Column generation is a linear programming method that, when combined with appropriate integer programming techniques, has been successfully used for solving huge integer programs. The method alternates between a restricted master problem and a column generation subproblem. The latter step is founded on dual information from the former one; often an optimal dual solution to the linear programming relaxation of the restricted master problem is used.
We consider a zero–one linear programming problem that is approached by column generation and present a generic sufficient optimality condition for the restricted master problem to contain the columns required to find an integer optimal solution to the complete problem. The condition is based on dual information, but not necessarily on an optimal dual solution. It is however most natural to apply the condition in a situation when an optimal or near-optimal dual solution is at hand.
We relate our result to a few special cases from the literature, and make some suggestions regarding possible exploitation of the optimality condition in the construction of column generation methods for integer programs.

This study investigated the life cycle greenhouse gases (GHG) mitigation effect of railway infrastructure through the application of an eco-concrete, using ground granulated blast furnace (GGBF) slag and electric arc furnace (EAF) slag as alternative materials in railway sleeper, and a concrete surface protection agent in railway track. A simplified life cycle assessment method was applied to compare GHG emissions and an instrumental analysis as well as a physical performance test were carried out to identify the protection mechanism between the agent and concrete along with field tests. From this study, it was found that two different approaches might contribute substantially to mitigate emissions from railway infrastructure. The surface protection agent with an anti-deterioration function showed high possibility of increasing lifespan of the concrete structure and the use of alternative materials, such as furnace slag, reduced the concrete consumption by more than 20% (w/w). It was estimated that the potential GHG mitigation effects from the surface protection agent and eco-concrete technology applied to a railway concrete track were at least 27 ton CO₂ eq. per km a year and 11.1 kg of CO₂ eq. per 1 sleeper, respectively.

Corrosion-induced deterioration of reinforced concrete (RC) structures results in premature failure of the RC structures. In practice concrete crack width is one of the most important criteria for the assessment of the serviceability of RC structures. It is therefore desirable to predict the growth of the crack width over time so that better informed decisions can be made concerning the repairs due to concrete cracking. Literature review shows that little research has been undertaken on numerical prediction of concrete crack width. The intention of this study was to develop a numerical method to predict concrete crack width for corrosion-affected concrete structures. A cohesive crack model for concrete is implemented in the numerical formulation to simulate crack initiation and propagation in concrete. Choices for evaluating the parameters of cohesive elements are extensively discussed which is a key for developing a plausible model employing cohesive elements. The surface crack width is obtained as a function of service time. Accurate prediction of crack width can allow timely maintenance which prolongs the service life of the reinforced concrete structures.

Polyaniline (PANI) involved materials have extended approved applications in different fields, including bio/chemical-sensors, electrical and electrochemical devices, electrochemically active membranes and stimuli-responsive systems. These materials have attracted most of the interest in the field of development of biomaterials for tissue engineering and drug delivery systems. PANI offers remarkable features such as the ease of synthesis, considerable electrical conductivity especially in the doped condition, simplicity in the modification to improve water processability and enhanced biocompatibility. An engineered PANI-based biomaterial can be applied in the process named the stimulation (ES), in which cells cultured on the prepared biomaterial can be electrically stimulated. The ES mimics the native role of bioelectricity and affects the cellular behavior. The main focus of this review is on the electrical conductivity and stimulation using biocompatible advanced PANI-based materials, documenting and discussing the relevant literature and providing key information and new insights into ES.

Artificial intelligence (AI) is proving to be an efficient alternative approach to classical modeling techniques. AI refers to the branch of computer science that develops machines and software with human-like intelligence. Compared to traditional methods, AI offers advantages to deal with problems associated with uncertainties and is an effective aid to solve such complex problems. In addition, AI-based solutions are good alternatives to determine engineering design parameters when testing is not possible, thus resulting in significant savings in terms of human time and effort spent in experiments. AI is also able to make the process of decision making faster, decrease error rates, and increase computational efficiency. Among the different AI techniques, machine learning (ML), pattern recognition (PR), and deep learning (DL) have recently acquired considerable attention and are establishing themselves as a new class of intelligent methods for use in structural engineering. The objective of this review paper is to summarize techniques concerning applications of the noted AI methods in structural engineering developed over the last decade. First, a general introduction to AI is presented and the importance of AI in structural engineering is described. Thereafter, a review of recent applications of ML, PR, and DL in the field is provided, and the capability of such methods to address the restrictions of conventional models are discussed. Further, the advantages of employing such algorithmic methods are discussed in detail. Finally, potential research avenues and emerging trends for employing ML, PR, and DL are presented, and their limitations are discussed.

This research tested the idea that the perception of the state of the macroeconomic environment impacts the psychology underlying an essential organizational function: The evaluation of employees’ work and the associated promotion and demotion decisions. We predicted that when the macroeconomic environment is perceived to be more (less) prosperous, people’s generalized sense of the extent to which individuals have control over outcomes increases (decreases), leading them to attribute more (less) responsibility for work outcomes to individuals rather than contextual influences. In Study 1, we tested this theory using data from 124,400 respondents surveyed across 57 countries and 19 years and data about objective indicators of their macroeconomic environments. We found that in more prosperous times, people reported a higher generalized sense of control and were less likely to believe that contextual influences, such as luck, matter for work success. In Studies 2 and 3, we manipulated the perception of the macroeconomic environment among employees working in organizations, and we found that those who perceived their economic environment to be more prosperous had a higher generalized sense of control and in turn attributed more responsibility for a work outcome to the individual performing the work, resulting in more extreme promotion and demotion decisions. The consideration of the macroeconomic context of organizational decision making bridges the macro–micro divide in organizational sciences to provide a novel explanation for individual psychology and behavior underlying fundamental organizational processes.

Recent financial turmoil (e.g., the 2008–2009 global financial crisis) has resulted in financial contagion-induced instability becoming one of the major concerns in the fields of economics and finance. In this paper, we extend the network analysis of financial contagion from three perspectives. First, given that cross-holding of claims and obligations among financial institutions can be viewed as input-output linkages, we model the financial system and the contagion mechanism by introducing the classic Leontief input–output framework. Second, based on this modeling process, we propose a simple contagion algorithm to study how financial system heterogeneity influences its stability. Third, to mitigate financial contagion, we propose several concrete intervention policies based on two widely used prudential approaches—forced mergers and capital injections. The performance of these intervention policies is then evaluated by comprehensive numerical experiments. Our study has significant implications for financial regulation and supervision.

Enterprise Information Systems are enjoying an extensive trajectory in the optimization of organizations worldwide, of which predominantly the Business Process Management (BPM) systems stand out for their great flexibility. BPM models describe business workflows and are highly useful in detecting errors and bottlenecks and in identifying possible improvements. On the other hand, educational management software tools offer a large number of functionalities, but have yet to take advantage of these techniques. Our main objective is to perform an empirical analysis in this unexplored area to evaluate the advantages of applying BPM in the implementation of innovative and dynamic teaching activities.

Using this methodology, we have designed RubricaSoft, a BPM system focused on providing dynamic educational processes. It automates multiple tasks, including peer evaluation, information integration and the management of deadlines. The results have been very promising from the point of view of the three axes upon which the evaluation has been carried out: satisfaction of students, improvement in academic results and increase in the productivity of teachers. In one of the processes, the time spent by the teacher has been reduced by 80% and student participation increased by 41%.

We study scheduling on multiple two-stage flowshops in which each job has to pass through an R-operation and a T-operation. Motivated by the current research in data centers, we consider two restricted versions of the problem in which the jobs are inclined: one restricts that for each job, the R-operation consumes no less time than the T-operation, while the other assumes that the T-operation consumes no less time than the R-operation for each job. For the first case, we present an online 2-competitive algorithm and an offline 11/6-approximation algorithm. For the second case, we give an online 5/2-competitive algorithm, and prove, for the offline setting, that the problem can be reduced to the problem in the first case.

How can family firms unlock their innovation potential? Despite the recent growth in research on family business innovation, existing literature has yielded controversial findings. Family firms are recognized as more conservative and steadfast to their tradition, however many of the most innovative firms worldwide are family businesses. This points to an apparent willingness-ability paradox in family business innovation. Drawing on family business innovation and family systems literature, we argue that family characteristics are an important yet overlooked driver of this paradoxical tension. We develop the construct of family business innovation posture, and identify a typology of four ideal types: Seasoner, Re-enactor, Digger, and Adventurer. Furthermore, we explore and illustrate with empirical data the necessary fit between the family business innovation posture and family-related dimensions to resolve the willingness-ability paradox. The article examines the implications of the typology for family business innovation research by exploring the effects of intra-family succession, outlining important directions for future research aimed at advancing current understanding of the role of the family in family business innovation, and providing practical insights for family business owners, managers, and consultants.