Finding Acceptable Solutions Research Articles

A weighted sum genetic algorithm to support multiple-party multiple-objective negotiations

Negotiations are a special class of group decision-making problems that can be formulated as constrained optimization problems and are characterized by high degrees of conflict among the negotiation participants. A variety of negotiation support techniques have been used to help find solutions acceptable to all parties in a negotiation. The paper presents an approach that employs a genetic algorithm (GA) for finding acceptable solutions for multiparty multiobjective negotiations. The GA approach is consistent with the complex nature of real-world negotiations and is therefore capable of addressing more realistic negotiation scenarios than previous techniques in the literature allow. In addition to the traditional genetic operators of reproduction, crossover, and mutation, the search is enhanced with a new operator called trade. The trade operator simulates concessions that might be made by parties during the negotiation process. GA performance with the trade operator is compared to a traditional GA, nonlinear programming, a hill-climber, and a random search. Experimental results show the GA with the trade operator performs better than these other more traditional approaches.

Novel semiconductor device approach for finding acceptable solutions to classes of graph-based computing problems not otherwise possible

The framework and practical applications of ongoing efforts to design novel classes of semiconductor devices making it possible to address highly complex problems that can not be currently solved in real-time, is presented. Important classes of such problems are identified and characterized by massively parallel and distributed graph-based architectures of ever-increasing complexity, national, and global scope. The etiology of the inability of current digital microprocessor-based computing schemes and devices to address such complex real-time problems is briefly reviewed. An overview of novel classes of information processing semiconductor devices, designated as “system-on-a-chip” devices that overcome these limitations, is given. Emanating from the application of a new graph-based methodology, they are designed to find acceptable domain-specific solutions in real-time. Examples and simulation results illustrate key concepts and capabilities of these devices for planning classes of problems.

Heuristics for hybrid flow shops with controllable processing times and assignable due dates

This paper considers a generalization of the permutation flow shop problem that combines the scheduling function with the planning stage. In this problem, each work center consists of parallel identical machines. Each job has a different release date and consists of ordered operations that have to be processed on machines from different machine centers in the same order. In addition, the processing times of the operations on some machines may vary between a minimum and a maximum value depending on the use of a continuously divisible resource. We consider a nonregular optimization criterion based on due dates which are not a priori given but can be fixed by a decision-maker. A due date assignment cost is included into the objective function. For this type of problems, we generalize well-known approaches for the heuristic solution of classical problems and propose constructive algorithms based on job insertion techniques and iterative algorithms based on local search. For the latter, we deal with the design of appropriate neighborhoods to find better quality solution. Computational results for problems with up to 20 jobs and 10 machine centers are given. Scope and purpose Traditional research to solve multi-stage scheduling problems has focused on regular measures of performance based on a single criterion and assumes that several decisions related to due dates and processing times have already been made. However, in many industrial scheduling practices, managers develop schedules based on multicriteria and have to decide the due dates and processing times as part of the scheduling activities. Further, in practical scheduling situations, there are multiple machines at each stage and the objective function often reflects the total cost of processing, earliness and tardiness. Such scheduling problems require significantly more effort in finding acceptable solutions and hence have not received much attention in the literature. For this reason, this paper considers one such hybrid flow shop scheduling problem involving nonregular measures of performance, controllable processing times, and assignable due dates. We combine and generalize the existing approaches for the classical flow shop problem to the problem under consideration. Computational experiments are used to evaluate the utility of the proposed algorithms for the generalized scheduling problems. Brah and Hunsucker (European Journal of Operational Research 1991;51:88–99) and Nowicki and Smutnicki (European Journal of Operational Research 1998;106:226–253) describe branch and bound and tabu search algorithms for the approach used in the development of heuristic algorithms can also be adapted to several other complex practical scheduling problems.

SUGGESTION STRATEGIES FOR CONSTRAINT-BASED MATCHMAKER AGENTS

In this paper we describe a paradigm for content-focused matchmaking, based on a recently proposed model for constraint acquisition and satisfaction. Matchmaking agents are constraint-based solvers that interact with other, possibly human, agents (Customers). The Matchmaker provides potential solutions ("suggestions") based on partial knowledge, while gaining further information about the problem itself from the other agent through the latter's evaluation of these suggestions. The dialogue between Matchmaker and Customer results in iterative improvement of solution quality, as demonstrated in simple simulations. We also show empirically that this paradigm supports "suggestion strategies" for finding acceptable solutions more efficiently or for increasing the amount of information obtained from the Customer. This work also indicates some ways in which the tradeoff between these two metrics for evaluating performance can be handled.

Search-based software engineering

This paper claims that a new field of software engineering research and practice is emerging: search-based software engineering. The paper argues that software engineering is ideal for the application of metaheuristic search techniques, such as genetic algorithms, simulated annealing and tabu search. Such search-based techniques could provide solutions to the difficult problems of balancing competing (and some times inconsistent) constraints and may suggest ways of finding acceptable solutions in situations where perfect solutions are either theoretically impossible or practically infeasible. In order to develop the field of search-based software engineering, a reformulation of classic software engineering problems as search problems is required. The paper briefly sets out key ingredients for successful reformulation and evaluation criteria for search-based software engineering.

Local search heuristics for two-stage flow shop problems with secondary criterion

This paper develops and compares different local search heuristics for the two-stage flow shop problem with makespan minimization as the primary criterion and the minimization of either the total flow time, total weighted flow time, or total weighted tardiness as the secondary criterion. We investigate several variants of simulated annealing, threshold accepting, tabu search, and multi-level search algorithms. The influence of the parameters of these heuristics and the starting solution are empirically analyzed. The proposed heuristic algorithms are empirically evaluated and found to be relatively more effective in finding better quality solutions than the existing algorithms. Scope and purpose Traditional research to solve multi-stage scheduling problems has focused on single criterion. However, in industrial scheduling practices, managers develop schedules based on multi-criteria. Scheduling problems involving multiple criteria require significantly more effort in finding acceptable solutions and hence have not received much attention in the literature. This paper considers one such multiple criteria scheduling problem, namely, the two-machine flow shop problem where the primary criterion is the minimization of makespan and the secondary criterion is one of the three most popular performance measures, namely, the total flow time, total weighted flow time, or total weighted tardiness. Based on the principles of local search, development of heuristic algorithms, that can be adapted for several multi-criteria scheduling problems, is discussed. Using the example of the two-machine flow shop problem with secondary criterion, computational experiments are used to evaluate the utility of the proposed algorithms for solving scheduling problems with a secondary criterion.

Heuristic constraints enforcement for training of and rule extraction from a fuzzy/neural architecture. II. Implementation and application

For part I, see ibid., p.143-50. This paper is the second of two companion papers. The foundations of the proposed method of heuristic constraint enforcement on membership functions for knowledge extraction from a fuzzy/neural architecture was given in Part I. Part II develops methods for forming constraint sets using the constraints and techniques for finding acceptable solutions that conform to all available a priori information Moreover, methods of integration of enforcement methods into the training of the fuzzy-neural architecture are discussed. The proposed technique is illustrated on a fuzzy-AND classification problem and a motor fault detection problem. The results indicate that heuristic constraint enforcement on membership functions leads to extraction of heuristically acceptable membership functions in the input and output spaces. Although the method is described on a specific fuzzy/neural architecture, it is applicable to any realization of a fuzzy inference system, including adaptive and/or static fuzzy inference systems.