Data-driven Algorithm for Scheduling with Total Tardiness

In this paper, we investigate the use of deep learning for solving a classical NP-Hard single machine scheduling problem where the criterion is to minimize the total tardiness. Instead of designing an end-to-end machine learning model, we utilize well known decomposition of the problem and we enhance it with a data-driven approach. We have designed a regressor containing a deep neural network that learns and predicts the criterion of a given set of jobs. The network acts as a polynomial-time estimator of the criterion that is used in a single-pass scheduling algorithm based on Lawler's decomposition theorem. Essentially, the regressor guides the algorithm to select the best position for each job. The experimental results show that our data-driven approach can efficiently generalize information from the training phase to significantly larger instances (up to 350 jobs) where it achieves an optimality gap of about 0.5%, which is four times less than the gap of the state-of-the-art NBR heuristic

Multi-mode resource constrained multi-project scheduling and resource portfolio problem

This paper introduces a multi-project problem environment which involves multiple projects with assigned due dates; with activities that have alternative resource usage modes; a resource dedication policy that does not allow sharing of resources among projects throughout the planning horizon; and a total budget. There are three issues to face when investigating this multiproject environment. First, the total budget should be distributed among different resource types to determine the general resource capacities which correspond to the total amount for each renewable resource to be dedicated to the projects. With the general resource capacities at hand, the next issue is to determine the amounts of resources to be dedicated to the individual projects. With the dedication of resources accomplished, the scheduling of the projects' activities reduces to the multi-mode resource constrained project scheduling problem (MRCPSP) for each individual project. Finally the last issue is the effcient solution of the resulting MRCPSPs. In this paper, this multi-project environment is modeled in an integrated fashion and designated as the Resource Portfolio Problem. A two-phase and a monolithic genetic algorithm are proposed as two solution approaches each of which employs a new improvement move designated as the combinatorial auction for resource portfolio and the combinatorial auction for resource dedication. Computational study using test problems demonstrated the effectiveness of the solution approach proposed

Flow shop scheduling with earliness, tardiness and intermediate inventory holding costs

We consider the problem of scheduling customer orders in a flow shop with the objective of minimizing the sum of tardiness, earliness (finished goods inventory holding) and intermediate (work-in-process) inventory holding costs. We formulate this problem as an integer program, and based on approximate solutions to two di erent, but closely related, Dantzig-Wolfe reformulations, we develop heuristics to minimize the total cost. We exploit the duality between Dantzig-Wolfe reformulation and Lagrangian relaxation to enhance our heuristics. This combined approach enables us to develop two di erent lower bounds on the optimal integer solution, together with intuitive approaches for obtaining near-optimal feasible integer solutions. To the best of our knowledge, this is the first paper that applies column generation to a scheduling problem with di erent types of strongly NP-hard pricing problems which are solved heuristically. The computational study demonstrates that our algorithms have a significant speed advantage over alternate methods, yield good lower bounds, and generate near-optimal feasible integer solutions for problem instances with many machines and a realistically large number of jobs

Using real-time information to reschedule jobs in a flowshop with variable processing times

Versión revisada. Embargo 36 mesesIn a time where detailed, instantaneous and accurate information on shop-floor status is becoming available in many manufacturing companies due to Information Technologies initiatives such as Smart Factory or Industry 4.0, a question arises regarding when and how this data can be used to improve scheduling decisions. While it is acknowledged that a continuous rescheduling based on the updated information may be beneficial as it serves to adapt the schedule to unplanned events, this rather general intuition has not been supported by a thorough experimentation, particularly for multi-stage manufacturing systems where such continuous rescheduling may introduce a high degree of nervousness in the system and deteriorates its performance. In order to study this research problem, in this paper we investigate how real-time information on the completion times of the jobs in a flowshop with variable processing times can be used to reschedule the jobs. In an exhaustive computational experience, we show that rescheduling policies pay off as long as the variability of the processing times is not very high, and only if the initially generated schedule is of good quality. Furthermore, we propose several rescheduling policies to improve the performance of continuous rescheduling while greatly reducing the frequency of rescheduling. One of these policies, based on the concept of critical path of a flowshop, outperforms the rest of policies for a wide range of scenarios.Ministerio de Ciencia e Innovación DPI2016-80750-

Order acceptance and scheduling in a single-machine environment: exact and heuristic algorithms.

In this paper, we develop exact and heuristic algorithms for the order acceptance and scheduling problem in a single-machine environment. We consider the case where a pool consisting of firm planned orders as well as potential orders is available from which an over-demanded company can select. The capacity available for processing the accepted orders is limited and orders are characterized by known processing times, delivery dates, revenues and the weight representing a penalty per unit-time delay beyond the delivery date promised to the customer. We prove the non-approximability of the problem and give two linear formulations that we solve with CPLEX. We devise two exact branch-and-bound procedures able to solve problem instances of practical dimensions. For the solution of large instances, we propose six heuristics. We provide a comparison and comments on the efficiency and quality of the results obtained using both the exact and heuristic algorithms, including the solution of the linear formulations using CPLEX.Order acceptance; Scheduling; Single machine; Branch-and-bound; Heuristics; Firm planned orders;