Omega-international Journal of Management Science最新文献

In most multi-component systems, the cost-efficiency of maintenance policies depends on technical structural dependencies. Motivated by the recognition that these dependencies must be accounted for in the development of optimal maintenance policies, we develop an optimization model to determine cost-efficient maintenance schedules for multi-component systems. Our main contribution is twofold. First, we introduce directed graphs as an expressive tool to represent the economic and structural dependencies of the system, including situations in which the maintenance of a given component may require other components to be disassembled or maintained. Second, we formulate a Markov Decision Process model, which is solved through the modified policy-iteration algorithm to determine the most cost-efficient policy. This policy indicates which maintenance actions consisting of disassembly and component replacement decisions are optimal when mandatory replacements must be made whenever the system fails, or the reliability of the system falls below a predefined reliability threshold. To our knowledge, this is the first model that provides optimal maintenance policies that comply with reliability requirements in the presence of constraints arising from technical structural dependencies. We illustrate the model with a realistic case study on the development of cost-efficient maintenance policies and show that its results compare favorably with heuristic maintenance policies.

The online commodity recommendation (OCR) model mines users’ historical behavior characteristics and recommends products that may be of interest according to user preferences. Online reviews are among the most important information sources for OCR. However, the explicit and implicit emotional words in online review texts have different structures in the expression of multi-attribute emotions. To fully utilize review information and improve the recommendation accuracy, we propose an OCR model that considers the interaction of multiple attributes and hierarchical emotions and calculates a score weighted by emotion intensity. First, to balance the efficiency and accuracy of information extraction while considering the coexistence of explicit and implicit expressions in online review text, a multi-attribute hierarchical emotion lexicon construction method is proposed. Second, based on the advantage of intuitionistic fuzzy sets in terms of information expression superiority, multi-attribute review text information expression of the affective polarity and intensity of online review text is realized. Then, combined with the weighted singular value decomposition and factorization machine method, we propose an OCR model for interactions between multi-attribute emotions and scores through fusion and recombination of the eigenvectors of users and products. Finally, tourism products on the LYCOM website are used as an example to verify the effectiveness of the proposed method.

Travelers expect integrated and multimodal itinerary planning while addressing their individual expectations. Besides common preferences such as travel time and price, further criteria such as walking and waiting times are of importance as well. The competing features of these preferences yield a variety of non-dominated itineraries. Finding the set of non-dominated multimodal travel itineraries in efficient run time remains a challenge in case multiple traveler preferences are considered.

In this work, we present a sampling framework to approximate the set of non-dominated travel itineraries that scales well in terms of considered preferences. In particular, we guide the search process dynamically to uncertain areas of the complex multimodal solution space. To this end, we learn the structure of the Pareto front during the search with Gaussian Process Regression (GPR). The GPR sampling framework is evaluated integrating an extensive amount of real-world data on mobility services. We analyze long-distance trips between major cities in Germany. Furthermore, we take up to five traveler preferences into account. We observe that the framework performs well, revealing the origin and destination specifics of Pareto fronts of multimodal travel itineraries.

Along with the increased use of digitization, platform-driven manufacturing-as-a-service (p-MaaS) is becoming an inevitable trend of the manufacturing industry. End-users openly share their personalized manufacturing tasks, which necessitates platform-based crowdsourcing to conduct manufacturing service collaboration and at last achieve efficient task-service matching (TSM). This crowdsourcing takes into account the autonomy of end-users, platforms, and manufacturing servicers, which challenges previous opinions that distributed manufacturing services must be centralized and controlled by platforms. This paper proposes a novel TSM problem for p-MaaS under the framework of crowdsourcing. The platform plays the role of allocating new emerged tasks and broadcasting to corresponding servicers. All servicers receive the broadcast information and conduct scheduling-based task acceptance (STA) independently. The above manufacturing task allocation (MTA) focuses on maximizing the net revenue of TSM and at the same time enables servicers to accept tasks as many as possible. In terms of the inherent interactive mechanism between MTA and STA, in which MTA generates a decision space for STA and STA feeds task acceptance schemes and the corresponding fulfillment costs back for use in MTA decision-making, a bilevel multi-objective optimization (BMO) is formulated to simultaneously address the two subproblems based on a Stackelberg game. The BMO is a type of multi-objective nonlinear programming, and a nested algorithm is designed to solve it. The better performance of the BMO is verified through a practical case study.

This article presents a novel model for building biweekly rosters for physicians according to the regulations of a German teaching hospital, while also ensuring the viability of breaks. Currently, rosters are manually prepared by experienced physicians with basic spreadsheet knowledge, leading to significant costs and time consumption because of the complexity of the problem and the individual working conditions of the physicians. Unfortunately, manually generated rosters frequently prove to be non-compliant with labor regulations and ergonomic agreements, resulting in potential overtime hours and employee dissatisfaction. A particular concern is the inability of physicians to take mandatory breaks, which negatively affects both employee motivation and the hospital service level. To address these challenges, we propose a data-driven formulation of an operational physician scheduling problem, considering overstaffing and overtime hours as primary cost drivers and integrating shift preferences and break viability as ergonomic objectives. We develop and train a survival regression model to predict the viability of breaks, allowing practitioners to define break-time windows appropriately. Given the limitations of standard solvers in producing high-quality solutions within a reasonable timeframe, we adopt a Dantzig–Wolfe decomposition to reformulate the proposed model. Furthermore, we develop a branch-and-price algorithm to achieve optimal solutions and introduce a problem-specific variable selection strategy for efficient branching. To assess the algorithm’s effectiveness and examine the impact of the new break assignment constraint, we conducted a comprehensive computational study using real-world data from a German training hospital. Using our approach, healthcare institutions can streamline the rostering process, minimize the costs associated with overstaffing and overtime hours, and improve employee satisfaction by ensuring that physicians can take their legally mandated breaks. Ultimately, this contributes to better employee motivation and improves the overall level of hospital service.

Regret theory is a classic behavioural decision theory, but existing literature related to the regret theory used different utility functions, reference points, and parameter values, which may lead to biased results. In this regard, this study proposes a preference disaggregation-driven multiple-criteria sorting model based on the regret theory, which can infer the values of risk- and regret-aversion parameters involved in the regret theory and category thresholds according to the preference information in both precise and imprecise forms provided by decision-makers. We analyze the utility functions applied in the regret theory, and find that the power function is more reasonable than the exponential function. We also test the reference points applied in the regret theory, and find that the results obtained using the average alternative as a reference point are similar to those obtained without setting reference points while reducing computational complexity. To infer the values of risk- and regret-aversion parameters involved in the regret theory and category thresholds, a preference disaggregation-driven multiple-criteria sorting model is developed according to the preference information in both precise and imprecise forms provided by decision-makers. An illustrative example on supplier resilience classification is provided to demonstrate the applicability of the proposed sorting model.

Conventional models of data envelopment analysis (DEA) typically assume that the underlying production technology is a convex set. It is known that such assumption may be clearly unsubstantiated in certain cases. Examples include studies in which some inputs or outputs are stated as proportions or percentages, or are represented by categorical measures. Excluding such “problematic” inputs and outputs from the assumption of convexity while assuming the latter for the remaining measures leads to the notion of selective convexity. Further examples of selectively convex technologies include technologies parameterized by an environmental factor and technologies in which only the input or output sets are convex. In this paper, we consider the identification of efficient targets and reference sets of decision making units in a selectively convex technology, which has not yet been explored in the literature. We show that, for such technologies, the conventional method based on the solution of the additive DEA model may not correctly identify the reference sets and needs an adjustment.

The increasing urbanisation and fast-paced lifestyle have heightened the importance of shopping malls in retail industry, altering traditional shopping patterns by designing efficient space and optimise time of shoppers. Due to this newly-acquired importance, these malls have become critical players in the retail industry sector. Despite their significance, current research lacks comprehensive scientific methods in two critical aspects: the classification of anchors (or magnet shops) and regular tenants, and the detailed analysis of interrelations among anchors and tenants within the shopping malls. Both aspects are heavily related to the strategic allocation of shops position within malls. For addressing these gaps, this paper introduces a multi-method framework expert system to classify anchors and tenants and to optimise their positions in the shopping mall, considering their categories and existing product relationships. This framework comprises a new sorting method, a modified ranking method, a product correlation technique based on implementing ecological dynamics, an ecological interrelation index, and a metaheuristic allocation algorithm. The practical application of this framework is demonstrated through a real-world case study, highlighting its potential to significantly improve shopping mall management and retail efficiency. The effect of the proposed framework is subject to empirical tests and comparison between layout modifications.

To improve the operating efficiency of container terminals, we investigate a closed-loop scheduling method in an autonomous inter-terminal system that employs unmanned shipment vessels (USVs) to transport containers among operational berths (Dedicated to USVs) in seaport terminals. Our USVs scheduling model is developed by considering energy replenishment, time windows, and berth restrictions, aiming to obtain cost-saving USV transportation solutions and conflict-free paths. To solve this optimization model more efficiently, we propose the multi-attention reinforcement learning (MARL) algorithm by integrating an encoder-decoder framework and an unsupervised auxiliary network. The MARL algorithm provides instant problem-solving capabilities and benefits from extensive offline training. Experimental results demonstrate that our method can obtain efficient solutions for our USVs scheduling problem, and our algorithm outperforms other compared algorithms on computing time and solution accuracy.

As one of the 17 Sustainable Development Goals, reducing carbon emissions is crucial to combat climate change. It has also prompted companies to comply with emission regulations and evaluate the environmental impacts of their supply chains. Yet, news and reports occasionally highlight industrial instances of noncompliance. In particular, supplier’s noncompliance is often mistakenly attributed to its downstream manufacturers. Due to this misconception, manufacturers might conduct audits to protect their reputation and sales. Moreover, because a supplier may provide components to multiple competing manufacturers, they may collaborate to share audit findings regarding the common supplier’s compliance with carbon emissions regulations. However, studies do not reveal how this audit cooperation affects stakeholder interests. Here, we introduce a stylised model to examine the effects of carbon audit cooperation on the environment, competing manufacturers, and their supplier. We identify two main effects: the free-riding and amplifying effects. The former benefits the supplier but harms the environment and competing manufacturers, while the latter presents the opposite effect. The net impact depends on the balance between these two effects, which challenges conventional beliefs about carbon emissions compliance and highlights the importance of sustainability in the industry. Finally, we explore various extensions to validate the robustness of our findings.