We establish the computational complexity of the problem of minimizing makespan in a flowshop, where each jobs requires a pallet the entire time, from the start of its first operation until the completion of the last operation. We prove that the problem is NP-hard in the strong sense for m =2...

Robust optimization is a methodology that can be applied to problems that are affected by uncertainty in the problem's parameters. The classical robust counterpart (RC) of the problem requires the solution to be feasible for all uncertain parameter values in a so-called uncertainty set, and...

Focuses on a study which developed a framework for forecast and decision horizons. Definition of finite and infinite horizon stochastic optimization problems for a given forecast; Description of the general framework; Conditions for the existence of a solution horizon; Development of sufficient...

We provide results for an efficient analytical valuation of partial moments of the multivariate Gaussian distribution over convex polyhedrons to aid the solution, sensitivity analysis and structural analysis of a large number of two-stage resource acquisition and allocation problems. These...

This paper proposes a new methodology to solve partially observed inventory problems. Generally, these problems have infinitedimensional states that are conditional distribution of the inventory level. Our methodology involves linearizing the state transitions via unnormalized probabilities. It...

This paper considers the dynamic lot sizing problem of H. M. Wagner and T. M. Whitin with the assumption that the total cost of n setups is a concave nondecreasing function of n. Such setup costs could arise from the worker learning in setups and/or technological improvements in setup methods....

We are concerned with a discrete-time undiscounted dynamic lot size model in which demand and the production setup cost are constant for an initial few periods and the holding cost of inventory is an arbitrary nondecreasing function assumed to be stationary (i.e., explicitly independent of time)...

In this paper, we treat an optimal control problem of a stochastic two-machine flowshop with machines subject to random breakdown and repair. While the problem is difficult to solve, it can be approximated by a deterministic problem when the rates of machine failure and repair become large....

We extend the linear programming (LP) model of deterministic supply-chain planning to take demand uncertainty and cash flows into account for the medium term. The resulting stochastic LP model is similar to that of Asset-Liability Management (ALM), for which the literature using stochastic...

A new smoothing approach based on entropic perturbationis proposed for solving mathematical programs withequilibrium constraints. Some of the desirableproperties of the smoothing function are shown. Theviability of the proposed approach is supported by acomputationalstudy on a set of well-known...