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For an unbalanced cooperative game, its grand coalition can be stabilized by some instruments, such as subsidization and penalization, that impose new cost terms to certain coalitions. In this paper, we study an alternative instrument, referred to as cost adjustment, that does not need to impose any new coalition-specific cost terms. Specifically, our approach is to adjust existing cost coefficients of the game under which (i) the game becomes balanced so that the grand coalition becomes stable, (ii) a desired way of cooperation is optimal for the grand coalition to adopt, and (iii) the total cost to be shared by the grand coalition is within a prescribed range. Focusing on a broad class of cooperative games, known as integer minimization games, we formulate the problem on how to optimize the cost adjustment as a constrained inverse optimization problem. We prove NP -hardness and derive easy-to-check feasibility conditions for the problem. Based on two linear programming reformulations, we develop two solution algorithms. One is a cutting-plane algorithm, which runs in polynomial time when the corresponding separation problem is polynomial time solvable. The other needs to explicitly derive all the inequalities of a linear program, which runs in polynomial time when the linear program contains only a polynomial number of inequalities. We apply our models and solution algorithms to two typical unbalanced games, including a weighted matching game and an uncapacitated facility location game, showing that their optimal cost adjustments can be obtained in polynomial time. History: Accepted by Area Editor Andrea Lodi for Design & Analysis of Algorithms—Discrete. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72022018 and 72091210]; the Research Grants Council of the Hong Kong SAR, China [Grant 16210020]; Hong Kong Polytechnic University [Grant P0032007]; and the Youth Innovation Promotion Association, Chinese Academy of Sciences [Grant 2021454]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoc.2022.0268. [ABSTRACT FROM AUTHOR]

This paper develops a cost-effective sequential route recommender system to provide real-time routing recommendations for vacant taxis searching for the next passenger. We propose a prediction-and-optimization framework to recommend the searching route that maximizes the expected profit of the next successful passenger pickup based on the dynamic taxi demand-supply distribution. Specifically, this system features a deep learning-based predictor that dynamically predicts the passenger pickup probability on a road segment and a recursive searching algorithm that recommends the optimal searching route. The predictor integrates a graph convolution network (GCN) to capture the spatial distribution and a long short-term memory (LSTM) to capture the temporal dynamics of taxi demand and supply. The GCN-LSTM model can accurately predict the pickup probability on a road segment with the consideration of potential taxi oversupply. Then, the dynamic distribution of pickup probability is fed into the route optimization algorithm to recommend the optimal searching routes sequentially as route inquiries emerge in the system. The recursion tree-based route optimization algorithm can significantly reduce the computational time and provide the optimal routes within seconds for real-time implementation. Finally, extensive experiments using Beijing Taxi GPS data demonstrate the effectiveness and efficiency of the proposed recommender system. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Funding: This work was partially supported by the Hong Kong Research Grants Council [Grants CityU 21500220, CityU 11504322] and the National Natural Science Foundation of China [Grant 72201222]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0112) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2021.0112). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

Many warehouses involved in e-commerce order fulfillment use robotic mobile fulfillment systems. Because demand and variability can be high, scheduling orders, robots, and storage pods in interaction with manual workstations are critical to obtaining high performance. Simultaneously, the scheduling problem is extremely complicated because of interactions between decisions, many of which must be taken timely because of short planning horizons and a constantly changing environment. This paper models all such scheduling decisions in combination to minimize order fulfillment time. We propose two decision methods for the above scheduling problem. The models batch the orders using different batching methods and assign orders and batches to pods and workstations in sequence and robots to jobs. Order picking and stock replenishment operations are included in the models. We conduct numerical experiments based on a real-world case to validate the efficacy and efficiency of the model and algorithm. Instances with 14 workstations, 400 orders, 300 stock-keeping units (SKUs), 160 pods, and 160 robots can be solved to near optimality within four minutes. Our methods can be applied to large instances, for example, using a rolling horizon. Because our model can be solved relatively fast, it can be used to take managerial decisions and obtain executive insights. Our results show that making integrated decisions, even when done heuristically, is more beneficial than sequential, isolated optimization. We also find that positioning pick stations close together along one of the system's long sides is efficient. The replenishment stations can be grouped along another side. Another finding is that SKU diversity per pod and SKU dispersion over pods have strong and positive impacts on the total completion time of handling order batches. Funding: This work was supported by National Natural Science Foundation of China [72025103, 72361137001, 71831008, 72071173] and the Research Grants Council of the Hong Kong Special Administrative Region, China [HKSAR RGC TRS T32-707/22-N]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2022.0265. [ABSTRACT FROM AUTHOR]

The ℓ 0 trend filtering ( ℓ 0 -TF) is a new effective tool for nonparametric regression with the power of automatic knot detection in function values or derivatives. It overcomes the drawback of ℓ 1 -TF that is known to have bias issues. To solve the ℓ 0 -TF problem, we propose an alternating minimization induced active set (AMIAS) search method based on the necessary optimality conditions derived from an augmented Lagrangian framework. The proposed method takes full advantage of the primal and dual variables with complementary supports, and decouples the high-dimensional problem into two subsystems on the active and inactive sets, respectively. A sequential AMIAS algorithm with warm start initialization is developed for efficient determination of the cardinality parameter, along with the output of solution paths. Theoretically, the oracle estimator of ℓ 0 -TF is justified to behave like regression splines under the continuous time setting with mild conditions. Our numerical experiments include simulation studies for comparing ℓ 0 -TF to ℓ 1 -TF and free-knot splines on several synthetic examples, and a real data application of time series segmentation on Hong Kong PM2.5 indexes. History: Accepted by Antonio Frangioni, Area Editor for Design & Analysis of Algorithms – Continuous. Funding: This work was supported in part by Hong Kong General Research Fund [No. 17306519]. C. Wen's research is partially supported by National Science Foundation of China [12171449] and Fundamental Research Funds for the Central Universities [WK3470000027, YD2040002019]. X. Wang's research is partially supported by National Natural Science Foundation of China [Grants 72171216, 12231017, 71921001, and 71991474], and the National Key R&D Program of China [No. 2022YFA1003803]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/ijoc.2021.0313. The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0313) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2021.0313). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

We extend the notion of globalized robustness to consider distributional information beyond the support of the ambiguous probability distribution. We propose the globalized distributionally robust counterpart that disallows any (respectively, allows limited) constraint violation for distributions residing (respectively, not residing) in the ambiguity set. By varying its inputs, our proposal recovers several existing perceptions of parameter uncertainty. Focusing on the type 1 Wasserstein distance, we show that the globalized distributionally robust counterpart has an insightful interpretation in terms of shadow price of globalized robustness, and it can be seamlessly integrated with many popular optimization models under uncertainty without incurring any extra computational cost. Such computational attractiveness also holds for other ambiguity sets, including the ones based on probability metric, optimal transport, ϕ-divergences, or moment conditions, as well as the event-wise ambiguity set. Numerical studies on an adaptive network lot-sizing problem demonstrate the modeling flexibility of our proposal and its emphases on globalized robustness to constraint violation. History: Antonio Frangioni, Area Editor for Design & Analysis of Algorithms—Continuous. Funding: Z. Chen was supported by [General Research Fund Grant 9043424, NSFC/RGC Joint Research Scheme N_CityU105/21] from the Hong Kong Research Grants Council. S. Wang was supported by the National Natural Science Foundation of China [Grants 71922020, 72171221, and 71988101, entitled "Econometric Modeling and Economic Policy Studies"] and the Fundamental Research Funds for the Central Universities [Grant UCAS-E2ET0808X2]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0274) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0274). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

A multidepot capacitated vehicle routing problem aims to serve customers' demands using a fleet of capacitated vehicles located in multiple depots, such that the total travel cost of the vehicles is minimized. We study a variant of this problem, the k-depot split delivery vehicle routing problem (or k-DSDVRP in short), for the situation where each customer's demand can be served by more than one vehicle, and the total number of depots, denoted by k ≥ 2 , is a fixed constant. This is a challenging problem with broad applications in the logistics industry, for which no constant ratio approximation algorithm is known. We develop a new approximation algorithm for the k-DSDVRP, ensuring an approximation ratio of (6 − 4 / k) and a polynomial running time for any fixed constant k ≥ 2. To achieve this, we propose a novel solution framework based on a new relaxation of the problem, a cycle splitting procedure, and a vehicle assignment procedure. To further enhance its efficiency for practical usage, we adapt the newly developed approximation algorithm to a heuristic, which runs in polynomial time even when k is arbitrarily large. Experimental results show that this heuristic outperforms a commercial optimization solver and a standard vehicle routing heuristic. Moreover, our newly proposed solution framework can be applied to developing new constant ratio approximation algorithms for several other variants of the k-DSDVRP with k ≥ 2 being a fixed constant. History: Accepted by Erwin Pesch, Area Editor for Heuristic Search & Approximation Algorithms. Funding: This work was supported in part by the National Natural Science Foundation of China [Grants 71971177, 71725001, U1811462], Research Grants Council of Hong Kong SAR, China [Grant 15221619], and Guangdong Basic and Applied Basic Research Foundation [Grant 2023A1515030260]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/ijoc.2021.0193. The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0193) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2021.0193). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

We study adjustable distributionally robust optimization problems, where their ambiguity sets can potentially encompass an infinite number of expectation constraints. Although such ambiguity sets have great modeling flexibility in characterizing uncertain probability distributions, the corresponding adjustable problems remain computationally intractable and challenging. To overcome this issue, we propose a greedy improvement procedure that consists of solving, via the (extended) linear decision rule approximation, a sequence of tractable subproblems—each of which considers a relaxed and finitely constrained ambiguity set that can be iteratively tightened to the infinitely constrained one. Through three numerical studies of adjustable distributionally robust optimization models, we show that our approach can yield improved solutions in a systematic way for both two-stage and multistage problems. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: Financial support by the Early Career Scheme from the Hong Kong Research Grants Council [Project No. CityU 21502820], the CityU Start-Up Grant [Project No. 9610481], the CityU Strategic Research Grant [Project No. 7005688], the National Natural Science Foundation of China [Project No. 72032005], and Chow Sang Sang Group Research Fund sponsored by Chow Sang Sang Holdings International Limited [Project No. 9229076] is gratefully acknowledged. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0181), as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2021.0181). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

75.6% of land comprising Hong Kong remains undeveloped according to the special administrative region's planning department. In turn, Hong Kong's constricted real estate, now estimated to be the world's costliest, has created eye-popping living arrangements historically and contemporarily. Denizens' colorful reputation and imagination for flouting city ordinances, zoning laws, and spatial management stand emblematic of tenacious self-sufficiency and a free-spirited brand of runaway capitalist initiative. Why is this conspicuous trademark of Hong Kong's societal fabric very much alive in the 21st Century? Why does this matter in a rapidly urbanizing world witnessing the ascension of mega-urban centers alongside ever-widening socioeconomic chasms? This paper intends to illuminate the peculiar origins and longevity of the KowloonWalled City, an urban monolith of notoriety and autonomy that blossomed in a semi-legal grey zone unencumbered under British protectorate rule for nearly a century. Parallels will connect the linear trajectory between Kowloon's hardnosed living to today's comparable Chungking Mansions and the hundreds of thousands of cage homes appearing in all corners of the city. This paper aims to answer why these residential paradoxes continue to function with efficiency and relevancy, posing solutions for indigent housing while exacerbating the stigma of social and economic ostracism. [ABSTRACT FROM AUTHOR]

Background: The new waves of COVID-19 outbreaks caused by the SARS-CoV-2 Omicron variant are developing rapidly and getting out of control around the world, especially in highly populated regions. The healthcare capacity (especially the testing resources, vaccination coverage, and hospital capacity) is becoming extremely insufficient as the demand will far exceed the supply. To address this time-critical issue, we need to answer a key question: How can we effectively infer the daily transmission risks in different districts using machine learning methods and thus lay out the corresponding resource prioritization strategies, so as to alleviate the impact of the Omicron outbreaks? Methods: We propose a computational method for future risk mapping and optimal resource allocation based on the quantitative characterization of spatiotemporal transmission patterns of the Omicron variant. We collect the publicly available data from the official website of the Hong Kong Special Administrative Region (HKSAR) Government and the study period in this paper is from December 27, 2021 to July 17, 2022 (including a period for future prediction). First, we construct the spatiotemporal transmission intensity matrices across different districts based on infection case records. With the constructed cross-district transmission matrices, we forecast the future risks of various locations daily by means of the Gaussian process. Finally, we develop a transmission-guided resource prioritization strategy that enables effective control of Omicron outbreaks under limited capacity. Results: We conduct a comprehensive investigation of risk mapping and resource allocation in Hong Kong, China. The maps of the district-level transmission risks clearly demonstrate the irregular and spatiotemporal varying patterns of the risks, making it difficult for the public health authority to foresee the outbreaks and plan the responses accordingly. With the guidance of the inferred transmission risks, the developed prioritization strategy enables the optimal testing resource allocation for integrative case management (including case detection, quarantine, and further treatment), i.e., with the 300,000 testing capacity per day; it could reduce the infection peak by 87.1% compared with the population-based allocation strategy (case number reduces from 20,860 to 2689) and by 24.2% compared with the case-based strategy (case number reduces from 3547 to 2689), significantly alleviating the burden of the healthcare system. Conclusions: Computationally characterizing spatiotemporal transmission patterns allows for the effective risk mapping and resource prioritization; such adaptive strategies are of critical importance in achieving timely outbreak control under insufficient capacity. The proposed method can help guide public-health responses not only to the Omicron outbreaks but also to the potential future outbreaks caused by other new variants. Moreover, the investigation conducted in Hong Kong, China provides useful suggestions on how to achieve effective disease control with insufficient capacity in other highly populated countries and regions. [ABSTRACT FROM AUTHOR]

• We compared the structural characteristics of 18 urban agglomerations in China. • Our framework contained analysis of 3 different scales based on 4 elements' flows. • Guangdong-Hong Kong-Macao urban Agglomeration had the greatest average flow (5.15). • Shandong Peninsula Urban Agglomeration had the best performance in overall network. • Cities with high centrality were in the bidirectional or main spillover subgroups. Flows of different elements among cities can affect urban organizational structures, leading to specific links between and roles for cities within urban agglomerations. Previous studies have mainly focused on flows produced by specific elements in a single urban agglomeration, and they cannot reveal differences in organizational structures affected by multiple elements among different urban agglomerations. Therefore we selected 18 typical urban agglomerations in China and explored their structural characteristics under comprehensive effects of four elements: economy, resources, population and technology, with gravity model and social network analysis. Guangdong-Hong Kong-Macao Urban Agglomeration had a maximum flow intensity of 5.15, this concentration range was the largest from 0.65 to 3.75, indicating that more elements flowed internally. Shandong Peninsula Urban Agglomeration had the largest network density at 0.412, the lowest efficiency at 0.567, and the lowest hierarchy at 0.000, showing that intercity relationships within SP were the tightest among the18 urban agglomerations. Geographical proximity and topological structure of subgroups proved that connections among subgroups were not complex, and cities of different provinces lacked close interactions. Furthermore, most of the key cities with high centrality indexes were municipalities directly under the Central Government or provincial capitals; these cities played important roles in networks and were distributed into "main spillover" and "bidirectional spillover" subgroups. This paper provides a unified analytical framework aimed at urban agglomerations that accounts for flows of multiple types of elements and explores urban organizational structures, and our results could provide scientific guidance for promoting the coordinated and sustainable development of urban agglomerations. [ABSTRACT FROM AUTHOR]

In order to achieve the purpose of sealing water between adjacent jacked pipes in construction of long-distance and large-section tunnel, an innovative method called Freeze-Sealing Pipe Roof (FSPR) was applied to the Gongbei tunnel, which is a key part of Hong Kong-Zhuhai-Macau Bridge in China. Since the pre-constructed jacking pipes provides great convenience for flexible placement of freezing tubes inside, it is crucial to study the temperature field of different freezing states. This paper first established a new mathematical model considering radial offset of jacking pipe according to the practical engineering, and then the analytical solution to steady-state temperature field was also derived. Moreover, temperature distribution of four typical cross-sections were analyzed to evaluate the freezing effect on different paths. Results show that the analytical results are in good agreement with the numerical results, and the quasi-double-circle freezing model can be well applied to the practical problem of radial offset. Soil between steel pipes will be lowered to a sufficiently low temperature and form a fairly reliable water-sealing curtain. By designing the radial offset between freezing tubes in solid and hollow pipes, the freezing effect can be better ensured. The conclusions have important guiding significance for design and construction of FSPR method in future projects. • Temperature field of a special double-circle-piped freezing model is solved analytically. • The boundary separation property of the harmonic equation is used to solve the heat conduction problem. • The accuracy of complex analytical expressions is verified by numerical simulation. • Water sealing effect of FSPR method considering radial offset of jacking pipes is analyzed. [ABSTRACT FROM AUTHOR]