During recent years, Shuttle-based Storage and Retrieval Systems (SBS/RSs) have been widely applied in distribution centers and production sites to meet the increasing demand for rapid and flexible large-scale warehousing activities. Recognizing the complex service dynamics due to the use of different types of S/R devices, both the configuration design problem and operational control problem need to be studied in order to improve efficient, sustainable and robust performance of the system. An animated, data-driven and datagenerated simulation model is developed to support the development of both the design and configuration methodology and operational control strategy of SBS/RS-based warehouse systems. The model enables detailed analysis of different system configurations and technology options including tier-captive and tierto-tier, multi-deep rack designs, multi-capacity lifts, etc., and provides visualized tracking of accurately simulated service processes of the S/R devices and performance evaluation under configurable demand scenarios.

Conducting field research with a vulnerable population is difficult under the most auspicious conditions, and these difficulties only increase during a pandemic. Here, we describe the practical challenges and ethical considerations surrounding a recent data collection effort with a high-risk population during the COVID-19 pandemic. We detail our strategies related to research design, site selection, and ethical review.

Background: Ecological momentary assessment (EMA) is an increasingly popular and feasible form of data collection, but it can be intensive and intrusive. Especially for at-risk, vulnerable populations like people who use drugs (PWUD), poor experiences with EMA may exacerbate existing chronic struggles while decreasing response rates. However, little research queries participants' experiences with EMA studies.Objectives: We explore participants' positive and negative experiences with EMA, identifying what they liked about the study, the problems they experienced, and suggested solutions to these problems.Methods: Results come from semi-structured interviews from 26 PWUD (6 women; 20 men) in Nebraska who participated in a two-week EMA pilot study on drug use with a study-provided smartphone. Participant responses were recorded by interviewers into open-text fields in Qualtrics. Data were analyzed with an iterative open coding procedure.Results: We found that many participants enjoyed the study and seamlessly incorporated the phone into their daily lives. There were a number of negative study aspects identified, however, as many participants experienced functional issues (e.g., running out of high-speed data, trouble keeping the phone charged, not able to answer questions within the two-hour timeframe) that detracted from their experience, especially if they were homeless.Conclusion: Our findings provide methodological considerations for studies with EMA components among at-risk, vulnerable populations, like PWUD. These suggestions are targeted toward the continued ethical collection of high-quality data in clinical and non-clinical settings.

Warehouses and warehouse-related operations have long been a field of interest for researchers. One of the areas that researchers focus on is the Storage Location Assigning Problem (SLAP or Slotting). The goal in this field is to find the best location in a warehouse to store the products. With the current COVID-19 pandemic, there is a shopping paradigm shift towards e-commerce, which even after the pandemic will not return to the old state. This paradigm shift raises the need for better performing multi-pick warehouses. In this paper, we propose a clustering method based on the gravity model. We show that for warehouses in which there is more than one pick per trip, our proposed method improves the performance.

During recent years, Autonomous Vehicle Storage and Retrieval Systems (AVS/RS) have been widely applied to meet the increasing demand for rapid and flexible large-scale storage and retrieval tasks. This paper focuses on the control strategies for coordinating the subsystem operations with regard to the conveyor system, rack storage system and pick-up system in order to maximize the system's throughput capacity and minimize the storage/retrieval times of items in an e-commerce picking warehouse. The study is based on a large-scale shoe manufacturer's warehouse with an eight-zone AVS/RS. We describe a simulation model that was built to validate the proposed control strategies and thus provides insights for system management.

Storing pallets of products on top of one another on the floor of a warehouse is called block stacking. The arrangement of lanes, aisles, and cross-aisles in this storage system affects both utilization of the storage space and material handling costs; however, the existing literature focuses exclusively on lane depths and their impact on space utilization. This paper fills this gap and studies the optimal layout design for block stacking, which includes determining the numbers of aisles and cross-aisles, bay depths, and cross-aisle types. We show that lane depths affect material handling cost in addition to space utilization and develop a simulation-based optimization algorithm to find optimal layouts with respect to both of these objectives. We also propose a closed-form solution to the optimal number of aisles in a layout. The results of a case study in the beverage industry show that the resulting layout can save up to ten percent of the operational costs of a warehouse. We present the computational efficiency of the algorithm and some insights into the problem through an exhaustive experimental analysis based on various test problems that cover small- to industrial-sized warehouses.

In block stacking warehouses, pallets of Stock Keeping Units (SKUs) are stacked on top of one another in lanes on the warehouse floor. A conventional layout consists of multiple bays of lanes separated by aisles. The depths of the bays and the number of aisles determine the storage space utilization. Using an analytical model, we show that the traditional lane depth model underestimates accessibility waste and therefore does not provide an optimal lane depth. We propose a new model of wasted storage space and embed it in a mixed-integer program to find the optimal bay depths. The model improves space utilization by allowing multiple bay depths and allocating SKUs to appropriate bays. Our computational study shows the proposed model is capable of solving large-scale problems with a relatively small optimality gap. We use simulation to evaluate performance of the proposed model on small to industrial-sized warehouses. We also include a case study from the beverage industry.

We focus on a service system in which the customer arrivals are non-stationary and our goal is to determine a server staffing schedule that ensures that arriving customers do not experience long and/or unpredictable queue times. An airport ticket counter is an example of such a system. Passengers arrivals are nonstationary, yet arriving passengers do not wish to wait in long lines to check into their flights. Moreover, unpredictability is a significant issue in these environments as it often forces passengers to arrive earlier than necessary "just in case." Unfortunately, we rarely know the precise form of the arrival process and must use observed samples to set the staffing policy. We show through a case study that simulation combined with a specialized input analysis tool can be used to determine good staffing policies in these environments.

This paper explores the optimal initial inventory at launch, sales plan, and introduction time for a new generation of a product. We propose a multi-generation demand model that accounts for supply constraints. A mathematical model of the supply-restricted multi-generation diffusion problem is then developed and the optimal sales policy is derived. Closed-form analytical expressions as well as numerical experiments are used to investigate the effect of consumers' backlogging, cost of production capacity, unit profit margin for the products, market expansion by the new generation, and cannibalization of older generations. Through consideration of supply restrictions and lost sales, we are able to generalize previous findings on market entry policy by showing that there is a continuous range of optimal introduction times. We show that the inter-dependence between production capacity and initial inventory varies based on the introduction time of the new generation. We also provide an application of the proposed model in the case of Sony's PlayStation®3 game console. The results suggest that the company introduced the product too late and overproduced inventory which, as supported by empirical evidence, had a negative impact on the product's performance. Limitations and considerations for the application of the model are also discussed.

Generally accepted data structures and predefined paradigms such as geographic information system (GIS) in research domains enhance researchers experience and efficiency by providing defined data structures and file formats. Warehousing and related topics represent one of the most studied areas in supply chain with no predefined standards or data structures when it comes to developing warehouse operation related programs and code. In this paper, we propose WODS, a new data structure that integrates, stores, edits, analyzes, and displays data related to all aspects of warehouse modeling and development. The paper also describes a set of tools to demonstrate the use of WODS which can also play as a starting point that facilitates the process of modeling a warehouse operation program. (C) 2017 Elsevier Ltd. All rights reserved.

Block stacking storage is an inexpensive storage system widely used in manufacturing systems where pallets of stock keeping units (SKUs) are stored in a warehouse at the finite production rates. However, determining the optimal lane depth that maximises space utilisation under a finite production rate constraint has not been adequately addressed in the literature and is an open problem. In this research, we propose mathematical models to obtain the optimal lane depth for single and multiple SKUs where the pallet production rates are finite. A simulation model is used to evaluate performance of the proposed models under stochastic uncertainty in the major production parameters and the demand.

This paper and the corresponding panel session focus on teaching undergraduate industrial engineering/operations research-related simulation courses. The format brings together four experienced instructors to discuss four questions involving the structure and topic outlines of courses, print and software teaching materials used, and general teaching methods/philosophy. The hope is to provide some experience-based teaching information for new and soon-to-be instructors and to generate discussion with the simulation education community.

When introducing a new product, firms face a hierarchy of decisions at the strategic and operational levels including capacity sizing, time to market or starting sales, initial inventory required by the product's release time and production management in response to changes in the demand ( hereafter referred to as production-sales policies). The goal of this paper was to show the importance of considering both supply and demand uncertainties in the determination of the production-sales policy which has been overlooked in the existing literature. More specifically, we test two main hypotheses: ( 1) ignoring supply and demand uncertainties may lead to potentially incorrect decisions; and, ( 2) the decision could be different if risk is used as the primary performance measure instead of the commonly used expected ( mean) profit. We perform extensive experimentation with a Monte Carlo simulation model of the stochastic supply-restricted new product diffusion and use different statistical procedures, namely, the Welch's t-test and a nonparametric double-bootstrap method to compare the average and percentiles of the profit for different policies, respectively. The results indicate that the correctness of the two hypotheses depends on the diffusion speed, consumers' backlogging behaviour, production capacity, price and variable production and inventory costs. The findings also have important implications for managers regarding market entry time, parameter estimation, production strategy and the implementation of the proposed model.

Emerging cyber-infrastructure tools are enabling scientists to transparently co-develop, share, and communicate about real-time diverse forms of knowledge artifacts. In these environments, communication preferences of scientists are posited as an important factor affecting innovation capacity and robustness of social and knowledge network structures. Scientific knowledge creation in such communities is called global participatory science (GPS). Recently, using agent-based modeling and collective action theory as a basis, a complex adaptive social communication network model (CollectiveInnoSim) is implemented. This work leverages CollectiveInnoSim implementing communication preferences of scientists. Social network metrics and knowledge production patterns are used as proxy metrics to infer innovation potential of emergent knowledge and collaboration networks. The objective is to present the underlying communication dynamics of GPS in a form of computational model and delineate the impacts of various communication preferences of scientists on innovation potential of the collaboration network. Gained insight can ultimately help policy-makers to design GPS environments and promote innovation.

We develop an Excel ® Add-In that automates the evaluation and visualization of measures of risk and error (MORE) for performance measures that change over time. We use an example with a non-stationary arrival process to demonstrate the applicability and importance of a such tool. The tool takes raw simulation output, automatically calculates the pertinent MORE values, and generates side-by-side MORE plots for different time ticks according to a user-specified interval to characterize how the mean and percentiles of a time-dependent statistic and their corresponding confidence intervals change over time. The ADD-MORE tool significantly reduces the burden on the simulation analyst and can potentially have a high impact on simulation practice as there is no easy way to perform such analysis using existing tools. The tool is made available online for free and can potentially be integrated into existing simulation and/or statistical software packages to support output analysis and decision-making.

When a customer arrives to a service system, how long should they expect to wait, and how long might their wait actually be? Computer simulation is an ideal tool for answering such questions for very general and complex queueing systems, but they are not always answered by the automatic statistical summary generated by commercial simulation languages. Using an illustration based on passenger check-in at an airport, we demonstrate how standard summary measures go wrong and provide methods that correctly answer these questions.

An important step in input modeling is the assessment of data being independently and identically distributed (IID). While this is straightforward when modeling stationary stochastic processes, it becomes more challenging when the stochastic process follows a non-stationary pattern where the probability distribution or its parameters depend on time. In this paper, we first discuss the challenges faced by using traditional approaches. We then introduce the Histograms and Rates for Input Analysis (HistoRIA) as a tool to facilitate input modeling. The tool automates the analysis process and significantly reduces the amount of time and effort required to test the IID assumptions. The generated HistoRIA plot is capable of effectively illustrating changes in the rate and distribution over time. Although originally designed and developed for simulation input analysis, the paper demonstrates how the tool can potentially be applicable in other areas where non-stationarity in the data is also common.

Once a new product is available, if the company starts the sales without building an initial inventory (myopic policy), the demand for the product grows rapidly due to extensive word of mouth spreading from past sales and soon may exceed the firm’s capacity resulting in lost sales. To avoid this problem, companies generally delay product launch to build sufficient inventory prior to starting sales (build-up policy). In this study, we use simulation to evaluate the expected profit and risk associated with myopic and build-up policies under production uncertainties. The results show that ignoring production uncertainties can result in potentially incorrect decisions regarding the number of inventory build-up periods and product launch time. We also show that the policy with the maximum expected profit does not necessarily minimize risk.