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The purpose of this paper is to develop the method of generating assembly sequences, which can be used in the shipbuilding industry. The method must take into account the assumptions specific for assembly processes of large-size steel ship hulls, among others, a large number of connections, multi-stage and parallel assembly, set priority relations between connections.

The assembly sequence is presented as a directed acyclic graph, whose vertices are mutually uniquely assigned to connections on a hull structure. The minimization of the number of unmet priority precedence of performing connections has been proposed as a criterion of optimization. The genetic algorithm has been proposed as a method to solve problems.

The proposed method allows to model the acyclic assembly process of welded structures and find solutions minimizing the objective function even for very complex problems. Because of this, the method has a chance to be used in shipbuilding.

Mathematical formulation of priority assumptions is quite laborious. The possibility of partial automation of this process should be considered. Due to the complexity of the problem, a relatively simplified objective function has been proposed. In assembling a hull, additional criteria should be taken into account. It is the direction of further research.

The method can be successfully used in shipbuilding and in planning the production of other steel welded structures, among others, tanks, components of bridges, offshore structures. Examples of calculations were performed on an actual structure of a hull fragment.

A new way of coding the acyclic serial-parallel sequence was designed. The proposed method allows to analyse the sequence using the graph theory. Original, two-part crossover and mutation operators for assembling sequence were proposed.

This study aims to investigate dynamic relations among office property price indices of 10 major cities in China for the years 2005–2021.

Using monthly data, the authors adopt vector error correction modeling and the directed acyclic graph for the characterization of contemporaneous causality among the 10 indices.

The PC algorithm identifies the causal pattern, and the linear non-Gaussian acyclic model algorithm further determines the causal path from which we perform innovation accounting analysis. Sophisticated price dynamics are found in price adjustment processes following price shocks, which are generally dominated by the top tier of cities.

This suggests that policies on office property prices, in the long run, might need to be planned with particular attention paid to the top tier of cities.

The increasing popularity of XML has generated a lot of interest in query processing over graph‐structured data. To support efficient evaluation of path expressions structured indexes have been proposed. Extending the proposed indexes to work with large XML graphs and to support intra‐ or inter‐document links requires a lot of computing power for the creation process and a lot of space to store the indexes. Moreover, the efficient evaluation of ancestors‐descendants queries over arbitrary graphs with long paths is a severe problem. This paper aims to propose a scalable path index which is based on the concept of 2‐hop covers as introduced by Cohen et al.

The problem of efficiently managing and querying XML documents poses interesting challenges on database research. The proposed algorithm for index creation scales down the original graph size substantially. As a result a directed acyclic graph with a smaller number of nodes and edges will emerge. This reduces the number of computing steps required for building the index. Thus, computing time and space will be reduced as well. The index also permits ancestors‐descendants relationships to be efficiently evaluated. Moreover, the proposed index has a nice property in comparison to most other work; it is optimized for descendants‐or‐self queries on arbitrary graphs with link relationships.

In this paper, a scalable path index is proposed. It can efficiently address the problem of querying large XML documents that contain links and have cycles. Cycles in the graph stress path‐indexing algorithms. An overview about 2‐hop cover and the algorithms that used to build the index are given.

This paper works on the updating problem. Since the construction of the index is quite complex its construction make sense for some time. However, this means it is currently dealing with the problem of updating XML‐documents.

This paper presents an efficient path index that can test the reachability between two nodes and evaluate ancestors‐descendants queries over arbitrary graphs with long paths.

Although a lot of attention has been paid to demand information sharing in the recent decade, few studies look at the value of supply information sharing. The purpose of this paper is to address the importance of timely supply information sharing to the supply chain management under disruption is addressed.

By introducing a Directed Acyclic Supply Network (DASN) model and an Impact Network (INet) model, the impact of a disruption on the performance of the supply chain is quantified. A comprehensive algorithm is developed to calculate the time and cost impact of the disruption. Insights about the value of timely supply information sharing are further discussed, based on quantitative relationships of material flows. Finally, an application of the above model in a main manufacturer of China is introduced. It is then compared to its performance in the case of timely supply information sharing with cases where information is not shared or is shared late.

By timely sharing of supply information, firms at downstream stages can alert a disruption at an upstream stage, derive the correct early warning time, and make proper decisions to offset the impact of the disruption. Information sharing therefore enhances the agility of firms while improving the stability and performance of the whole supply chain.

This paper only considers the time and cost impact from a single source of disruption. Further work may investigate other disruptions, which may arise from multiple sources.

This paper provides an effective method to quantify the impact of a disruption. The method has been successfully applied in a supply chain management information system.

This paper is among the initial studies of understanding and quantifying the value of supply information sharing. The work indicates the importance of timely supply information sharing to improve the performance of a supply chain.

This study aims to investigate dynamic relationships among residential housing price indices of ten major Chinese cities for the years 2005–2021.

Using monthly data, this study uses vector error correction modeling and the directed acyclic graph for characterization of contemporaneous causality among the ten indices.

The PC algorithm identifies the causal pattern and the Linear Non-Gaussian Acyclic Model algorithm further determines the causal path, from which this study conducts innovation accounting analysis. Sophisticated price dynamics are found in price adjustment processes following price shocks, which are generally dominated by the top tiers of cities.

This study suggests that policies on residential housing prices in the long run might need to be planned with particular attention paid to these top tiers of cities.

The RMB Internationalization has a great impact on China’s domestic economy. This chapter applies the Gap Estimation approach to estimate the RMB overseas circulation amount from 1997 to 2015, as the indicator of RMB internationalization. Using the recently developed Directed Acyclic Graph (DAG) method for the model identification and contemporaneous causality analysis. The structural vector auto regression (SVAR) model is constructed between the economic indicators (the interest rate, the CPI, and the exchange rate) and the RMB overseas circulation. The dynamic relationship and degree of mutual influence are further studied between the economic indicators and overseas circulation. The results show that there exist contemporaneous causalities of “from RMB overseas circulation to inflation rate,” “from exchange rate to overseas circulation,” and “from exchange rate to the inflation rate.” The influence of interest rate adjustment on macro economy presents the time lag effect. The internationalization of the RMB encourages the currency appreciation. The China’s Central Bank passively looses monetary policy to meet the needs of internationalization and reduce the shock of the international hot money, thereby further deepening the domestic inflation.

As agent‐based systems are increasingly used to model real‐life applications such as the internet, electronic markets or disaster management scenarios, it is important to study the computational complexity of such usually combinatorial systems with respect to some desirable properties. The purpose of this paper is to consider two computational models: graphical games encoding the interactions between rational and selfish agents; and weighted directed acyclic graphs (DAG) for evaluating derivatives of numerical functions. The author studies the complexity of a certain number of search problems in both models.

The author's approach is essentially theoretical, studying the problem of verifying game‐theoretic properties for graphical games representing interactions between self‐motivated and rational agents, as well as the problem of searching for an optimal elimination ordering in a weighted DAG for evaluating derivatives of functions represented by computer programs.

A certain class of games has been identified for which Nash or Bayesian Nash equilibria can be verified in polynomial time; then, it has been shown that verifying a dominant strategy equilibrium is non‐deterministic polynomial (NP)‐complete even for normal form games. Finally, it has been shown that the optimal vertex elimination ordering for weighted DAGs is NP‐complete.

This paper presents a general framework for graphical games. The presented results are novel and illustrate how modeling real‐life scenarios involving intelligent agents can lead to computationally hard problems while showing interesting cases that are tractable.

The purpose of this paper is to propose data mining techniques to model the return on investment from various types of promotional spending to market a drug and then use the model to draw conclusions on how the pharmaceutical industry might go about allocating promotion expenditures in a more efficient manner, potentially reducing costs to the consumer. The main contributions of the paper are two-fold. First, it demonstrates how to undertake a promotion mix optimization process in the pharmaceutical context and carry it through from the beginning to the end. Second, the paper proposes using directed acyclic graphs (DAGs) to help unravel the direct and indirect effects of various promotional media on sales volume.

A synthetic data set was constructed to prototype proposed data mining techniques and two analyses approaches were investigated.

The two methods were found to yield insights into the problem of the promotion mix in the context of the healthcare industry. First, a factor analysis followed by a regression analysis and an optimization algorithm applied to the resulting equation were used. Second, DAG was used to unravel direct and indirect effects of promotional expenditures on new prescriptions.

The data are synthetic and do not incorporate any time autocorrelations.

The promotion mix optimization process is demonstrated from the beginning to the end, and the issue of negative coefficient in promotion mix models are addressed. In addition, a method is proposed to identify direct and indirect effects on new prescriptions.

A better allocation of promotional expenditures has the potential for reducing the cost of healthcare to consumers.

The contributions of the paper are two-fold: for the first time in the literature (to the best of the authors’ knowledge), the authors have undertaken a promotion mix optimization process and have carried it through from the beginning to the end Second, the authors propose the use of DAGs to help unravel the effects of various promotion media on sales volume, notably direct and indirect effects.

In order to initialize a student model in intelligent tutoring systems, some form of initial knowledge test should be given to a student. Since the authors cannot include all domain knowledge in that initial test, a domain knowledge subset should be selected. The paper aims to discuss this issue.

In order to generate a knowledge sample that represents truly a certain domain knowledge, the authors can use sampling algorithms. In this paper, the authors present five sampling algorithms (Random Walk, Metropolis-Hastings Random Walk, Forest Fire, Snowball and Represent algorithm) and investigate which structural properties of the domain knowledge sample are preserved after sampling process is conducted.

The samples that the authors got using these algorithms are compared and the authors have compared their cumulative node degree distributions, clustering coefficients and the length of the shortest paths in a sampled graph in order to find the best one.

This approach is original as the authors could not find any similar work that uses graph sampling methods for student modeling.

The purpose of this paper is to propose a method to determine the optimal number of operators to be assigned to a given number of machines, as well as the number of machines that will be run by each operator (a numerical partition). This determination should be made with the objective of minimizing production costs or maximizing profits.

The method calculates the machines interference rate via the binomial distribution function. The optimal assignment is calculated by transformation of a partition problem into a problem of finding the shortest path on a directed acyclic graph.

The method enables the authors to calculate the adjusted cycle time, the workload of the operators, and the utility of the machines, as well as the production yield, the total cost per unit, and the hourly profit for each potential assignment of operators to machines. In a case study, the deviation of the output per hour of the proposed method from the actual value was about 2 percent.

The paper provides formulas and tables that give machine interference rates through the application of binomial distribution. The practicability of the proposed method is demonstrated by a real-life case study.

The method can be applied in a wide variety of manufacturing systems that use many identical machines. This includes tire presses in tire manufacturing operations, ovens in pastry manufacturing systems, textile machines, and so on.