F. Brian Talbot

Computational experience with a backtracking algorithm for solving a general class of precedence and resource-constrained scheduling problems

In this paper computational results are presented with a very general, yet powerful backtracking procedure for solving the duration minimization and net present value maximization problems in a precedence and resource-constrained network. These networks are generally of the PERT/CPM variety, although it is not required that they be so. Among the advantages cited for our approach are low computer memory (storage) requirements and the ability to obtain improved solutions rapidly (heuristic properties). Since the resource-constrained project scheduling problem subsumes the job shop, flow shop, assembly line balancing, and related scheduling problems, our procedure can be used with little or no modification to solve a wide variety of problem types. Computational experience is reported for both mainframe and personal computer implementations.

Scheduling a project to maximize its net present value: An integer programming approach

We describe an integer programming algorithm for determining scheduled start and finish times for the activities of a project subject to resource limitations during each period of the schedule duration. The objective is to maximize the net present value of the project to the firm. A depth-first branch and bound solution procedure searches over the feasible set of finish or completion times for each of the activities of the project. Fathoming criteria based upon the concept of a network cut originally developed to solve the duration minimization version of this problem are extended in this paper to solve the net present value problem. These fathoming decision rules prevent many potentially inferior solutions from being explicitly evaluated. Computational experience reported demonstrates the efficacy of the approach.

The job shop tardiness problem: A decomposition approach

An important criterion for evaluating the effectiveness of many manufacturing firms is their ability to meet due dates. In low to medium volume discrete manufacturing, typified by traditional job shops and more recently by flexible manufacturing systems, this criterion is usually operationalized on the shop floor through the use of prioritizing dispatching rules. The widespread use of dispatching rules has led to a number of investigations where the due date performance of various rules is compared. In contrast to previous research on dispatching rules, this paper proposes a new approach that _decomposes the dynamic problem into a series of static problems. These static problems are solved in their entirely, and then implemented dynamically on a rolling basis. To illustrate this approach, a specific heuristic is developed that constructs the schedule for the entire system by focusing on the bottleneck machine. Computational results indicate that significant due date performance improvement over traditional dispatching rules can be obtained by using this new approach.

Load planning for shipments of low density products

This paper presents a complex computer-based heuristic procedure for sizing customer orders and developing three dimensional load diagrams for rail and truck shipment of low density products. This heuristic procedure was developed for, and is in various phases of implementation at, a large multinational U.S.-based consumer products company. Products are shipped daily in high volume from inventory in corrugated containers of various sizes depending on the product package sizes and customer requirements. Vehicles used include railcars, truck trailers and tandem truck trailers, which also vary in size depending upon need and availability. In most cases, product volume or material handling considerations limit the amount of product loaded into vehicles before weight restrictions are met. Hence, the emphasis here is on low density products. The procedure developed has been demonstrated to significantly increase vehicle utilization, and improve customer service. It is fast and accurate enough to be used in real time during the order entry process. It has also been used successfully in a vehicle feasibility study of single versus tandem trailers.

An Approach to Integrating Environmental Considerations within Managerial Decision-Making

Recent environmental trends, including (1) an expansion of existing command and control directives, (2) the introduction of market-based policy instruments, and (3) the adoption of extended producer responsibility, have created a need for new tools to help managerial decision-making. To address this need, we develop a nonlinear mathematical programming model from a profit-maximizing firms perspective, which can be tailored as a decision-support tool for firms facing environmental goals and constraints. We typify our approach using the specific context of diesel engine manufacturing and remanufacturing. Our model constructs are based on detailed interviews with top managers from two leading competitors in the medium and heavy-duty diesel engine industry. The approach allows the incorporation of traditional operations planning considerations — in particular, capacity, production, and inventory — together with environmental considerations that range from product design through production to product end of life. A current hurdle to implementing such a model is the availability of input data. We therefore highlight the need not only to involve all departments within businesses but also for industrial ecologists and business managers to work together to implement meaningful decision models that are based on accurate and timely data and can have positive economic and environmental impact.

Heuristics for loading flexible manufacturing systems

Abstract The flexible manufacturing system (FMS) is an alternative to conventional discretemanufacturing processes that permits highly automated, efficient, and simultaneous machining of a variety of part types. In managing these systems, technological requirements indicate that several decisions must be made prior to system start-up. To this end, previous research has defined a set of FMS production planning problems. The final production planning problem is called the loading problem, which is described as follows. Given a set of part types chosen for immediate simultaneous production, allocate the operations and associated tooling of these part types among the machines subject to the capacity and technological constraints, and according to some loading objective. this problem has previously been formulated as a nonlinear mixed integer program for several loading objectives. Although it has been shown that the nonlinear MIPs are solvable on large computer systems, real-time FMS control requirements and the typical availability of minicomputers in shop environments make it impractical and cost inefficient to optimally solve the loading problem in many plants today. As a result, the authors develop several heuristic algorithms that provide goodsolutions to various versions of the FMS loading problem. We expect that these rules can be executed in essentially real-time on minicomputers available today.