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In view of the complicated information of wargaming system, which is not conducive for users to understand the combat situation, a trajectory clustering algorithm CTUW (clustering trajectories of units in wargame) based on space-time and combat grouping is proposed. The algorithm is divided into four parts: trajectory compression, similarity measurement, trajectory segments’ clustering and visualization. The main content can be extracted from the complicated track information and summarized, so as to achieve the purpose of concise overview of the overall situation changes of the military chess deduction without losing the important details in the process of chess maneuver.The experiment shows that the trajectory clustering effect of the CTUW algorithm is more refined than that of the TRACLUS algorithm and the CTECW algorithm, and the computational complexity is lower. It can maintain a good clustering effect even when dealing with special trajectory data with abnormal maneuvering speed, special trajectory shape and trajectories overlap.

We study a shipment consolidation problem commonly faced by companies that outsource logistics operations and operate in a commit-to-delivery mode. It involves delivering a given set of orders to their destinations by their committed due times using multiple shipping methods at the minimum total shipping and inventory cost. The shipping cost is generally nonlinear in shipping quantity and can be represented by a subadditive piecewise linear function. We investigate two shipping scenarios, one involving long-haul shipping only and the other involving joint long-haul and short-haul shipping. We develop analytical results and solution algorithms for the shipment consolidation problem under each shipping scenario. The problem under the first shipping scenario is shown to be strongly NP -hard. We find that a simple policy, called the First-Due-First-Delivered (FDFD) policy, which assigns orders with earlier delivery due times to shipping methods with earlier destination arrival times, is very effective. This policy enables us to develop a polynomial time algorithm, which not only solves the problem under the concave shipping cost structure optimally but also achieves a performance guarantee of 2 for the problem under the general subadditive shipping cost structure. For the problem under the second shipping scenario, we extend the FDFD policy for long-haul shipping and derive another policy, called the No-Wait policy, for short-haul shipping. We use these policies to develop a polynomial time algorithm and analyze its performance guarantee. Our computational experiments show that the algorithm significantly outperforms a commercial optimization solver, and its performance is robust across different parameter settings that reflect various practical situations. This paper was accepted by Jeannette Song, operations management. Supplemental Material: The data and e-companion are available at https://doi.org/10.1287/mnsc.2023.4835. [ABSTRACT FROM AUTHOR]

Abstract. We consider a practical dynamic pricing problem with two substitutable products involving a number of business rules commonly seen in practice. Demand substitution exists between the two products (interproduct [...]

Abstract. We consider several integrated production, inventory, and delivery problems that arise in a number of practical settings where customer orders have pre-specified delivery time windows. These orders are first [...]

This paper investigates a practical batching decision problem that arises in the batch annealing operations in the cold rolling stage of steel production faced by most large iron and steel [...]

Abstract As a fast developing alternative of traditional therapeutics, photodynamic therapy (PDT) is an effective, noninvasive, nontoxic therapeutics for cancer, senile macular degeneration, and so on. But the efficacy of PDT was compromised by insufficient selectivity and low solubility. In this study, novel multifunctional silica-based magnetic nanoparticles (SMNPs) were strategically designed and prepared as targeting drug delivery system to achieve higher specificity and better solubility. 2,7,12,18-Tetramethyl-3,8-di-(1-propoxyethyl)-13,17-bis-(3-hydroxypropyl) porphyrin, shorted as PHPP, was used as photosensitizer, which was first synthesized by our lab with good PDT effects. Magnetite nanoparticles (Fe3O4) and PHPP were incorporated into silica nanoparticles by microemulsion and sol–gel methods. The prepared nanoparticles were characterized by transmission electron microscopy, X-ray diffraction, Fourier transform infrared spectroscopy and fluorescence spectroscopy. The nanoparticles were approximately spherical with 20–30 nm diameter. Intense fluorescence of PHPP was monitored in the cytoplasm of SW480 cells. The nanoparticles possessed good biocompatibility and could generate singlet oxygen to cause remarkable photodynamic anti-tumor effects. These suggested that PHPP-SMNPs had great potential as effective drug delivery system in targeting photodynamic therapy, diagnostic magnetic resonance imaging and magnetic hyperthermia therapy.

This paper considers the coordination of pricing and scheduling decisions in a make-to-order environment. Following common industry practice, we assume knowledge of a deterministic demand function that is nonincreasing in [...]

The classical scheduling literature considers many problems where a given set of jobs must be processed at minimum cost, subject to various resource constraints. The literature only considers the issue [...]

Two special cases of the problem of scheduling several batches of jobs on a single machine are considered. The problem, which involves setup times as well as earliness and tardiness penalties, assigns a common due date for all jobs in a single batch. The first case requires finding an optimal schedule that will minimize the total penalties for an externally given due date while the second requires an optimal schedule and due date that will minimize both total penalties and due date penalty for due dates that are decision variables.

A polynomial dynamic programming algorithm is proposed as a solution to a single machine scheduling problem which involves both the scheduling of job processing and the scheduling of job delivery. A common due date for all the jobs and a delivery date for each job need to be determined to minimize the sum of earliness and tardiness penalties, due date penalty and delivery costs. Finished jobs are delivered in batches.