Renato de Matta

Production and inter-facility transportation scheduling for a process industry

Abstract We study in this paper the problem of coordinating the short-term production and inter facility transportation scheduling decisions between a plant that produces intermediate products and a finishing plant which processes the intermediate products into finished goods. Our goal is to develop a better understanding of the general relationships between production and transportation scheduling decisions, in particular, how changes in plant capacity and costs affect the coordination of scheduling decisions as well as the choice of transportation modes and carriers. We formulate the problem as a mixed integer programming model and use a solution procedure that exploits the underlying structure of our problem. Using real and simulated data from a process industry firm, our computational study, which compares the production and transportation schedules obtained from coordinated scheduling and sequential scheduling, shows that coordinated schedules yield significant cost savings resulting from the modest use of the expensive fast transport mode, coordinated product changeovers between plants and reduced intermediate product inventories. Discussions of managerial implications of coordinated scheduling are presented.

Developing work schedules for an inter-city transit system with multiple driver types and fleet types

In developing work schedules, the job assignment flexibility exploits the variety of available skills, thus enabling the assignment of workers to perform different jobs. In this study, we investigate the problem of finding the mix of primary and secondary jobs in short term work schedules to meet, at minimum cost, the daily service requirements of an inter-city bus transit firm in Andra Pradesh India operating multiple fleet types. We formulate the problem as a set covering model with resource allocation constraints. We develop a branch-and-price procedure to solve the model. Computational results are provided.

An Efficient Heuristic Approach to Recognize the Infeasibility of a Loading Problem

The success of hierarchical production planning approaches for flexible manufacturing systems lies in the consistency of decision outcomes at various decision levels. For instance, the loading problem, which is solved at a lower level, may not yield a feasible loading solution to a set of part types selected at a higher level. This paper attemps to address the issue of recognizing the infeasibility of a loading solution. We present a modified loading model that includes a penalty for each operation not assigned to any machine. We develop a Lagrangian-based heuristic procedure and provide a sufficient condition on the quality of heuristic solutions that, if satisfied, will enable us to use the heuristic solutions to recognize the infeasibility of a loading problem. The proposed model and the dual-based heuristic can be effectively incorporated in an FMS hierarchical production planning approach that finds a good loading solution by iteratively comparing different part grouping scenarios.

Short-term work scheduling with job assignment flexibility for a multi-fleet transport system

In recent years, planning and management of operations of transportation companies has become increasingly complex as tighter financial constraints affect the ability to respond to changing demands for travel. This paper investigates how a firm can reduce its total labor cost, enhance the flexibility of its operations and improve worker productivity and utilization by determining the right mix of jobs in work schedules. Using a method we have developed to generate low cost work schedules for bus drivers of an inter-city transport system in India, we study the changes in worker productivity and utilization when a mix of primary and secondary jobs is built in work schedules. We also investigate other factors that can strongly influence worker productivity and utilization such as the amount of overtime in work schedules, the level of job assignment flexibility, and staff size. Test results and discussions of managerial implications are presented.

Capacitated selection problem

Optimizing the selection of resources to accomplish a set of tasks involves evaluating the tradeoffs between the cost of maintaining the resources necessary to accomplish the tasks and the penalty cost associated with unfinished tasks. We consider the case where resources are categorized into types, and limits (capacity) are imposed on the number of each type that can be selected. The objective is to minimize the sum of penalty costs and resource costs. This problem has several practical applications including production planning, new product design, menu selection and inventory management. We develop a branch-and-bound algorithm to find exact solutions to the problem. To generate bounds, we utilize a dual ascent procedure which exploits the special structure of the problem. Information from the dual and recovered primal solutions are used to select branching variables. We generate strong valid inequalities and use them to fix other variables at each branching step. Results of tests performed on reasonably sized problems are presented.

Scheduling a manufacturing process with restrictions on resource availability

This paper examines the scheduling of a drug packaging operation which requires assigning resources to production lines during set-up in order to satisfy product specifications. We present mixed integer programming models of the problem and develop a Lagrangian heuristic to solve the models. Real data from a drug company and randomly generated data are used to test the models. Results show the efficacy of our solution approach to find good feasible schedules. We discuss some of the practical benefits and potential issues about the production schedules obtained using the models.This paper examines the scheduling of a drug packaging operation which requires assigning resources to production lines during set-up in order to satisfy product specifications. We present mixed integer programming models of the problem and develop a Lagrangian heuristic to solve the models. Real data from a drug company and randomly generated data are used to test the models. Results show the efficacy of our solution approach to find good feasible schedules. We discuss some of the practical benefits and potential issues about the production schedules obtained using the models.

Short-term capacity adjustment with offline production for a flexible manufacturing system under abnormal disturbances

Large production variations caused by abnormal disturbances can significantly reduce the production capacity of a flexible manufacturing system (FMS). To prevent production delays, short-term capacity adjustment strategies can be used to augment the capacity of the FMS, such as working overtime, using alternative tools that are suited for faster processing, and producing parts outside of the FMS. We propose a mixed integer programming (MIP) model to obtain an optimal production plan for a multi-machine FMS. Our model evaluates both the FMS loading decision and the effective use of short-term capacity adjustment strategies to minimize the total part production cost. We develop an iterative procedure to solve the model that uses the Lagrangian relaxation method for finding lower bounds and a Lagrangian heuristic for obtaining feasible solutions. The procedure exploits certain special structures found in the Lagrangian multipliers which enable us to obtain good solutions to reasonably large test problems quickly.

Minimizing the total waiting time of intermediate products in a manufacturing process

A mixed model production plan may use a schedule that will consecutively produce a small batch of each product model on a production line. However, it may be necessary to deviate from this schedule and produce the models in larger batches at a manufacturing cell in the production line if the manufacturing cell cannot support many setups. A potential problem with large batches is that products that are produced early must wait for its turn at the next step of the production process. When additional operations are performed on products that wait in order to restore its quality, it is imperative to schedule production such that the total time products wait is minimized. We model the problem as scheduling jobs on a single machine with setups and deadlines such that the total waiting time is minimized. We develop a tabu search based heuristic and branch-and-bound algorithm to solve the model. Results show that the heuristic solution to 30-product–3-product model problems is within 3% of its optimal solution.

The selection allocation problem

The Selection Allocation Problem (SAP) is a single period decision problem which involves selecting profit-maximizing (or cost-minimizing) activities from various distinct groups, and determining the volume of those activities. The activities in each group are selected subject to the availability of that groups resource, which is provided by either pooling or blending raw inputs from several potential sources. Imbedded in the decision process is the additional task of determining how much raw input is to be allocated to each group to form the resource for that group. Instances of this problem can be found in many different areas, such as in tool selection for flexible manufacturing systems, facility location, and funding for social services. Our goal in this paper is to identify and exploit special structures in the (SAP) and use those structures to develop an efficient solution procedure.