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The purpose of this study is to first investigate the efficiency of the most commonly used performance measures for minimizing the number of workstations (NWs) in approaches addressing simple assembly line balancing problem (SALBP) for both straight and U-shaped line, and second to provide a comparative evaluation of 20 constructive heuristics to find solutions to the SALBP-1.

A total of 200 problems are solved by 20 different constructive heuristics for both straight and U-shaped assembly line. Moreover, several comparisons have been made to evaluate the performance of constructive heuristics.

Minimizing the smoothness index is not necessarily equivalent to minimizing the NWs; therefore, it should not be used as the fitness function in approaches addressing the SALBP-1. Line efficiency and the idle time are indeed reliable performance measures for minimizing the NWs. The most promising heuristics for straight and U-shaped line configurations for SALBP-1 are also ranked and introduced.

Results are expected to help scholars and industrial practitioners to better design effective solution methods for having the most balanced assembly line. This study will further help with choosing the most proper heuristic with regard to the problem specifications and line configuration.

There is limited research assessing the efficiency of the common objectives for SALBP-1. This study is among the first to prove that minimizing the workload smoothness is not equivalent to minimizing the NWs in SALBP-1 studies. This work is also one of the first attempts for evaluating the constructive heuristics for both straight and U-shaped line configurations.

The purpose of this paper is to explain how two task characteristics and two individual differences influence which heuristics individuals use, and as a results explain their decision performance when choosing performance measures (PMs) for incentive compensation.

In total, 76 MS accounting students volunteered to participate in an experiment. A between-subjects experimental design was used to test the hypotheses.

The experimental evidence suggests that individuals, while using high-complexity heuristics, can choose an incorrect PM when PM attribute conflict is present and the difference between PM attribute differences is small. Individuals with high goal commitment are more likely to make the correct choice than individuals with low goal commitment, because they focus more on the PMs’ goal congruence than on the PMs’ noise when making tradeoffs between the conflicting PMs’ attributes.

The social context can stimulate individuals’ empathic concern and/or goal commitment and thus explain individuals’ performance when PM attribute conflict is present and the difference between PM attribute differences is small.

The results of this study are important to those responsible for designing incentive systems give greater importance to considering not just congruency attributes in PM but precision attributes as well.

This paper develops predictions and provides experimental evidence on two task characteristics that influence individuals’ use of heuristics when choosing PMs for incentive compensation. In addition, it provides evidence that individual differences can affect individuals’ PM choice performance when tradeoffs between PMs’ congruity and precision are required.

The purpose of this paper is to present three heuristics for choosing supplier selection criteria. By considering the balance between the expected relative effort and benefit of using different selection criteria, the heuristics suggest which criteria should be prioritized. The heuristics serve to develop our understanding of the search and evaluation heuristics used in supplier selection and to facilitate further research.

The research is primarily theoretical, yet draws on empirical studies of supplier selection. The theoretical basis is Simon’s notion of procedural rationality (Simon, 1976). The author makes the general notion of procedural rationality more concrete for supplier selection by formally describing three heuristics for choosing selection criteria. The heuristics share the same logic but differ in terms of the precision of the input information required from the purchaser. The paper provides illustrations of the heuristics.

It appears that procedural rationality can be specified for the process of designing the supplier selection process by explicitly recognizing the cost and value of selection criteria. There is no one way of doing this, but at the most basic level, it requires an ordinal ranking of criteria. Already such a rudimentary, qualitative, assessment can help identifying suitable criteria. The heuristics developed appear compatible with established approaches for the subsequent selection of suppliers.

The paper addresses the early stage of supplier selection which has been largely ignored in the literature.

The purpose of this paper is to compare the effectiveness of two meta‐heuristics in solving the problem of scheduling maintenance operations and jobs processing on a single machine.

The two meta‐heuristic algorithms, tabu search and simulated annealing are hybridized using the properties of an optimal schedule identified in the existing literature to the problem. A lower bound is also suggested utilizing these properties.

In a numerical experimentation with large size problems, the best‐known heuristic algorithm to the problem is compared with the tabu search and simulated annealing algorithms. The study shows that the meta‐heuristic algorithms outperform the heuristic algorithm. In addition, the developed meta‐heuristics tend to be more robust against the problem‐related parameters than the existing algorithm.

A future work may consider the possibility of machine failure along with the preventive maintenance. This relaxes the assumption that the machine cannot fail but it is rather maintained preventively. The multi‐criteria scheduling can also be considered as an avenue of future work. The problem can also be considered with stochastic parameters such that the processing times of the jobs and the maintenance related parameters are random and follow a known probability distribution function.

The usefulness of meta‐heuristic algorithms is demonstrated for solving a large scale NP‐hard combinatorial optimization problem. The paper also shows that the utilization of the directed search methods such as hybridization could substantially improve the performance of a meta‐heuristic.

This research highlights the impact of utilizing the directed search methods to cause hybridization in meta‐heuristic and the resulting improvement in their performance for large‐scale optimization.

How does algorithmic information processing affect the thoughts and behavior of artificial intelligence (AI) users? In this study, the authors address this question by focusing on algorithm-based chatbots and examine the influence of culture on algorithms as a form of digital intermediation.

The authors conducted a study comparing the United States (US) and Japan to examine how users in the two countries perceive the features of chatbot services and how the perceived features affect user trust and emotion.

Clear differences emerged after comparing algorithmic information processes involved in using and interacting with chatbots. Major attitudes toward chatbots are similar between the two cultures, although the weights placed on qualities differ. Japanese users put more weight on the functional qualities of chatbots, and US users place greater emphasis on non-functional qualities of algorithms in chatbots. US users appear more likely to anthropomorphize and accept explanations of algorithmic features than Japanese users.

Different patterns of chatbot news adoption reveal that the acceptance of chatbots involves a cultural dimension as the algorithms reflect the values and interests of their constituencies. How users perceive chatbots and how they consume and interact with the chatbots depends on the cultural context in which the experience is situated.

A comparative juxtaposition of cultural-algorithmic interactions offers a useful way to examine how cultural values influence user behaviors and identify factors that influence attitude and user acceptance. Results imply that chatbots can be a cultural artifact, and chatbot journalism (CJ) can be a socially contextualized practice that is driven by the user's input and behavior, which are reflections of cultural values and practices.

316 L stainless steel alloy is potentially the most used material in the selective laser melting (SLM) process because of its versatility and broad fields of applications (e.g. medical devices, tooling, automotive, etc.). That is why producing fully functional parts through optimal printing configuration is still a key issue to be addressed. This paper aims to present an entirely new framework for simultaneously reducing surface roughness (SR) while increasing the material processing rate in the SLM process of 316L stainless steel, keeping fundamental mechanical properties within their allowable range.

Considering the nonlinear relationship between the printing parameters and features analyzed in the entire experimental space, machine learning and statistical modeling methods were defined to describe the behavior of the selected variables in the as-built conditions. First, the Box–Behnken design was adopted and corresponding experimental planning was conducted to measure the required variables. Second, the relationship between the laser power, scanning speed, hatch distance, layer thickness and selected responses was modeled using empirical methods. Subsequently, three heuristic algorithms (nonsorting genetic algorithm, multi-objective particle swarm optimization and cross-entropy method) were used and compared to search for the Pareto solutions of the formulated multi-objective problem.

A minimum SR value of approximately 12.83 μm and a maximum material processing rate of 2.35 mm³/s were achieved. Finally, some verification experiments recommended by the decision-making system implemented strongly confirmed the reliability of the proposed optimization methodology by providing the ultimate part qualities and their mechanical properties nearly identical to those defined in the literature, with only approximately 10% of error at the maximum.

To the best of the authors’ knowledge, this is the first study dealing with an entirely different and more comprehensive approach for optimizing the 316 L SLM process, embedding it in a unique framework of mechanical and surface properties and material processing rate.

This article aims to systematically review the literature published in recognized journals focused on cognitive heuristic-driven biases and their effect on investment management activities and market efficiency. It also includes some of the research work on the origins and foundations of behavioral finance, and how this has grown substantially to become an established and particular subject of study in its own right. The study also aims to provide future direction to the researchers working in this field.

For doing research synthesis, a systematic literature review (SLR) approach was applied considering research studies published within the time period, i.e. 1970–2021. This study attempted to accomplish a critical review of 176 studies out of 256 studies identified, which were published in reputable journals to synthesize the existing literature in the behavioral finance domain-related explicitly to cognitive heuristic-driven biases and their effect on investment management activities and market efficiency as well as on the origins and foundations of behavioral finance.

This review reveals that investors often use cognitive heuristics to reduce the risk of losses in uncertain situations, but that leads to errors in judgment; as a result, investors make irrational decisions, which may cause the market to overreact or underreact – in both situations, the market becomes inefficient. Overall, the literature demonstrates that there is currently no consensus on the usefulness of cognitive heuristics in the context of investment management activities and market efficiency. Therefore, a lack of consensus about this topic suggests that further studies may bring relevant contributions to the literature. Based on the gaps analysis, three major categories of gaps, namely theoretical and methodological gaps, and contextual gaps, are found, where research is needed.

The skillful understanding and knowledge of the cognitive heuristic-driven biases will help the investors, financial institutions and policymakers to overcome the adverse effect of these behavioral biases in the stock market. This article provides a detailed explanation of cognitive heuristic-driven biases and their influence on investment management activities and market efficiency, which could be very useful for finance practitioners, such as an investor who plays at the stock exchange, a portfolio manager, a financial strategist/advisor in an investment firm, a financial planner, an investment banker, a trader/broker at the stock exchange or a financial analyst. But most importantly, the term also includes all those persons who manage corporate entities and are responsible for making their financial management strategies.

Currently, no recent study exists, which reviews and evaluates the empirical research on cognitive heuristic-driven biases displayed by investors. The current study is original in discussing the role of cognitive heuristic-driven biases in investment management activities and market efficiency as well as the history and foundations of behavioral finance by means of research synthesis. This paper is useful to researchers, academicians, policymakers and those working in the area of behavioral finance in understanding the role that cognitive heuristic plays in investment management activities and market efficiency.

The role of managerial cognitive processes has so far been largely neglected within strategy research which examines organizational decline and recovery. Proposes that heuristics used by managers such as: availability, representativeness, adjustment and anchoring may contribute to the declining performance of the organization. Suggests that mental imagery could be used to adjust these heuristics and change the cognitive processes of the existing managers instead of replacing the top management team in declining organizations. This change in cognitive processes could help increase the ability of a declining firm to improve its performance.

The purpose of this paper is to compare the performance of the genetic algorithm (GA), simulate annealing (SA) and shuffled frog-leaping algorithm (SFLA) in solving discrete versus continuous-variable optimization problems of the finance-based scheduling. This involves the minimization of the project duration and consequently the time-related cost components of construction contractors including overheads, finance costs and delay penalties.

The meta-heuristics of the GA, SA and SFLA have been implemented to solve non-deterministic polynomial-time hard (NP-hard) finance-based scheduling problem employing the objective of minimizing the project duration. The traditional problem of generating unfeasible solutions in scheduling problems is adequately tackled in the implementations of the meta-heuristics in this paper.

The obtained results indicated that the SA outperformed the SFLA and GA in terms of the quality of solutions as well as the computational cost based on the small-size networks of 30 activities, whereas it exhibited the least total duration based on the large-size networks of 120 and 210 activities after prolonged processing time.

From researchers’ perspective, finance-based scheduling is one of the few domain problems which can be formulated as discrete and continuous-variable optimization problems and, thus, can be used by researchers as a test bed to give more insight into the performance of new developments of meta-heuristics in solving discrete and continuous-variable optimization problems.

Finance-based scheduling discrete-variable optimization problem is of high relevance to the practitioners, as it allows schedulers to devise finance-feasible schedules of minimum duration. The minimization of project duration is focal for the minimization of time-related cost components of construction contractors including overheads, finance costs and delay penalties. Moreover, planning for the expedient project completion is a major time-management aspect of construction contractors towards the achievement of the objective of client satisfaction through the expedient delivery of the completed project for clients to start reaping the anticipated benefits.

Planning for the expedient project completion is a major time-management aspect of construction contractors towards the achievement of the objective of client satisfaction.

SFLA represents a relatively recent meta-heuristic that proved to be promising, based on its limited number of applications in the literature. This paper is to implement SFLA to solve the discrete-variable optimization problem of the finance-based scheduling and assess its performance by comparing its results against those of the GA and SA.

Investigates group scheduling heuristics in a flow shop cellular system with two types of order shipment environment: permitted early shipment (PES) and forbidden early shipment (FES). Once an order is completed in the PES environment, it leaves the system without delay. In the FES environment, however, the suppliers must deliver materials to the system on a just‐in‐time basis; and a completed order cannot leave the system earlier than the customer has specified. The FES environment therefore captures many of the features of the currently popular just‐in‐time (JIT) approach to manufacturing. Examines shop factors such as the order shipment environment, demand pattern variability, and order release policy. Shows that the performance of heuristics in the FES environment differs from their performance in the PES environment, except for tardiness‐related measures. Identifies the best performing heuristics, which can provide guidance for schedulers or decision makers in the selection of preferable heuristics.