An interactive paired comparison method for bicriterion integer programming

This article proposes an interactive paired comparison region elimination method for bicriterion integer mathematical programming problems. The new method isolates the best compromise solution by successively evaluating a pair of associated supported non-dominated solutions. The efficiency of the method is tested by solving randomly generated problems based on varying shapes of efficient frontiers. When compared with the existing branch-and-bound method, the method was effective in reducing the burden on the decision maker. © 1994 John Wiley & Sons, Inc.     

A multi-run interactive method for bicriterion optimization problems

This article introduces a new conceptual and methodological framework for the use of decision makers and their interactions with the computer in bicriterion decision making. The new method, called the multirun interactive method, attempts to estimate the prior of the decision maker on his uncertain preference nature using the minimum cross-entropy principle. A computational study is performed with four hypothesized prior distributions under various interaction conditions. Other important aspects related to the method, such as implementation of the method, decision making under certainty, decision making with multiple decision makers, and bicriterion integer programming, are also discussed.     

Comparative evaluation of prior versus progressive articulation of preference in bicriterion optimization

Procedures for solving multiple criteria problems are receiving increasing attention. Two major solution approaches are those involving prior articulation and progressive articulation of preference information. A progressive articulation (interactive) optimization approach, called the Paired Comparison Method (PCM) is compared to the prior articulation approach of a priori utility function measurement in a quality control decision environment from the perspective of the decision maker. The three major issues investigated included: (1) the ease of use of each method, (2) the preferences of solutions obtained, and (3) the insight provided by the methodology into the nature and structure of the problem. The problem setting involved management students who were rquired to determine an acceptance sampling plan using both methods. The PCM provided the most preferred solutions and was considered easier to use and understand. The prior articulation of preference method was found to give more insight into the problem structure. The results suggest that a hybrid approach, combining both prior preference assessment and an interactive procedure exploiting the advantages of each, should be employed to solve multiple criteria problems.     

Approximating the nondominated frontiers of multi‐objective combinatorial optimization problems

Finding all nondominated vectors for multi‐objective combinatorial optimization (MOCO) problems is computationally very hard in general. We approximate the nondominated frontiers of MOCO problems by fitting smooth hypersurfaces. For a given problem, we fit the hypersurface using a single nondominated reference vector. We experiment with different types of MOCO problems and demonstrate that in all cases the fitted hypersurfaces approximate all nondominated vectors well. We discuss that such an approximation is useful to find the neighborhood of preferred regions of the nondominated vectors with very little computational effort. Further computational effort can then be spent in the identified region to find the actual nondominated vectors the decision maker will prefer. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2009     

A descent approach to solving the complementary programming problem

In recent years, much attention has focused on mathematical programming problems with equilibrium constraints. In this article we consider the case where the constraints are complementarity constraints. Problems of this type arise, for instance, in the design of traffic networks. We develop here a descent algorithm for this problem that will converge to a local optimum in a finite number of iterations. The method involves solving a sequence of subproblems that are linear programs. Computational tests comparing our algorithm with the branch-and-bound algorithm in [7] bear out the efficacy of our method. When solving large problems, there is a definite advantage to coupling both methods. A local optimum incumbent provided by our algorithm can significantly reduce the computational effort required by the branch-and-bound algorithm.     

Sequencing with setup time and order tardiness trade-offs

We consider the problem of scheduling customer orders on a single facility where each order consists of several jobs that can be clustered into several groups. When a facility is changed over to another group, a setup time associated with the new group is required. Two particular problems are considered in this context. One is to consider the total setup time and the number of tardy orders jointly. The other is to consider the total setup time and the maximum tardiness jointly. The total setup time in both problems represents a measure of internal efficiency, whereas the number of tardy orders and the maximum tardiness represent a measure of external efficiency. In any shop, the decision maker must consider the tradeoffs between large setup costs associated with a more frequent changeover schedule versus the cost of tardy orders that might be induced by a less-frequent changeover schedule. In this article branch-and-bound algorithms are proposed to identify the set of nondominated schedules for the two bicriteria problems. © 1996 John Wiley & Sons, Inc.     

Identifying and ranking a most preferred subset of alternatives in the presence of multiple criteria

In this article an interactive method is developed to identify and rank a most preferred subset, T, of alternatives assuming that the decision maker has an implicit quasiconcave nondecreasing utility function. The method requires the decision maker to compare pairs of selected alternatives. Based on the responses of the decision maker, convex cones are constructed to eliminate alternatives that are proved to be inferior to alternatives in set T. The method aims at keeping the number of pairwise comparisons small. Computational experience with the method indicates that the required number of pairwise comparisons to form set T is usually small. However, the number of pairwise comparisons needed to confirm that this set is best may be large.     

A penalty for concave minimization derived from the tuy cutting plane

A wide variety of optimization problems have been approached with branch-and-bound methodology, most notably integer programming and continuous nonconvex programming. Penalty calculations provide a means to reduce the number of subproblems solved during the branch-and-bound search. We develop a new penalty based on the Tuy cutting plane for the nonconvex problem of globally minimizing a concave function over linear constraints and continuous variables. Computational testing with a branch-and-bound algorithm for concave minimization indicates that, for the problems solved, the penalty reduces solution time by a factor ranging from 1.2 to 7.2. © 1994 John Wiley & Sons, Inc.     

Risk preference,multiple attributes,and multiple criteria in bayesian acceptance sampling plans

Although there has been considerable research directed toward developing Bayesian acceptance sampling plans, little consideration has been given to incorporating a decision-maker's risk attitude or treating such problems in terms of multiple-type defects and multiple criteria. We review our own work which is focused on the above issues. A model incorporating risk preference is shown to yield substantial differences in the characteristics of an optimal sampling plan relative to a typical Bayesian linear cost (risk neutral) model. Bayesian models and optimization schemes for a variety of multiple-type defect plans are reviewed. A bicriterion acceptance model, employing average outgoing quality and average inspection cost is also formulated. Traditional versus interactive optimization procedures are compared empirically in terms of ease of use, satisfaction with solutions, and insight gained into the problem.     

Interactive multicriteria linear programming: An extension of the method of Zionts and Wallenius

This article presents an extension of the interactive multicriteria linear-programming method of Zionts and Wallenius [see Management Science, 29 (5) (1983)]. The decision maker's underlying utility function is assumed to be pseudoconcave, and his preference structure is assessed through pairwise comparison questions. In the method of Zionts and Wallenius, the decision maker's preference structure is represented as constraints on the weights on the objectives derived from his responses. This representation is only a linear approximation to the underlying nonlinear utility function. Accordingly, inconsistency among the constraints on the weights arises while solving the problem. Therefore, some of the constraints have to be dropped, resulting in a loss of information on the preference structure, and hence an increase in the total number of questions. In this article we develop a hybrid representation scheme to avoid this problem. The proposed scheme is implemented within the algorithmic framework of the method of Zionts and Wallenius, and its underlying theory is developed. Computational results show that the number of questions required by the Zionts and Wallenius method can be sinificantly reduced using the proposed scheme.     

一种锥比率数据包络分析模型及有关结论

本文研究一种锥比率数据包络分析模型,它既能反映各项输入或输出的相对重要性和对某些决策单元的偏好,又可单纯地评价决策单元间的相对技术有效性。本文着重讨论这一模型的DEA有效性与多目标规划非支配解的关系以及有效决策单元的存在性等问题,论证了有关结论。     

Interactive solution of bi-criteria mathematical programs

In this paper, we develop efficient interactive methods for the solution of bicriteria nonlinear programming problems. The methods do not require trade-off information from the decision maker, pose less cognitive burden and converge to the “best compromise solution” fast. Two methods, called the paired comparison method and comparative trade-off method, are presented with examples. A real application of the interactive method to a bicriteria problem that arose in the planning of the cardiovascular disease control program in the U.S. Air Force is also presented.     

An exact branch‐and‐price algorithm for multitasking scheduling on unrelated parallel machines

We consider the multitasking scheduling problem on unrelated parallel machines to minimize the total weighted completion time. In this problem, each machine processes a set of jobs, while the processing of a selected job on a machine may be interrupted by other available jobs scheduled on the same machine but unfinished. To solve this problem, we propose an exact branch‐and‐price algorithm, where the master problem at each search node is solved by a novel column generation scheme, called in‐out column generation, to maintain the stability of the dual variables. We use a greedy heuristic to obtain a set of initial columns to start the in‐out column generation, and a hybrid strategy combining a genetic algorithm and an exact dynamic programming algorithm to solve the pricing subproblems approximately and exactly, respectively. Using randomly generated data, we conduct numerical studies to evaluate the performance of the proposed solution approach. We also examine the effects of multitasking on the scheduling outcomes, with which the decision maker can justify making investments to adopt or avoid multitasking.     

New product introduction against a predator: A bilevel mixed‐integer programming approach

We consider a scenario with two firms determining which products to develop and introduce to the market. In this problem, there exists a finite set of potential products and market segments. Each market segment has a preference list of products and will buy its most preferred product among those available. The firms play a Stackelberg game in which the leader firm first introduces a set of products, and the follower responds with its own set of products. The leader's goal is to maximize its profit subject to a product introduction budget, assuming that the follower will attempt to minimize the leader's profit using a budget of its own. We formulate this problem as a multistage integer program amenable to decomposition techniques. Using this formulation, we develop three variations of an exact mathematical programming method for solving the multistage problem, along with a family of heuristic procedures for estimating the follower solution. The efficacy of our approaches is demonstrated on randomly generated test instances. This article contributes to the operations research literature a multistage algorithm that directly addresses difficulties posed by degeneracy, and contributes to the product variety literature an exact optimization algorithm for a novel competitive product introduction problem. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2009     

A finite algorithm for concave minimization over a polyhedron

We present a new algorithm for solving the problem of minimizing a nonseparable concave function over a polyhedron. The algorithm is of the branch-and-bound type. It finds a globally optimal extreme point solution for this problem in a finite number of steps. One of the major advantages of the algorithm is that the linear programming subproblems solved during the branch-and-bound search each have the same feasible region. We discuss this and other advantages and disadvantages of the algorithm. We also discuss some preliminary computational experience we have had with our computer code for implementing the algorithm. This computational experience involved solving several bilinear programming problems with the code.     

A combined approach to solve binary multicriteria problems

An implicit enumeration algorithm is developed to determine the set of efficient points in zero-one multiple criteria problems. The algorithm is specialized for the solution of a particular class of facility location problems. The procedure is complemented with the use of the utility function of the decision maker to identify a subset of efficient point candidates for the final selection. Computational results are provided and discussed.     

多目标的分布式协同进化MDO算法

通过引入非优超排序和排挤的多目标处理机制 ,将分布式协同进化MDO算法的能力扩展到多目标的多学科设计优化问题。多目标的分布式协同进化MDO算法在保持各学科充分自治和各学科并行设计优化协同的基础上 ,通过一次运行即可获得具有良好分布的多个Pareto最优解 ,逼近整个Pareto最优前沿。应用于导弹气动 /发动机 /控制三学科两目标设计优化问题 ,与约束法计算结果的对比表明算法能够有效逼近该问题的Pareto最优前沿 ,为设计决策提供了丰富的信息     

Using multiple searchers in constrained-path,moving-target search problems

The search theory open literature has paid little, if any, attention to the multiple-searcher, moving-target search problem. We develop an optimal branch-and-bound procedure and six heuristics for solving constrained-path problems with multiple searchers. Our optimal procedure outperforms existing approaches when used with only a single searcher. For more than one searcher, the time needed to guarantee an optimal solution is prohibitive. Our heuristics represent a wide variety of approaches: One solves partial problems optimally, two use paths based on maximizing the expected number of detections, two are genetic algorithm implementations, and one is local search with random restarts. A heuristic based on the expected number of detections obtains solutions within 2% of the best known for each one-, two-, and three-searcher test problem considered. For one- and two-searcher problems, the same heuristic's solution time is less than that of other heuristics. For three-searcher problems, a genetic algorithm implementation obtains the best-known solution in as little as 20% of other heuristic solution times. © 1996 John Wiley & Sons, Inc.     

一种基于多目标优化的QoS路由交互式算法

为了满足通信网络中一些特定业务对于多个网络指标性能的同时要求 ,研究了一类基于多目标决策的QoS路由算法。通过选取带宽作为约束条件 ,把时延和丢失率作为优化目标 ,建立了QoS路由选择的多目标非线性整数规划模型 ,并给出了一种求解模型的交互式算法。该算法通过逐步调整目标函数的上界 ,压缩目标函数的搜索空间来满足决策者的要求和网络条件。实例计算结果表明了算法的可行性