A novel formulation of material nonlinear analysis in structural mechanics

This article demonstrates a novel approach for material nonlinear analysis. This analysis procedure eliminates tedious and lengthy step by step incremental and then iterative procedure adopted classically and gives direct results in the linear as well as in nonlinear range of the material behavior. Use of elastic moduli is eliminated. Instead, stress and strain functions are used as the material input in the analysis procedure. These stress and strain functions are directly derived from the stress-strain behavior of the material by the method of curve fitting. This way, the whole stress-strain diagram is utilized in the analysis which naturally exposes the response of structure when loading is in nonlinear range of the material behavior. It is found that it is an excellent computational procedure adopted so far for material nonlinear analysis which gives very accurate results, easy to adopt and simple in calculations. The method eliminates all types of linearity assumptions in basic derivations of equations and hence, eliminates all types of possibility of errors in the analysis procedure as well. As it is required to know stress distribution in the structural body by proper modelling and structural idealization, the proposed analysis approach can be regarded as stress-based analysis procedure. Basic problems such as uniaxial problem, beam bending, and torsion problems are solved. It is found that approach is very suitable for solving the problems of fracture mechanics. Energy release rate for plate with center crack and double cantilever beam specimen is also evaluated. The approach solves the fracture problem with relative ease in strength of material style calculations. For all problems, results are compared with the classical displacement-based liner theory.     

Coordinated replenishments from multiple suppliers with price discounts

In this study we present an integer programming model for determining an optimal inbound consolidation strategy for a purchasing manager who receives items from several suppliers. The model considers multiple suppliers with limited capacity, transportation economies, and quantity discounts. We propose an integrated branch and bound procedure for solving the model. This procedure, applied to a Lagrangean dual at every node of the search tree, combines the subgradient method with a primal heuristic that interact to change the Lagrangean multipliers and tighten the upper and lower bounds. An enhancement to the branch and bound procedure is developed using surrogate constraints, which is found to be beneficial for solving large problems. We report computational results for a variety of problems, with as many as 70,200 variables and 3665 constraints. Computational testing indicates that our procedure is significantly faster than the general purpose integer programming code OSL. A regression analysis is performed to determine the most significant parameters of our model. © 1998 John Wiley & Sons, Inc. Naval Research Logistics 45: 579–598, 1998     

Heuristic procedure for a multiproduct dynamic lot-sizing problem with coordinated replenishments

In this article we develop a heuristic procedure for a multiproduct dynamic lot-sizing problem. In this problem a joint setup cost is incurred when at least one product is ordered in a period. In addition to the joint setup cost a separate setup cost for each product ordered is also incurred. The objective is to determine the product lot sizes, over a finite planning horizon, that will minimize the total relevant cost such that the demand in each period for each product is satisfied without backlogging. In this article we present an effective heuristic procedure for this problem. Computational results for the heuristic procedure are also reported. Our computational experience leads us to conclude that the heuristic procedure may be of considerable value as a decision-making aid to production planners in a real-world setting. © 1994 John Wiley & Sons, Inc.     

An efficient heuristic procedure for the capacitated warehouse location problem

This paper introduces an efficient heuristic procedure for solving a special class of mixed integer programming problem called the capacitated warehouse (plant) location problem. This procedure parallels the work reported earlier in [9] on the uncapacitated warehouse location problem. The procedure can be viewed as tracing a judiciously selected path of the branch and bound tree (from the initial node to the terminal node) to arrive at a candidate solution. A simple backtracking scheme is also incorporated in the procedure to investigate possible improvement in the solution. Computational results on problems found in the literature look quite encouraging.     

A heuristic procedure for allocating tasks in fault-tolerant distributed computer systems

One way of achieving the increased levels of system reliability and availability demanded by critical computer-based control systems is through the use of fault-tolerant distributed computer systems. This article addresses the problem of allocating a set of m tasks among a set of n processors in a manner that will satisfy various task assignment, system capacity, and task scheduling constraints while balancing the workload across processors. We discuss problem background, problem formulation, and a known heuristic procedure for the problem. A new solution-improving heuristic procedure is introduced, and computational experience with the heuristics is presented. With only a modest increase in the amount of computational effort, the new procedure is demonstrated to improve dramatically solution quality as well as obtain near-optimal solutions to the test problems.     

舰载直升机兵力使用方案优化建模与仿真

依据舰载直升机兵力使用计划活动的思维机制,分析了计划活动中所涉及的各种主要因素,采用定量与定性分析相结合的方法,建立了兵力使用方案优化的益损值模型,并以舰载直升机反潜作战样式为例,采用软件编程方式实现了该模型,实例表明该方法可为决策者做出科学、有效的决策提供支持。     

The permutation flow shop problem with sum-of-completion times performance criterion

In this article we address the non-preemptive flow shop scheduling problem for minimization of the sum of the completion times. We present a new modeling framework and give a novel game-theoretic interpretation of the scheduling problem. A lower-bound generation scheme is developed by solving appropriately defined linear assignment problems. This scheme can also be used as a heuristic approach for the solution of the problem with satisfactory results. Its main use, however, is as a bounding scheme within a branch-and-bound procedure. Our branch-and-bound procedure improves significantly upon the best available enu-merative procedures in the current literature. Extensive computational results are used to qualify the above statements. © 1993 John Wiley & Sons, Inc.     

An efficient heuristic procedure for the uncapacitated warehouse location problem

This paper introduces an efficient heuristic procedure for a special class of mixed integer programming problems called the uncapacitated warehouse (plant) location problem. This procedure is derived from the branching decision rules proposed for the branch and bound algorithm by the author in an earlier paper. It can be viewed as tracing a single path of the branch and bound tree (from the initial node to the terminal node), the path being determined by the particular branching decision rule used. Unlike branch and bound the computational efficiency of this procedure is substantially less than linearly related to the number of potential warehouse locations (integer variables) in the problem. Its computational efficiency is tested on problems found in the literature.     

Successive approximation in separable programming: An improved procedure for convex separable programs

We implement a solution procedure for general convex separable programs where a series of relatively small piecewise linear programs are solved as opposed to a single large one, and where, based on bound calculations developed in [13] and [14], the ranges of linearization are systematically reduced for successive programs. The procedure inherits ε-convergence to the global optimum in a finite number of steps, but perhaps its most distinct feature is the rigorous way in which ranges containing an optimal solution are reduced from iteration to iteration. This paper describes the procedure, called successive approximation, discusses its convergence, tightness of the bounds, bound-calculation overhead, and its robustness. It presents a computer implementation to demonstrate its effectiveness for general problems and compares it (1) with the more standard separable programming approach and (2) with one of the recent augmented Lagrangian methods [10] included in a comprehensive study of nonlinear programming codes [12]. It seems clear from over 130 cases resulting from 80 distinct problems studied here that significant savings in terms of computational effort can be realized by a judicious use of the procedure, and the ease with which it can be used is appreciably increased by the robustness it shows. Moreover, for most of these problems, the advantage increases as the size, nonlinearity, and the degree of desired accuracy increase. Other important benefits include significantly smaller storage requirements, the ability to estimate the error in the current solution, and to terminate the algorithm as soon as the acceptable level of accuracy has been achieved. Problems requiring up to about 10,000 nonzero elements in their specification and about 45,000 nonzero elements in the generated separable programs resulting from up to 70 original nonlinear variables and 70 nonlinear constraints are included in the computations.     

Determining optimal growth paths in logistics operations

This paper considers a logistics system modelled as a transportation problem with a linear cost structure and lower bounds on supply from each origin and to each destination. We provide an algorithm for obtaining the growth path of such a system, i. e., determining the optimum shipment patterns and supply levels from origins and to destinations, when the total volume handled in the system is increased. Extensions of the procedure for the case when the costs of supplying are convex and piecewise linear and for solving transportation problems that are not in “standard form” are discussed. A procedure is provided for determining optimal plant capacities when the market requirements have prespecified growth rates. A goal programming growth model where the minimum requirements are treated as goals rather than as absolute requirements is also formulated.     

An optimal sequential procedure for selecting the best bernoulli process—a review

This article describes a new closed adaptive sequential procedure proposed by Bechhofer and Kulkarni for selecting the Bernoulli population which has the largest success probability. It can be used effectively for selecting the production process with the largest proportion of conforming items, and thus is applicable in vendor selection situations. The performance of this procedure is compared to that of the Sobel-Huyett single-stage procedure, and to a curtailed version of the single-stage procedure, all of which guarantee the same probability of a correct selection. Optimal properties of the Bechhofer-Kulkarni procedure are stated; quantitative assessments of important performance characteristics of the procedure are given. These demonstrate conclusively the superiority of the new procedure over that of the competing procedures. Relevant areas of application (including clinical trials) are described. Appropriate literature references are provided.     

Solving the multiperiod assignment problem with start-up costs using dual ascent

In this article we consider a multiperiod assignment problem where the assignment cost of assigning job i to machine j varies from one time period to the next. A start-up cost is incurred whenever the job processed by a machine in the current time period is different from the one processed in the previous time period. This problem is modeled as an integer programming problem for which a dual ascent approximate procedure is developed. Our computational results show that our procedure outperforms the more common Lagrangian-relaxation-based subgradient procedure by a significant margin. It is also found to be faster than MPSX/370 by many orders of magnitude. © 1993 John Wiley & Sons, Inc.     

Control charts for exponentially distributed product life

This paper introduces a special control chart procedure for exponentially distributed product life. Statistical control of product life in manufacturing requires continuing life tests of manufactured product so as to detect changes in product life and take appropriate corrective action. These life testing experiments may become exceedingly time consuming and thus can be both impractical because of serious time delays in implementing corrective action on the process when indicated, and quite uneconomical. It is desirable to inquire into the character of life testing by means of a control chart procedure based on the real time to first failure within given samples. Measuring the real minimum life provides a considerable reduction in duration of the testing procedure and in the number of specimens destroyed, yielding a considerable economy over the Shewhart's X control chart.     

A two-phase procedure for allotting geostationary orbital locations to communications satellites

We consider the problem of allotting locations in the geostationary orbit to communication satellites, subject to angle of elevation and electromagnetic interference constraints. An optimization framework is used as a means of finding feasible allotment plans. Specifically, we present a two-phase solution procedure for the satellite location problem (SLP). The objective in SLP is to allot geostationary orbital locations to satellites so as to minimize the sum of the absolute differences between the locations prescribed for the satellites and corresponding specified desired locations. We describe two heuristics, an ordering procedure and a k-permutation algorithm, that are used in tandem to find solutions to SLP. Solutions to a worldwide example problem with 183 satellites serving 208 service areas are summarized.     

A heuristic approach to bicriteria scheduling

We consider the problem of sequencing jobs on a single machine while minimizing a nondecreasing function of two criteria. We develop a heuristic procedure that quickly finds a good solution for bicriteria scheduling. The procedure is based on using several arcs in the criterion space that are representative of the possible locations of nondominated solutions. By sampling a small number of points on these arcs, a promising point is identified in the criterion space for each arc. An efficient sequence in the neighborhood of each of the promising points is found and the best of these efficient sequences is selected as the heuristic solution. We implement the procedure for two different bicriteria scheduling problems: (i) minimizing total flowtime and maximum tardiness and (ii) minimizing total flowtime and maximum earliness. The computational experience on a wide variety of problem instances show that the heuristic approach is very robust and yields good solutions. © 1999 John Wiley & Sons, Inc. Naval Research Logistics 46: 777–789, 1999     

A worst‐case formulation for constrained ranking and selection with input uncertainty

In this research, we consider robust simulation optimization with stochastic constraints. In particular, we focus on the ranking and selection problem in which the computing time is sufficient to evaluate all the designs (solutions) under consideration. Given a fixed simulation budget, we aim at maximizing the probability of correct selection (PCS) for the best feasible design, where the objective and constraint measures are assessed by their worst‐case performances. To simplify the complexity of PCS, we develop an approximated probability measure and derive the asymptotic optimality condition (optimality condition as the simulation budget goes to infinity) of the resulting problem. A sequential selection procedure is then designed within the optimal computing budget allocation framework. The high efficiency of the proposed procedure is tested via a number of numerical examples. In addition, we provide some useful insights into the efficiency of a budget allocation procedure.     

A new approach for dealing with the startup problem in discrete event simulation

This article proposes a practical, data-based statistical procedure which can be used to reduce or remove bias owing to artificial startup conditions in simulations aimed at estimating steady-state means. We discuss results of experiments designed to choose good parameter values for this procedure, and present results of extensive testing of the procedure on a variety of stochastic models for which partial analytical results are available. The article closes with two illustrations of the application of the procedure to more complex statistical problems which are more representative of the kinds of purposes for which real-world steady-state simulation studies might be undertaken.     

Determining adjacent vertices on assignment polytopes

To rank the solutions to the assignment problem using an extreme point method, it is necessary to be able to find all extreme points which are adjacent to a given extreme solution. Recent work has shown a procedure for determining adjacent vertices on transportation polytopes using a modification of the Chernikova Algorithm. We present here a procedure for assignment polytopes which is a simplification of the more general procedure for transportation polytopes and which also allows for implicit enumeration of adjacent vertices.     

OPTIMAL FACILITY LOCATION UNDER RANDOM DEMAND WITH GENERAL COST STRUCTURE

This paper investigates the problem of determining the optimal location of plants, and their respective production and distribution levels, in order to meet demand at a finite number of centers. The possible locations of plants are restricted to a finite set of sites, and the demands are allowed to be random. The cost structure of operating a plant is dependent on its location and is assumed to be a piecewise linear function of the production level, though not necessarily concave or convex. The paper is organized in three parts. In the first part, a branch and bound procedure for the general piecewise linear cost problem is presented, assuming that the demand is known. In the second part, a solution procedure is presented for the case when the demand is random, assuming a linear cost of production. Finally, in the third part, a solution procedure is presented for the general problem utilizing the results of the earlier parts. Certain extensions, such as capacity expansion or reduction at existing plants, and geopolitical configuration constraints can be easily incorporated within this framework.     

Single-period inventory models with demand uncertainty and quantity discounts: Behavioral implications and a new solution procedure

Quantity discounts are considered in the context of the single-period inventory model known as “the newsboy problem.” It is argued that the behavioral implications of the all-units discount schedule are more complex and interesting than the literature has suggested. Consideration of this behavior and the use of marginal analysis lead to a new method for solving this problem that is both conceptually simpler and more efficient than the traditional approach. This marginal-cost solution procedure is described graphically, an algorithm is presented, and an example is used to demonstrate that this solution procedure can be extended easily to handle complex discount schedules, such as some combined (simultaneously applied) purchasing and transportation cost discount schedules.