Make-to-order is a production process where the businesses build the product only after an order from the customer is received. A large enterprise may have many such make-to-order shops distributed geographically. The cost and time for executing a job in each of these shops may vary. Therefore, it is important for a multisite enterprise to judiciously decide on where to process the jobs. Ideally, an enterprise would like to minimize the cost (or maximize the profit) while meeting the deadlines and at the same time maximize the utilization of the shops. The time to execute jobs can vary based on how the shops are laid out (the design of shops) and the decision of how jobs are routed (among the various shops). Predicting (or estimating) the likely turnaround time (and cost) for various jobs across the different shops enables the routing decision process. In this paper, we address the two important problems of (i) cell-design and (ii) turnaround time prediction and routing of jobs across various shops. We propose (i) a novel approach based on graph partitioning and set cover heuristic to generate a set of cell designs for a shop, (ii) a framework based on machine learning techniques to predict the turnaround time of jobs across various shops, and (iii) a routing algorithm based on dynamic programming and local search heuristic to route jobs such that the overall profit is maximized. We present results of applying the proposed approaches on real-life datasets from a multisite print shop enterprise.

Nowadays, brick-and-mortar stores are finding it extremely difficult to retain their customers due to the ever increasing competition from the online stores. One of the key reasons for this is the lack of personalized shopping experience offered by the brick-and-mortar stores. This work considers the problem of persona based shopping recommendation for such stores to maximize the value for money of the shoppers. For this problem, it proposes a non-polynomial time-complexity optimal dynamic program and a polynomial time-complexity non-optimal heuristic, for making top-k recommendations by taking into account shopper persona and her time and budget constraints. In our empirical evaluations with a mix of real-world data and simulated data, the performance of the heuristic in terms of the persona based recommendations (quantified by similarity scores and items recommended) closely matched (differed by only 8% each with) that of the dynamic program and at the same time heuristic ran at least twice faster compared to the dynamic program.

Hence, they do not address mobile resource devices, it has now become practically feasible to enable constraints (e.g., energy, bandwidth) and also result in unnecessary people to share information about dynamic events (e.g., trees spam. On the other hand, multi-cast approaches randomly fallen on roads due to a storm, sudden truck breakdowns send the queries to some of the users to preserve mobile and unscheduled processions) in their current location. This resources, but they do not ensure the direction of queries strongly motivates facilitation of various kinds of locationdependent to the most relevant users.

Speech to personal assistants (e.g., reminders, calendar entries, messaging, voice search) is often uttered under cognitive load, causing nonfinal pausing that can result in premature recognition cut-offs. Prior research suggests that prepausal features can discriminate final from nonfinal pauses, but it does not reveal how speakers would behave if given longer to pause. To this end, we collected and compared two elicitation corpora differing in naturalness and task complexity. The Template Corpus (4409 nonfinal pauses) uses keyword-based prompts; the Freeform Corpus (8061 nonfinal pauses) elicits open-ended speech. While nonfinal pauses are longer and twice as frequent in the Freeform data, prepausal feature modelling is roughly equally effective in both corpora. At a response latency of 100 ms, prepausal features modelled by an SVM reduced cut-off rates from 100% to 20% for both corpora. Results have implications for enhancing turn-taking efficiency and naturalness in personal-assistant technology.

Crowd workers exhibit varying work patterns, expertise, and quality leading to wide variability in the performance of crowdsourcing platforms. The onus of choosing a suitable platform to post tasks is mostly with the requester, often leading to poor guarantees and unmet requirements due to the dynamism in performance of crowd platforms. Towards this end, we demonstrate CrowdUtility, a statistical modelling based tool for evaluating multiple crowdsourcing platforms and recommending a platform that best suits the requirements of the requester. CrowdUtility uses an online Multi-Armed Bandit framework, to schedule tasks while optimizing platform performance. We demonstrate an end-to end system starting from requirements specification, to platform recommendation, to real-time monitoring.