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Order picking is one of the most costly logistics processes in warehouses. As a result, the optimization of order picking processes has received an increased attention in recent years. One potential source for improving order picking is the reduction of picker blocking. The purpose of this paper is to investigate picker blocking under different storage assignment and order picker-route combinations and evaluate its effects on the performance of manual order picking processes.

This study develops an agent-based simulation model (ABS) for order picking in a rectangular warehouse. By employing an ABS, we are able to study the behaviour of individual order pickers and their interactions with the environment.

The simulation model determines shortest mean throughput times when the same routing policy is assigned to all order pickers. In addition, it evaluates the efficiency of alternative routing policies–storage assignment combinations.

The paper implies that ABS is well-suited for further investigations in the field of picker blocking, for example, with respect to the individual behaviour of agents.

Based on the results of this paper, warehouse managers can choose an appropriate routing policy that best matches their storage assignment policy and the number of order pickers employed.

This paper is the first to comprehensively study the effects of different combinations of order picker routing and storage assignment policies on the occurrence of picker blocking.

This brief article seeks to answer, with examples, some of the more common questions that policy‐makers and practitioners in children's services often ask about randomised controlled trials (RCTs). It is essentially a primer, and those wishing to read further on these issues might find it helpful to start with the books discussed in the review article by Hobbs and colleagues in this special edition (p40).

This paper aims to represent a novel framework for optimization of robotic handling from disarray to structure where the products are randomly distributed on a surface, the initial location of the products are known (with the aid of image processing, laser position sensors, etc.) and there is a set of final positions for the products.

Pick‐and‐place is one of the main solutions especially for the food products where the products are prone to damage, have adhesive surfaces and the grippers can be complicated. The aim of this paper is to maximize the utilization of the pick‐and‐place robotic system. In order to do so the handling process is modelled mathematically and the pick‐and‐place problem is formulated based on assignment problem where Hungarian algorithm is utilized to minimize the total distance travelled by the robot. Furthermore, a simulation program is developed to demonstrate the possible improvements of the algorithm in comparison with the existing algorithms.

Utilizing the proposed algorithm can significantly increase the utilization of robots in the pick‐and‐place operation.

The new optimization algorithm can be applied to any industry with pick‐and‐place where time efficiency and maximum utilization matters.

To highlight the differences and common features of taboo search (TS) and genetic algorithms (GA) in solving the problem of board‐type sequencing on the assembly line simultaneously with the combined problem of feeder assignment and component placement sequencing in the printed circuit board (PCB) industry.

Two metaheuristics (search techniques) are used to solve three problems associated with the PCB assembly line: TS and GA. The implemented approach is used to solve the three problems on a single pick‐and‐place sequential machine with a stationary board table and stationary feeders, and with the use of the Euclidean metric.

The achieved results show a satisfactory reduction in assembly time, when TS and GA are compared with a random solution, with a slight superiority of TS over GA. However, the program running time is longer for TS.

The hypothetical case study used shows that in real life the savings could reach an average of 6 per cent when TS is used. Slightly lower savings are possible when GA is used.

This paper provides a clear insight into how some of the problems associated with the production of PCBs can be solved simultaneously using metaheuristics such as TS and GA.

Vertical lift module (VLM) is a parts-to-picker system for order picking of small products, which are stored into two columns of trays served by a lifting crane. A dual-bay VLM order picking (dual-bay VLM-OP) system is a particular solution where the operator works in parallel with the crane, allowing higher throughput performance. The purpose of this paper is to define models for different operating configurations able to improve the total throughput of the dual-bay VLM-OP system.

Analytical models are developed to estimate the throughput of a dual-bay VLM-OP. A deep evaluation has been carried out, considering different storage assignment policies and the sequencing retrieval of trays.

A more accurate estimation of the throughput is demonstrated, compared to the application of previous models. Some use guidelines for practitioners and academics are derived from the analysis based on real data.

Differing from previous contributions, these models include the acceleration/deceleration of the crane and the probability of storage and retrieve of each single tray. This permits to apply these models to different storage assignment policies and to suggest when these policies can be profitably applied. They can also model the sequencing retrieval of trays.

This paper investigates the importance of team formation in entrepreneurship education, and the authors ask: how do different team formation strategies influence teamwork in higher education experiential learning-based entrepreneurship courses?

Employing a multiple case study design, the authors examine 38 student teams from three different entrepreneurship courses with different team formation paths to uncover potential links between team formation and learning outcomes.

The authors find that team formation mode matters. Randomly assigned teams, while diverse, struggle with handling uncertainty and feedback from potential stakeholders. In contrast, student self-selected teams are less diverse but more robust in handling this pressure. Results suggest that in randomly assigned teams, the entrepreneurial project becomes the team's sole reference point for well-being. Seeking to protect the project, the team's ability to deal with uncertainty and external feedback is limited, stifling development. In student self-select teams, team well-being becomes a discrete reference point. This enables these teams to respond effectively to external project feedback while nurturing team well-being independently.

Education theories' implications about the benefit of team diversity may not apply to experiential learning-based entrepreneurship education's typical level of ambiguity and uncertainty. Therefore, educators may have to reconsider the unique dynamics of team formation strategies to ensure strong teamwork and teamwork outcomes.

The purpose of this paper is to discuss some common pitfalls in experimental research in the field of hospitality and tourism. It aims to offer recommendations on how to avoid such problems to enhance theory development.

This paper highlights some common pitfalls in hospitality research regarding manipulations, samples and data analyses. The challenges imposed by the global pandemic are also discussed.

Researchers in hospitality are recommended to refine their experimental designs, to recruit appropriate and sufficient samples and to avoid the abuse of “researcher degrees of freedom” in data analysis.

This is the first study to review common mistakes in experimental research in hospitality research and to recommend some remedies. The findings of this study can contribute to stronger theory development.

This paper aims to present the Difference-in-Differences (DiD) method in an accessible language to a broad research audience from a variety of management-related fields.

The paper describes the DiD method, starting with an intuitive explanation, goes through the main assumptions and the regression specification and covers the use of several robustness methods. Recurrent examples from the literature are used to illustrate the different concepts.

By providing an overview of the method, the authors cover the main issues involved when conducting DiD studies, including the fundamentals as well as some recent developments.

The paper can hopefully be of value to a broad range of management scholars interested in applying impact evaluation methods.

Among the gold standards in human resource development (HRD) research are studies that test theoretically developed hypotheses and use experimental designs. A somewhat typical experimental design would involve collecting pretest and posttest data on individuals assigned to a control or experimental group. Data from such a design that considered if training made a difference in knowledge, skills or attitudes, for example, could help advance practice. Using simulated datasets, situated in the example of a scenario-planning intervention, this paper aims to show that choosing a data analysis path that does not consider the associated assumptions can misrepresent findings and resulting conclusions. A review of HRD articles in a select set of journals indicated that some researchers reporting on pretest-posttest designs with two groups were not reporting associated statistical assumptions and reported results from repeated-measures analysis of variance that are considered of minimal utility.

Using heuristic datasets, situated in the example of a scenario-planning intervention, this paper will show that choosing a data analysis path that does not consider the associated assumptions can misrepresent findings and resulting conclusions. Journals in the HRD field that conducted pretest-posttest control group designs were coded.

The authors' illustrations provide evidence for the importance of testing assumptions and the need for researchers to consider alternate analyses when assumptions fail, particularly the homogeneity of regression slopes assumption.

This paper provides guidance to researchers faced with analyzing data from a pretest-posttest control group experimental design, so that they may select the most parsimonious solution that honors the ecological validity of the data.

The purpose of this paper is to lay out the data competence maturity model (DCMM) and discuss how the application of the model can serve as a foundation for a measured and deliberate use of data in secondary education.

Although the model is new, its implications, and its application are derived from key findings and best practices from the software development, data analytics and secondary education performance literature. These principles can guide educators to better manage student and operational outcomes. This work builds and applies the DCMM model to secondary education.

The conceptual model reveals significant opportunities to improve data-driven decision making in schools and local education agencies (LEAs). Moving past the first and second stages of the data competency maturity model should allow educators to better incorporate data into the regular decision-making process.

Moving up the DCMM to better integrate data into their decision-making process has the potential to produce profound improvements for schools and LEAs. Data science is about making better decisions. Understanding the path laid out in the DCMM to helping an organization move to a more mature data-driven decision-making process will help improve both student and operational outcomes.

This paper brings a new concept, the DCMM, to the educational literature and discusses how these principles can be applied to improve decision making by integrating them into their decision-making process and trying to help the organization mature within this framework.