Optimizing the Disposition and Retrograde of United States Air Force Class VII Equipment from Afghanistan

To meet the President\u27s established OPERATION ENDURING FREEDOM drawdown date of 1 December 2014, the United States Air Force, while continuing to conduct and support combat operations, must begin to plan disposition and execute retrograde its Class VII equipment. Calling upon concepts utilized in the management of closed-loop supply chains and optimization, this research proposes a multiple objective linear program to optimize the alignment of Bagram and Kandahar Air Base positioned equipment with in-garrison demand. Closed-loop supply chains provide planning process guidelines necessary for second use value creation and efficient reverse logistical flows. Optimization concepts provide the methodology for model development and output. The proposed multiple objective model provides solutions and equipment disposition instructions that minimize the deviations from the lowest total surface transportation cost and maximum average demand satisfaction values. To ensure compliance with Air Force guidance on equipment prioritization, cost-efficient transportation and maximum amounts of supply, multiple pre-process and model constraints limit the allocation of supply to demand bases. Combining situational specific user input values and constraints provides United States Air Force equipment managers the ability to test multiple courses of action for both real-time and future equipment movements

On the inventory routing problem with stationary stochastic demand rate

One of the most significant paradigm shifts of present business management is that individual businesses no longer participate as solely independent entities, but rather as supply chains (Lambert and Cooper, 2000). Therefore, the management of multiple relationships across the supply chain such as flow of materials, information, and finances is being referred to as supply chain management (SCM). SCM involves coordinating and integrating these multiple relationships within and among companies, so that it can improve the global performance of the supply chain. In this dissertation, we discuss the issue of integrating the two processes in the supply chain related, respectively, to inventory management and routing policies. The challenging problem of coordinating the inventory management and transportation planning decisions in the same time, is known as the inventory routing problem (IRP). The IRP is one of the challenging optimization problems in logis-tics and supply chain management. It aims at optimally integrating inventory control and vehicle routing operations in a supply network. In general, IRP arises as an underlying optimization problem in situations involving simultaneous optimization of inventory and distribution decisions. Its main goal is to determine an optimal distribution policy, consisting of a set of vehicle routes, delivery quantities and delivery times that minimizes the total inventory holding and transportation costs. This is a typical logistical optimization problem that arises in supply chains implementing a vendor managed inventory (VMI) policy. VMI is an agreement between a supplier and his regular retailers according to which retailers agree to the alternative that the supplier decides the timing and size of the deliveries. This agreement grants the supplier the full authority to manage inventories at his retailers'. This allows the supplier to act proactively and take responsibility for the inventory management of his regular retailers, instead of reacting to the orders placed by these retailers. In practice, implementing policies such as VMI has proven to considerably improve the overall performance of the supply network, see for example Lee and Seungjin (2008), Andersson et al. (2010) and Coelho et al. (2014). This dissertation focuses mainly on the single-warehouse, multiple-retailer (SWMR) system, in which a supplier serves a set of retailers from a single warehouse. In the first situation, we assume that all retailers face a deterministic, constant demand rate and in the second condition, we assume that all retailers consume the product at a stochastic stationary rate. The primary objective is to decide when and how many units to be delivered from the supplier to the warehouse and from the warehouse to retailers so as to minimize total transportation and inventory holding costs over the finite horizon without any shortages

Optimization strategies for the integrated management of perishable supply chains: A literature review

Purpose: The main purpose of this article is to systematically review the papers published in the period 2005-2020 about the integration of production, inventory and distribution activities in perishable supply chains. Design/methodology/approach: The proposed research methodology is based on several steps. First, database and keywords are selected, with the aim to search and collect the main papers, dealing with the integration of production, inventory, distribution activities in perishable supply chains. Then, a bibliometric analysis is carried out, to detect: the main publishing sources, the chronological distribution, the most used keywords, the featured authors, about the selected papers. A five-dimension classification framework is proposed to carry out a content analysis, where the papers of the literature review are classified and discussed, according to: supply chain structure, objective, perishability type, solution approach, approach validation. Findings: Interest in the application of optimization models for integrated decision-making along perishable supply chains is strongly growing. Integrating multiple stages of the supply chain into a single framework is complex, especially when referring to perishable products. The vast majority of the problems addressed are then NP-Hard. Only a limited quantity of the selected papers aims to solve real-life case studies. There is a need for further research, which is capable of modeling and quantitatively improving existing supply chains. The potentials of Industry 4.0 are currently little explored. Originality/value: Based on the analysis of the papers published, this article outlines the current state of the art on the optimization strategies for the integrated management of perishable supply chains, which are very complex to be managed. Research trends and gaps are discussed, future challenges are presentedPeer Reviewe

A blockchain-based framework for trusted quality data sharing towards zero-defect manufacturing

There is a current wave of a new generation of digital solutions based on intelligent systems, hybrid digital twins and AI-driven optimization tools to assure quality in smart factories. Such digital solutions heavily depend on quality-related information within the supply chain business ecosystem to drive zero-waste value chains. To empower zero-waste value chain strategies with meaningful, reliable, and trustful data, there must be a solution for end-to-end industrial data traceability, trust, and security across multiple process chains or even inter-organizational supply chains. In this paper, we first present Product, Process, and Data quality services to drive zero-waste value chain strategies. Following this, we present the Trusted Framework (TF), which is a key enabler for the secure and effective sharing of quality-related information within the supply chain business ecosystem, and thus for quality optimization actions towards zero-defect manufacturing. The TF specification includes the data model and format of the Process/Product/Data (PPD) Quality Hallmark, the OpenAPI exposed to factory system and a comprehensive Identity Management layer, for secure horizontal- and vertical quality data integration. The PPD hallmark and the TF already address some of the industrial needs to have a trusted approach to share quality data between the different stakeholders of the production chain to empower zero-waste value chain strategies.publishedVersio

An Optimized Resource Allocation Approach to Identify and Mitigate Supply Chain Risks using Fault Tree Analysis

Low volume high value (LVHV) supply chains such as airline manufacturing, power plant construction, and shipbuilding are especially susceptible to risks. These industries are characterized by long lead times and a limited number of suppliers that have both the technical know-how and manufacturing capabilities to deliver the requisite goods and services. Disruptions within the supply chain are common and can cause significant and costly delays. Although supply chain risk management and supply chain reliability are topics that have been studied extensively, most research in these areas focus on high vol- ume supply chains and few studies proactively identify risks. In this research, we develop methodologies to proactively and quantitatively identify and mitigate supply chain risks within LVHV supply chains. First, we propose a framework to model the supply chain system using fault-tree analysis based on the bill of material of the product being sourced. Next, we put forward a set of mathematical optimization models to proactively identify, mitigate, and resource at-risk suppliers in a LVHV supply chain with consideration for a firm’s budgetary constraints. Lastly, we propose a machine learning methodology to quan- tify the risk of an individual procurement using multiple logistic regression and industry available data, which can be used as the primary input to the fault tree when analyzing overall supply chain system risk. Altogether, the novel approaches proposed within this dissertation provide a set of tools for industry practitioners to predict supply chain risks, optimally choose which risks to mitigate, and make better informed decisions with respect to supplier selection and risk mitigation while avoiding costly delays due to disruptions in LVHV supply chains

Determination of Network Configuration Considering Inventory Cost in a Supply Chain

AbstractIn this paper we show the importance of applying mathematical optimization when designing the distribution network in a supply chain, specifically in making decisions related location of facilities and inventory management, which are associated with different levels of planning but are closely related.The addressed problem is an extension of the classic capacitated facility location problem. The distinguishing features are: the inventory management, the presence of multiple plants, and the single source constraints in both echelons. A key issue is that demand at each distribution center is a function of the demands at the retailers assigned, which is a random variable whose value is not known at the time of designing the network. We focus on the mathematical modeling of the problem and the evaluation of the performance of the developed models, so, it can be observed the troubles that arise when modeling supply chains that consider different types of decisions

Decision support for build-to-order supply chain management through multiobjective optimization

This paper aims to identify the gaps in decision-making support based on multiobjective optimization for build-to-order supply chain management (BTOSCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting multiobjective optimization (MOO) techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted and future research directions to better exploit the decision support capabilities of MOO are proposed