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It is worth mentioning that in supply chain management (SCM), managerial decisions are often based on optimization of resources. Till the early 2000s, supply chain optimization problems were being addressed by conventional programming approaches such as Linear Programming, Mixed-Integer Linear Programming and Branch-and-Bound methods. However, the solution convergence in such approaches was slow. But with the advent of Swarm Intelligence (SI)-based algorithms like particle swarm optimization and ant colony optimization, a significant improvement in solution of these problems has been observed. The purpose of this paper is to present and analyze the application of SI algorithms in SCM. The analysis will eventually lead to development of a generalized SI implementation framework for optimization problems in SCM.

A structured state-of-the-art literature review is presented, which explores the applications of SI algorithms in SCM. It reviews 56 articles published in peer-reviewed journals since 1999 and uses several classification schemes which are critical in designing and solving a supply chain optimization problem using SI algorithms.

The paper revels growth of swarm-based algorithms and seems to be dominant among all nature-inspired algorithms. The SI algorithms have been used extensively in most of the realms of supply chain network design because of the flexibility in their design and rapid convergence. Large size problems, difficult to manage using exact algorithms could be efficiently handled using SI algorithms. A generalized framework for SI implementation in SCM is proposed which is beneficial to industry practitioners and researchers.

The paper proposes a generic formulation of optimization problems in distribution network design, vehicle routing, resource allocation, inventory management and supplier management areas of SCM which could be solved using SI algorithms. This review also provides a generic framework for SI implementation in supply chain network design and identifies promising emerging issues for further study in this area.

Ensuring the sufficient service level is essential for critical materials in industrial maintenance. This study aims to evaluate the use of statistically imperfect data in a stochastic simulation-based inventory optimization where items' failure characteristics are derived from historical consumption data, which represents a real-life situation in the implementation of such an optimization model.

The risks of undesired shortages were evaluated through a service-level sensitivity analysis. The service levels were simulated within the error of margin of the key input variables by using StockOptim optimization software and real data from a Finnish steel mill. A random sample of 100 inventory items was selected.

Service-level sensitivity is item specific, but, for many items, statistical imprecision in the input data causes significant uncertainty in the service level. On the other hand, some items seem to be more resistant to variations in the input data than others.

The case approach, with one simulation model, limits the generalization of the results. The possibility that the simulation model is not totally realistic exists, due to the model's normality assumptions.

Margin of error in input data estimation causes a significant risk of not achieving the required service level. It is proposed that managers work to improve the preciseness of the data, while the sensitivity analysis against statistical uncertainty, and a correction mechanism if necessary, should be integrated into optimization models.

The output limitations in the optimization, i.e. service level, are typically stated precisely, but the capabilities of the input data have not been addressed adequately. This study provides valuable insights into ensuring the availability of critical materials.

The purpose of this paper is to study joint inventory and pricing strategy for a continuous inventory review system. While dynamic pricing decisions are often studied in the literature along with inventory management, the authors' aim in this study is to obtain a single long‐run optimal price; also to gain insight about how to obtain the optimal price and inventory control variables simultaneously and then the benefits of joint optimization of the inventory and pricing decisions over the sequential optimization policy often followed in practice.

A general (R;Q) policy system with fixed cost of ordering is modelled and then the case where unsatisfied demand is lost is studied. General forms of both the additive and multiplicative demand models are used to obtain structural results.

By showing optimality conditions on the price and inventory decision variables, two algorithms on how to obtain optimal decision variables, one for additive and another for multiplicative demand‐price model are provided. Through extensive numerical analyses, the potential profit increases are reported if the price and inventory problem are solved simultaneously instead of sequentially. In addition, the sensitivities of optimal decision variables to system parameters are revealed.

Although there are several studies in the literature investigating emergency price change models, they use arbitrary exogenous prices menus. However, the value of a price change can be better appreciated if the long‐run price is optimal for the system.

Very few researchers have investigated constant price and inventory optimization, and while there are several past studies demonstrating the benefits of dynamic pricing over a static one, there still are not many findings on the benefit of joint price and inventory optimization.

The purpose of this paper is to explore new methods to improve supply chain management in uncertain environment, more specifically, to tackle the uncertain demand problem and the inventory optimization problem faced by most supply chain systems.

The paper develops a multi‐objective inventory optimization model, which combines the classic grey prediction GM(1,1) model with the metaheuristic method. The former is applied to achieve the forecasting mechanism in supply chain operations, and the latter is applied to optimize the model solution.

Results show that the grey‐based forecasting mechanism performs better than other prediction methods, such as the double exponential smoothing method used in this paper. The solution of the multi‐objective inventory optimization model is also improved with the integration of grey prediction method. These indicate the importance of a forecasting mechanism in supply chain management.

The paper succeeds in constructing a novel inventory optimization model and in providing a novel supply chain management framework. It shows for the first time that grey prediction method combined with metaheuristic method may be a valid approach to supply chain management under uncertain environment.

The purpose of this paper is to model the distribution stage of the public distribution system (PDS) and optimize the inventory policy during this stage of the PDS to address some of the inefficiencies present in the system. This study models this supply chain as a multistage supply chain consisting of storage depots, issue centers, fair price shops and card holders.

A two-stage modeling approach is used to model the distribution stage in the PDS. In the first stage, the authors developed a simulation model for periodic review-based stock policy with appropriate assumptions. This helped minimize the total supply chain cost (TSCC). The TSCC consists of three cost elements, namely, ordering cost, holding cost and shortage cost. These three cost elements, in turn, depend on inventory policy parameters, such as review periods and base stock levels, at various echelons. In the second stage, a Genetic Algorithm based optimization approach was used.

A set of optimal policy parameters was identified. It is observed that base stock levels at issue centers are higher as compared to those in the FPS and the TSCC is less in scenario, when backorder cost is equal to the holding cost.

Present study will be useful to policy makers in improving PDS performance. This optimization of inventory policies helps actors in the PDS supply chain to choose appropriate policy parameters in the present inventory policy so as to reduce the overall distribution cost.

Unlike the previous researchers who examined the PDS from the social security perspective and tried to address specific problems to improve functioning of the PDS, this study looked at the problem as a supply chain-related problem and optimized the inventory parameters in one of the subsets of the PDS supply chain.

This project attempts to present a space component inventory classification system for space inventory replenishment and management. The authors propose to adopt a classification system that can incorporate all the different variables in a multi-criteria configuration. Fuzzy logic is applied as an effective way for formulating classification problems in space inventory replenishment.

A fuzzy-based approach with ABC classification is proposed to incorporate all the different variables in a multi-criteria configuration. Fuzzy logic is applied as an effective way for formulating classification problems in space inventory replenishment of the soil preparation system (SOPSYS) which is used in grinding and sifting Phobos rocks to sub-millimeter size in the Phobos-Grunt space mission. An information system was developed using the existing platform and was used to support the key aspects in performing inventory classification and purchasing optimization.

The proposed classification system was found to be able to classify the inventory and optimize the purchasing decision efficiency. Based on the information provided from the system, implementation plans for the SOPSYS project and related space projects can be proposed.

The paper addresses one of the main difficulties in handling qualitative or quantitative classification criteria. The model can be implemented using mathematical calculation tools and integrated into the existing inventory management system. The proposed model has important implications in optimizing the purchasing decisions to shorten the research and development of other space instruments in space missions.

Inventory management in the manufacture of space instruments is one of the major problems due to the complexity of the manufacturing process and the large variety of items. The classification system can optimize purchasing decision-making in the inventory management process. It is also designed to be flexible and can be implemented for the manufacture of other space mission instruments.

To develop a systematic procedure and a computerized tool for optimizing the delivery and inventory of materials, as part of a comprehensive material management system in construction projects.

A newly devised approach that employs genetic algorithms (GAs) for the optimization of material delivery schedules and their associated inventory control is presented. The approach is based on the project material requirement plans, and employs an objective function that minimizes the total costs associated with material deliveries. Furthermore, the computer system developed is used to examine and validate the adopted approach.

GA proved to be a satisfactory approach for optimizing material delivery schedules and its associated inventory levels. The selected case study particularly showed the system to produce material delivery plans that have reduced costs compared with their actual counterparts. Also, the computer processing time for developing the optimized plans was rather minimal, which promote its practical use.

The paper addresses one part of the comprehensive material management system; that is the optimization of the material delivery schedules and inventory control. Other future publications by the same authors will address the issues of probabilistic lead time calculations and development of material ordering schedules.

The paper partially fulfills a long‐sought research need for developing comprehensive material management systems specifically tailored to construction projects. The system takes into account several parameters that are not typically incorporated in the economic order quantity models for material management. Furthermore, practicality of the introduced system is augmented by the fact that it is interlinked with one of the most commonly used scheduling software.

This paper aims to introduce a novel structure for the physical internet (PI)–enabled sustainable supplier selection and inventory management problem under uncertain environments.

To address hybrid uncertainty both in the objective function and constraints, a novel interactive hybrid multi-objective optimization solution approach combining Me-based fuzzy possibilistic programming and interval programming approaches is tailored.

Various numerical experiments are introduced to validate the feasibility of the established model and the proposed solution method.

Due to its interconnectedness, the PI has the opportunity to support firms in addressing sustainability challenges and reducing initial impact. The sustainable supplier selection and inventory management have become critical operational challenges in PI-enabled supply chain problems. This is the first attempt on this issue, which uses the presented novel interactive possibilistic programming method.

Logistics is the part of the supply chain (SC) that plans, executes and handles forward and reverse movement and storage of products, services and related information, in order to respond to customers' needs effectively and efficiently. The main concern for logistics is to ensure that the correct product is placed at the right time. This paper introduces a linear model of shipping focused on decision-making, which includes configuration of shipping network, choosing of transport means and transfer of individual customer shipments through a particular transport system.

In this study, authors try to address the problem of supply chain network (SCN) where the primary goal is to determine the appropriate order allocation of products from different sources to different destinations. They also seek to minimize total transportation cost and inventory cost by simultaneously determining optimal locations, flows and shipment composition. The formulated problem of getting optimal allocation turns out to be a problem of multi-objective programming, and it is solved by using the max-addition fuzzy goal programming approach, for obtaining optimal order allocation of products. Furthermore, the problem demand and supply parameters have been considered random in nature, and the maximum likelihood estimation approach has been used to assess the unknown probabilistic distribution parameters with a specified probability level (SPL).

A case study has also been applied for examining the effectiveness and applicability of the developed multi-objective model and the proposed solution methods. Results of this study are very relevant for the manufacturing sector in particular, for those facing logistics issues in SCN. It enables researchers and managers to cope with various types of uncertainty and logistics risks associated with SCN.

The principal contribution of the proposed model is the improved modelling of transportation and inventory, which are affected by different characteristics of SCN. To demonstrate computational information of the suggested methods and proposed model, a case illustration of SCN is provided. Also, environmentalism is increasingly becoming a significant global concern. Hence, the concept proposed could be extended to include environmental aspects as an objective function or constraint.

Efficient integration of logistical cost components, such as transportation costs, inventory costs, with mathematical programming models is an important open issue in logistics optimization. This study expands conventional facility location models to incorporate a range of logistic system elements such as transportation cost and different types of inventory cost, in a multi-product, multi-site network. The research is original and is focused on case studies of real life.

The research mainly aims at the hotspot of inventory management by knowledge mapping and provides a visualization reference in this research field.

First, inventory management journals during 1986 to 2017 were selected as the research object and text formatting in the Web of Science (WOS) database is exported. Then inventory management knowledge mapping is done and clustering keywords are extracted by using CiteSpace and VOSviewer software. Based on co-word analysis, the three special clusters are exported: inventory optimization strategy, inventory pricing and inventory technology. Besides, the clustering structure and time evolution are analysed. Finally, bibliographic item co-occurrence matrix builder (BICOMB) was used to extract the “journal” and “researchers” keywords in the inventory management research fields. Setting three parameters such as the cited half-life, centrality, frequency and keywords for data mining, it can infer the trend keywords of future research.

Results showed that inventory management research has been abundant in literature over the past 30 years and has experienced a change from focusing on inventory optimization strategy to inventory pricing and inventory technology in process. It shows that inventory management research focused on the classic topics and includes economic order quantity, dynamic pricing, design and technology, and the new topics include channel coordination, hierarchical price and simulation.

Based on knowledge mapping, this study is still relatively macro and cannot cover all areas of inventory management. This study only investigated the state of correlational research in WOS and Google Trends and not additional databases.

The current research mainly builds on knowledge mapping for the research hotspot of inventory management and provides visual references for future research in this field.