be effective.

Transfer method of geometric tolerance items based on assembly joints

In order to reduce the uncertainty in the selection of geometric tolerance items, a qualitative method for top-down transfer of geometric tolerance items is proposed. The assembly joint which is composed of two mating surfaces with functional requirements or structural constraints is acted as the basis of geometric tolerance items transmission. According to the structural characteristics, the assembly joints are divided into meta-assembly joints and composite assembly joints, and the priority rules for assembly joints are proposed. The transfer path of part-level geometric tolerance items is established according to the functional requirements and structural constraints among parts. On this basis, by adding information about the composition and constraint types of assembly joints between parts and the position constraint relationship of the general structure surface in the part, the transfer path of part-level geometric tolerance items is extended to the transfer path of geometric feature surface-level. With the development of product design, the initial functional requirements will be transformed into structural constraints between parts and geometric feature surfaces, and the structural transformation model of functional requirements is constructed. The generation specifications of geometric tolerance items based on structural constraints and the transfer specifications of datums are established. And based on the above specifications, the mapping relationship between functional requirements, structural constraints, and geometric tolerance items is defined. The synchronous transmission of geometric tolerance items along with the product design process are realized which provides an effective analysis tool for the top-down design of geometric tolerance items. Finally, the effectiveness of the method is verified by taking the transmission parts and connection parts in the crankshaft-piston mechanism as an example.

Simulation-based priority rules for the stochastic resource-constrained net present value and risk problem

In this paper, the performance of activity-based and resource-based policies with newly designed priority rules for the stochastic resource-constrained project scheduling problem is considered. The aim of the paper is to maximize the expected net present value (eNPV) and to minimize the risk of the project's net present value by semivariance risk measurement. 16 (mainly cash flow-based) heuristic rules are considered, among which 10 rules are derived from the deterministic NPV literature which are referred to as direct priority rules, and 6 rules are developed based on simulation information. The performance of these rules in the mentioned objective functions has been evaluated on the benchmark data set project scheduling problem library. The priority rules have been ranked separately in maximization of eNPV and minimization of semivariance. The computational results on various test problems with 120 activities show that the top rank priority rules in optimizing each objective function under both policies are similar. Based on the obtained results, the priority rules which produce a schedule with a high eNPV, cause a schedule with high semivariance and this indicates the trade-off between eNPV and risk. The selection of the best priority rules is dependent on the risk-aversion level of the project manager. A risk-averse project manager is recommended to use the rule discounted cash flow weight based on activity duration in direct priority rules, and the rule statistical discounted cash flow weight at earliest finish time in simulation-based priority rules under both policies. But for a risk-preferent manager it is better to use the discounted cumulative cash flow weight at latest finish time in direct priority rules, and the statistical discounted cumulative cash flow weight at latest finish time in simulation-based priority rules under both policies.

Is it Time to Include Post‐Transplant Survival in Heart Transplantation Allocation Rules?

Identifying an efficient and fair allocation of limited donated hearts to patients on the waiting list is one of the top priorities in heart transplantation management. The recent heart allocation rule by the United Network for Organ Sharing (UNOS) has emphasized medical urgency to address the heart transplant crisis by further dividing the previous sickest patient group into three subgroups. However, there is a significant debate on optimality and fairness of this policy because although it can help reduce pre‐transplant mortality, it may reduce post‐transplant survival. We undertake a rigorous study to address this debate by measuring the impacts of a variety of perspectives on the waiting list and patients. We show that the optimal policy of our proposed fluid model is a dynamic priority rule, and provide insight on the impact of fairness constraints on such priorities. We quantify the price that the society pays for following a medical urgency approach, which favors the sickest patients, compared to a utilitarian approach, which seeks to maximize total quality‐adjusted life years (QALYs). Our results, produced by a validated simulation model, reveal that the said price is 7.7% of total QALYs and increases to 11% by considering a broader regional sharing aligned with four‐hour heart cold ischemic time. We study other relevant objectives/measures in transplantation and our results show that the utilitarian policy outperforms the medical urgency policy in other measures as well. Our analysis provides novel insights on optimal patient allocation and sheds light on the debate around this challenging problem.

New closed-loop approximate dynamic programming for solving stochastic decentralized multi-project scheduling problem with resource transfers

Resource transfers and uncertain activity durations are two practical factors that are difficult to cope with in real project management. This paper studies a new decentralized multi-project scheduling problem with both resource transfers and uncertain activity durations, we name it the stochastic decentralized multi-project scheduling problem with resource transfers (SDRCMPSPTT). To tackle the curse-of-dimensionality of an exact solution approach, we develop a new and effective rollout policy-based approximate dynamic programming (ADP) algorithm for SDRCMPSPTT. Based on the benchmark instances, we build a new data set and examine the performance of 12 priority rule heuristics, then select the two best ones as the base policy for our rollout algorithm. The proposed approach is verified under a stochastic environment by assessing different base heuristic policies, computational results show that the rollout algorithm can further improve the solution quality of the base heuristics. Compared with the state-of-the-art algorithm for solving the decentralized multi-project scheduling problem under a deterministic environment, our rollout policy-based ADP algorithm can also obtain competitive solutions.

Could Chapter 11 redeem itself? Wealth and welfare effects of the redemption option

A redemption option granted to junior creditors has been advocated to accelerate Chapter 11 negotiations and rebalance junior recovery with respect to senior claims. We develop a game-theoretic, continuous-time model of the leveraged firm under Chapter 11 to assess the wealth transfers and welfare impacts of such an amendment to the bankruptcy procedure. After fitting the model to Chapter 11 current outcomes, we show that the redemption option design overcompensates junior creditors, leading to different, but not less frequent, Absolute Priority Rule violations. Since the reform shifts negotiations from a three- to a two-player game, it reduces the scope for concessions in the bargaining process and raises the risk of liquidation. Importantly, the redemption option aligns junior creditors’ interests with those of shareholders, thereby increasing the incentives for risk-shifting prior to bankruptcy.

A three-stage decomposition algorithm for decentralized multi-project scheduling under uncertainty

Decentralized management of multiple projects is a common practice in areas such as maintenance service, supply chain management, and globalized manufacturing. Nevertheless, the resolution of resource conflicts among multiple projects caused by management independence is a challenging task in generating a multi-project baseline schedule. Various unexpected events may also affect the project execution phase, leading to resource disruptions that can cause the baseline schedule to become infeasible and necessitate its repair. Here, we study the decentralized multi-project scheduling problem subject to global resource disruption, which is NP-hard. To obtain high-quality baseline schedules and effectively repair disrupted schedules, we formulated a three-stage decomposition model. Then, we developed a three-stage decomposition algorithm with a task-scoring mechanism (TDA-TS), which is generic for the processes of schedule generation and repair. Proposed new priority rules were developed that used information about the local schedules and were organized as TDA-TS to further improve its performance. A comprehensive experiment was conducted on the Multi-Project Scheduling Problem LIBrary dataset. The experimental results indicated that the proposed algorithm outperformed several existing centralized and distributed algorithms in the generation of baseline schedules and that it could effectively repair disrupted schedules even for large instances. Furthermore, we saw that the newly proposed priority rule embedded in TDA-TS significantly outperformed other priority rules in repairing the disrupted schedules. The results of this research are valuable for managers who need to effectively repair disrupted schedules in a decentralized multi-project environment facing various disruptions.

Genetic programming with local search to evolve priority rules for scheduling jobs on a machine with time-varying capacity

Priority rules combined with schedule generation schemes are a usual approach to online scheduling. These rules are commonly designed by experts on the problem domain. However, some automatic method may be better as it could capture some characteristics of the problem that are not evident to the human eye. Furthermore, automatic methods could devise priority rules adapted to particular sets of instances of the problem at hand. In this paper we propose a Memetic Algorithm, which combines a Genetic Program and a Local Search algorithm, to evolve priority rules for the problem of scheduling a set of jobs on a machine with time-varying capacity. We propose a number of neighbourhood structures that are specifically designed to this problem. These structures were analyzed theoretically and also experimentally on the version of the problem with tardiness minimization, which provided interesting insights on this problem. The results of the experimental study show that a proper selection and combination of neighbourhood structures allows the Memetic Algorithm to outperform previous approaches to the same problem.

Constitutional proportionality and moral deontology

ABSTRACT I come to grips with the deontological critique of constitutional proportionality that asserts that this doctrine ignores rights and slips into the utilitarian maximisation of societal interests. I offer a four-pronged classification of the deontological structures underlying the settlement of conflicts of rights and principles. Two of these structures are scalar: balance deontology and threshold deontology. The other two are nonscalar: derogation deontology and hierarchy deontology. I argue that proportionality relies on the scalar structures, but nonetheless sets much store by rights. Therefore, the deontological critique of proportionality is misplaced. However, I claim that proportionality is similar to utilitarian balancing in its practical import. Like utilitarian balancing, proportionality allows judicial arbitrariness by recommending courts to adopt an intuitionist decision-making procedure. Based on this point, I offer a republican argument against proportionality. Instead of an intuitionist approach, I defend a committal method of constitutional adjudication based on hierarchy deontology. The point of this procedure is to introduce priority rules via moral deliberation. I submit that a rule-based kind of deontologism coheres better with rule of law values.

Families of sequential priority rules and random arrival rules with withdrawal limits

This paper deals with extensions of the family of sequential priority rules and the random arrival rule for bankruptcy problems when withdrawal limits are fixed by an arbitrator. We assume two approaches to limit withdrawals. The first is based on the principle that agents can only obtain at most a fixed part of the endowment each time they qualify for an award, and the second that agents can only receive at most a proportion of their claims each time they are attended. For each approach we introduce two pairs of families, each consisting of a sequential priority rule-like family and a random arrival rule-like family. Applying the first approach, we show that the constrained equal awards rule belongs to each of the families. In the second, we prove the same for the proportional rule. We study in detail one pair of families for each approach, and the others are examined in relation to them.

Ensembles of priority rules for resource constrained project scheduling problem

Resource constrained project scheduling problem is an NP-hard problem that attracts many researchers because of its complexity and daily use. In literature there are a lot of various solving methods for this problem. The priority rules are one of the prominent methods used in practice. Because of their simplicity, speed, and possibility to react to changes in the system, they can be used in a dynamic environment. In this paper, ensembles of priority rules were created to improve the performance of priority rules created with genetic programming. For ensemble creation, four different methods will be considered: simple ensemble combination, BagGP, BoostGP, and cooperative coevolution. The priority rules that are part of the ensemble will be combined with the sum and vote methods in reaching the final decision. Additionally, the ensemble subset search method will be applied to the created ensembles to find the optimal subset of priority rules. The results achieved in this paper show that ensembles of priority rules can achieve significantly better results than those achieved when using only a single priority rule.

Performance of Heuristic Priority Rule for Multiskilled Resource-Constrained Scheduling

Nowadays, without a classification system that helps the project manager to decide which heuristic applies when mitigating the multiskilled resource-constrainedscheduling, they must try several of rules until they find one that compares favorably (shortest duration) with the results of the other heuristic priority rules. This study explored the twenty-three existing heuristics’ performance for multiskilled resource-constrained scheduling. The results found that the heuristics with good performance are TIMROS, TIMRES, ACROS, WCS ACS and ACTRES. Overall, they outperform the others in shortening the project duration. It should be pointed out that the heuristics dealing with the use of several information are likely better to get shorter project duration. There are enough statistical evidences to conclude that their criterions have a significant effect on reducing project duration by approximately 1-2 times of the standard deviation. The top four heuristics: TIMROS, TIMRES, ACROS and ACTRESS classified into the composite rule produced the lowest average of project duration. It is also found that Serial Schedule Scheme (SSS) underperformthe Partial Schedule Scheme (PSS). This study has the contribution for the project managers to decide which heuristic applies when mitigating the multiskilled resource overallocation problem in term of minimum project duration

Resolving Priority Competitions between PPSA Security Interests and Non-PPS Interests

This article examines competitions between security interests under the Personal Property Securities Act 2009 (Cth) (‘PPSA’) and property interests outside the statutory regime (‘non-PPS interests’). Part II examines the statutory priority rules which govern such competitions. It considers their scope, how they should be construed, and the extent to which they may require amendment in order to align with their policy rationales. Part III then examines those competitions which fall outside the statutory priority rules, and are thus determined by the general law. In particular, it considers whether all security interests must be characterised as legal interests for the purposes of such competitions, both as a question of statutory construction and from a policy perspective. It concludes that, for the limited purposes of competitions with non-PPS interests which remain governed by the general law, a PPSA security interest should retain its general law legal or equitable character.

Improving efficiency in school choice under partial priorities

We provide a school choice model where the student priority orders for schools are allowed not to be total. We introduce a class of algorithms, each of which derives a student optimal stable matching once we have an initial stable matching, when the priorities are represented by partial orders. Since a method to derive a stable matching exists when the priorities are partial orders, we can use it to derive a student optimal stable matching. Moreover, we show that any student optimal stable matchings that Pareto dominate the starting stable one are obtained via an algorithm within this class. For the problem of improving efficiency by allowing some priorities to be violated, the algorithms can also be applied, with a weaker assumption on the violations than in the previous study. Finally, we suggest some specific rules of priorities that can be introduced by weakening the requirement of total ordering.

Effect of two-wheeler proportion on passenger car units at roundabout in Indian urban scenario

ABSTRACT The present study focuses on modeling roundabout in heterogeneous condition and understanding the effect of change in two-wheeler composition on the entry capacity using microscopic simulation technique. A four-legged roundabout of 25m diameter, located in the city of Chandigarh, India was considered as the study area. As Indian traffic flow is highly heterogeneous due to the presence of vehicles with varying physical and operational characteristics, each vehicle modelled separately in PTV VISSIM. Model calibrations were performed using speed distributions, conflict areas, priority rules, and driving behavior parameters. Comparison with the field data obtained from the study roundabout (indicates the applicability of the developed simulation model in assessing the hypothetical scenarios. Study results indicate that the PCU values remain steady under varying compositions of two-wheelers in the traffic stream. In addition, the effect of two-wheeler proportion on roundabout capacity is insignificant in the study area.

Exploring task features that predict psychometric quality of test items: the case for the Dutch driving theory exam

This study reports on an Evidence Centered Design (ECD) project in the Netherlands, involving the theory exam for prospective car drivers. In particular, we illustrate how cognitive load theory, task-analysis, response process models, and explanatory item-response theory can be used to systematically develop and refine task models. Based on a cognitive model for driving, 353 existing items involving rules of priority at intersections, were coded on intrinsic task features and task presentation features. Hierarchical regression analyses were carried out to determine the contribution of task features to item difficulty and item discrimination. A substantial proportion of variance in both item difficulty and item discrimination parameters could be explained by intrinsic task-features, including rules and signs (25%, 18.6%), task-intersection features (13.4%, 14.1%), and a smaller small proportion to item presentation features (3.5%, 7.1%) of the total variance. It is concluded that the systematic approach of discerning task features and determining the impact on item parameters has added value as an ECD-tool for evaluating existing assessments that are planned to be innovated. The paper concludes with a discussion of practical implications.

Optimization In a Casing Manufacturing Industry Using Simulation Study

This project describes how Simulation can be applied to optimize a job shop scheduling problem (JSSP) at a precision aerospace components manufacturing company. The product selected for the simulation study is casing. As 8 different casings are manufactured in a single casing manufacturing line, the demand requirements are not met. To identify the bottleneck operations, the existing manufacturing process is simulated using Arena simulation software. The model is simulated for various batch sizes and Scheduling rules and the performance parameters like total casing output, average work in process, average utilization and average flow time were determined. Design of experiments and Interaction plot is used to determine the optimum batch size and scheduling rule. The results showed that for maximum casing output and Resource utilization, the optimum batch size is 5 and optimum scheduling rule is Shortest processing time (SPT) priority rule. To minimize the Average WIP and flow time, the optimum batch size is 2 and optimum scheduling rule is Shortest processing time (SPT) priority rule.

How automated vehicles should operate to avoid fatal crashes with cyclists?

The study assesses what kind of features would allow highly automated vehicles’ (HAVs) safe operation in encounters with cyclists and allow avoiding fatal crashes between cyclists and passenger cars. Five features of HAVs’ capabilities are formed based on previous studies and evaluated qualitatively using data from fatal crashes between driver-managed passenger cars and cyclist in Finland. By analysing these crashes, it is assessed which features HAVs should have in order to avoid each crash in a hypothetical setting, in which driver-managed cars would be replaced by HAVs. The necessary features of HAVs for crash avoidance are analysed crash-by-crash by considering the obligation to yield, visual obstacles at the crash scene and driver’s behaviour prior to the crash. In order to avoid different types of fatal crashes with cyclists, the HAVs should be able to recognize nearby cyclists (feature 1), be aware of the priority rules in various intersections and traffic situations (2), indicate its intentions to cyclists (3), maintain safe driving patterns and anticipate future situations (4), and assess cyclists’ intentions (5). Albeit the number of different features to allow crash avoidance is only five, implementing these features is a considerable challenge for HAVs’ programming and design, as these should function in various and complex traffic situations. The study discloses the complexity in the encounters between HAVs and cyclists, which are to be considered in further studies and real-world implementations.

Digital Twin and Reinforcement Learning-Based Resilient Production Control for Micro Smart Factory

To achieve efficient personalized production at an affordable cost, a modular manufacturing system (MMS) can be utilized. MMS enables restructuring of its configuration to accommodate product changes and is thus an efficient solution to reduce the costs involved in personalized production. A micro smart factory (MSF) is an MMS with heterogeneous production processes to enable personalized production. Similar to MMS, MSF also enables the restructuring of production configuration; additionally, it comprises cyber-physical production systems (CPPSs) that help achieve resilience. However, MSFs need to overcome performance hurdles with respect to production control. Therefore, this paper proposes a digital twin (DT) and reinforcement learning (RL)-based production control method. This method replaces the existing dispatching rule in the type and instance phases of the MSF. In this method, the RL policy network is learned and evaluated by coordination between DT and RL. The DT provides virtual event logs that include states, actions, and rewards to support learning. These virtual event logs are returned based on vertical integration with the MSF. As a result, the proposed method provides a resilient solution to the CPPS architectural framework and achieves appropriate actions to the dynamic situation of MSF. Additionally, applying DT with RL helps decide what-next/where-next in the production cycle. Moreover, the proposed concept can be extended to various manufacturing domains because the priority rule concept is frequently applied.

A Novel Recursive Heuristic Algorithm for Straight and U-Shaped Assembly Line Balancing with Fuzzy Processing Time

The paper aims to address the straight and U–type assembly line balancing problems by developing a novel recursive heuristic algorithm based on the idea of the depth of search. The dynamic fuzzy processing time (DFPT) model is employed to represent uncertainty and ambiguity related to the processing time in the actual production systems. The novel algorithm, the minimum cycle time objective is considered for a set of imposed considerers. They are arranged in an appropriate strategy in which three-stages are proposed and presented as a solution approach. Finally, the validity of the developed solution approach is evaluated through a tested numerical example conducted over a test problem taken from literature to assess its performance. This study proofs their ability and efficiency in assisting decision-making by determining the contribution proportion for significant assignment variables represented by skill level, work stability, type layout, and priority rule