Omega-international Journal of Management Science最新文献

In this paper, we study the interaction between the lot-sizing problem and the storage assignment problem. Traditional lot-sizing problems have been studied for decades. However, only recent studies have further considered decisions related to the assignment of items to inventory locations, aiming to better model the complex reality. In our problem, the storage space is divided into several separate locations, and the inventory is assigned to the storage locations taking into account specific compatibility conditions. Relocation of inventory is also possible if needed. In addition to the traditional cost elements from the lot-sizing problem, we consider others related to holding inventory, such as fixed storage costs, handling costs, and relocation costs. We model the problem using a general mathematical model, as well as a transportation reformulation, which provides better lower bounds. We propose several heuristics to solve the problem by splitting it into smaller subproblems, which are then solved sequentially. A series of computational experiments is carried out in order to evaluate the impact of the integration between the lot-sizing and the storage assignment decisions, as well as the behavior of the different solution approaches. The results show that the proposed heuristics are highly effective in finding feasible solutions that are very close to the best solutions, while spending 97% less computational time compared to solving the full mathematical model. When compared to the relax-and-fix heuristic (benchmark), certain versions of the heuristics can find better solutions using less computational effort, underscoring the benefit of employing more specialized heuristics. Additionally, we conduct a sensitivity analysis with the aim of understanding the impact of key input parameters on the problem. The results indicate a significant influence of compatibility levels on the problem complexity. Limited item–item compatibility notably increases complexity, whereas restricted item–location compatibility reduces computational time.

Online reviews of hotels provide important information to consumers. The process of extracting useful information from diverse online reviews is crucial for making the best final decisions. To explore the hidden intrinsic information behind online reviews, this paper optimizes information extraction by integrating multiple sources, and gives the recommendation alternative. First, to meet quantitative requirements, the probabilistic linguistic term set is introduced to demonstrate the massive number of comments crawled. Second, considering preference and fluctuation, the relative importance of multiple attributes is determined. Because multiple attributes typically have cooperative or mutually exclusive relationships, a novel model is presented by introducing such relationship to modify relative importance. Third, inspired by the 2-additive Choquet integral operator and the Mahalanobis-Taguchi System, a bi-objective optimization model is proposed to illustrate the interactive effect of comments and develop an attribute correlation network. The specific relationships between attributes are reflected, including the positive and negative interactions. The relative importance, interactive imporantce and subgroup utility can be obtained. Fourth, to guarantee the operability and interpretability of the recommendation results, this paper presents a new information fusion operator and an probabilistic linguistic three-way recommendation process. Finally, a case study is used to demonstrate the complete procedures, and the parameter and comparative analyses highlight the effectiveness of the new operator and recommendation method.

As the urgency and importance of global carbon emission reduction have escalated in recent years, numerous firms are considering ideas to develop innovative green technologies in order to contribute to the low-carbon economy. This paper examines two types of green innovations adopted by firms, namely process innovation (improving production processes to enhance energy efficiency) and recycling innovation (establishing a circular economy to recycle and re-manufacture waste products). By developing a game-theoretical model, our analytical results reveal the following findings. First, we find that both process and recycling innovations can attract more consumers and improve the firm’s profitability under specific conditions. Second, when the cost advantage of remanufacturing is sufficiently low, the adoption of process innovation is always more profitable than recycling innovation. Otherwise, the profitability largely depends on the trade-off between consumers’ perception of the two green innovations. Third, compared to a single innovation, the adoption of both process and recycling innovations (dual green innovation) leads to a further increase in consumer demand and firm’s profit when the cost advantage of remanufacturing exceeds a certain threshold. Furthermore, the optimal pricing set by the firm and consumer demand become more sensitive to consumers’ perception of greenness when dual green innovation is adopted. Finally, the equilibrium result suggests the existence of a “synergistic promoting effect” when the firm implements dual green innovation, which indicates that the advantages of each innovation are amplified when the firm adopts dual green innovation. These results can serve as guidelines for firms aiming to utilize green innovations in order to reduce carbon emissions.

The notion of environmental, social and governance (ESG) has been used by firms as a tool to resist crises. The aspect of ESG that enhances firm viability remains uncertain, as does whether this aspect varies across firms that exhibit distinct supply chain concentrations (SCCs). Clarifying these issues is essential for companies’ attempts to tailor their ESG portfolios in the context of operations to suit their unique circumstances and to improve supply chain viability. The COVID-19 pandemic offers an opportunity to examine these issues. Our study utilizes data regarding Chinese listed firms to conduct an empirical assessment of how ESG affects stock market performance in response to the increase in COVID-19 cases both within China (the national outbreak) and at the global level excluding China (the global pandemic). The results reveal that a significant decline in stock returns occurred as COVID-19 cases increased. Firms that exhibited higher levels of preexisting ESG performance were associated with a less pronounced decrease in stock returns during the national outbreak. Notably, environmental and social responsibilities emerged as key protective factors in response to the national COVID-19 outbreak, while corporate governance proved to be more effective with regard to addressing the global pandemic. In addition, our study reveals that the safeguard provided by environmental and social responsibilities functions as a form of insurance primarily among low-SCC firms. On the other hand, the protective effect of high corporate governance is more evident among high-SCC firms. These findings offer valuable insights for companies seeking to fine-tune their ESG portfolios and navigate the interactions between ESG and SCC.

The aim of this paper is to design, carry out and analyze an experiment with over 100 respondents to measure the precision of the respondents when they compare (by evaluation) the areas of nine geometric figures and five distances between cities by using direct evaluations, Pairwise Comparisons Method and Best-Worst Method. The outcomes of the experiment indicate that the direct evaluations are the most imprecise and that there is not statistically significant difference between Best-Worst Method and Pairwise Comparisons Method.

A weight vector is assigned to the attributes in multiple-attribute decision-making to show their relative importance. The interdependencies among the attributes often influence this weight vector. The decision-making trial and evaluation laboratory (DEMATEL) and weighted influence non-linear gauge system (WINGS) are among those methods that consider these interdependencies. These methods require matrix manipulation with several metrics to evaluate interdependencies. This study investigates the potential irregularities within the metrics employed by these two methods for weighing criteria. It examines these metrics and analyzes their sensitivity to the direction and the level of influence among attributes. We provide several numerical examples and mathematical analyses to evaluate their consistency by comparing the expected outcomes with the outcomes of the metrics. Although the metrics are expected to assign higher importance to the more influencing criteria, the total engagement/prominence metric is not sensitive to the direction and level of influence among attributes. We conclude these metrics are inconsistent and can not be used reliably as a composite indicator. In contrast, we show that the total impact factor reflects both the direction and the level of influence and is a reliable choice for this purpose.

The equilibrium efficient frontier data envelopment analysis (EEFDEA) has been extensively used to evaluate efficiencies of the decision-making units (DMUs) with fixed-sum outputs. This study develops a new EEFDEA approach based on a proportional frontier-shifting strategy. Our approach applies an iterative procedure to find the equilibrium efficient frontier (EEF). Each iteration uses a proportional frontier-shifting model to improve an inefficient DMU to the efficient frontier by increasing its fixed-sum outputs. Meanwhile, the DMUs on the efficient frontier decrease fixed-sum outputs proportionally to ensure the total fixed-sum outputs are unchanged. Our theoretical developments show that the proportional frontier-shifting strategy is feasible and can finally obtain a unique EEF. The new approach allows DMUs to use their preferred input and output weights when determining the EEF. This generates an EEF that better aligns with real-world practices and avoids the need to construct it as a single hyperplane, as required by conventional EEFDEA methods. It also avoids unfair adjustments in fixed-sum outputs among the DMUs and eliminates the problem of peculiar efficiency evaluation results (i.e., some DMUs obtain extremely high, or infinity, efficiencies). Finally, we apply our approach to a case study of Chinese vehicle industry companies to demonstrate its usefulness and compare it with the previous representative approach.

The utilization of drones to conduct inspections on industrial electricity facilities, including large-sized wind turbines and power transmission towers, has recently received significant attention, mainly due to its potential to enhance inspection efficiency and save maintenance costs. Motivated by the advantages of drones for facility inspection, we present a novel station-based drone inspection problem (SDIP) for large-scale facilities. The objective of SDIP is to determine the locations of multiple homogeneous automatic battery swap stations (ABSSs) equipped with drones, assign facility inspection tasks to the ABSSs with operation duration constraints, and design drone inspection routes with battery capacity constraints, such that minimize the sum of fixed ABSS costs and drone travel costs. The SDIP can be regarded as a variant of the location-routing problem, which is NP-hard and difficult to solve optimally. To obtain the optimal solution of SDIP efficiently, we firstly formulate this problem into an arc based formulation and a route based formulation, and then develop a logic-based Benders decomposition (LBBD) algorithm to solve it. The SDIP is decomposed into a master problem (MP) and a set of subproblems (SPs). The MP is solved by a branch-and-cut (BC) procedure. Once a feasible integer solution is found, the linear relaxation of SPs are solved by a stabilized column generation to generate Benders cuts. If the cost of all the SPs’ optimal LP solutions plus the cost of the MP’s solution is less that current best cost, the SPs are exactly solved by a Branch-and-Price (BP) algorithm to generate the logic cuts. The numerical results on five scales of randomly generated instances validate the effectiveness of the LBBD algorithm. Specifically, the LBBD can solve all small- and middle-sized instances, and seven out of ten large-sized instances in 1000 s. Furthermore, we conduct a sensitivity analysis by varying the attributes of ABSSs and drones, and provide valuable managerial insights for large-scale facility inspection.

The robotic mobile fulfillment system (RMFS), with wide application in warehousing and logistics, requires many robots powered by electricity, which significantly impacts energy consumption. This paper investigates the energy consumption in the RMFS under a classic e-business environment, which classifies the orders into regular orders and expedited orders. We evaluate the impact of three dynamic priority policies (the earliest deadline first policy, waiting time-dependent policy, and weighted waiting time first policy) on throughput time and energy consumption. This paper proposes multi-class semi-open queuing network models (SOQN) with dynamic priority policies to investigate energy consumption. We validate the accuracy of the analytical models by simulation models. This paper makes the following contributions: (1) In methodology, we propose new methods to solve the SOQN with dynamic priority policies. (2) In operational planning and control, we are among the earliest to investigate the impact of dynamic priority policies on order throughput time and energy consumption in an RMFS. (3) In design optimization, we propose a decision tool to optimize the robot number for realizing the required throughput time with minimal energy consumption. Our model can also decide the optimal warehouse shape to minimize energy consumption. (4) In system analysis, we estimate the energy consumption per transaction in an RMFS, providing logistics managers insights into energy saving of warehouses.