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The purpose of this paper is to investigate the total cost function of an inventory system with a reorder point/order quantity policy where the lead time is controllable based on the cost paid by the buyer for the service.

Cost functions are presented to investigate how the changes in lead time affect different components of inventory cost in the present of random demand. Two methods including an iteration technique and Simulated Annealing (SA) algorithm are presented to deal with the cost optimization issue. The application of proposed model is illustrated using numerical case scenarios.

The cost functions show that besides ordering cost, change in stochastic demand during lead time is the major factor that affects the other cost components such as holding and penalty costs. This finding is validated by numerical study. Results also show that performance of SA algorithm is highly similar to iteration methodology, while the former one is easier in application.

This paper develops less complex, more pragmatic methods, easily adoptable by logistics managers for cost minimization. This paper also analyzes and highlights the unique characteristics and features of these two approaches that can help practitioners in making the right choice when faced with the identified logistics issue.

This research explicitly investigate impacts of changing lead time on inventory cost components which enables informed decision making and inventory system planning for cost optimization by logistics practitioners. Two methodologies that can be easily used by practitioners without deep mathematical analysis and is cost effective are introduced to solve the optimization problem. Detailed roadmaps of how to implement proposed approaches have been illustrated by different case scenarios.

Almost all inventory models assume that lead time is prescribed and thus is not subject to control. In many practical situations, however, lead time is controllable; that is, lead time can be shortened, at the expense of extra costs, so as to improve customer service, reduce inventory investment in safety stocks, and improve system responsiveness. Although some authors recognise the advantage of short lead time and suggest that it should be considered a variable for management to control instead of a given, there is a lack of a suitable inventory model for determining the optimal lead time. A probabilistic inventory model in which the lead time is a decision variable is presented. It is assumed that the demand follows normal distribution and the lead time consists of n components each having a different cost for reduced lead time. The objective is to determine the lead time that minimises the sum of the expected holding cost and the additional cost.

The purpose of the study is to describe the implications of strategic lead times (SLTs) for return on investment (ROI).

This study was part of an interactive research project and is based on the logic of theory application leading to theory building. It uses a multiple case study with five holistic single cases. Empirical data (ED) have mainly been collected from interviews and focus groups.

The length of and uncertainty in SLTs have implications for companies' financial performance. These implications vary in strength and can be either direct or indirect. These findings are incorporated into a framework on SLTs' implications for ROI.

The presented array of SLTs' implications for ROI could be further investigated, focussing on their strength. Additionally, it would be interesting to substantiate the findings in the context of environmental and social sustainability (i.e. the triple bottom line).

The findings offer practitioners a rich description and understanding of SLTs' actual implications for financial performance in terms of ROI. This knowledge can support practitioners in analysing supply chain designs based on financial performance.

Using a combination of a relative financial performance measure (ROI) and a set of SLTs (systems perspective), this study focuses on SLTs' actual implications for ROI. The findings provide evidence that different sections of a supply chain can have different implications for revenue, cost and investment (i.e. the three absolute measures related to ROI).

Unprecedented endeavors have been made to take autonomous trucks to the open road. This study aims to provide relevant information on autonomous truck technology and to help logistics managers gain insight into assessing optimal shipment sizes for autonomous trucks.

Empirical data of estimated autonomous truck costs are collected to help revise classic, conceptual models of assessing optimal shipment sizes. Numerical experiments are conducted to illustrate the optimal shipment size when varying the autonomous truck technology cost and transportation lead time reduction.

Autonomous truck technology can cost as much as 70% of the price of a truck. Logistics managers using classic models that disregard the additional cost could underestimate the optimal shipment size for autonomous trucks. This study also predicts the possibility of inventory centralization in the supply chain network.

The findings are based on information collected from trade articles and academic journals in the domain of logistics management. Other technical or engineering discussions on autonomous trucks are not included in the literature review.

Logistics managers must consider the latest cost information when deciding on shipment sizes of road freight for autonomous trucks. When the economies of scale in autonomous technology prevail, the classic economic order quantity solution might again suffice as a good approximation for optimal shipment size.

This study shows that some models in the literature might no longer be applicable after the introduction of autonomous trucks. We also develop a new cost expression that is a function of the lead time reduction by adopting autonomous trucks.

This paper proposes a two-level supply chain including suppliers and manufacturers. The purpose of this paper is to design a resilient fuzzy risk-averse supply portfolio selection approach with lead-time sensitive manufacturers under partial and complete supply facility disruption in addition to the operational risk of imprecise demand to minimize the mean-risk costs. This problem is analyzed for a risk-averse decision maker, and the authors use the conditional value-at-risk (CVaR) as a risk measure, which has particular applications in financial engineering.

The methodology of the current research includes two phases of conceptual model and mathematical model. In the conceptual model phase, a new supply portfolio selection problem is presented under disruption and operational risks for lead-time sensitive manufacturers and considers resilience strategies for risk-averse decision makers. In the mathematical model phase, the stages of risk-averse two-stage fuzzy-stochastic programming model are formulated according to the above conceptual model, which minimizes the mean-CVaR costs.

In this paper, several computational experiments were conducted with sensitivity analysis by GAMS (General algebraic modeling system) software to determine the efficiency and significance of the developed model. Results show that the sensitivity of manufacturers to the lead time as well as the occurrence of disruption and operational risks, significantly affect the structure of the supply portfolio selection; hence, manufacturers should be taken into account in the design of this problem.

The study proposes a new two-stage fuzzy-stochastic scenario-based mathematical programming model for the resilient supply portfolio selection for risk-averse decision-makers under disruption and operational risks. This model assumes that the manufacturers are sensitive to lead time, so the demand of manufacturers depends on the suppliers who provide them with services. To manage risks, this model also considers proactive (supplier fortification, pre-positioned emergency inventory) and reactive (revision of allocation decisions) resilience strategies.

While numerous mathematical models of production‐inventory systems have been developed and are in operation there is no commonly accepted approach to guide the professional in managing the system effectively and comprehensively. A higher proportion of these models are not readily acceptable as they are strictly technical and canonical in form and content. This article attempts to develop a hybridised information system while recognising the existing operational problems and planning for the accommodation of change. The attempt here has been specifically to derive some factual premises to facilitate professionals to develop meaningful insights into the solution process.

This paper aims to propose a supply model of periodic review with joint replenishment for multi-products grouped by several variables with random and time dependence demand.

The products are grouped by multivariate cluster analysis. The stochastic inventory model describes the random demand of each product, considering the temporal dependency through a generalized autoregressive moving average model. Stochastic programming for the total cost of inventory is obtained considering the expected value of the demand per unit of time.

The total costs for the products grouped with the proposed model are 6% lower than for the individual inventory policy. The expected shortage units decrease significantly in the proposed grouped model with temporary dependence. In addition, the proposal with temporary dependency has lower costs than when the independent and identically distributed demand is considered.

The proposed policy is exemplified with real-world data from a Chilean hospital, where the products (drugs) are segmented by grouping variables, forming clusters of drugs with homogeneous behavior within the groups and heterogeneous behavior between groups.

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.

Supply chains (SCs) have been growingly virtualized in response to the market challenges and opportunities that are presented by new and cost-effective internet-based technologies today. This paper designed a virtual closed-loop supply chain (VCLSC) network based on multiperiod, multiproduct and by using the Internet of Things (IoT). The purpose of the paper is the optimization of the VCLSC network.

The proposed model considers the maximization of profit. For this purpose, costs related to virtualization such as security, energy consumption, recall and IoT facilities along with the usual costs of the SC are considered in the model. Due to real-world demand fluctuations, in this model, demand is considered fuzzy. Finally, the problem is solved using the Grey Wolf algorithm and Firefly algorithm. A numerical example and sensitivity analysis on the main parameters of the model are used to describe the importance and applicability of the developed model.

The findings showed that the Firefly algorithm performed better and identified more profit for the SC in each period. Also, the results of the sensitivity analysis using the IoT in a VCLSC showed that the profit of the virtual supply chain (VSC) is higher compared to not using IoT due to tracking defective parts and identifying reversible products. In proposed model, chain members can help improve chain operations by tracking raw materials and products, delivering products faster and with higher quality to customers, bringing a new level of SC efficiency to industries. As a result, VSCs can be controlled, programmed and optimized remotely over the Internet based on virtual objects rather than direct observation.

There are limited researches on designing and optimizing the VCLSC network. This study is one of the first studies that optimize the VSC networks considering minimization of virtual costs and maximization of profits. In most researches, the theory of VSC and its advantages have been described, while in this research, mathematical optimization and modeling of the VSC have been done, and it has been tried to apply SC virtualization using the IoT. Considering virtual costs in VSC optimization is another originality of this research. Also, considering the uncertainty in the SC brings the issue closer to the real world. In this study, virtualization costs including security, recall and energy consumption in SC optimization are considered.

1. Investigates the role of IoT for virtual supply chain profit optimization and mathematical optimization of virtual closed-loop supply chain (VCLSC) based on multiperiod, multiproduct with emphasis on using the IoT under uncertainty.

2. Considering the most important costs of virtualization of supply chain include: cost of IoT information security, cost of IoT energy consumption, cost of recall the production department, cost of IoT facilities.

3. Selection of the optimal suppliers in each period and determination of the price of each returned product in virtual supply chain.

4. Solving and validating the proposed model with two meta-heuristic algorithms (the Grey Wolf algorithm and Firefly algorithm).

Investigates the role of IoT for virtual supply chain profit optimization and mathematical optimization of virtual closed-loop supply chain (VCLSC) based on multiperiod, multiproduct with emphasis on using the IoT under uncertainty.

Considering the most important costs of virtualization of supply chain include: cost of IoT information security, cost of IoT energy consumption, cost of recall the production department, cost of IoT facilities.

Selection of the optimal suppliers in each period and determination of the price of each returned product in virtual supply chain.

Solving and validating the proposed model with two meta-heuristic algorithms (the Grey Wolf algorithm and Firefly algorithm).

The purpose of this paper is to formulate a coordinated inventory control model for growing items in a supply chain with farming, processing and retail operations. The farmer grows newborn items and then delivers them to a processor once the items mature. At the processing plant, the items are slaughtered, cut and packaged at a specified rate. The processor then delivers a certain number of equally sized shipments of processed items to a retailer who satisfies customer demand.

A cost minimisation inventory model describing the problem at hand is formulated with the number of shipments and the cycle time being the decision variables. A solution algorithm for solving the problem is presented and applied to a numerical example.

Opting for an integrated policy is favourable to all supply chain members. When the proposed model is compared to equivalent independent and equal-cycle time replenishment policies, the total cost savings amount to 3 and 14 per cent, respectively.

The model can serve as a guideline for procurement managers dealing with growing items to better their inventory management practices. Considerable cost savings in food production chains can be achieved through improved inventory control, and these savings can be used to cushion consumers against rising food prices.

Most previously published models on inventory management for growing items were formulated under the assumption that the items are grown and then sold to consumers instantaneously. In real food production systems, the items need to be transformed and packaged into a consumable form before customer demand is met. The model presented in this paper accounts for this and is therefore more realistic.