Transition Constraints Research Articles

Managing product-inherent constraints with artificial intelligence: production control for time constraints in semiconductor manufacturing

Continuous product individualization and customization led to the advent of lot size one in production and ultimately to product-inherent uniqueness. As complexities in individualization and processes grow, production systems need to adapt to unique, product-inherent constraints by advancing production control beyond predictive, rigid schedules. While complex processes, production systems and production constraints are not a novelty per se, modern production control approaches fall short of simultaneously regarding the flexibility of complex job shops and product unique constraints imposed on production control. To close this gap, this paper develops a novel, data driven, artificial intelligence based production control approach for complex job shops. For this purpose, product-inherent constraints are resolved by restricting the solution space of the production control according to a prediction based decision model. The approach validation is performed in a real semiconductor fab as a job shop that includes transitional time constraints as product-inherent constraints. Not violating these time constraints is essential to avoid scrap and similarly increase quality-based yield. To that end, transition times are forecasted and the adherence to these product-inherent constraints is evaluated based on one-sided prediction intervals and point estimators. The inclusion of product-inherent constraints leads to significant adherence improvements in the production system as indicated in the real-world semiconductor manufacturing case study and, hence, contributes a novel, data driven approach for production control. As a conclusion, the ability to avoid a large majority of violations of time constraints shows the approaches effectiveness and the future requirement to more accurately integrate such product-inherent constraints into production control.

An iterative two‐stage PSCOPF method considering line fault uncertainty under typhoon influence

AbstractTyphoon weather poses a significant threat to the security and stability of the power system, as it can potentially lead to the disconnection of transmission lines. Concurrently, there exists substantial uncertainty regarding which specific lines may become disconnected under such weather conditions. The preventive security constrained optimal power flow (PSCOPF) is one of the main methods to address this issue. However, the current PSCOPF exhibits low computational efficiency due to the requirement of analysing all line faults through the N‐1 check. Therefore, this paper proposes a PSCOPF method based on two‐stage optimization. Firstly, the concentric relaxation method is employed to screen and obtain the critical line faults, thereby reducing the total quantity of security constraints associated with N‐1 line fault scenarios. Secondly, the incorporation of scenario transition constraints is introduced to decompose the PSCOPF model into OPF (optimal power flow) models under N‐0 scenarios and N‐1 critical line fault scenarios, then solve iteratively to achieve multi‐scenario decoupling. Finally, the proposed method was validated on the IEEE30 test system to demonstrate its capability to ensure economics in the N‐0 scenario and security in N‐1 scenarios.

A Methodological Framework for Assessing the Agroecological Performance of Farms in Portugal: Integrating TAPE and ACT Approaches

Agroecology integrates science, social movements, and agricultural practices, playing a central role in the sustainability of food systems. It addresses agroecosystems and food systems holistically; however, defining whether a farm is agroecological remains a challenge. This article proposes a methodology to measure farms’ agroecological performance, adapted to the family farming context in Portugal. The aim of the developed methodology is to compare the agroecological performance of family farms (conventional and non-conventional), providing information about anchors for agroecological transition and supporting public policies. A literature review identified existing farm evaluation methodologies, with Tool for Agroecological Performance Evaluation (TAPE) and Agroecology Criteria Tool (ACT) scoring highest in an assessment process. Questions from both were integrated into a questionnaire for family farmers. This field work provided critical insights towards the methodologies: (1) territorial adaptability; (2) transition constraints’ origin; and (3) use of non-academic language. The results were incorporated into the developed methodology, which combines the TAPE indicator matrix and Gliessman’s five levels of food system change, the latter of which provides the framework for the ACT. This study made it possible to identify the most relevant aspects for characterizing family farmers/farms and the importance of how the evaluation criteria/indicators are ordered by element/theme, as it alters the values of each farm’s agroecological performance.

The role of financial constraints in firm transition—Evidence from Indian manufacturing

AbstractIn developing countries' manufacturing, the prevalence of small firms poses a conundrum, as few successfully transition to larger categories. Scholarly attention to the reasons behind this limited upward mobility remains scarce. This study, focusing on Indian manufacturing, explores the role of limited financial access as a significant obstacle hindering small firms' transition. Analysing data spanning 2001–2016, encompassing both formal and informal firms, we find robust evidence that access to finance is crucial for small firm transition in Indian manufacturing. The findings are robust to alternate specifications and methods, and also to concerns arising from reverse causality.

Greening Finance? What Institutional Options for a Sustainable Transition?

Research on sustainability and energy transition aims to identify the structural changes that should occur and how they could be achieved and managed to render society more viable. Institutions are the providers of an appropriate environment that should offer those conditions and allow citizens to undertake activities that meet both individual and societal goals. Climate emergency and transition constraints recast economic and political structures and put institutions under pressure in the face of global risks. Two of them, climate risks and financial risks, can be regarded as sources of systemic risk since they determine the conditions of viability of our economies and societies. This article argues that an appropriate institutional reform is required to frame macroprudential policies for financial regulation and tackle climate change by greening finance and containing climate-related financial risks. The article then assesses the scope of a macro-regulatory institutional framework to provide relevant support for sustainable finance and financial markets to improve our ability to secure a viable future for our societies. Long-term sustainable financial tools for financing the transition require definancialization of the economy to prevent the perversion of financial practices that undermine societal sustainability and sabotage financing resources that a greener future inevitably needs.

Bargaining with Patriarchy in Peacemaking: The Failure of Women, Peace, and Security in Afghanistan

Abstract After two decades of effort to embed Women, Peace, and Security (WPS), why and how did the agenda fail to transform patriarchal gendered structures and prevent the regression of women’s rights and security in Afghanistan? To address this question, the paper investigates how the WPS agenda was conceptualized and operationalized in Afghanistan. We apply Kandiyoti’s concept of patriarchal bargains to analyze multi-level power dynamics across four peace and conflict phases between 2001 and 2022, drawing from primary and secondary sources, including interviews with women leaders and key experts. While 20 years of engagement could not be expected to transform the country’s conservative gender order, we argue that WPS efforts lacked a grounded understanding of the gendered constraints in a conflict transition as well as a political strategy for achieving its goals. While the case of Afghanistan is unique, it is nonetheless highly relevant to other settings making significant investments in WPS. For advancing WPS, the paper suggests that the international normative framework must be connected to local voices that understand the context-specific, patriarchal power dynamics and how to bargain with them to achieve outcomes and gains for women’s security and rights.

Climate risk performance and returns integration of Chinese listed energy companies

Traditional energy and new energy stock returns are becoming more closely related under the energy transition constraint. In this paper, the core correlation between traditional energy stock returns and new energy stock returns in China is portrayed using the minimum spanning tree (MST) approach and the integration characteristics of energy companies are dynamically analyzed. On this basis, we identify the impact of climate factors on the integration of energy markets by constructing a GARCH-MIDAS model with three dimensions: climate concern, climate physical risk, and climate policy. We find that returns are more likely to be transmitted within traditional energy industries and new energy industries, and firms with the same characteristics tend to form clusters. In a dynamic context, the degree of energy system integration has significant time-varying characteristics and is susceptible to unforeseen events, market policies, and supply-demand relationships. In addition, there is heterogeneity in the effect of climate concern on the integration between traditional energy and new energy markets, and the effects of both climate physical risk and climate policy on the integration of the energy system are significantly negative.

Adaptive agricultural transitions to droughts and salinity intrusion in the Mekong delta: Policy issues

This study aims to determine major issues of policies and policy implementation to facilitate enablers and remove constraints of agricultural transition by farmers to adapt to droughts and salinity intrusion in the fresh–brackish water inter-facing zone in the Mekong delta. Results show that farmers shifted rice and sugarcane to fruits (freshwater) or shrimp (brackish or saline water). Resource-richer farmers tended to gradually intensify fruit or shrimp production for increased farm income, which would cause potential economic risks from disease outbreaks or unstable market prices of farm products. The poor capacity of farmers for farm management, financial resources, and information access are considered important constraints for adaptive agriculture production in the future. Thus, synchronous implementation of specialised policies at the local level through an economic sector approach through coordination between the management sector and localities is necessary.

Deep Reinforcement Learning for the Agile Earth Observation Satellite Scheduling Problem

The agile earth observation satellite scheduling problem (AEOSSP) is a combinatorial optimization problem with time-dependent constraints. Recently, many construction heuristics and meta-heuristics have been proposed; however, existing methods cannot balance the requirements of efficiency and timeliness. In this paper, we propose a graph attention network-based decision neural network (GDNN) to solve the AEOSSP. Specifically, we first represent the task and time-dependent attitude transition constraints by a graph. We then describe the problem as a Markov decision process and perform feature engineering. On this basis, we design a GDNN to guide the construction of the solution sequence and train it with proximal policy optimization (PPO). Experimental results show that the proposed method outperforms construction heuristics at scheduling profit by at least 45%. The proposed method can also calculate the approximate profits of the state-of-the-art method with an error of less than 7% and reduce scheduling time markedly. Finally, we demonstrate the scalability of the proposed method.

Transparency and Involvement of Society and Policy in a Data Sharing Platform

Today, energy system models are becoming increasingly powerful and detailed regarding techno-economic parameters. However, current models rarely include social and political factors, although these factors constitute important determinants for the design of energy systems. In Task Area 2 of the nfdi4energy research project, we therefore explore social and political drivers and constraints of the energy transition, generate and link the relevant data, and prepare it for incorporation on a data sharing platform. The aim of this task area is to co-design a scientific energy data and research sharing platform that can feed into new or existing energy models to help inform the public and political decision makers to determine socially acceptable energy pathways of the future. In addition, we will involve citizens during the project lifetime in the development of a platform that enables and incentivizes the active participation of public stakeholders in energy system research.Consequentially, the intention of this abstract within the “Linking RDM Track” is to provide an overview of the platform engagement design process for society and policy.

Reference trajectory output tracking for Boolean control networks with controls in output

<abstract><p>This article investigates the reference trajectory output tracking issue of Boolean control networks (BCNs) that have controls in the output. Firstly, to solve the problem, some necessary and sufficient conditions are obtained. The tracking problem is studied from the perspective of set and matrix calculation. Next, an algorithm for determining whether the output tracking issue is solvable is proposed. Furthermore, the controller design algorithm satisfying the solvability condition is given. Using our methods, we can track some trajectories that cannot be tracked in BCNs without controls in output. In addition, for better application in practice, the corresponding changes in the network transition matrix and output matrix under state, transition, and input constraints are considered. Finally, some examples are presented to illustrate the validity of our results.</p></abstract>

Consumption Upgrading and Industrial Structural Change: A General Equilibrium Analysis and Empirical Test with Low-Carbon Green Transition Constraints

To clarify the relations between low-carbon green transition, consumption upgrading, and industrial structure change, this paper firstly builds a dynamic model of the three, then uses the PVAR Model and panel data of 30 Chinese provinces from 2008 to 2020 to carry out empirical study from the rationalization and upgrading dimensions of industrial structural change, respectively. The results are as follows: (1) Low-carbon green transition and consumption upgrading are Granger causes of each other. In this causal relationship, low-carbon green transition hinders consumption upgrading, but consumption upgrading significantly promotes low-carbon green transition. (2) Low-carbon green transition plays a facilitating and hindering role in industrial structure rationalization and upgrading, respectively. However, from the different dimensions of industrial structure change, only industrial structure upgrading has a significant reverse hindering effect on low-carbon green transition, and the reverse effect of industrial structure rationalization on low-carbon green transition is not significant. (3) Consumption upgrading has a hindering and promoting effect on the rationalization of industrial structure in the short- and long-run respectively, and a promoting and hindering effect on the industrial structure upgrading respectively; however, only industrial structure upgrading significantly promotes consumption upgrading in the opposite direction, while industrial structure rationalization has no significant effect on consumption upgrading. These findings propose some suggestions such as advocating the new way of green consumption, constructing and improving the green whole industry chain, and strengthening the synergy between imitative innovation and independent innovation.

A Conflict-Driven Interface Between Symbolic Planning and Nonlinear Constraint Solving

Robotic planning in real-world scenarios typically requires joint optimization of logic and continuous variables. A core challenge to combine the strengths of logic planners and continuous solvers is the design of an efficient interface that informs the logical search about continuous infeasibilities. In this paper we present a novel iterative algorithm that connects logic planning with nonlinear optimization through a bidirectional interface, achieved by the detection of minimal subsets of nonlinear constraints that are infeasible. The algorithm continuously builds a database of graphs that represent (in)feasible subsets of continuous variables and constraints, and encodes this knowledge in the logical description. As a foundation for this algorithm, we introduce Planning with Nonlinear Transition Constraints (PNTC), a novel planning formulation that clarifies the exact assumptions our algorithm requires and can be applied to model Task and Motion Planning (TAMP) efficiently. Our experimental results show that our framework significantly outperforms alternative optimization-based approaches for TAMP.

An integrated two‐dimensional merging trajectory optimization model for automated vehicles considering mixed traffic flow

Recently, automated vehicles have been recognized as a promising tool to address the problem of on-ramp merging. However, the complexity of the problem includes ignorance of the vehicle's lateral dynamics, the limitation of the fixed merging point, the mismatch problem caused by the sequential optimization scheme, and the fixed terminal states assumption. These factors make it intractable in practical driving scenarios. This paper proposed an integrated optimization model for on-ramp automated vehicles, which can guide the vehicle to complete the merging behaviour from the ramp lane to the main lane safely and efficiently. A merging path is first constructed in the two-dimensional plane with two critical parameters recommended by a neural network predictor, enabling it to incorporate both longitudinal and lateral dynamics. Then, an integrated Mixed Integer Quadratic Program (MIQP) model is built to determine the optimal velocity profiles and merging gap simultaneously while considering the safety and efficiency factors. Specifically, with the introduction of discretization and linearization techniques, linear collision avoidance constraints and smooth transition constraints can be built, which can address the challenging computational problem. The effectiveness of the proposed model is validated through the microscopic and macroscopic numerical results under different levels of mixed traffic.

Constraints of transition to online distance learning in Higher Education Institutions during COVID-19 in developing countries: A systematic review

This paper addresses the paradox of transition to online distance learning during COVID-19 pandemic in Higher Education Institutions (HEIs) in developing countries. The systematic review methodological procedures were employed to draw some limitations and possible opportunities that may inform future practices on online distance learning. The findings reveal several limitations such as digital inequalities, lack of reliable internet access, low readiness and technological competence among instructors and students, and limited availability of digital solutions. The findings also reveal that most students faced social emotional challenges due to rapid and blind transition to online distance learning. It was concluded that most limitations were due to lack of digital culture even before the outbreak of COVID-19 pandemic. It is recommended that deliberate investment should be made to promote digital culture and equity. Further possible areas for research recommended in this paper include exploration of digital inclusion among marginalized groups in developing countries.

Concise integer linear programming formulation for clique partitioning problems

A Clique Partitioning Problem (CPP) finds an optimal partition of a given edge-weighted undirected graph, such that the sum of the weights is maximized. This general graph problem has a wide range of real-world applications, including correlation clustering, group technology, community detection, and coalition structure generation. Although a CPP is NP-hard, due to the recent advance of Integer Linear Programming (ILP) solvers, we can solve reasonably large problem instances by formulating a CPP as an ILP instance. The first ILP formulation was introduced by Grötschel and Wakabayashi (Mathematical Programming, 45(1-3), 59–96, 1989). Recently, Miyauchi et al. (2018) proposed a more concise ILP formulation that can significantly reduce transitivity constraints as compared to previously introduced models. In this paper, we introduce a series of concise ILP formulations that can reduce even more transitivity constraints. We theoretically evaluate the amount of reduction based on a simple model in which edge signs (positive/negative) are chosen independently. We show that the reduction can be up to 50% (dependent of the ratio of negative edges) and experimentally evaluate the amount of reduction and the performance of our proposed formulation using a variety of graph data sets. Experimental evaluations show that the reduction can exceed 50% (where edge signs can be correlated), and our formulation outperforms the existing state-of-the-art formulations both in terms of memory usage and computational time for most problem instances.

Low-melting metal bonded MM′X/In composite with largely enhanced mechanical property and anisotropic negative thermal expansion

MM′X (M, M′ = transition metals, X = carbon or boron group elements) compounds experiencing magnetostructural transition have great potential in multifunctional applications due to the various magnetoresponsive effects. However, the poor mechanical properties hinder their practical applications. In the present work, the low-melting metal bonded MM′X/In composites are fabricated. The dense structure with low porosity, formed due to the high ductility of In, greatly improves the machinability and thermal conductivity. The thermal cycling tests prove the excellent integrality and stability of MM′X/In composites. The introduction of ductile In reduces the constraints of structural transition so that weakens the first-order magnetostructural transition of MM′X compounds, thus lowering the hysteresis loss. Besides, the MM′X/In composite with 30 wt.% In (In30) exhibits a larger magnetocaloric effect than that of the benchmark magnetocaloric material Gd. In addition, zero thermal expansion (ZTE) property is achieved in In30 composite over a wide temperature range. Moreover, magnetic oriented In30 composite displays a strongly anisotropic thermal expansion performance. An obvious positive thermal expansion (PTE) with linear PTE coefficient of 5.2 × 10−6 K−1 is observed along the direction parallel to pressure, while large negative thermal expansion (NTE) can be obtained along the directions perpendicular and parallel to field. The NTE in the direction parallel to field is as high as −40.6 × 10−6 K−1, which is comparable to those of some typical NTE materials. Consequently, the present result highlights the potential multifunctional applications of low-melting metal bonded MM′X/In composites, especially as magnetic refrigerants and NTE materials around room temperature.

Sequencing jobs with asymmetric costs and transition constraints in a finishing line: A real case study

Production scheduling plays a vital role in industrial manufacturing due to the potential impact on the production costs and service levels of a company. It consists in finding the best sequence in which some items should be produced, optimizing one or multiple performance indicators, such as the production cost or total time span. In this work we study the real-world problem of sequencing steel coils in a continuous galvanizing line and the challenges it poses. The production of new steel grades and the growing necessity or reducing the stock levels at the galvanizing line have brought an important increase in the number of sequencing constraints, challenging feasibility and the algorithms in use. We explain some issues of the current Ant Colony Optimization algorithms and introduce a new hybrid version, the Ant System with Interval Reconstruction (AS-IR), that notably enhances the feasibility performance. The new hybrid algorithm uses the Interval Reconstruction (IR), a novel constructive local search algorithm initially developed to solve constraint violations, and then extended to also help reduce the sequencing costs. All the key features of the IR and how it is used in the hybrid algorithm are explained in detail. The experiments conducted with 30 real instances show how the proposed AS-IR hybrid algorithm achieves much better results, guaranteeing feasible sequences when the set of coils is sequenceable, as well as finding lower-cost solutions.

Model Predictive Control of Permanent Magnet Synchronous Motor Based on State Transition Constraint Method

Permanent magnet synchronous motors are widely used and have sufficient development prospects in the drive systems of electric vehicles. Traditional model predictive control (MPC) methods are shown to achieve good control performance by tracking the d- and q-axis current as well as limiting the current amplitude. However, the dynamic response performance and current harmonics during the switching process are not considered in the traditional MPC. Therefore, this paper proposes an MPC that can effectively improve control performance, where the switch transfer sequence in the switch constraint module is considered in the improved model. The state transition error is obtained from the switch constraint module according to the current switch state and the transition probability, after which, the integration into the cost function in which the driving error, tracking error, and constraint error are considered. A reinforcement learning (RL) algorithm is used to obtain the weight coefficient of the transition error term in the constraint module for automatically determining the best switch state for the next control period using the cost function. Simulation tests show that the total harmonic distortion of the phase current based on the improved MPC is 978.4%, less than 2843.0% of the traditional MPC method under 20 Nm at 1000 rpm. The torque response time of the motor is reduced by 0.026 s, whereas the simulation results indicate that the 100 km acceleration performance of an electric vehicle is improved by 9.9%.

Mitigating Increased Driving after the COVID-19 Pandemic: An Analysis on Mode Share, Travel Demand, and Public Transport Capacity.

Reduced transit capacity to accommodate social distancing during the COVID-19 pandemic was a sudden constraint that along with a large reduction in total travel volume and a shift in activity patterns contributed to abrupt changes in transportation mode shares across cities worldwide. There are major concerns that as the total travel demand rises back toward prepandemic levels, the overall transport system capacity with transit constraints will be insufficient for the increasing demand. This paper uses city-level scenario analysis to examine the potential increase in post-COVID-19 car use and the feasibility of shifting to active transportation, based on prepandemic mode shares and varying levels of reduction in transit capacity. An application of the analysis to a sample of cities in Europe and North America is presented. Mitigating an increase in driving requires a substantial increase in active transportation mode share, particularly in cities with high pre-COVID-19 transit ridership; however, such a shift may be possible based on the high percentage of short-distance motorized trips. The results highlight the importance of making active transportation attractive and reinforce the value of multimodal transportation systems as a strategy for urban resilience. This paper provides a strategic planning tool for policy makers facing challenging transportation system decisions in the aftermath of the COVID-19 pandemic.