With the development of online real estate trading platforms, multi-modal housing trading data, including structural information, location, and interior image data, are being accumulated. The accurate appraisal of real estate makes sense for government officials, urban policymakers, real estate sellers, and personal purchasers. In this study, we propose an interpretable multi-modal stacking-based ensemble learning (IMSEL) method that deals with various modalities for real estate appraisals. We crawl the structural and image data of real estate in Chengdu city, China from the nation's largest real estate transaction platform with the location information, including public services, within 2 km of the real estate using Baidu map. We then compare the predictive results from IMSEL with those from previous state-of-art methods in the literature in terms of the root mean square error, mean absolute percentage error, mean absolute error, and coefficient of determination (R2). The comparison results show that IMSEL outperformed the other methods. We verified the improvement of introducing a data transformation strategy and deep visual features through a 10-fold cross-validation. We also discuss the managerial implications of our research findings.

A solar power plant is a large-scale photovoltaic (PV) system designed to supply usable solar power to the electricity grid. Building a solar power plant needs consideration of arrangements of several important components, such as PV arrays, solar inverters, combiner boxes, cables, and other electrical accessories. The design of solar power plants is very complex because of various optimization parameters and design regulations. In this study, we address the cable-routing problem arising in the planning of large-scale solar power plants, which aims to determine the partition of the PV arrays, the location of combiner boxes, and cable routing such that the installation cost of the cables connecting the components is minimized. We formulate the problem as a mathematical programming problem, which can be viewed as a generalized capacitated minimum spanning tree (CMST) problem, and then devise a branch-and-price-and-cut (BPC) algorithm to solve it. The BPC algorithm uses two important valid inequalities, namely the capacity inequalities and the subset-row inequalities, to tighten the lower bounds. We also adopt several acceleration strategies to speed up the algorithm. Using real-world data sets, we show by numerical experiments that our BPC algorithm is superior to the typical manual-based planning approach used by many electric power planning companies. In addition, when solving the CMST problem with unitary demands, our algorithm is highly competitive compared with the best exact algorithm in the literature.

Most theories of information security policy (ISP), except a few focused on the insider-centric view, are grounded in the control-centric perspective, and most ISP compliance models stem from Western countries. Regulatory focus theory (RFT) proposes two modes of motivational regulation, promotion and prevention focused that are supposed to motivate employee compliance in a trade-off. Culture is crucial to the study of ISP that puts control over human connections. Chinese guanxi, a specific dimension of Chinese culture, is better understood underlying the trust-distrust frame. To bridge the theoretical gap between the control-centric and the insider-centric perspectives, we develop an ISP behavioral model by taking an integrated approach from RFT and the trust-distrust frame. We employed scenario-based events about information security misconduct in the workplace to examine employees’ compliance intention and non-violation choice of ISP upon counterfactual thinking. Our empirical results improve the theoretical and practical implications of security practices.

The problem of searching for hidden or lost objects (called targets) by autonomous smart robots in an unknown environment arises in many applications, e.g., searching for and rescuing lost people during incidents in high-rise buildings, searching for fire sources and hazardous materials, searching for safe paths through the rubble during an emergency evacuation etc. Until the target is found, it may cause loss or damage whose extent depends on the location of the target and the search duration. The problem is to efficiently search for and detect the target as soon as possible with the help of a smart mobile robot. The autonomous mobile robot has no operator on board, as it is guided and controlled by on-board sensors and computer programs. In this paper we construct a mathematical model for the search process in an uncertain environment and provide a new fast algorithm for scheduling the activities of the autonomous robot used during an emergency evacuation.

Consumer-generated self-disclosure is better than firm-generated advertising and sales reports in increasing contact opportunities and also more credible for firms to foster alignment with future market expectations. Previous research mostly assesses online self-disclosure from the rational approach of anticipated benefits and privacy risks without considering the “privacy paradox” phenomenon (users behave contrarily to privacy concern) in social networking sites (SNSs). We develop a theoretical model, grounded in constraint-based (lock-in) and dedication-based (trust-building) mechanisms and social identity theory, to predict online self-disclosure. We test the proposed theoretical model by surveying 395 consumers with participation experience in an online SNS. Different from the rational approach behind personalization, we advance knowledge on how to apply social identity, as well as constraint-based and dedication-based mechanisms, to motivate online self-disclosure induced by consumers. We provide theoretical and practical insights based on our research findings for managing the motivational mechanisms of online self-disclosure.

Recently, Shabtay considered a scheduling problem on a single serial-batching machine with rejection to minimize the dual criteria of total completion time and total rejection cost, where the number of jobs to be included in each batch is not restricted. He studied four variants of the problem: the first is to minimize the sum of the two criteria; the second and third are to minimize one criterion, subject to the other criterion not exceeding a given value; and the last is to find the Pareto-optimal solutions for the bicriterion problem. Shabtay provided an${O}$($\textit{n}^{\mathbf {5}}$) algorithm for the first variant and an${O}$(${n} ^{\mathbf {6}}$/$\boldsymbol {\varepsilon }^{\mathbf {2}}$) fully polynomial-time approximation scheme (FPTAS) for the fourth variant. In this paper, we provide an alternative${O}$(${n} ^{\mathbf {4}}$) algorithm to solve the first variant and an${O}$(${n} ^{\mathbf {5}}$/$\boldsymbol {\varepsilon }$) FPTAS for the fourth variant, which are more efficient than those developed by Shabtay from a theoretical perspective. However, when the size of each batch is bounded by a given number${b}~\boldsymbol {>}1$, the corresponding time complexities of our algorithms for the first and fourth variants reduce to${O}$(bn$^{\mathbf {3}}$) and${O}$(bn$^{\mathbf {4}}$/$\boldsymbol {\varepsilon }$), respectively.

Facing horizontal channel competition in a hierarchical distribution system, independent intermediaries such as wholesalers and retailers are keen to find the optimal location and pricing strategies that enable them to adapt to the increasingly competitive business environment. To help market intermediaries to address their challenges, we propose in this paper a spatial agent-based model (SAM), grounded in complex adaptive systems, which comprises four types of agents, namely the world, the manufacturer, firms, and consumers. We derive the firms' optimal behaviors in response to competition by evaluating the evolutionary location and pricing strategies using a genetic algorithm. We observe that a pyramid structure and the bullwhip effect in demand emerge from the evolutionary behavior of the SAM. We also find that buyers' searching ability enhanced by information technology has a significant effect on the degree of competition in a hierarchical distribution system. In addition, we find that firms that distribute elastic goods are likely to lower their prices to attract more buyers and move closer to their suppliers to save transport costs. In the case that the product demand is inelastic, intermediaries will move as close to their buyers as possible because they can maximize their profits in the SAM.

This paper is motivated by observed industrial practices. We conduct a mean variance (MV) analysis of a fast fashion supply chain with returns policy. Different from the conventional newsvendor type products, fast fashion brands plan to have stock-out because it is a feature of fast fashion and can bring some benefit. Based on the fast fashion features, we build an analytical MV optimization model for a two-echelon fast fashion supply chain to address the following research questions. 1) What are the differences and similarities in the structural properties between the supply chains that carry fast fashion products and conventional newsvendor type products? 2) How do we optimize a fast fashion supply chain with multiple retailers under the MV framework? 3) Can a simple returns policy optimize (and “coordinate”) such a multiretailer supply chain? 4) How do individual retailers' degrees of risk aversion affect the achievability of coordination? 5) Can the above simple contract help coordinate the supply chain under information asymmetry? We propose a novel approach called “negotiated space” in the analysis. We generate several important insights which include an interesting finding that a simple returns policy can be applied to coordinate the fast fashion supply chain even in the presence of multiple retailers.

We develop an organization design (OD) framework and sample based on sound theories in this paper. We first present a new total relationship flow management theorem (TRFMT), which, combined with the two previously published TRFMTs, provides a theoretical foundation for the OD framework. We then apply the TRFMTs to determine the factors of OD and their logical relationships, which constitute an OD framework with a focus on the design of the total relationship flow of an organization. As an example, we conduct an OD exercise following the framework with a set of preselected parameters, and produce an organization sample, called an OD sample. The OD framework and sample enable managers to develop a blueprint of their organization, facilitating their work on conducting systematic and efficient OD. The TRFMTs, and the OD framework and sample suggest the emergence of a new organization theory-the total relationship flow management theory.

In this paper, we study the impacts of imposing a minimum order quantity (MOQ) on a two-echelon supply chain implementing quick response (QR) and consider the issue of coordination for such a system. By exploring the QR-MOQ supply chain system, we analytically prove that the retailer's expected profit (REP) is nonincreasing in the MOQ. We further find that the MOQ that maximizes the manufacturer's expected profit can significantly reduce the REP and the supply chain's efficiency. Understanding that the static nature of the preagreed MOQ hinders the information updating capability brought about by QR, which, in turn, decreases the supply chain's efficiency, we propose an innovative dynamic MOQ policy and derive the analytical conditions under which channel coordination with Pareto improvement is achieved.

In this note we consider the NP-complete one-setup version of the two-machine flexible manufacturing cell scheduling problem studied by Lee and Mirchandani (1988). We provide a pseudopolynomial dynamic programming algorithm to solve the problem, thus establishing that the problem is NP-complete in the ordinary sense. We derive a tight worst-case error bound for the heuristic presented by Lee and Mirchandani, and propose another heuristic with a tight worst-case error bound of 3/2.

The paper describes the research and development of a management support system (MSS) for total quality management (TQM). A prototype system called management support system for total quality management (MSS4TQM) is designed and developed to facilitate management or a quality team in conducting quality barrier assessment and critical factor analysis of TQM for an organization. MSS4TQM is implemented on personal computers under a Microsoft Windows/sup TM/ environment. To evaluate the performance of MSS4TQM, selected quality practitioners in manufacturing and service industries in Hong Kong, as well as quality experts from thirteen nations, are invited to participate in testing the system. The evaluation study of the prototype system in the context of a proposed MSS evaluation framework is described. The results of prototype evaluation are satisfactory and support the contention that MSS4TQM performs its functions as expected.