Problem Definition Research Articles

On a sharp estimate of overlapping Schwarz methods in H(curl; Ω) and H(div; Ω)

Abstract The previously proved upper bound is $C\big(1+\frac{H^{2}}{\delta ^{2}}\big)$ for the condition number of the discrete system arising from the classical two-level overlapping Schwarz method for symmetric positive definite problems in $\boldsymbol{H}(\textbf{curl}; \varOmega )$ and $\boldsymbol{H}(\textrm{div}; \varOmega )$, where $H$ and $\delta $ are the sizes of subdomains and overlap, respectively. But all numerical results indicate that a sharper bound is $C\big (1+ \frac{H}{\delta } \big )$. In this work, we prove that the condition number is indeed bounded above by $C\big(1+\frac{H}{\delta }\big)$.

Balancing parallel assembly lines with human-robot collaboration: problem definition, mathematical model and tabu search approach

Human-robot collaboration (HRC), as an emerging production mode, has garnered significant attention in the context of the growing smart manufacturing. This study aims to investigate the application of human-robot collaboration in parallel assembly lines and explore its potential for improving productivity, resource utilisation and flexibility. To the best of our knowledge, this is one of the first attempts to consider the combination of parallel assembly line balancing problem (PALBP) and human-robot collaboration. In this study, the parallel assembly line balancing problem with human-robot collaboration (PALBP-HRC) is introduced and characterised. The collaboration mode allows human workers and robots to execute tasks both in parallel and in collaboration. A mixed-integer programming model (MIP) that minimises the cycle time is formulated and a lower bound is proposed. Due to the NP-hardness, we develop an adapted tabu search (TS) algorithm specifically tailored for problem characterisation. Experimental results indicate that the proposed TS algorithm achieves competing performance compared to the MIP and other algorithms, including genetic algorithm (GA) and simulated annealing (SA) algorithm. The comparison between PALBP and PALBP-HRC demonstrates that the integration of human-robot collaboration to parallel assembly line can achieve significant improvements in efficiency and flexibility.

Resolving the Definitional Quagmire of Terrorism and Providing Guidance to States

The study examined the definition of terrorism from the early years to the modern day. The study analyzed the attempts at definitions of terrorism at international law, other definitions in international relations and scholarly definitions. The study aimed at solving the problem of definition of terrorism, particularly the widely acclaimed notion that there is no precise definition of terrorism. The importance of having a definition is to know what exactly to fight, the appropriate penalties for such acts and to ensure that all violent acts are not subsumed under terrorism. The study revealed that international law had provided a guidance as to what the definition of terrorism should be. This guidance could be gleaned from the various instruments of the United Nations, other definitions in international relations and scholarly definitions. The study concluded that the common elements of terrorism from the attempts at definition at international law could form a consensual definition and serve as a foundation for States to build upon in defining acts of terrorism in domestic laws.

Voting according to one’s political stances is difficult: Problems definition, computational hardness, and approximate solutions

This paper studies the computational complexity of two voting problems where the goal is deciding how a given voter should vote to favour their personal stances. In the first problem, given (a) the voter stance towards each law that will be voted by the parliament and (b) the political stance of each party towards each law (all party members are assumed to vote according to it), the goal is finding the parliamentary seats distribution maximizing the number of laws that will be approved/rejected as desired by the voter. In the second problem no parliament is involved, but a single issue with several possible answers is voted by citizens in a presidential election with several candidates. The problem consists in deciding how a group of voters, split in different electoral districts, all of them supporting the same candidate, should vote to make their candidate president. It is assumed that (a) all delegates of each electoral district are assigned to the candidate winning in the district, (b) after the election day, candidates may ask their assigned delegates to support other candidates receiving more votes than them, and these post-electoral supporting stances are known in advance by the electorate, and (c) the group of voters that is coordinated knows the votes that will be cast by the rest of the electorate. For each problem, its NP-hardness as well as its inapproximability are proved. This implies that something as essential as exercising the democratic right to vote, in such a way that the voting choice will be the best for the voter’s political stances, is at least NP-hard. It is also shown how genetic algorithms can be used to obtain reasonable solutions in practice despite the limitations of theoretical approximation hardness.

Frontiers in Operations: Employees vs. Contractors: An Operational Perspective

Problem definition: We consider a platform’s problem of how to staff its operations given the possibilities of hiring employees and setting up a contractor marketplace. We aim to understand the operational difference between these two work arrangement models. Methodology/results: We consider a model where demand is not only stochastic but also evolving over time, which we capture via a state of the world that determines the demand distribution. In the case of employees, the platform controls the number of employee hours it uses for serving demand, whereas in the case of contractors, it sets the wage paid to them per utilized hour. We show that although the employee problem is equivalent to a standard newsvendor, the contractor one corresponds to an unusual version of the newsvendor model where utilization is the control variable. Managerial implications: This distinction makes the contractor model more flexible, allowing us to prove that it performs significantly better, especially if the order of magnitude of demand is unknown. Meanwhile, hybrid solutions that combine both employees and contractors have complex optimal solutions and offer relatively limited benefits relative to a contractor marketplace. History: This paper has been accepted in the Manufacturing & Service Operations Management Frontiers in Operations Initiative. Funding: This research was partially supported by the National Natural Science Foundation of China [Grant 71821002]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0029 .

Elephants in the Room - Analyzing Local Discourses for Sustainable Management of Bannerghatta National Park, South India.

Landscape governance challenges, particularly in peri-urban contexts like the Bannerghatta National Park (BNP) region in South India, exemplify 'wicked' problems due to their inherent complexities. These challenges arise from a mix of conflicting interests, policy ambiguities, and sociocultural dynamics, which often blur the definition of problems and hinder effective solutions. Despite apparent options for resolution, stakeholder disagreements and deep uncertainties about implementation strategies complicate governance. This study, therefore, has two broad objectives. The first objective is to analyze the local discourses surrounding planned policy interventions around the BNP region in South India. Based on the findings, the second objective is to draw insights for sustainable natural resource governance research and practice. We applied Q-methodology to understand the discourses that underpin various conflicts in the rapidly urbanizing elephant corridor at BNP. We elicited information on how various local actors frame solutions to current collective action challenges in the BNP landscape and their perspectives on the proposed eco-sensitive zone notification, as well as the functioning of current policy interventions concerning conservation and development. The study uncovers the micropolitics and power regimes underpinning various natural resource governance challenges and demonstrates the utility of the Q-methodology in bringing diverse perspectives together in response to 'wicked' governance challenges.

Logistics distribution route optimization of electric vehicles based on distributed intelligent system

Abstract The data management system of health cloud authentication plays an important role in the optimization of logistics vehicle routing. It can not only help logistics vehicles choose the best distribution path, but also save time and cost and improve economic efficiency. At present, logistics has not yet formed a complete service system. High distribution costs and low distribution efficiency limit the development of the entire logistics. The reduction of logistics costs and the improvement of distribution efficiency have become the top priorities of the society. The optimization of the distribution route is the key to cost saving and distribution logistics. It is particularly important to study and optimize the distribution route, because the distribution route affects the logistics transportation efficiency and the loss cost during transportation. Therefore, this paper adjusted the scheduling system of logistics vehicles through a distributed intelligent system, and optimized the path of logistics vehicles according to the improved genetic algorithm, thereby reducing the transportation cost of logistics and improving the efficiency of logistics distribution. This article first explains the definition, classification, and main components of the delivery vehicle routing problem. Then, using cloud authentication path optimization, a distributed intelligent system is constructed. Finally, an improved ant colony algorithm is used to analyze and study the distance constraints of vehicles. By improving the ant colony algorithm, it can be seen that the optimized path pheromone concentration and the optimized sub-function have gradually increased with time. The mean pheromone concentration was 40 %, and the seventh day was 15 % higher than the first. The mean value of the optimized subfunction was 0.34 %, and the seventh day was 20 % higher than the first. The distribution cost and distribution efficiency of the optimized logistics vehicle distribution path were much higher than those of the traditional logistics distribution path. Moreover, the distribution cost of the logistics distribution path was 9 % lower than the traditional one, and the distribution efficiency was 13 % higher. The average smoothness of the optimized logistics path is about 90 %, and the seventh day is 11 % higher than the first day. The average fitness of the optimized logistics path is 88 %, and the seventh day is 14 % higher than the first day. In a word, the data management system can uniformly schedule logistics vehicles and improve the efficiency of distribution.

A Systematic Survey of Deep Learning-Based Single-Image Super-Resolution

Single-image super-resolution (SISR) is an important task in image processing, which aims to enhance the resolution of imaging systems. Recently, SISR has made a huge leap and has achieved promising results with the help of deep learning (DL). In this survey, we give an overview of DL-based SISR methods and group them according to their design targets. Specifically, we first introduce the problem definition, research background, and the significance of SISR. Secondly, we introduce some related works, including benchmark datasets, upsampling methods, optimization objectives, and image quality assessment methods. Thirdly, we provide a detailed investigation of SISR and give some domain-specific applications of it. Fourthly, we present the reconstruction results of some classic SISR methods to intuitively know their performance. Finally, we discuss some issues that still exist in SISR and summarize some new trends and future directions. This is an exhaustive survey of SISR, which can help researchers better understand SISR and inspire more exciting research in this field. An investigation project for SISR is provided at https://github.com/CV-JunchengLi/SISR-Survey.

Contractual Diversification Advantage: A Relational View of the Link Between Supplier Diversification and Performance

Contractual Diversification Advantage: A Relational View of the Link Between Supplier Diversification and Performance

Robust Drone Delivery with Weather Information

Problem definition: Drone delivery has recently garnered significant attention due to its potential for faster delivery at a lower cost than other delivery options. When scheduling drones from a depot for delivery to various destinations, the dispatcher must take into account the uncertain wind conditions, which affect the delivery times of drones to their destinations, leading to late deliveries. Methodology/results: To mitigate the risk of delivery delays caused by wind uncertainty, we propose a two-period drone scheduling model to robustly optimize the delivery schedule. In this framework, the scheduling decisions are made in the morning, with the provision for different delivery schedules in the afternoon that adapt to updated weather information available by midday. Our approach minimizes the essential riskiness index, which can simultaneously account for the probability of tardy delivery and the magnitude of lateness. Using wind observation data, we characterize the uncertain flight times via a cluster-wise ambiguity set, which has the benefit of tractability while avoiding overfitting the empirical distribution. A branch-and-cut (B&C) algorithm is developed for this adaptive distributionally framework to improve its scalability. Our adaptive distributionally robust model can effectively reduce lateness in out-of-sample tests compared with other classical models. The proposed B&C algorithm can solve instances to optimality within a shorter time frame than a general modeling toolbox. Managerial implications: Decision makers can use the adaptive robust model together with the cluster-wise ambiguity set to effectively reduce service lateness at customers for drone delivery systems. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72101049 and 72232001], the Natural Science Foundation of Liaoning Province [Grant 2023-BS-091], the Fundamental Research Funds for the Central Universities [Grant DUT23RC(3)045], and the Major Project of the National Social Science Foundation [Grant 22&ZD151]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.0339 .

Retail Category Management with Slotting Fees

Problem definition: Slotting fees are lump-sum payments retailers demand from manufacturers to include manufacturers’ products in their assortments. Although retailers regard slotting fees as part of doing business, some manufacturers claim that slotting fees limit their ability to compete on a level playing field with other manufacturers. Considering these conflicting views, we study the role of manufacturer competition in the emergence of slotting fees and how slotting fees affect retailers’ category management (i.e., assortment and pricing) decisions. Methodology/results: We consider a game-theoretic model with a single retailer and two competing manufacturers, each offering a single product. The retailer makes slotting fee, assortment, and pricing decisions in the presence of an operational cost term that increases in the assortment size. The manufacturers that can afford the slotting fee set the wholesale prices for their products. This study leads to three key findings. First, slotting fees can be suboptimal when their absence would trigger intense wholesale price competition. Second, depending on the retailer’s operational cost and the intensity of manufacturer competition, slotting fees create three distinct effects -category expansion, rent extraction, and competitive exclusion under which product variety (i.e., the retailer’s assortment size) increases, remains unchanged, and decreases, respectively. Third, slotting fees are most (least) beneficial for the retailer when they lead to a decrease (increase) in product variety. Managerial implications: This study not only illustrates that retailers can use slotting fees as a strategic tool to control the intensity of manufacturer competition but also reveals how slotting fees impact retailers’ assortment and pricing decisions, with implications for manufacturers and policy makers. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0143 .

Interdisciplinary Transition Innovation, Management, and Engineering (InTIME) Design: an intersection analysis of design approaches for whole-system sustainability

AbstractInterdisciplinary transition innovation, management, and engineering (InTIME) Design has been developed to overcome sustainability transition challenges in complex systems. The intersections of InTIME Design with a range of reported design for sustainability (DfS) approaches were analysed. Results demonstrate similar core principles across DfS approaches. InTIME Design accomplishes convergence of the studied approaches, and organises the DfS approaches into workflow phases, adds a complimentary wicked problem definition, and deploys systems engineering problem solving.

Visual Semantic Segmentation Based on Few/Zero-Shot Learning: An Overview

Visual semantic segmentation aims at separating a visual sample into diverse blocks with specific semantic attributes and identifying the category for each block, and it plays a crucial role in environmental perception. Conventional learning-based visual semantic segmentation approaches count heavily on large-scale training data with dense annotations and consistently fail to estimate accurate semantic labels for unseen categories. This obstruction spurs a craze for studying visual semantic segmentation with the assistance of few/zero-shot learning. The emergence and rapid progress of few/zero-shot visual semantic segmentation make it possible to learn unseen categories from a few labeled or even zero-labeled samples, which advances the extension to practical applications. Therefore, this paper focuses on the recently published few/zero-shot visual semantic segmentation methods varying from 2D to 3D space and explores the commonalities and discrepancies of technical settlements under different segmentation circumstances. Specifically, the preliminaries on few/zero-shot visual semantic segmentation, including the problem definitions, typical datasets, and technical remedies, are briefly reviewed and discussed. Moreover, three typical instantiations are involved to uncover the interactions of few/zero-shot learning with visual semantic segmentation, including image semantic segmentation, video object segmentation, and 3D segmentation. Finally, the future challenges of few/zero-shot visual semantic segmentation are discussed.

Wasserstein Robust Classification with Fairness Constraints

Problem definition: Data analytics models and machine learning algorithms are increasingly deployed to support consequential decision-making processes, from deciding which applicants will receive job offers and loans to university enrollments and medical interventions. However, recent studies show these models may unintentionally amplify human bias and yield significant unfavorable decisions to specific groups. Methodology/results: We propose a distributionally robust classification model with a fairness constraint that encourages the classifier to be fair in the equality of opportunity criterion. We use a type-[Formula: see text] Wasserstein ambiguity set centered at the empirical distribution to represent distributional uncertainty and derive a conservative reformulation for the worst-case equal opportunity unfairness measure. We show that the model is equivalent to a mixed binary conic optimization problem, which standard off-the-shelf solvers can solve. We propose a convex, hinge-loss-based model for large problem instances whose reformulation does not incur binary variables to improve scalability. Moreover, we also consider the distributionally robust learning problem with a generic ground transportation cost to hedge against the label and sensitive attribute uncertainties. We numerically examine the performance of our proposed models on five real-world data sets related to individual analysis. Compared with the state-of-the-art methods, our proposed approaches significantly improve fairness with negligible loss of predictive accuracy in the testing data set. Managerial implications: Our paper raises awareness that bias may arise when predictive models are used in service and operations. It generally comes from human bias, for example, imbalanced data collection or low sample sizes, and is further amplified by algorithms. Incorporating fairness constraints and the distributionally robust optimization (DRO) scheme is a powerful way to alleviate algorithmic biases. Funding: This work was supported by the National Science Foundation [Grants 2342505 and 2343869] and the Chinese University of Hong Kong [Grant 4055191]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.0230 .

Empowering Internal Control: A Case Study in Leveraging Data and Technologies

This essay responds to the PCAOB's call for insights into data analytics and emerging technologies in auditing by developing a Social Construction of Technology (SCOT) framework to embed data analytics and technologies to improve internal control performance. SCOT outlines four phases of institutionalization: problem definition, interpretative flexibility management, stabilization, and social construction. This paradigm is validated through a documented procedure of institutionalizing a data-driven analyticproject optimizing the purchase-to-payment process at a state university. The study used a value-focused thinking approach to observe and identify the values at stake in the decisions about how communication among IT, auditors, and management impacts the adoption of technologies. The collected evidence highlights the importance of identifying suitable algorithms and qualified data as the starting point for technology’s institutionalization. Additionally, the case study demonstrates mechanisms for preparingan integrated and transparent data platform, emphasizing the role of integrating relevant social groups and technology in circulating emerging technology. This study offers a practical roadmap about how to fit AI and technologies into an institutional social management ecosystem. It contributes to the literature by addressing the emerging technology usage trap and improving analytics performance, offering practical value through a social construct roadmap for institutionalizing emerging technologies.

Gestión de proyectos de ciencia abierta. Una experiencia de investigación acción participativa

Participatory action research is characterized because it involves people in the definition of problems and action alternatives in order to help transform a situation. As such, it is found between the gaze of the researcher and the participants, who intertwine to form a mode of intersubjective interpretation and co-responsible action of the situation to overcome. Therefore, participatory action research favors learning processes among those who serve as formal researchers, the primary participants and the interested parties, who make up a plural human group that accounts for a process of investigation and change in the environment. This essay presents a participatory action research experience in the field of knowledge project management, as part of the foundation of an open science knowledge asset creation approach. As such, it is proposed to offer two contributions: a consideration of participatory action research in the design of research goods development processes, and an argument in favor of participatory research processes in the field of open science.

Shared-Ride Efficiency of Ride-Hailing Platforms

Problem definition: Ride-hailing platforms offering shared rides devote effort to reducing the trip-lengthening detours that accommodate fellow customers’ divergent transportation needs. By reducing shared-ride delay, improving shared-ride efficiency has the twin benefits of making shared rides more attractive to customers and increasing the number of customers a driver can serve per unit time. Methodology/results: We analytically model a ride-hailing platform that can offer individual rides and shared rides. We establish results that are counter to naive intuition: greater customer sensitivity to shared-ride delay and greater labor cost can reduce the value of improving shared-ride efficiency, and an increase in shared-ride efficiency can prompt a platform to add individual-ride service. We show that when network effects are small, increasing shared-ride efficiency pushes wages to extremes: if the current wage is high (low), increasing shared-ride efficiency pushes the wage higher (lower). We provide a sharp characterization of whether shared-ride efficiency and labor supply are complements or substitutes. We provide simple conditions under which increasing shared-ride efficiency reduces (alternatively, increases) labor welfare. We provide evidence that increasing shared-ride efficiency increases consumer surplus. Managerial implications: Our results inform a platform’s decision of whether to invest in improving shared-ride efficiency, as well as how to change its service offering and wage, as shared-ride efficiency improves. Supplemental Material: The online supplement is available at https://doi.org/10.1287/msom.2021.0545 .

Top-down characterization of resource use in LCA: from problem definition of resource use to operational characterization factors for resource inaccessibility of elements in a short-term time perspective

PurposeWhen resources are extracted and used by society, they are not necessarily lost for future generations. Therefore, recent publications on impact assessment of abiotic resource use in life cycle assessment focus on a decreased accessibility of resources due to dissipation, rather than depletion. In a previous study, dissipation was defined as a function of the global change in accessible stock due to human actions, and the global amount of the accessible stock, assuming a very long-term time perspective (more than 500 years). In this paper, a short-term time perspective (25 years) is adopted.MethodsThe same generic characterization model is used, but different choices are outlined to derive characterization factors for a short-term perspective (25 years). To illustrate how the short term might be approached, a preliminary set of characterization factors is developed, based on assumptions and estimates.ResultsThe problem of resource use is defined as follows: the decrease of accessibility on a global level of primary and/or secondary elements over the short term due to the net result of compromising actions (i.e., emissions, dissipation in the technosphere, occupation in use, and exploration for new stocks). Characterization factors are derived based on assumptions, like the following: the accessible stock is based on present estimates of accessible stocks in the environment and the technosphere; estimates of accessible stocks in the technosphere are based on past extractions and generic recycling rates; all flows that are presently not recycled are assumed to be inaccessible. Finally, weighting between elements and the functions they have for the present society is based on the added value of the economic sector that is affected due to the decreased accessibility.Discussion and conclusionA preliminary set of characterization factors is proposed for 55 elements. They assess the impact of the present use of resources on the decreased accessibility in the short term due to emissions and dissipation in the technosphere. However, calculation of impact category scores is still hampered by a lack of appropriate data for dissipative flows in life cycle inventory databases. The presented calculations are based on several simplifications and proxies. A more detailed distinction of dissipative flows and estimates of stocks in the technosphere may be possible based on (dynamic) SFA modelling of elements in different applications. To derive a more mature set of characterization factors, it is recommended to use the presented model as a basis and further elaborate or replace the proxies.

GHQ: grouped hybrid Q-learning for cooperative heterogeneous multi-agent reinforcement learning

Previous deep multi-agent reinforcement learning (MARL) algorithms have achieved impressive results, typically in symmetric and homogeneous scenarios. However, asymmetric heterogeneous scenarios are prevalent and usually harder to solve. In this paper, the main discussion is about the cooperative heterogeneous MARL problem in asymmetric heterogeneous maps of the Starcraft Multi-Agent Challenges (SMAC) environment. Recent mainstream approaches use policy-based actor-critic algorithms to solve the heterogeneous MARL problem with various individual agent policies. However, these approaches lack formal definition and further analysis of the heterogeneity problem. Therefore, a formal definition of the Local Transition Heterogeneity (LTH) problem is first given. Then, the LTH problem in SMAC environment can be studied. To comprehensively reveal and study the LTH problem, some new asymmetric heterogeneous maps in SMAC are designed. It has been observed that baseline algorithms fail to perform well in the new maps. Then, the authors propose the Grouped Individual-Global-Max (GIGM) consistency and a novel MARL algorithm, Grouped Hybrid Q-Learning (GHQ). GHQ separates agents into several groups and keeps individual parameters for each group. To enhance cooperation between groups, GHQ maximizes the mutual information between trajectories of different groups. A novel hybrid structure for value factorization in GHQ is also proposed. Finally, experiments on the original and the new maps show the fabulous performance of GHQ compared to other state-of-the-art algorithms.

A machine learning based method for parametric environmental impact model for electric vehicles

Environmental managers attempt to increasingly incorporate precautionary principles into decision making. The literature lacks Machine Learning -based approaches for forewarning lifecycle environmental impacts. This paper proposes a method to support electric vehicle design. The main innovation of the work lays in merging Life Cycle Assessment (LCA) and Machine Learning foundations to provide support and awareness to designers. The present approach overcomes the present literature because it provides a method for the design phase, is based on a well-established methodology (LCA) and provides quantitative results from little inputs. The approach exploits machine Learning Methods to develop models with the design features of a generic electric vehicle (such as vehicle mass and distance traveled) in six phases (Problem definition; Data collection; Data Preparation; Modeling; Model evaluation; Model interpretation). Differently from existing environmental analyses, all stages of the product life cycle have been considered in building the database; moreover, the model provides quantitative results. Regression models and supervised algorithms were used. The obtained model can be used by product engineers, as well as those not experts on LCA. Moreover, the model guarantees the database and hypothesis's uniqueness, ensuring the results coherence and comparability. The level of accuracy obtained in the case study (error or 17%) is comparable with studies handling full environmental analysis (that should be more accurate), and outstanding, as the present case is for the design phase. Future works will focus on additional significative indicators, similar electric vehicle design and integration with prospective LCA approaches.