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To develop and test three different inventory management strategies as applied to the complex emergency in south Sudan.

Quantitative modeling, simulation, and statistics.

This research identified critical system factors that contributed most significantly to inventory system performance, and identified strengths and weaknesses of each inventory management strategy.

This research represents a first step in developing inventory management systems for humanitarian relief. Future work would include modeling correlation among relief items, multiple items, and considering the impact of information.

In a domain that has seen limited application of quantitative models, this work demonstrates the performance benefits of using quantitative methods to manage inventory in a relief setting.

This research has value for relief organizations by providing a real‐world application of quantitative inventory management strategies applied to a complex emergency, and demonstrated performance advantages of quantitative versus ad hoc methods. This research has value for researchers by providing a new application of simulation and mathematical modeling (humanitarian relief).

The purpose of this paper is to review and analyze the perishable inventory models along various dimensions such as its evolution, scope, demand, shelf life, replenishment policy, modeling techniques and research gaps.

In total, 418 relevant and scholarly articles of various researchers and practitioners during 1990-2016 were reviewed. They were critically analyzed along author profile, nature of perishability, research contributions of different countries, publication along time, research methodologies adopted, etc. to draw fruitful conclusions. The future research for perishable inventory modeling was also discussed and suggested.

There are plethora of perishable inventory studies with divergent objectives and scope. Besides demand and perishable rate in perishable inventory models, other factors such as price discount, allow shortage or not, inflation, time value of money and so on were found to be combined to make it more realistic. The modeling of inventory systems with two or more perishable items is limited. The multi-echelon inventory with centralized decision and information sharing is acquiring lot of importance because of supply chain integration in the competitive market.

Only peer-reviewed journals and conference papers were analyzed, whereas the manuals, reports, white papers and blood-related articles were excluded. Clustering of literature revealed that future studies should focus on stochastic modeling.

Stress had been laid to identify future research gaps that will help in developing realistic models. The present work will form a guideline to choose the appropriate methodology(s) and mathematical technique(s) in different situations with perishable inventory.

The current review analyzed 419 research papers available in the literature on perishable inventory modeling to summarize its current status and identify its potential future directions. Also the future research gaps were uncovered. This systemic review is strongly felt to fill the gap in the perishable inventory literature and help in formulating effective strategies to design of an effective and efficient inventory management system for perishable items.

Information distortion results in demand variance amplification in upstream supply chain members, known as the bullwhip effect, and inventory inaccuracy in the inventory records. As inventory inaccuracy contributes to the bullwhip effect, the purpose of this paper is to investigate the impact of inventory inaccuracy on the bullwhip effect in radio-frequency identification (RFID)-enabled supply chains and, in this context, to evaluate supply chain performance because of the RFID technology.

A simulation modeling method based on hierarchical timed colored petri nets is presented to model inventory management in multi-stage serial supply chains subject to inventory inaccuracy for various traditional and information sharing configurations in the presence and absence of RFID. Validation of the method is done by comparing results obtained for the bullwhip effect with published literature results.

The bullwhip effect is increased in RFID-enabled multi-stage serial supply chains subject to inventory inaccuracy. The information sharing supply chain is more sensitive to the impact of inventory inaccuracy.

Information sharing involves collaboration in market demand and inventory inaccuracy, whereas RFID is implemented by all echelons. To obtain the full benefits of RFID adoption and collaboration, different collaboration strategies should be investigated.

Colored petri nets simulation modeling of the inventory management process is a novel approach to study supply chain dynamics. In the context of inventory errors, information on RFID impact on the dynamic behavior of multi-stage serial supply chains is provided.

The purpose of this paper is to create the inventory scheduling model for supply chain system.

Aiming at the problems of analysis and modeling of multi‐agent system, an agent‐oriented Petri net (AOPN) model is proposed by introducing the characteristics of agent based on Petri net, and formalization definition of AOPN is presented. Combined with the inventory scheduling in supply chain system, inventory scheduling model based on AOPN is constructed. On the basis of the inventory scheduling model, its program model is designed. Then the reachability of the inventory scheduling model is analyzed and its soundness is verified.

The AOPN model and inventory scheduling model based on AOPN in supply chain system are found.

The reasonable basic function design of various agents are main limitations.

A very useful tool of analysis for the modeling of inventory scheduling in supply chain system.

The paper presents a new modeling method of inventory scheduling in supply chain system. This paper is aimed at researchers and engineers.

This paper aims to give a comprehensive review, summary, and discussion on inventory models that have appeared in the literature. During these past ten decades, no seminal paper reviewing the field of inventory lot sizing has even been published. This limitation has been identified in the literature by several researchers over the years, with the sheer volume of the number of published inventory lot sizing models acting as a factor which has limited a research endeavor to review the literature on inventory lot sizing models.

This article reviews research on inventory lot size models and provides a review of previously published literature review papers on inventory models. Based on this initial review, the literature extending current research practices on inventory modeling in supply chains and in sustainable practices is presented. Directions for expanding research in these two areas are examined in light of concerns expressed in the historical use of inventory models and in light of a new inventory research paradigm.

In our paper, we have adopted a novel strategy to overcome this limitation by focusing our review on a review of inventory lot sizing review papers.

By adopting the methodology of reviewing published inventory review papers, we can contribute a comprehensive review of the inventory lot sizing literature that serves to provide in one paper a consolidation of inventory research that can serve as a single source to keep researchers up to date with the research developments in inventory lot sizing models. We also identify gaps in the field which could stimulate new research agendas in the areas of supply chain management and sustainable inventory practices.

This paper aims to propose an alternate, efficient and scalable modeling framework to simulate large-scale bike-sharing systems using discrete-event simulation. This study uses this model to evaluate several initial bike inventory policies inspired by the operation of the bike-sharing system in Mexico City, which is one of the largest around the world. The model captures the heterogeneous demand (in time and space) and this paper analyzes the trade-offs between the performance to take and return bikes. This study also includes a simulation-optimization algorithm to determine the initial inventory and present a method to deal with the bias caused by dynamic rebalancing on observed demand.

This paper is based on the analysis of an alternate and efficient discrete-event simulation modeling framework. This framework captures the heterogeneity of demand and allows one to experiment with large-scale models. This study uses this model to test several initial bike inventory policies and also combined them with an optimization engine. The results, provide valuable insights not only for the particular system that motivated the study but also for the administrators of any bike-sharing system.

The findings of this paper include: most of the best policies use a ratio of bikes: docks near to 1:2; however, it is important the way they are initially allocated; a policy that contradicts the demand profile of the stations can lead to poor performance, regardless the quick and dynamic changes of bike locations during the morning period; the proposed simulation-optimization algorithm achieves the best results.

The findings are limited to the initial inventory of the system under study. The model assumes a homogeneous probability distribution function for the travel time. This assumption seems reasonable for the system under study. This paper limits the tested inventory policies to simple practical rules. There might be other sophisticated methods to obtain better solutions, but they might be system-specific.

The insights of this paper are valuable for operators of bike-sharing systems because this study focuses on the analysis of the impact of the initial inventory assuming that dynamic rebalancing may not be existing during the morning peak-time. This paper finds that initial inventory has a great impact on the performance, regardless of how quickly the bikes are dispersed across the system. This study also provides insights into the effect of dynamic rebalancing on observed demand.

Increasing knowledge about the operation of the bike-sharing system has a positive effect on society because more cities around the world could consider implementing these systems as a public transportation mode. Furthermore, delivering suggestions on how to increase the user service level could incentivize people to adopt bikes as a mobility option, which would contribute to improve their health and also reduce air pollution caused by motorized vehicles.

This paper considers that the contributions of this work to existing literature are the following: this study proposes a novel efficient and scalable simulation framework to evaluate initial bike inventory policies; the analysis presented in the paper includes an approach to deal with the bias in the observed demand caused by dynamic rebalancing and the analysis includes the value of demand information to determine an effective initial bike inventory policy.

Due to the randomness and unpredictability of many disasters, it is essential to be prepared to face difficult conditions after a disaster to reduce human casualties and meet the needs of the people. After the disaster, one of the most essential measures is to deliver relief supplies to those affected by the disaster. Therefore, this paper aims to assign demand points to the warehouses as well as routing their related relief vehicles after a disaster considering convergence in the border warehouses.

This research proposes a multi-objective, multi-commodity and multi-period queueing-inventory-routing problem in which a queuing system has been applied to reduce the congestion in the borders of the affected zones. To show the validity of the proposed model, a small-size problem has been solved using exact methods. Moreover, to deal with the complexity of the problem, a metaheuristic algorithm has been utilized to solve the large dimensions of the problem. Finally, various sensitivity analyses have been performed to determine the effects of different parameters on the optimal response.

According to the results, the proposed model can optimize the objective functions simultaneously, in which decision-makers can determine their priority according to the condition by using the sensitivity analysis results.

The focus of the research is on delivering relief items to the affected people on time and at the lowest cost, in addition to preventing long queues at the entrances to the affected areas.

Nowadays, in many organizations, products are not delivered instantly. So, the customers should wait to receive their needed products, which will form a queueing-inventory model. Waiting a long time in the queue to receive products may cause dissatisfaction and churn of loyal customers, which can be a significant loss for organizations. Although many studies have been done on queueing-inventory models, more practical models in this area are needed, such as considering customer prioritization. Moreover, in many models, minimizing the total cost for the organization has been overlooked.

This paper will compare several machine learning (ML) algorithms to prioritize customers. Moreover, benefiting from the best ML algorithm, customers will be categorized into different classes based on their value and importance. Finally, a mathematical model will be developed to determine the allocation policy of on-hand products to each group of customers through multi-channel service retailing to minimize the organization’s total costs and increase the loyal customers' satisfaction level.

To investigate the application of the proposed method, a real-life case study on vaccine distribution at Imam Khomeini Hospital in Tehran has been addressed to ensure model validation. The proposed model’s accuracy was assessed as excellent based on the results generated by the ML algorithms, problem modeling and case study.

Prioritizing customers based on their value with the help of ML algorithms and optimizing the waiting queues to reduce customers' waiting time based on a mathematical model could lead to an increase in satisfaction levels among loyal customers and prevent their churn. This study’s uniqueness lies in its focus on determining the policy in which customers receive products based on their value in the queue, which is a relatively rare topic of research in queueing management systems. Additionally, the results obtained from the study provide strong validation for the model’s functionality.

The purpose of this paper is to provide an approach for determining inventory levels that result in a minimum cost customer service level for specific products based on their demand characteristics and profit margin.

The paper uses logistic regression to quantify the relationship between customer service level and inventory on‐hand in relation to forecasted demand, as well to estimate the impact of factors such as forecast accuracy, customer lead‐times, and demand variability on this relationship. It then performs financial analysis in order to associate a cost with customer service level.

Empirical results based on data from a semiconductor manufacturer indicate significant cost‐savings can be achieved by applying the proposed method over the organization's current ad hoc practices.

The minimum cost customer service level identified via the methodology is based on values of dynamic factors that are specific to the time when data were collected. Therefore, frequent updating is necessary to ensure the customer service level remains close to the minimum cost. Future research could identify the ideal frequency for updating inventory levels based on cost minimization and production stability.

This research presents an inventory management methodology for organizations with variable, non‐stationary demand. In contrast to much of the current inventory modeling literature, in which service level goals are selected in an ad hoc or a priori manner, this research determines an ideal (minimum cost) customer service level from the supplier's perspective based on products' unique characteristics.