Stochastic leadtimes in two-level distribution-type networks

Leadtime uncertainty occurs in both manufacturing and distribution systems and can cause difficult coordination problems. We analyze a simple three-location, two-level distribution-type system with stochastic leadtimes with the objective of determining planned leadtimes which minimize the sum of expected inventory-holding and tardiness costs. In a manufacturing context, the system can be viewed as one in which common processing or procurement is done first, whereupon another manufacturing stage differentiates this common product. Within a distribution framework, the system is one in which material is transported to a central facility (such as a regional warehouse) and subsequently transported to smaller local distributors or retailers. We investigate two heuristic policies which are simple adjustments to optimal solutions for serial systems resulting from a decoupling of the distribution network. We also report computational experience which indicates that the optimal “location” and quantity of safety time depends largely upon the relationship among the due dates and average leadtimes for the final stages of processing or delivery.     

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 different, 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 different 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 different types of strongly ????‐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. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004.     

Minimizing mean tardiness and earliness in single-machine scheduling problems with unequal due dates

We consider a single-machine scheduling problem with the objective of minimizing the mean (or equivalently, total) tardiness and earliness when due dates may differ among jobs. Some properties of the optimal solution are discussed, and these properties are used to develop both optimal and heuristic algorithms. Results of computational tests indicate that optimal solutions can be found for problems with up to 20 jobs, and that two of the heuristic procedures provide optimal or very near optimal solutions in many instances. © 1994 John Wiley & Sons, Inc.     

New algorithms for (Q,r) systems with complete backordering using a fill-rate criterion

We present an algorithm which determines optimal parameter values for order quantity-reorder point systems with complete backordering. The service level is measured as fraction of demand satisfied directly from shelf, also known as “fill-rate.” This algorithm differs from existing algorithms because an exact cost function is used rather than an approximation. We also present a new heuristic algorithm, which is more efficient computationally than the optimal procedure and provides excellent results. Results of extensive computational experience also are reported.     

Lot sizing when yields increase during the production run

We investigate the problem of determining lot sizes for multiple items when the expected percentage of acceptable output increases with the duration of the production run, usually due to adjustments made during the early part of the production run. Such problems arise in metal stamping, textile finishing processes, and a variety of other industries. The goal is to minimize the total cost of production, inventory holding costs, and setup costs (where applicable). We develop a heuristic procedure based on a Lagrangian relaxation that differs from relaxations used in earlier studies. We use various properties of the objective function to guide the adjustment of the initial solution from the relaxation toward feasibility. Computational results indicate that, on the average, the heuristic produces solutions within 4.9% of the lower bound obtained from the Lagrangian relaxation. © 1996 John Wiley & Sons, Inc.     

Forecast horizons for the discounted dynamic lot-size problem allowing speculative motive

This article considers the dynamic lot-size problem under discounting, allowing speculative motive for holding inventory. A variable rolling-horizon procedure is presented, which, under certain regularity conditions, is guaranteed to generate an infinite-horizon optimal-production plan. We also discuss a fixed rolling-horizon procedure which provides a production plan that achieves an infinite-horizon cost within a user-specified tolerance ϵ of optimality. The fixed-horizon length T* needed in this procedure is given in terms of a closed-form formula that is independent of specific forecasted demands. We also present computational results for problems with a range of cost parameters and demand characteristics.