Movie Scheduling Research Articles

Impact of vertical integration on movie scheduling in Korea

ABSTRACT This study investigates the impact of vertical integration on movie screening decisions using data from the Korean movie industry (Korea in the text refers to the Republic of Korea, also known as South Korea). Previous studies have found that movie exhibitors (i.e. theaters) that own distributors allocated more screens when they release their affiliated movies. The novelty of this study is that it focuses on the impact of vertical integration on showtime scheduling of integrated players that own both distribution services and theaters. Controlling for seasonality, levels of competition, and expected quality of movies, the study found evidence of the dominating power of integrated players in the market that occupied more screens and put their movies in more favorable time slots. However, this power weakened after the opening week and did not appear by the fourth week. Although the integrated exhibitors are likely to screen their affiliated distributors’ movies more in the opening week, they did not block other distributors’ movies over time. Our study suggests that the integrated theaters’ screening decisions favorable to their own movies are better understood as the result of pursuing incentives rather than as the result of blocking competitors’ movies.

Predicting and ranking box office revenue of movies based on big data

Predicting box office revenue (BOR) of movies before releasing on big screens successfully becomes an emerging need, as it informs investment decisions on the stock market, the design of promotion strategies by advertisement companies, movie scheduling by cinemas, etc. However, the task is very challenging as it is affected by a lot of complex factors. In this paper, we first provide a strategic investigation of these influential factors. Then, we put forward a novel framework to predict a movie’s BOR by modeling these factors using big data. Specifically, the framework consists of a series of feature learning models and a prediction and ranking model. In particular, there are two models devised for learning features: (1) a novel dynamic heterogeneous network embedding model to simultaneously learn latent representations of actors, directors, and companies, capable of capturing their cooperation relationship collectively; (2) a deep neural network-based model designed to uncover high-level representations of movie quality from trailers. Based on the learned features, we train a mutually-enhanced prediction and ranking model to obtain the BOR prediction results. Finally, we apply the framework to the Chinese film market and conduct a comprehensive performance evaluation using real-world data. Experimental results demonstrate the superior performance of both extracted knowledge and the prediction results.

Optimal Movie Schedule and Prediction.

With a global revenue of over 40 billion US dollars and predicted astronomic growth, the movie industry remains a viable economic sector. It becomes imperative for media professionals to come up with more proficient ways of scheduling movies, and predicting performance of movies, so as to maximize their earning capacity. In this article, the movie scheduling and prediction problem is both an optimization and data analytic problem. The optimization part of the problem is abstracted as a binary integer programming (BIP) problem, and the BIP model is solved using gurobi in MATLAB. While there is a reasonable exploration as to the data analytical solution to the predictiove part of the problem.

On the problem of movie selection and scheduling to maximize revenue

On the problem of movie selection and scheduling to maximize revenue

Demand-driven scheduling of movies in a multiplex

This paper is about a marketing decision support system in the movie industry. The decision support system of interest is a model that generates weekly movie schedules in a multiplex movie theater. A movie schedule specifies, for each day of the week, on which screen(s) different movies will be played, and at which time(s). The model integrates elements from marketing (the generation of demand figures) with approaches from operations research (the optimization procedure). Therefore, it consists of two parts: (i) conditional forecasts of the number of visitors per show for any possible starting time, and (ii) a scheduling procedure that quickly finds a near optimal schedule (which can be demonstrated to be close to the optimal schedule). To generate this schedule, we formulate the “movie scheduling problem” as a generalized set partitioning problem. The latter is solved with an algorithm based on column generation techniques. We tested the combined demand forecasting/schedule optimization procedure in a multiplex in Amsterdam, generating movie schedules for fourteen weeks. The proposed model not only makes movie scheduling easier and less time consuming, but also generates schedules that attract more visitors than current “intuition-based” schedules.

Evolutionary approach to the development of decision support systems in the movie industry

This paper reports the development and implementation of a decision support system in a non-traditional domain — the motion picture industry. The approach reported here is evolutionary, and the model was designed to assist exhibition executives in movie scheduling. After an earlier successful collaboration in scheduling a single theater with multiple screens, we now turn to the multi-theater multi screens situation, describing the problems encountered in that situation and how we have dealt with them. Using a quasi-experimental design, the decision support system was estimated to improve the net margin by over US $ 900,000 on an annual basis. The paper describes the implementation process and the performance evaluation metrics that had been agreed upon with the management.

How Complex Do Movie Channel Contracts Need to Be?

The motion picture industry is characterized by a dynamic market environment, limited shelf space and product category management, and consequently, complex channel contracts specifying the split of box office revenue between distributors and exhibitors. Although such a contracting practice creates a considerable administrative effort and channel conflict, it is not clear whether such complexity is necessary for superior channel performance. This study investigates this question by analyzing the impact of movie contract structure on movie scheduling and channel member profitability. We develop and analyze a game-theoretic model using the genetic algorithm approach and a decision support system, SilverScreener, to capture strategic behaviors of channel members in a complex market environment. We find that simpler two-part tariff or 50/50 split contracts perform as well as the current contracts. Thus, the complexity of the market environment need not be reflected in the complexity of the channel contracts. Channel contract structure has significant impact on channel member profitability and the exhibitor's movie-scheduling behavior. In particular, our results indicate that the flat rate contract structure represents an attractive alternative to the current practice for distributors.

Single machine scheduling with time deteriorating job values

This paper considers the problem of scheduling n jobs on a single machine where the job value deteriorates with its starting time. The objective of the problem is to maximize the cumulative value of all jobs. The problem is motivated from the real-life applications, such as movie scheduling, remanufacturing of high-technology products, web object transmission, and banner advertising. Unrestricted, truncated, and capacity constrained job value functions are considered. Some special cases of the problem, such as the unrestricted linear job value function, are polynomially solvable. The general problem is shown to be unary NP-hard and is modelled as a time index integer program. For the NP-hard cases, several heuristics are proposed. Results of the empirical evaluation of the relative performance of the proposed heuristics on critical parameters are reported.

Sharing video on demand

We formulate the problem of video on demand ( VOD) from a resource allocation perspective. We introduce the decision element into a movie vending environment, which complements the current approaches. In contrast with the traditional resource allocation problems (e.g., machine scheduling), the problem possesses the distinctive batching property, which stands for the feasibility of several requests being served by one resource. First, we investigate the problem in an on-line fashion, namely, having to accept or reject a request for a movie without any knowledge about future requests. We show upper and lower bounds on the competitive ratio of on-line movie scheduling algorithms for a variety of scenarios depending on the notification time. In particular, when the length of the movie and the notification time on requests are linearly related, we present a simple competitive algorithm. Second, we propose a new approach— Shared VOD—which gives a more balanced functionality/price tradeoff than the two popular approaches full VOD and near VOD. In this approach, several users may share the same channels, but the movies are selected by the users themselves. In this framework, we evaluate a variety of on-line channel allocation algorithms under “typical” conditions. We compare these strategies to the off-line strategy, and show that the algorithm we introduce substantially outperforms the rest.

The Strategic Effects of Vertical Market Structure: Common Agency and Divisionalization in the US Motion Picture Industry

I examine the release date scheduling of all motion pictures that went into wide release in the US in 1995 and 1996 to investigate the effects of vertical market structure on competition. The evidence suggests that complex vertical structures involving multiple upstream or downstream firms generally do not achieve efficient outcomes in movie scheduling. In addition, analysis of the data suggests that the production divisions of the major studios act as integrated parts of the studio, rather than as independent competing firms.

The Strategic Effects of Vertical Market Structure: Common Agency and Divisionalization in the US Motion Picture Industry

I examine the release‐date scheduling of all motion pictures that went into wide release in the US in 1995 and 1996 to investigate the effects of vertical market structure on competition. The evidence suggests that complex vertical structures involving multiple upstream or downstream firms generally do not achieve efficient outcomes in movie scheduling. In addition, analysis of the data suggests that the production divisions of the major studios act as integrated parts of the studio, rather than as independent competing firms.

Real-Time Communication Scheduling in a Multicomputer Video Server

In this paper, we address the problem of scheduling communication over the interconnection network of a distributed-memory multicomputer video server. We show that this problem is closely related to the problem of data distribution and movie scheduling in such a system. A solution is proposed in this paper that addresses these three issues at once. The movies are distributed evenly over all nodes of the multicomputer. The proposed solution minimizes the contention for links over the switch. The proposed solution makes movie scheduling very simple—if the first block of the movie is scheduled, the rest of the movie is automatically scheduled. Moreover, if the first block of the movie stream is scheduled without network contention, the proposed solution guarantees that there will be no network contention during the entire duration of playback of that movie. We show that the proposed approach to communication scheduling is optimal in utilizing the network resources. Extensive simulation results are presented to show the effectiveness of the proposed approach.