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Background Chylothorax is a disease that has various causes such as neoplasm, infection, post-surgery trauma, congenital, and venous thrombosis. In approximately 15% of cases of chylothorax, the exact cause is unknown. We report a case of delayed occurrence of right chylothorax in a patient who had multiple segmental ribs fracture on his left side. Case presentation A 70-year-old Asian man had a “rollover” accident in which the cultivator he was driving overturned. He presented to our hospital with the main complaint of severe dyspnea. On chest computed tomography, multiple ribs fracture from the first to the eighth rib of the left side of his chest and left-sided hemopneumothorax were presented, but there was no evidence of fracture in the right side of his chest. After closed thoracostomy, an emergency operation for open reduction of fractured ribs was performed. On the fifth postoperative day, tubal feeding was performed. On the next day, a plain chest X-ray image showed pleural effusion of the right side of his chest. After insertion of a small-bore chest tube, 3390 ml of fluid for 24 hours was drained. The body fluid analysis revealed triglycerides levels of 1000 mg/dL, which led to a diagnosis of chylothorax. Although non-oral feeding and total parenteral nutrition were sustained, drain amount was increased on the fifth day. Surgical treatment (thoracoscopic thoracic duct ligation and pleurectomy) was performed in the early phase. The right chest tube was removed on the 14th postoperative day after the effusion completely resolved and he was uneventfully discharged. Conclusions In this case, as our patient was in old age and had multiple traumas, surgical treatment for chylothorax needed to be performed in the early phase.

We consider a healthcare facility location problem in which there are two types of patients, low-income patients and middle- and high-income patients. The former can use only public facilities, while the latter can use both public facilities and private facilities. We focus on the problem of determining locations of public healthcare facilities to be established within a given budget and allocating the patients to the facilities for the objective of maximizing the number of served patients while considering preference of the patients for the public and private facilities. We present an integer programming formulation for the problem and develop a heuristic algorithm based on Lagrangian relaxation and subgradient optimization methods. Results of computational experiments on a number of problem instances show that the algorithm gives good solutions in a reasonable computation time and may be effectively used by the healthcare authorities of the government.

This paper focuses on a scheduling problem in a manufacturing system composed of multiple parallel assembly lines. There are multiple orders to be processed in this system, and each order is specified by the product type, the number of products to be processed, and the due date. Each product is composed of two types of subassemblies, one unit of an external subassembly and one or more units of an internal subassembly. In the system, the parallel assembly lines are not identical, and certain lines are designated for certain product types. We present heuristic algorithms for the scheduling problem with the objective of minimizing total tardiness of orders. For an evaluation of the performance of the suggested algorithms, computational experiments are performed on a number of problem instances and results show that the suggested algorithms work better than the method used in a real manufacturing system.

This paper presents a model for integrating inventory replenishment and delivery planning in a two-level supply chain consisting of a supplier and a retailer. The supplier is authorized to manage the inventory level of the retailer by using the information on demands from final customers and the inventory level of the retailer. The problem considered here is to determine order-up-to levels of the supplier and the retailer simultaneously for the objective of minimizing the expected long-run average cost, which is composed of replenishment cost at the supplier, delivery cost to the retailer, and inventory holding costs at both members. We develop a renewal theoretic optimization model for a case of compound Poisson demands with distribution-free order quantities and present an approximation method for obtaining a solution of the problem. In addition, a closed-form solution is derived for the problem with a special case of compound Poisson demands, in which demand quantities follow an exponential distribution. A series of simulation tests show that the solutions obtained from the approximated cost functions are reasonably good.

In this paper, we exactly quantify the bullwhip effect, the variance amplification in replenishment orders, for cases of stochastic demand and stochastic lead time in a simple two-stage supply chain with one supplier and one retailer. In most of the previous research, the impact of order lead time on the bullwhip effect in supply chains with pre-specified demand processes is investigated mostly for cases of deterministic lead time. In this paper, we deal with a first-order autoregressive, AR(1), demand process and investigate the behavior of a measure for the bullwhip effect with respect to autoregressive coefficient and stochastic order lead time. Extension to a mixed first-order autoregressive-moving average, ARMA(1,1), demand process is also considered.

Uncertainties inherent in customer demands make it difficult for supply chains to achieve just-in-time inventory replenishment, resulting in loosing sales opportunities or keeping excessive chain-wide inventories. In this paper, we propose two adaptive inventory-control models for a supply chain consisting of one supplier and multiple retailers. The one is a centralized model and the other is a decentralized model. The objective of the two models is to satisfy a target service level predefined for each retailer. The inventory-control parameters of the supplier and retailers are safety lead time and safety stocks, respectively. Unlike most extant inventory-control approaches, modelling the uncertainty of customer demand as a statistical distribution is not a prerequisite in the two models. Instead, using a reinforcement learning technique called action-value method, the control parameters are designed to adaptively change as customer-demand patterns changes. A simulation-based experiment was performed to compare the performance of the two inventory-control models.

This paper focuses on facility layout problems with shape constraints. A heuristic algorithm is developed for the problems with the objective of minimizing the sum of rectilinear distances weighted by flow amounts between the facilities. The suggested algorithm is a simulated annealing algorithm in which a solution is encoded as a matrix that has information about relative locations of the facilities on the floor. A block layout is constructed by partitioning the floor into a set of rectangular blocks according to the information while satisfying the areas of the facilities. In this paper, three methods are suggested for the partitioning procedure and they are employed in the simulated annealing algorithm. The results of computational experiments show that the proposed algorithm performs better than two existing algorithms.

Loading problems in flexible manufacturing systems involve assigning operations for selected part types and their associated tools to machines or machine groups. One of the objectives might be to maximize the expected production rate (throughput) of the system. Because of the difficulty in dealing with this objective directly, a commonly used surrogate objective is the closeness of the actual workload allocation to the continuous workload allocation that maximizes throughput. We test several measures of closeness and discuss correlations between these measures and throughput. Using the best measure, we show how to modify an existing branch and bound algorithm which was developed for the case of equal target workloads for all machine groups to accommodate unequal target workloads. We also develop a new branch and bound algorithm which can be used for both types of problems. The efficiency of the algorithm in finding optimal solutions is achieved through the application of better branching rules and improved dominance results. Computational results on randomly generated test problems indicate that the new algorithm performs well.

System setup problems in flexible manufacturing systems deal with short-term planning problems such as part type selection, machine grouping, operation assignment, tooling, fixture and pallet allocation, and routing. In this article, we consider three of the subproblems: part type selection, machine grouping, and loading. We suggest a heuristic approach to solve the subproblems consistently with the objective of maximizing the expected production rate. The proposed procedure includes routines to generate all possible machine grouping alternatives for a given set of machines, to obtain optimal target workloads for each grouping alternative, and to allocate operations and tools to machine groups. These routines are executed iteratively until a good solution to the system setup problem is obtained. Computational experience is reported.