This paper investigates and compares the bene?ts of sensor embedded cell phones (SECPs) over conventional cell phones (CCPs) in product recovery. Sensors embedded into cell phones during the production process are important data acquisition tools for condition monitoring during the product life cycle. SECPs provide reduction in the costs of disassembly, inspection, inventory, and stock-outs. With the data captured from sensors, the recovery system is able to detect failed or missing components in SECP before disassembling it. In addition, the captured data can be used to estimate the remaining lives of components and cell phones. In order to ascertain the impact of sensors on the performance of processes involving disassembly and remanufacturing, two separate and detailed discrete event simulation case studies with experiments based on orthogonal arrays (OAs) are carried out for the CCPs and the SECPs. The results are then compared for the two cases. Results con?rm that sensors are greatly bene?cial in reducing the processing costs. The paper concludes by determining the extent of the bene?t of using sensors which provide an upper limit of the cost of the sensors for the SECPs to remain viable.

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This paper considers a product recovery facility which receives sensors and Radio Frequency Identi?cation (RFID) tags embedded End-Of-Life (EOL) products. Sensors and RFID tags acquire components’ dynamic and static life cycle data during their economic lives. The captured life cycle data provides information about products’ contents and conditions. The information also helps to determine the remaining lives of products and components which are then translated into quality levels. The example considered here presents an advanced-repair-to-order-disassembly-to-order system. It disassembles the components to meet the components’ demands, repairs the products to meet the products’ demands and recycles the materials to meet the materials’ demands. The received EOL products may have different design alternatives. The EOL product of any design can be utilized to ful?ll the components’, products’, and materials’ demands. The objective of the proposed goal programming model is to determine how to process the EOL products in order to meet the components products’ and materials ’demands.

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Though service sector in Japan has developed, number of traditional inns (ryokan) has declined over the last 30 years. One of the reasons for this decline is that many ryokan managers tend to rely more on experience and intuition than on data analysis. Moreover, few studies have focused on hotel inventory control. Therefore, considering that safety stock levels and inventory costs have become crucial factors in the hotel industry, this study proposes methods for determining an appropriate level of safety stock using demand forecasting that considers the peculiarities of the hotel business. It applies multiple regression analysis and neural networks to forecast beer demand based on sales data from company A, a traditional Japanese inn. This study also compares the proposed safety stock level with that of company A. Furthermore, it provides an empirical analysis of inventory management in the hotel business. The results not only demonstrate that the reorder criteria determined by the multiple regression analysis and ANN are superior to the method currently used by company A but also verify the efficacy of the proposed methods.

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This study investigates vehicle operating time as a constraint within the vehicle routing problem (VRP) with multiple trips (VRPMT). In the basic VRP, a single route is assigned to each vehicle and the solution must satisfy both the load and distance (or travel time) constraints. However, in the real-world problem, one vehicle is assigned to multiple routes per day. Since this becomes a large-scale combinatorial optimization problem, it becomes difficult to ?nd an exact solution. Therefore, in previous research, a heuristic method using a two-phase algorithm was proposed. However, since the precision of the two-phase algorithm is greatly in?uenced by the solution selected for the ?rst phase, selection of solutions in this phase is crucial. In this study, the advantages of the one- and two-phase methods are integrated in a new proposed method.

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For environmentally conscious manufacturing, end-of-life (EOL) assembly products should be disassembled for reuse and recycling. In this remanufacturing, the disassembly systems essentially consist of disassembly and sorting processes, and the product sorting process involves reverse blocking which impacts and reduces the total productivity of the remanufacturing. This paper models and analyzes the sorting process in the disassembly system with reverse blocking in consideration of traffic and buffer capacities by a queueing theory. First, the traffic and buffer capacities in the sorting stations are modeled on a sorting queueing system,and the evaluation functions of this system are set as the reverse blocking probability and the system throughput. Next, a transition diagram of the system is drawn, and the stationary state equations of the system are generally formulated with the traffic and buffer capacities. Finally, the system performance is discussed in terms of the buffer capacities, the system balancing among arrival, sorting and disassembly abilities, and a buffer allocation plan.

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