End-of-Life (EOL) products can be recovered using various processes such as disassembly, reuse, recycling and remanufacturing. A facility receives EOL products from suppliers and performs various operations to fulfill the component and material demands. Depending on the design of the EOL product, the parameters such as total time required for disassembly, cost of remanufacturing and ease of retrieving its components will be different. In this paper,we consider EOL products coming from two different suppliers. A supplier is selected based on the total profit earned by remanufacturing, the quality of remanufactured products, the material sales revenue and the total disposal weight. Partitioning algorithm is used to solve the model formulated by goal programming.

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In product recovery the disassembly process has an important role since it allows for separation and retrieval of desired parts and materials. End-of-life (EOL) products with missing and/or nonfunctional components increase the uncertainty associated with disassembly yield. Sensor-embedded products (SEPs) eliminate a majority of uncertainties involved in EOL management by providing life-cycle information of products. This information includes the content of each product and component conditions, and enables the estimation of remaining useful life of the components. Once the data on the products are captured, it is possible to make optimal EOL decisions without any preliminary disassembly or inspection operations. This paper presents an Advanced Remanufacture-To-Order, Disassembly-To-Order and Refurbishment-To-Order (ARTODTORTO) model with disassembly precedence relationships among components of an air conditioner (AC). It also inspects and analyzes the impact of using smart sensors in End-of-Life products (EOLPs) on system performance. Various experimental design studies are conducted based on orthogonal arrays (OAs). The customers' demands may be satisfied either by purchasing new components, reassembling components from the returned used products, refurbishing products, or remanufacturing used products based on customers' needs. Discrete event simulation models are used to calculate various performance measures under different experimental conditions.

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This paper proposes a mathematical model of multi-period food supply chain optimization maximizing the total profit based on the data-driven approach from the information system. Our propose model is focused on accommodating surplus foods among stores in a regional area. Our proposed model is formulated as a stochastic programming problem. With respect to a previous model, the explicit optimal ordering quantity at each time slot is obtained. However,it is hard to solve the formulated problem directly. Therefore, a scenario-based approach derived from POS data using the information system and deterministic equivalent transformations are introduced to apply our proposed model.

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Recently, supply chains have come to be used globally, not only in developed countries such as Japan but also in emerging countries such as China. Supplier selections are one of the key decisions to be made in a strategic planning of the supply chains, where the manufacturers for assembly products have to select the suppliers that are appropriate for their specific purposes, especially for lower procurement costs of parts. Additionally, global warming is worsening due to increased greenhouse gases (GHG), CO₂ being the major culprit. In order to achieve a low-carbon society that is against global warming, it is necessary to reduce the CO₂ emissions across the globe by means of a low-carbon supply chain; before that, the CO₂ emissions in the supply chains should be visualized and used for any decision making in each process design. However, it is often difficult for assembly companies to share their environmental and cost information with their suppliers since there are other companies in the supply chains. For visualizing the CO₂ emissions,“Life Cycle Assessment (LCA) ”and“Life Cycle Inventory (LCI) Database ”have been established. According to the LCI database, the CO₂ emissions of the parts depend on types of materials and weights. Therefore, it is important to select the suppliers considering the types of materials and the weights for each part, in order to reduce not only the procurement cost but also the CO₂ emissions. This study proposes a low-carbon supplier selection method with an estimation of CO₂ emissions and cost based on material analysis, so as to aim to achieve both the procurement cost minimization and the CO₂ emissions reduction using the LCI database.

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Several mathematical optimization models for agricultural management have been proposed and developed. Crop planning model is one of these models that maximizes the farmers ’incomes by finding an optimum assignment of crops in their farmlands. Although the crop planning model is formulated as a linear programming problem and considers cultivation areas (sizes) for objective crops, it does not indicate the assignment location of crops. In addition,the problem deals with the farmer income in a certain season. In many actual cases, if we continue to cultivate a crop at the same farmland for some seasons, the harvest will change with the season because of replant failures, i.e., the optimum assignment is not applicable for all seasons. Therefore, we have to formulate more useful models that consider multiple seasons. In this paper, we propose a mathematical model that discusses crop assignment in a farmland and a multi-period crop rotation programming problem with replant failure effects to develop an innovative crop planning model. Furthermore, we try to describe the harvest transition using a network and discuss methods that yield an optimum crop rotation sequence using network programming and quality-control techniques.

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