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The purpose of this paper is to construct a dynamic information aggregation decision-making model based on variable precision rough set.

To deal with the dynamic decision-making problems, the grey relational analysis method, grey fixed weight clustering based on the centre triangle whitening weight function and maximum entropy principle is used to establish the dynamic information aggregation decision-making model based on variable precision rough set. The method, to begin with, the grey relational analysis method is used to determine the attributes weights of each stage; taking the proximity of the attribute measurement value and positive and negative desired effect value and the uncertainty of time weight into account, a multi-objective optimisation model based on maximum entropy principle is established to solve the model with Lagrange multiplier method, so that time weights expression are acquired; what is more, the decision-making attribute is obtained by grey fixed weight clustering based on the centre triangle whitening weight function, so that multi-decision-making table with dynamic characteristics is established, and then probabilistic decision rules from multi-criteria decision table are derived by applying variable precision rough set. Finally, a decision-making model validates the feasibility and effectiveness of the model.

The results show that it the proposed model can well aggregate the multi-stage dynamic decision-making information, realise the extraction of decision-making rules.

The method exposed in the paper can be used to deal with the decision-making problems with the multi-stage dynamic characteristics, and decision-making attributes contain noise data and the attribute values are interval grey numbers.

The paper succeeds in realising both the aggregation of dynamic decision-making information and the extraction of decision-making rules.

Many dynamic problems in economics are characterized by large state spaces which make both computing and estimating the model infeasible. We introduce a method for approximating the value function of high-dimensional dynamic models based on sieves and establish results for the (a) consistency, (b) rates of convergence, and (c) bounds on the error of approximation. We embed this method for approximating the solution to the dynamic problem within an estimation routine and prove that it provides consistent estimates of the modelik’s parameters. We provide Monte Carlo evidence that our method can successfully be used to approximate models that would otherwise be infeasible to compute, suggesting that these techniques may substantially broaden the class of models that can be solved and estimated.

Grey target decision-making serves as a pivotal analytical tool for addressing dynamic multi-attribute group decision-making amidst uncertain information. However, the setting of bull's eye is frequently subjective, and each stage is considered independent of the others. Interference effects between each stage can easily influence one another. To address these challenges effectively, this paper employs quantum probability theory to construct quantum-like Bayesian networks, addressing interference effects in dynamic multi-attribute group decision-making.

Firstly, the bull's eye matrix of the scheme stage is derived based on the principle of group negotiation and maximum satisfaction deviation. Secondly, a nonlinear programming model for stage weight is constructed by using an improved Orness measure constraint to determine the stage weight. Finally, the quantum-like Bayesian network is constructed to explore the interference effect between stages. In this process, the decision of each stage is regarded as a wave function which occurs synchronously, with mutual interference impacting the aggregate result. Finally, the effectiveness and rationality of the model are verified through a public health emergency.

The research shows that there are interference effects between each stage. Both the dynamic grey target group decision model and the dynamic multi-attribute group decision model based on quantum-like Bayesian network proposed in this paper are scientific and effective. They enhance the flexibility and stability of actual decision-making and provide significant practical value.

To address issues like stage interference effects, subjective bull's eye settings and the absence of participative behavior in decision-making groups, this paper develops a grey target decision model grounded in group negotiation and maximum satisfaction deviation. Furthermore, by integrating the quantum-like Bayesian network model, this paper offers a novel perspective for addressing information fusion and subjective cognitive biases during decision-making.

This paper explores the role of neural networks for decision making in dynamic environments which are characterized by risks and uncertainties, and also provides experimental evidence from a simulated data. Theoretical support is derived from theories of affective balance, and self‐organized criticality. The simulation is conducted for a two‐person‐constant sum game. The findings of the experiment are helpful in extending to managerial decision making which involves varying degrees of uncertainties. Such decisions are affected by forces both internal and external to the company, and making judgments in such a fuzzy future is highly probabilistic. It is suggested, therefore that neural networks are better able to capture the interactive dynamics of variables operating in a managerial decision environment. In sum, the findings indicate that decisions in general and business decisions in particular can greatly benefit from the parallel computational capabilities of neural networks.

The purpose of this paper is to present a new method about dynamic decision problems with three-parameter grey numbers from other angle of view which not only aggregates the attribute values of alternatives of all the periods, but also excavates changes of attribute values about alternatives between the adjacent periods.

The authors adopt grey target method to calculate the distance between every alternative and the best, worst bull’s eye, the distance between change series and the best, worst change bull’s eye, then both distances can be aggregated to reflect information about two aspects.

This dynamic decision-making method not only aggregates the existing state of alternatives all of the stages, but also excavates the change information from vertical and horizontal direction, the decision result conforming to decision maker’s psychological behavior is obtained though adjusting the priority parameter.

The paper considers on change of alternative’s attribute values from one period to the next period, and the dynamic characteristic has been reflected adequately. The grey target decision-making method reflects the distance between alternative and bull’s eye, the comprehensive target distance between alternative and positive, worst bull’s eye about change series are separately provided. And the final target distance reflecting both existing state and change trend is constructed.

The authors construct an intertemporal model of rent-maximizing behavior on the part of a timber harvester under potentially multidimensional risk as well as geographical heterogeneity. Subsequently, the authors use recursive methods (specifically, the method of stochastic dynamic programing) to characterize the optimal policy function – the rent-maximizing timber-harvesting profile. One noteworthy feature of their application to forestry in the province of British Columbia, Canada is the unique and detailed information the authors have organized in the form of a dynamic geographic information system to account for site-specific cost heterogeneity in harvesting and transportation, as well as uneven-aged stand dynamics in timber growth and yield across space and time in the presence of stochastic lumber prices. Their framework is a powerful tool with which to conduct policy analysis at scale.

Vehicle allocation problems (VAPs), which are frequently confronted in many transportation activities, primarily including but not limited to full truckload freight transportation operations, induce a significant economic impact. Despite the increasing academic attention to the field, literature still fails to match the needs of and opportunities in the growing industrial practices. In particular, the literature can grow upon the ideas on sustainability, Industry 4.0 and collaboration, which shape future practices not only in logistics but also in many other industries. This review has the potential to enhance and accelerate the development of relevant literature that matches the challenges confronted in industrial problems. Furthermore, this review can help to explore the existing methods, algorithms and techniques employed to address this problem, reveal directions and generate inspiration for potential improvements.

This study provides a literature review on VAPs, focusing on quantitative models that incorporate any of the following emerging logistics trends: sustainability, Industry 4.0 and logistics collaboration.

In the literature, sustainability interactions have been limited to environmental externalities (mostly reducing operational-level emissions) and economic considerations; however, emissions generated throughout the supply chain, other environmental externalities such as waste and product deterioration, or the level of stakeholder engagement, etc., are to be monitored in order to achieve overall climate-neutral services to the society. Moreover, even though there are many types of collaboration (such as co-opetition and vertical collaboration) and Industry 4.0 opportunities (such as sharing information and comanaging distribution operations) that could improve vehicle allocation operations, these topics have not yet received sufficient attention from researchers.

The scientific contribution of this study is twofold: (1) This study analyses decision models of each reviewed article in terms of decision variable, constraint and assumption sets, objectives, modeling and solving approaches, the contribution of the article and the way that any of sustainability, Industry 4.0 and collaboration aspects are incorporated into the model. (2) The authors provide a discussion on the gaps in the related literature, particularly focusing on practical opportunities and serving climate-neutrality targets, carried out under four main streams: logistics collaboration possibilities, supply chain risks, smart solutions and various other potential practices. As a result, the review provides several gaps in the literature and/or potential research ideas that can improve the literature and may provide positive industrial impacts, particularly on how logistics collaboration may be further engaged, which supply chain risks are to be incorporated into decision models, and how smart solutions can be employed to cope with uncertainty and improve the effectiveness and efficiency of operations.

Freight carriers operating in a spot-market environment are faced with uncertain future capacity demand, actual revenues, and properties of freight items. They require information about the expected future consumption of limited capacity to derive suitable request acceptance decisions. The purpose of this paper is to present a new idea to improve the handling of inaccurate information on the weight and volume of upcoming requests.

The authors start with the definition of a new mathematical optimization model as the backbone of a capacity control system. This model is embedded within a rolling-horizon decision-making process involving consecutively arriving requests. Computational simulation experiments are carried out to evaluate the applicability and efficiency of the proposed decision support system. The authors investigate how the new model contributes towards keeping the negative impacts of inadequate forecasts of the expected volume of future requests as low as possible.

In traditional application fields of capacity control (airline ticketing or hotel reservations) the physical extent of a request is always 1 (set/bed/room). In road-based freight transportation the variety of the physical extent of requests is much more complicated and complex. The major finding is that existing capacity control approaches are unable to meet the special requirements of road-haulage. Innovative capacity control features are necessary in order to cope with the higher request portfolio complexity.

This paper addresses the requirements of a capacity control system for road-based freight transportation. An innovative decision support system is evaluated. For the first time, the authors present a comprehensive quantitative simulation study dedicated to this complicated decision-making situation.

Throughout the 1990s, the supply of new condominiums in Tokyo significantly increased while prices persistently fell. This article investigates whether the market power of condominium developers is a factor in explaining the outcome in this market and whether there is a relationship between production cost trend and the degree of market power that the developers were able to exercise. In order to respond to these questions, we construct and structurally estimate a dynamic durable goods oligopoly model of the condominium market – one incorporating time-variant costs and a secondary market – using a nested GMM procedure. We find that the data provide no evidence that firms in the primary market have substantial market power in this industry. Moreover, the counterfactual experiment provides evidence that inflationary and deflationary expectations on production cost trends have asymmetric effects to the market power of condominium producers. The increase in their markup when cost inflation is anticipated is significantly higher than the decrease in the markup when the same magnitude of cost deflation is anticipated.

A layout problem may deal with the assignment and arrangement of buildings in a green field, location and/or relocation of machines/departments in manufacturing facilities, and so on. If multi‐periods are considered, the problem is called a dynamic layout problem in manufacturing environments. Designing web pages, especially internet newspaper layouts, might also be considered dynamic layout problems. This study aims to introduce a layout procedure for the front page of internet newspapers.

The news contents are ranked and selected based on their characteristic attributes. Then they are assigned to locations on dynamic content area of the front page. Layout optimization is made by use of Clonal Selection Algorithm (CSA). Finally, an illustrative example is provided and concepts for real life applications are discussed. The proposed method is based on CSA, which is a nature‐inspired technique. The novel heuristic is applied to a simulated system to depict how the news content layouts can be optimized in dynamic environments.

The capacity for addition of new news contents and removal of the old turns the internet newspaper environment into a dynamic structure. A systematic layout method for internet newspapers is developed to fill the gap.

The results are encouraging for real life applications of internet newspapers. The study has also introduced a new site for the manufacturing area. In the classic dynamic layout model, the machine locations and the number of available machines are assumed to be fixed. But the concept of introducing/removing news contents can be adapted to the machines at the manufacturing facilities.

The paper's value lies in showing that the front page layout is not considered to optimize the locations of the articles. Also, the proposed algorithm is applied to solve these kinds of problems.