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Introduction Immature teratomas are very rare tumors, representing only 1% of ovarian cancers. Distant metastases to the liver effect late disease manifestation, rendering chemotherapy the only viable option. Case presentation Here, we report the successful surgical management of stage IVB immature teratoma after cisplatin-based chemotherapy. A 20-year-old woman presented with a huge abdominal palpable mass and dyspnea. Postoperative pathology confirmed immature teratoma with metastases to the liver, diaphragm, and thoracic cavity (stage IVB). The patient underwent right hemihepatectomy, diaphragm resection, thoracic mass resection, and diaphragm repair using aortic artificial graft. She did not receive postoperative adjuvant chemotherapy. After the complete surgical resection of the tumor, the patient had no recurrence during the 44-month follow-up period. Conclusion No established treatment modalities have been developed for further treatment, once the first-line combination chemotherapy achieves unfavorable results in stage IVB immature teratoma. Surgical resection may offer hope for excellent disease control in this dismal stage. To obtain best possible outcomes, coordinated care between oncologists and general surgeons is required.

Despite the lack of high-level evidence, laparoscopic distal pancreatectomy (LDP) is frequently performed in patients with pancreatic ductal adenocarcinoma (PDAC) owing to advancements in surgical techniques. The aim of this study was to investigate the long-term oncologic outcomes of LDP in patients with PDAC via propensity score matching (PSM) analysis using data from a large-scale national database.A total of 1202 patients who were treated for PDAC via distal pancreatectomy across 16 hospitals were included in the Korean Tumor Registry System-Biliary Pancreas. The 5-year overall (5YOSR) and disease-free (5YDFSR) survival rates were compared between LDP and open DP (ODP).ODP and LDP were performed in 846 and 356 patients, respectively. The ODP group included more aggressive surgeries with higher pathologic stage, R0 resection rate, and number of retrieved lymph nodes. After PSM, the 5YOSRs for ODP and LDP were 37.3% and 41.4% (p = 0.150), while the 5YDFSRs were 23.4% and 27.2% (p = 0.332), respectively. Prognostic factors for 5YOSR included R status, T stage, N stage, differentiation, and lymphovascular invasion.LDP was performed in a selected group of patients with PDAC. Within this group, long-term oncologic outcomes were comparable to those observed following ODP.

Gallbladder paraganglioma (GP) is a rare tumor, with only 12 cases reported in the literature to date. Due to its rarity, clinical information of GP is insufficient. We present a case of GP in a 48-year-old female along with a literature review of all GP cases described to date. A 48-year-old female presented with intermittent right upper abdominal pain. Preoperative imaging revealed a hematoma in the gallbladder lumen without any definite etiology. Laparoscopic cholecystectomy was performed. Gross examination of the gallbladder revealed multiple small stones and a large hematoma as well as a 1.6-cm-sized polypoid mass at the gallbladder fundus. Microscopic study of the polypoid mass showed a zellballen appearance. Immunohistochemical analysis revealed that the mass was positive for synaptophysin, CD56, and chromogranin, suggesting GP. GP is difficult to diagnose because of non-specific clinical findings. Almost all GP cases are diagnosed based on histologic findings after cholecystectomy. Simple cholecystectomy was performed as a treatment in all reported cases of GP, including our case. There was no postoperative tumor recurrence or metastasis after surgery.

The goal of the present study was to evaluate the prognostic value of lymph node ratio (LNR) in distal cholangiocarcinoma (DCC) after curative intended surgery.Clinicopathological data of 162 DCC patients who underwent radical intended surgery between 2012 and 2020 were analyzed retrospectively. Prognostic factors related to overall survival (OS) and disease-free survival (DFS) were evaluated.Median OS time and DFS time were 41 and 29 months, and 5-year OS rate and DFS rate were 44.7% and 38.1%, respectively. In the univariate analysis, significant prognostic factors for OS were histologic differentiation, American Joint Committee on Cancer (AJCC) stage, positive lymph node count, LNR, R1 resection, and perineural invasion. Preoperative carcinoembryonic antigen, carbohydrate antigen 19-9, infiltrative type, histologic differentiation, AJCC stage, positive lymph node count, LNR, R1 resection, perineural invasion, and lymph-vascular invasion were significant prognostic factors for DFS in the univariate analysis. In the multivariate analysis, histologic differentiation, R1 resection, and LNR were the independent prognostic factors for both OS and DFS. The LNR ≥ 0.2 group had a significantly poor prognosis in terms of OS (hazard ratio, 3.915;LNR has significant value as a prognostic factor of DCC related to OS and DFS. LNR has the potential to be used as a modified staging system with furthermore studies.

Backgrounds/Aims The purpose of this study is to demonstrate that laparoscopic distal pancreatectomy in benign disease is is safer and more favorable to patients than open distal pancreatectomy. Methods We retrospectively reviewed data of 150 patients who underwent laparoscopic (n=69) or open (=81) distal pancreatectomy at a double institutes from 2008 to 2018. We reviewed each patient's history for age, sex, pathologic diagnosis. Specific outcomes that were included hospital stay, operative time (in minutes), operative blood loss (in milliliters), 30-daymajormorbidity and mortality (Clavian-Dindo classification), pancreatic leak rate (grade of leak A, B, or C), pancreatic hemorrhage. Results From 2008 to 2018, there were 150 patients underwent distal pancreatectomy with or without splenectomy for benign pancreatic disease. 81 patients underwent open and 69 patients underwent laparoscopic distal pancreatectomy (LDP) Intra-operative estimated blood loss was significantly lower in the LDP group than in the OPD group (200 vs. 400 ml p

District heating system is designed to minimize energy consumption and environmental pollution by employing centralized production facilities connected to demand regions. Traditionally, optimization based algorithms were applied to the heat production planning problem in the district heating systems. Optimization-based models provide near optimal solutions, while it takes a while to generate solutions due to the characteristics of the underlying solution mechanism. When prompt re-planning due to any parameter changes is necessary, the traditional approaches might be inefficient to generate modified solutions quickly. In this study, we developed a two-phase solution mechanism, where deep learning algorithm is applied to learn optimal production patterns from optimization module. In the first training phase, the optimization module generates optimal production plans for the input scenarios derived from operations history, which are provided to the deep learning module for training. In the second planning phase, the deep learning module with trained parameters predicts production plan for the test scenarios. The computational experiments show that after the training process is completed, it has the characteristic of quickly deriving results appropriate to the situation. By combining optimization and deep learning modules in a solution framework, it is expected that the proposed algorithm could be applied to online optimization of district heating systems.

With the growing interest and popularity of electric vehicles (EVs), the electrification of buses has been progressing recently. To achieve the seamless operation of electric buses (e-Buses) for public transportation, some bus stations should play the role of battery swapping station due to the limited travel range of e-Buses. In this study, we consider the problem of locating battery swapping stations for e-Buses on a passenger bus traffic network. For this purpose, we propose three integer programming models (set-covering-based model, flow-based model and path-based model) to model the problem of minimizing the number of stations needed. The models are applied and tested on the current bus routes in the Seoul metropolitan area of South Korea.

Kidney exchange programs, which allow a potential living donor whose kidney is incompatible with his or her intended recipient to donate a kidney to another patient in return for a kidney that is compatible for their intended recipient, usually aims to maximize the number of possible kidney exchanges or the total utility of the program. However, the fairness of these exchanges is an issue that has often been ignored. In this paper, as a way to overcome the problems arising in previous studies, we take fairness to be the degree to which individual patient-donor pairs feel satisfied, rather than the extent to which the exchange increases social benefits. A kidney exchange has to occur on the basis of the value of the kidneys themselves because the process is similar to bartering. If the matched kidneys are not of the level expected by the patient-donor pairs involved, the match may break and the kidney exchange transplantation may fail. This study attempts to classify possible scenarios for such failures and incorporate these into a stochastic programming framework. We apply a two-stage stochastic programming method using total utility in the first stage and the sum of the penalties for failure in the second stage when an exceptional event occurs. Computational results are provided to demonstrate the improvement of the proposed model compared to that of previous deterministic models.

For companies facing challenge of managing unreliable supply sources, one of the operational goals is to increase the overall profit by improving performance of uncertain supply. We here develop stochastic decision frameworks which evaluate necessary information about uncertainties and help newsvendor and suppliers to enhance supply reliability. We first analyze the newsvendor’s purchasing decision under supply uncertainty and the derived decision framework is used to identify stochastic dominance conditions. We found that partial supply risk information is sufficient to determine regular ordering quantity, but to improve the overall profit, it is important to gather more information about stochastic dominance conditions.

Production design is a key decision problem of steel making industry but due to its complexity, solution properties are not well understood. The design problem can be viewed as a multi-stage problem of bin packing, matching and sequencing sub-problems. Traditionally a sequential approach which treats each sub-problem separately has been applied to the simple case where mould width change is not allowed. However, for more general cases where casting with width change and different width is allowed, the sequential approach fails to generate good solutions. In this paper, we propose a mathematical programming formulation which can solve the design problem in an integrative way. By introducing multi-layer network representation of the cast design problem, it is now possible to generate an integrated formulation for the proposed problem. Based on the formulation, we derive a heuristic algorithm. The algorithm is adopted and tested at a large international integrated steel mill. The computational tests with rea...

This paper considers a problem of locating both distribution centers and retailers in a zone-dependent two-level distribution network where either a distribution center or a retailer should be located in each zone. Customer demands of each zone should be satisfied directly from either its own distribution center or its own retailer being supplied from a distribution center of another zone. The objective of the proposed problem is to minimize total cost being composed of distribution center/retailer setup costs and transportation costs. In the analysis, the problem is proved to be NP-hard, so that a branch-and-bound algorithm is derived for the problem. Numerical experiments show that the proposed branch-and-bound algorithm provides the optimal solution efficiently for some small problems.

This paper considers logistics decision support system which deals with transportation mode selection considering transportation and carbon emission cost. Transportation and carbon emission costs vary with the choice of transportation modes and to become competitive companies need to find proper transportation modes for their logistics services. However, due to the restricted capacity of transportation modes, it is difficult to balance transportation and carbon emission costs when designing logistics network including transportation mode choice for each service. Therefore this paper aims to analyze the trade-off relationship between transportation and carbon emission cost in mode selection of intermodal transportation and to provide optimal green logistics strategy. In this paper, the logistics decision support system is designed based on mixed integer programming model. To understand the trade-off relationship of transportation and carbon emission cost, the system is tested with various scenarios including transportation of containers between Seoul and Busan. The analysis results show that, even though sea transportation combined with trucking is competitive in carbon emission per unit distance travelled, the total cost of carbon emission and transportation for the sea transportation may not have competitive advantage over other transportation modes including rail and truck transportation modes. The sea-based intermodal logistics service may induce detours which have negative impacts on the overall carbon emission. The proposed logistics decision support system is expected to play key role in green logistics and supply chain management.

District heating is a system of distributing heated water from centralized facilities to local homes and buildings. In this paper, we model the distribution planning problem as a supply chain planning problem and propose an explicit column generation algorithm to handle large-scale data and nonlinear constraints. The algorithm is successfully applied to a Korean district heating company and computational experiments show that the integrated operation of the district heating network increases the total profit compared to previous isolated networks.

This paper addresses one of the key operational decision problems in the steel industry which is related to the allocation of orders to stock materials in the surplus inventory. The meta slab allocation problem can be stated as the ‘multi-stage multiple knapsack problem’ where the problem is to design slabs using orders with similar properties (the first stage) and allocating these designed slabs into the existing meta slabs in the inventory yard (the second stage). The objective of the problem is to maximise the allocated order weights in slabs and meta slabs. For the given problem, we propose a column generation algorithm, where a column in the master problem represents meta slabs and the sub problem is to design ‘bins’, here slabs with the given orders. The sub problem itself also becomes a multiple knapsack problem and we proposed a practical set-partitioning heuristic. The proposed algorithm was tested with daily operation data given from an integrated steel company in the Asia Pacific. The computati...

We investigate how the choice of coopetition of the simultaneous pursuit of collaboration and competition dynamically impacts both the participating firms and also the other self-developing ones in the same market. A conceptual framework of mathematical models obtained from the arguments and insights in the literature is used to undertake an in-depth study through a multiperiod analysis from 2013 to 2020 of an exemplar case of coopetition, the two concurrently ongoing coopetition partnerships in the US electric vehicle (EV) market, the Tesla Motors-Daimler AG alliance and the Tesla Motors-Toyota alliance and the other firms which are not involved in coopetition.

This paper considers an integrated service network design problem for a given set of freight demands that is concerned with integration of locating cross-docking (CD) centers and allocating vehicles for the associated direct (transportation) services from origin node to a CD center or from a CD center to the destination node. For the vehicle allocation, direct services (sub-routes) should be determined for the given freight demands, and then the vehicle allocation has to be made in consideration of routing for the associated direct service fulfillment subject to vehicle capacity and service time restriction. The problem is modeled as a path-based formulation for which a tabu-search-based solution algorithm is proposed. To guarantee the performance of the proposed solution algorithm, strong valid inequalities are derived based on the polyhedral characteristics of the problem domain and an efficient separation heuristic is derived for identifying any violated valid inequalities. Computational experiments are performed to test the performance of the proposed solution algorithm and also that of a valid-inequality separation algorithm, which finds that the solution algorithm works quite well and the separation algorithm provides strengthened lower bounds. Its immediate application may be made to strategic (integrated) service network designs and to tactical service network planning for the CD network.

This paper considers a combined problem of establishing virtual paths (VPs) and routing traffic (packet) demands through the virtual paths in a layered communication network where each physical link is subject to its capacity constraints. The problem is modeled as a path-based formulation for which a branch-and-price solution algorithm is derived. The solution algorithm is composed of an efficient pricing algorithm, and also a branching rule based on a variable dichotomy, which does not destroy the structure of the associated pricing problems. Computational experiments are performed to test the efficiency of the algorithm, which show that the algorithm works quite well in finding optimal solutions (for the test instances) within reasonable time. Its immediate application may be made to a centralized network management on mid-term global reconfiguration and long-term VP planning.

This paper considers a multiperiod vehicle lease planning problem for urban freight consolidation centers (UFCCs) in the urban freight transport network where short-term-leased and long-term-leased vehicles are hired together. The objective is to allocate the two kinds of leased vehicles optimally for direct transportation services from the associated origin node to the associated UFCC or from the associated UFCC to the associated destinations so as to satisfy a given set of period-to-period freight demands over a given planning horizon at total minimum vehicle allocation cost subject to demand-dependent transportation time restriction. The problem is formulated as an integer programming model and proven to be NP-hard in a strong sense. Thus, a Lagrangian heuristic is proposed to find a good solution efficiently. Numerical experiments show that the proposed algorithm finds good lower and upper bounds within reasonable time.

Two distributed heuristic clustering schemes are proposed that will minimize the required transmission power in two-tiered mobile ad hoc networks. Both schemes can be implemented and executed in real time and can be adopted for periodic or event-driven cluster reconfiguration. Scheme performance is simulated and compared with optimum configurations based on the mean transmission power and the call drop rate as performance measures. Numerical results show that the proposed schemes deliver performance similar to optimum results.

Two distributed heuristic clustering schemes are proposed which will minimize the required transmission power in two-tiered mobile ad-hoc networks. Both schemes can be implemented and executed in real-time and can be adopted for periodic or event-driven cluster reconfiguration. Scheme performance is simulated and compared with optimum configurations based on the mean transmission power and the call drop rate as performance measures. Numerical results show that the proposed schemes deliver performance similar to optimum results.

Two distributed heuristic clustering schemes for multicasting are proposed to minimize the transmission power in 2-tiered mobile ad-hoc networks. The proposed schemes are implementable, and the average consumed power per a master node in cases of using the proposed schemes are only slightly higher than the optimal results.

A distributed heuristic clustering scheme for multicasting is proposed to minimise transmission power in two-tier mobile ad-hoc networks. When the proposed scheme is implemented the average consumed power per master node is only slightly higher than the optimum result.

A new distributed heuristic clustering scheme that minimises the required transmission power in two-tiered mobile ad hoc networks is proposed. This scheme, which can be implemented and executed in real-time, delivers performance similar to optimum results.