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1. Modular supply chain optimization considering demand uncertainty to manage risk

   https://doi.org/10.1002/aic.17367  

   Bhosekar, Atharv ; Badejo, Oluwadare ; Ierapetritou, Marianthi ( August 2021 , AIChE Journal)

   Supply chain under demand uncertainty has been a challenging problem due to increased competition and market volatility in modern markets. Flexibility in planning decisions makes modular manufacturing a promising way to address this problem. In this work, the problem of multiperiod process and supply chain network design is considered under demand uncertainty. A mixed integer two‐stage stochastic programming problem is formulated with integer variables indicating the process design and continuous variables to represent the material flow in the supply chain. The problem is solved using a rolling horizon approach. Benders decomposition is used to reduce the computational complexity of the optimization problem. To promote risk‐averse decisions, a downside risk measure is incorporated in the model. The results demonstrate the several advantages of modular designs in meeting product demands. A pareto‐optimal curve for minimizing the objectives of expected cost and downside risk is obtained.

    

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2. Automating the Search and Discovery of Manufacturing Service Providers to Enable a Digital Supply Chain Network

   https://doi.org/doi.org/10.1520/SSMS20200061  

   B. Starly, P. Cohen ( September 2020 , Smart and sustainable manufacturing systems)

   null (Ed.)

   Uncertainty in manufacturing networks has created barriers to closing the gap between design enterprises and the American industrial base. Uncertainty arises from the lack of transparent access to manufacturer capabilities, the inability to auto-discover service providers who are best capable for a given job request, and the dependence on human word-of-mouth trust network relationships that exist in the manufacturing supply chain. This uncertainty slows down the pace of product development lifecycles from a viewpoint of inefficient forms of supplier assessment, vetting, selection, and compliance, leading to a trust tax tacked onto the final price of products. In times of global crisis such as the coronavirus disease pandemic, this uncertainty also leads to inefficient forms of gathering information on manufacturing capability, available capacity, and registered licenses and assessing compliance. This technical note outlines solution pathways that can help ease the search and discovery process of connecting clients and manufacturing service providers through digitally enabled technologies. 

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3. Eye-Track Modeling of Problem-Solving in Virtual Manufacturing Environments

   Zhu, Rui ; Aqlan, Faisal ; Zhao, Richard ; Yang, Hui ( July 2021 , ASEE annual conference proceedings)

   null (Ed.)

   Problem-solving focuses on defining and analyzing problems, then finding viable solutions through an iterative process that requires brainstorming and understanding of what is known and what is unknown in the problem space. With rapid changes of economic landscape in the United States, new types of jobs emerge when new industries are created. Employers report that problem-solving is the most important skill they are looking for in job applicants. However, there are major concerns about the lack of problem-solving skills in engineering students. This lack of problem-solving skills calls for an approach to measure and enhance these skills. In this research, we propose to understand and improve problem-solving skills in engineering education by integrating eye-tracking sensing with virtual reality (VR) manufacturing. First, we simulate a manufacturing system in a VR game environment that we call a VR learning factory. The VR learning factory is built in the Unity game engine with the HTC Vive VR system for navigation and motion tracking. The headset is custom-fitted with Tobii eye-tracking technology, allowing the system to identify the coordinates and objects that a user is looking at, at any given time during the simulation. In the environment, engineering students can see through the headset a virtual manufacturing environment composed of a series of workstations and are able to interact with workpieces in the virtual environment. For example, a student can pick up virtual plastic bricks and assemble them together using the wireless controller in hand. Second, engineering students are asked to design and assemble car toys that satisfy predefined customer requirements while minimizing the total cost of production. Third, data-driven models are developed to analyze eye-movement patterns of engineering students. For instance, problem-solving skills are measured by the extent to which the eye-movement patterns of engineering students are similar to the pattern of a subject matter expert (SME), an ideal person who sets the expert criterion for the car toy assembly process. Benchmark experiments are conducted with a comprehensive measure of performance metrics such as cycle time, the number of station switches, weight, price, and quality of car toys. Experimental results show that eye-tracking modeling is efficient and effective to measure problem-solving skills of engineering students. The proposed VR learning factory was integrated into undergraduate manufacturing courses to enhance student learning and problem-solving skills. 

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4. A decomposition approach to determining fleet size and structure with network flow effects and demand uncertainty

   https://doi.org/10.1002/atr.1410  

   Wang, Yu ; Zhu, Jinfu ; Sun, Hong ( September 2016 , Journal of Advanced Transportation)

   This paper presents a new methodology to determine fleet size and structure for those airlines operating on hub‐and‐spoke networks. The methodology highlights the impact of stochastic traffic network flow effects on fleet planning process and is employed to construct an enhanced revenue model by incorporating the expected revenue optimization model into fleet planning process. The objective of the model is to find a feasible allocation of aircraft fleet types to route legs using minimum fleet purchasing cost, thus ensuring that the expected fleet profit is maximized subject to several critical resource constraints. By using a linear approximation to the total network revenue function, the fleet planning model with enhanced revenue modeling is decomposed into the nonlinear aspects of expected revenue optimization and the linear aspects of determining fleet size and structure by optimal allocation of aircraft fleet types to route legs. To illustrate this methodology and its economic benefits, an example consisting of 6 chosen aircraft fleet types, 12 route legs, and 57 path‐specific origin‐destination markets is presented and compared with the results found using revenue prorated fleet planning formulation. The results show that the fleet size and structure of the methodology proposed in this paper gain 211.4% improvement in fleet profit over the use of the revenue prorated fleet planning approach. In addition, comparison with the deterministic model reveals that the fleet size and structure of this proposed methodology are more adaptable to the fluctuations of passenger demands. Copyright © 2016 John Wiley & Sons, Ltd.

    

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5. Mapping local food self-sufficiency in the U.S. and the tradeoffs for food system diversity

   https://doi.org/10.1016/j.apgeog.2022.102687  

   Bingham, D. R. ; Rushforth, R. R. ; Stevens, B. ; Ruddell, B. L. ( April 2022 , Applied geography)

   The demand for ‘local food’ by U.S. consumers has grown markedly over the last several decades, accompanied by confusion over how to define local food. Is ‘local’ food defined by the location of the farm, food processing factory, distribution warehouse, or all three? Is ‘local’ food defined by geographic, political, or biophysical boundaries? Is ‘local’ solely farm-to-table or can it include factories? This study evaluates food commodity flow ‘localness’ using jurisdictional boundaries and physical distance to investigate the potential for food system transformation and the tradeoffs inherent to ‘localizing’ food production. We take a supply chain approach by making data-driven distinctions between farm-based flows of food and industrial, energy and nonfood (IENF) crops, and manufacturing/distribution flows of food and agriculturally-derived industrial inputs. We analyze the diversity, distance (a proxy for environmental impact), political boundaries, population, weight, and price (net selling value) of food commodity flows. The diversity of a community's food supply has an optimal range of zero to four-hundred miles. We find tradeoffs between food system diversity and local food sourcing, sustainability, and self-sufficiency. As communities look to improve food system resilience, they will need to balance food-miles and the other values associated with local food. 

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