Multiperiod capacity expansion and shipment planning with linear costs

In this paper we consider a multiperiod deterministic capacity expansion and shipment planning problem for a single product. The product can be manufactured in several producing regions and is required in a number of markets. The demands for each of the markets are non-decreasing over time and must be met exactly during each time period (i.e., no backlogging or inventorying for future periods is permitted). Each region is assumed to have an initial production capacity, which may be increased at a given cost in any period. The demand in a market can be satisfied by production and shipment from any of the regions. The problem is to find a schedule of capacity expansions for the regions and a schedule of shipments from the regions to the markets so as to minimize the discounted capacity expansion and shipment costs. The problem is formulated as a linear programming model, and solved by an efficient algorithm using the operator theory of parametric programming for the transporation problem. Extensions to the infinite horizon case are also provided.     

Some bayes tests and their asymptotic properties for the multivariate,multisample goodness-of-fit problem

Independent samples are taken from C multivariate populations with continuous but unknown cumulative distribution function c.d.f.). The problem is to test the hypothesis that the C population c.d.f's are identical to a specified c.d.f. We approach this problem by first transforming the data so that the hypothesis being tested is that the common distribution is uniform over a unit hypercube. We then construct some Bayes tests and investigate their asymptotic properties. These tests are based on the asymptotic normality of the number of observations falling in the “asymptotically sufficient groupings”.     

Inventory control of a multiproduct system with a limited production resource

Optimal control of the production of several products subject to restricted production resources is considered. Linear production, holding and penalty costs are assumed although this is crucial only for production costs. Excess demand results in lost sales. A single point plays an important role in the definition of the optimal policy.     

Models for production planning under power interruptions

We present a robust optimization model for production planning under the assumption that electricity supply is subject to uncertain interruptions caused by participation in interruptible load contracts (ILCs). The objective is to minimize the cost of electricity used for production while providing a robust production plan which ensures demand satisfaction under all possible interruption scenarios. The combinatorial size of the set of interruption scenarios makes this a challenging problem. Furthermore, we assume that no probabilistic information is known about the supply uncertainty: we only use the information given in the ILC to identify an uncertainty set that captures the possible scenarios. We construct a general robust framework to handle this uncertainty and present a heuristic to compute a good feasible solution of the robust model. We provide computational experiments on a real‐world example and compare the performance of an exact solver applied to the robust model with that of the heuristic procedure. Finally, we include the operational impact of interruptions such as “recovery modes” in the definition of the uncertainty set. © 2013 Wiley Periodicals, Inc. Naval Research Logistics, 2013     

Generation and storage dispatch in electricity networks with generator disruptions

We present methods for optimizing generation and storage decisions in an electricity network with multiple unreliable generators, each colocated with one energy storage unit (e.g., battery), and multiple loads under power flow constraints. Our model chooses the amount of energy produced by each generator and the amount of energy stored in each battery in every time period in order to minimize power generation and storage costs when each generator faces stochastic Markovian supply disruptions. This problem cannot be optimized easily using stochastic programming and/or dynamic programming approaches. Therefore, in this study, we present several heuristic methods to find an approximate optimal solution for this system. Each heuristic involves decomposing the network into several single‐generator, single‐battery, multiload systems and solving them optimally using dynamic programming, then obtaining a solution for the original problem by recombining. We discuss the computational performance of the proposed heuristics as well as insights gained from the models. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 493–511, 2015     

Bullwhip and reverse bullwhip effects under the rationing game

When an unreliable supplier serves multiple retailers, the retailers may compete with each other by inflating their order quantities in order to obtain their desired allocation from the supplier, a behavior known as the rationing game. We introduce capacity information sharing and a capacity reservation mechanism in the rationing game and show that a Nash equilibrium always exists. Moreover, we provide conditions guaranteeing the existence of the reverse bullwhip effect upstream, a consequence of the disruption caused by the supplier. In contrast, we also provide conditions under which the bullwhip effect does not exist. In addition, we show that a smaller unit reservation payment leads to more bullwhip and reverse bullwhip effects, while a large unit underage cost results in a more severe bullwhip effect. © 2017 Wiley Periodicals, Inc. Naval Research Logistics 64: 203–216, 2017     

An exact solution approach for the time-dependent traveling-salesman problem

We present an algorithm for solving the time-dependent traveling-salesman problem (TDTSP), a generalization of the classical traveling salesman problem in which the cost of travel between two cities depends on the distance between the cities and the position of the transition in the tour. The algorithm is derived by applying Benders decomposition to a mixed-integer linear programming formulation for the problem. We identify trivial TDTSPs for which a standard implementation of the algorithm requires an exponential number of iterations to converge. This motivates the development of an efficient, network-flow-based method for finding Pareto-optimal dual solutions of a highly degenerate subproblem. Preliminary computational experience demonstrates that the use of these Pareto-optimal solutions has a dramatic impact on the performance of the algorithm. © 1996 John Wiley & Sons, Inc.     

Note: Dual sourcing with nonidentical suppliers

We analyze a dual-sourcing inventory model with exponential lead times and constant unit demand in which the order quantity is split in some proportion between two sources of supply. Unlike earlier studies, we do not require that the two sources be identical in terms of the lead-time parameters or the supply prices. We compare the expected total annual costs for the two-source and the traditional single-source models over a wide range of parameter values. We confirm the findings of earlier studies that, under stochastic lead times, dual sourcing yields savings in holding and shortage costs that could outweigh the incremental ordering costs. With this more general model, we demonstrate that savings from dual sourcing are possible even where the mean or the variability of the second source is higher. © 1993 John Wiley & Sons, Inc.     

On scheduling with ready-times,due-dates and vacations

A single machine sequencing problem is considered in which there are ready-time and due-date constraints on jobs and vacation constraints on the machine. Each vacation has fixed starting and finish time and no preemption is allowed for the jobs. The objective is to minimize maximum lateness. An intriguing feature of this formulation is that it allows sequencing in disconnected time windows. A relaxation of the problem is obtained by modeling the vacations as a set of jobs with flexible ready-times and artificial due-dates and a branch and bound algorithm is developed for the problem. In the algorithm, the search is not only guided by the bounds but also by a careful manipulation of the artificial due-dates. Consequently; while searching in the relaxed solution space, solutions of the original problem are implicitly enumerated. Computational results indicate that the algorithm can satisfactorily solve problems with multiple vacations.