Finite dimensional approximation in infinite dimensional mathematical programming

We consider the problem of approximating an optimal solution to a separable, doubly infinite mathematical program (P) with lower staircase structure by solutions to the programs (P(N)) obtained by truncating after the firstN variables andN constraints of (P). Viewing the surplus vector variable associated with theNth constraint as a state, and assuming that all feasible states are eventually reachable from any feasible state, we show that the efficient set of all solutions optimal to all possible feasible surplus states for (P(N)) converges to the set of optimal solutions to (P). A tie-breaking algorithm which selects a nearest-point efficient solution for (P(N)) is shown (for convex programs) to converge to an optimal solution to (P). A stopping rule is provided for discovering a value ofN sufficiently large to guarantee any prespecified level of accuracy. The theory is illustrated by an application to production planning.The work of Robert L. Smith was partially supported by the National Science Foundation under Grant ECS-8700836.     

Two-phase optimization of coal strategies at a power station

Development of optimal coal mining and stockpiling strategies for a new 960 MW thermal power station depends strongly on the operation of New Zealand's mixed hydro/thermal power system. To overcome the large dimensionality of the problem the optimization proceeds in two phases for each future scenario studied. Phase 1 finds optimal hydro operating strategies with thermal generation in merit order of marginal fuel costs. It uses stochastic dynamic programming. The hydro generating strategies are used as input to the phase 2 stochastic simulation of the coal mining and stockpiling operation at the new thermal station. Finally, a strategy is identified which is a priori best over all future scenarios of industrial development and oil prices studied.     

Solving certain singly constrained convex optimization problems in production planning

This paper considers the problem of minimizing a special convex function subject to one linear constraint. Based upon a theorem for lower and upper bounds on the Lagrange multiplier a fully polynomial time approximation scheme is proposed. The efficiency of the algorithm is demonstrated by a computational experiment.     

The acquisition of new technology and its impact on a firm's competitive position

The strategic decision concerning the optimal and dynamic acquisition of new technology is examined. The model focuses on a profit maximizing firm that optimally derives its price, level of output, and its level and composition of productive capacity over time. The acquisition of new technology and reduction of existing capacity may occur simultaneously, so that the composition of the firm's productive resources may be upgraded over time. It is assumed that the acquisition of new technology causes a reduction in production costs and a direct increase in the firm's demand. The demand experienced by the firm may be directly increased as a result of acquiring new technology due to benefits such as expanded product-mix or volume capabilities, improved quality of output, or improved customer service (shorter production lead time). In addition, it is shown that demand is indirectly increased due to the reduced production costs that enable the firm to charge a lower price. Therefore, the strategic impact of acquiring new technology is captured, since its effect on future demand and the firm's ability to meet the demand are considered. The importance of capturing the increased demand potential offered by the new technology is demonstrated through the analysis of numerical examples. In addition, the effect on the optimal solution caused by a variety of environmental conditions is examined. For example, the impact of technological innovation is observed by defining (i) the cost of acquiring technology as a decreasing function of time, and (ii) the effectiveness of new technology on reducing operating costs as an increasing function of time.     

新世纪建筑的发展方向—绿色建筑

本文结合中国国情，从绿色美学思想，生态学规划，绿色标志三方面论述了新世纪建筑的发展方向-走绿色建筑之路。     

Integer linear programming formulation of the material requirements planning problem

Lot sizing procedures for discrete and dynamic demand form a distinct class of inventory control problems, usually referred to asmaterial requirements planning. A general integer programming formulation is presented, covering an extensive range of problems: single-item, multi-item, and multi-level optimization; conditions on lot sizes and time phasing; conditions on storage and production capacities; and changes in production and storage costs per unit. The formulation serves as a uniform framework for presenting a problem and a starting point for developing and evaluating heuristic and tailor-made optimum-seeking techniques.     

Facility location models for planning a transatlantic communications network

This paper presents two facility location models for the problem of determining how to optimally serve the requirements for communication circuits between the United States and various European and Middle Eastern countries. Given a projection of future requirements, the problem is to plan for the economic growth of a communications network to satisfy these requirements. Both satellite and submarine cable facilities may be used. The objective is to find an optimal placement of cables (type, location, and timing) and the routing of individual circuits between demand points (over both satellites and cables) such that the total discounted cost over a T-period horizon is minimized. This problem is cast as a multiperiod, capacitated facility location problem. Two mathematical models differing in their provisions for network reliability are presented. Solution approaches are outlined and compared by means of computational experience. Use of the models both in planning the growth of the network and in the economic evaluation of different cable technologies is also discussed.     

Balancing capacity and lot sizes

In many companies there is an on-going discussion about capacity, capacity utilization and capital tied up in inventories. However, traditional models such as the EOQ model only include capacity considerations in the set-up cost, or in the cost of a replenishment order. This implies e.g. that they do not consider the set-up time as a capacity constraint. Furthermore, in these models the set-up cost is usually treated as a constant, even though the opportunity cost for capacity in general is dependent upon the capacity utilization.The purpose of this paper is to derive an analytical model for the balancing of capacity and lot sizes. The model includes costs for capacity, work-in-process (queueing, set-up, and processing time), and finished goods inventory. The total costs are minimized with respect to capacity. Then, the corresponding, recommended lot sizes are determined. The model was tested with data from a Swedish manufacturing company. The results turned out to coincide with experiences of the company in many important respects. The model offers production management an opportunity to discuss the relationship between capacity, work-in-process, and lot sizes.     

Heuristic optimization methods for motion planning of autonomous agricultural vehicles

In this paper, two heuristic optimization techniques are tested and compared in the application of motion planning for autonomous agricultural vehicles: Simulated Annealing and Genetic Algorithms. Several preliminary experimentations are performed for both algorithms, so that the best neighborhood definitions and algorithm parameters are found. Then, the two tuned algorithms are run extensively, but for no more than 2000 cost function evaluations, as run-time is the critical factor for this application. The comparison of the two algorithms showed that the Simulated Annealing algorithm achieves the better performance and outperforms the Genetic Algorithm. The final optimum found by the Simulated Annealing algorithm is considered to be satisfactory for the specific motion planning application.