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When port authorities or terminal operators set the free time or increase storage density, the decision is often made without a clear understanding of their effects on throughput and rehandling productivity. This is partly because practical methods that deal specifically with the effect of dwell time on throughput and productivity are limited in the literature; hence the motivation for this work. This paper introduces simple methods to evaluate the effect of container dwell time and storage policies on import throughput, storage density, and rehandling productivity. The analysis considers two import storage strategies 1) non-mixed - no stacking of new import containers on top of old ones, and 2) mixed - stacking of new import containers on top of old ones. The results highlight the effect dwell time has on throughput and rehandling productivity. For the non-mixed storage policy, the increasing container dwell time lowers throughput and average stack height - resulting in an increase in rehandling productivity. On the other hand, for the mixed storage policy, the increasing container dwell time raises throughput and average stack height - resulting in a decrease in rehandling productivity. Using the presented methods, port authorities and terminal operators are able to assess and quantify the benefits of their decisions regarding container free time and subsequently make an informed decision.

Port throughput analysis is a challenging task, as it consists of intertwined interactions between a variety of cargos and numerous influencing factors. This study aims to propose a quantitative method to facilitate port throughput analysis by identification of important cargos and key macroeconomic variables.

Mutual information is applied to measure the linear and nonlinear correlation among variables. The method gives a unique measure of dependence between two variables by quantifying the amount of information held in one variable through another variable.

This study uses the mutual information to the Port of Isafjordur in Iceland to underpin the port throughput analysis. The results show that marine products are the main export cargo, whereas most imports are fuel oil, industrial materials and marine product. The aggregation of these cargos, handled in the port, meaningfully determines the non-containerized port throughput. The relation between non-containerized export and the national gross domestic product (GDP) is relatively high. However, non-containerized import is mostly related to the world GDP. The non-containerized throughput shows a strong relation to the national GDP. Furthermore, the results reveal that the volume of national export trade is the key influencing macroeconomic variable to the containerized throughput.

Application of the mutual information in port throughput analysis effectively reduces epistemic uncertainty in the identification of important cargos and key influencing macroeconomic variables. Thus, it increases the reliability of the port throughput forecast.

Seru (cell) manufacturing system has achieved huge success in production. However, related research is limited, especially, the problem of cross-trained worker assignment. The purpose of this paper is to solve this problem for two representative seru types, divisional and rotating seru, and subsequently, compare throughput performance between the two seru types under reasonable worker-task assignment.

For the cross-trained worker assignment problem, this research presents new models aiming at maximum throughput of seru and workload balance of workers under considering skill levels (SLs) and several practical constraints. Furthermore, factorial experiments that involve four factors, the number of tasks (NT), gap of task time, SL and gap of SL, are performed to compare throughput performance between divisional and rotating seru.

First, the maximum throughput of the divisional seru is better than that of the rotating seru under suitable worker assignment. Second, in the seru which has less difference of task time, throughput performance of the rotating seru is better than the divisional seru when the NT is close to the number of assigned workers. Moreover, the influence tendency of the four factors on throughput gap between the two seru types is significant.

This research addresses the worker-task assignment for divisional and rotating seru based on their characteristics. Several findings can help decision maker select more applicable seru type according to various production environments from the perspective of optimum throughput.

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.

The purpose of this paper is to analyze the applicability of lean accounting and throughput accounting in a company with considerable investments in advanced manufacturing technology (AMT).

The paper compares lean accounting and throughput accounting with the traditional accounting system the company is using today. The authors investigate the differences between the three alternative approaches and use a case study approach to illustrate the effects of applying different modern accounting approaches in a complex manufacturing setting.

Pair-wise comparisons of the three approaches provide some interesting cost information as to the role of bottlenecks and value streams.

The specific results of this study are limited to the case company, but can hopefully contribute to further research on how to combine lean and throughput accounting for mixed manufacturing environments, involving both value streams and bottlenecks.

Lean and throughput accounting provide other perspectives on cost information to traditional accounting, and can therefore be used in combination. The authors identify some issues and challenges involved in using lean accounting and throughput accounting in an AMT company.

This paper contributes with a comparison of traditional, lean, and throughput accounting in a specific industrial setting characterized by AMT and complex manufacturing.

This study aims to identify the characteristics of the maritime shipping network in Northeast Asia as well as compare the level of port connectivity among these container ports in the region. In addition, this study analyses the change in role and position of 20 ports in the region by clustering these ports based on connectivity index and container throughput and route index.

This study employs Social Network Analysis (SNA) to delineate the international connectivity of major container ports in Northeast Asia. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to identify each port's connectivity index and container throughput index, and the resulting indexes are employed as the basis to cluster 20 major ports by fuzzy C-mean (FCM).

The results revealed that Northeast Asia is a highly connected maritime shipping network with the domination of Shanghai, Shenzhen, Hong Kong and Busan. Furthermore, both container throughput and connectivity in almost all container ports in the region have decreased significantly due to the coronavirus disease 2019 (COVID-19) pandemic. The rapid growth of Shenzhen and Ningbo has allowed them to join Cluster 1 with Shanghai while maintaining high connectivity, yet decreasing container throughput has pushed Busan down to Cluster 2.

The originality of this study is to combine indexes of SNA into connectivity index reflecting characteristics of the maritime shipping network in Northeast Asia and categorize 20 major ports by FCM.

In the face of growing demand for care and tightening resource constraints, hospitals need to ensure access to care that is affordable and effective. Yet, the multiplicity of objectives is a key challenge in this industry. An understanding of the interrelationships (tradeoffs) between the multiple outcome objectives of care (throughput, experiential and financial performance) and returns to operational inputs (diversification of care) is fundamental to improving access to care that is effective and affordable. This study serves to address this need.

The empirical analysis in the study builds on an output-oriented distance function model and uses a longitudinal panel dataset from 153 hospitals in California.

This study results point to key insights related to output–output tradeoffs along the production frontier. Specifically, the authors find that higher throughput rates may lead to significantly lower levels of experiential quality, and net revenue from operations, accounting for the clinical quality of care. Similarly, the authors’ findings highlight the resource intensity and operational challenges of improving experiential quality of care. In regards to input–output relationships, this study finds diversification of care is associated with increased throughput, improvements in service satisfaction and a corresponding increase in the net revenue from operations.

Highlighting the tradeoffs along the production frontier among the various outcomes of interest (throughput, experiential quality and net revenue from operations), and highlighting the link between diversification of care and care delivery outcomes at the hospital level are key contributions of this study. An understanding of the tradeoffs and returns in healthcare delivery serves to inform policy-making with key managerial implications in the delivery of care.

A simulation procedure is presented to estimate the throughput availability of a serially interconnected manufacturing system. The model presented in the article is able to analyse, predict and suggest ways for improving productivity. The model is applicable to systems with branching and converging process streams, recycling, and intermediate buffer storages. The simulation has been carried out for a wire‐rope manufacturing plant and has been implemented by using a personal computer.

A number of attempts have been made to develop theories in operations management (OM) (e.g. trade‐off theory by Skinner, customer‐contact model by Chase and Tansik, product‐process matrix by Hayes and Wheelwright). Researchers in OM acknowledge that there is no widely‐accepted theory on which OM rests or which serves as a unified OM theory to integrate existing theory‐like principles or informal theories. Constraints management (CM) has been developed over the past 20 years by consultants and practitioners but has received little attention from OM researchers. The authors believe that constraints management may serve as a broad theory within operations that will allow integration of a great deal of existing OM research. The main objectives of this paper are to propose a construct, throughput orientation, discuss its core dimensions, and develop a theoretical model of CM. The paper also suggests several hypotheses that might be empirically tested to establish CM as a recognized theory in the field of operations management. The paper concludes with suggestions for future research.

Better forecasting always leads to better management and planning of the operations. The container throughput data are complex and often have multiple seasonality. This makes it difficult to forecast accurately. The purpose of this paper is to forecast container throughput using deep learning methods and benchmark its performance over other traditional time-series methods.

In this study, long short-term memory (LSTM) networks are implemented to forecast container throughput. The container throughput data of the Port of Singapore are used for empirical analysis. The forecasting performance of the LSTM model is compared with seven different time-series forecasting methods, namely, autoregressive integrated moving average (ARIMA), simple exponential smoothing, Holt–Winter’s, error-trend-seasonality, trigonometric regressors (TBATS), neural network (NN) and ARIMA + NN. The relative error matrix is used to analyze the performance of the different models with respect to bias, accuracy and uncertainty.

The results showed that LSTM outperformed all other benchmark methods. From a statistical perspective, the Diebold–Mariano test is also conducted to further substantiate better forecasting performance of LSTM over other counterpart methods.

The proposed study is a contribution to the literature on the container throughput forecasting and adds value to the supply chain theory of forecasting. Second, this study explained the architecture of the deep-learning-based LSTM method and discussed in detail the steps to implement it.