Goal programming

About: Goal programming is a research topic. Over the lifetime, 4330 publications have been published within this topic receiving 117758 citations.

An evaluation framework for product planning using FANP, QFD and multi-choice goal programming

Abstract: Technological innovation and satisfaction of customer needs are the keys to survival and success for firms, especially in global competitive high-tech industries. Since new products are usually a source of new sales and profits, the success of new product development (NPD) is essential to maintain a competitive edge and to make a decent profit in a longer term. Therefore, how to develop products that deliver the quality and functionality customers demand while generating the desired profits becomes an important task for the manufacturers. In this paper, a framework with two phases is constructed for facilitating the selection of engineering characteristics (ECs) for product design. In the first phase, quality function deployment (QFD) is incorporated with the supermatrix approach of analytic network process (ANP) and the fuzzy set theory to calculate the priorities of ECs with the consideration of the interrelationship among factors and the impreciseness and vagueness in human judgments and information. I...

Manufacturing supply chain design and evaluation

Abstract: Successful supply chain management calls for robust supply chain design and evaluation tools. Many published papers focused on high level strategic aspects of supply chain design and the results are usually generic guidelines for business executives rather than specific tools for plant managers. In this paper, we present a decision-based methodology for supply chain design that a plant manager can use to select suppliers. The methodology utilizes the techniques of analytic hierarchy process and preemptive goal programming. Supply chain operations reference model level I performance metrics are incorporated into the methodology as the decision criteria. In addition, a set of performance metrics is developed to evaluate the overall supply chain effectiveness, which allows direct comparison of different supply chain designs.

An Integrated Approach for Supplier Selection

Abstract: Purchasing decisions are no longer made based on best price but companies are increasingly considering both tangible and intangible benefits that suppliers bring to the long term growth of the company. This paper addresses the multi-objective criteria pertaining to supplier selection process by a combination of Quality Function Deployment (QFD), Analytical Hierarchy Process (AHP) and Preemptive Goal Programming (PGP) techniques. QFD facilitates in blending the requirement for suppliers and supplier evaluating criteria. AHP then helps in systematically prioritizing the relative importance of the requirements enumerated as part of the QFD. Finally, PGP aids in the formulation to maximize the value proposition and to minimize the cost involved by exploiting volume discounts.

Merging Separately Generated Plans with Restricted Interactions

Abstract: Generating action sequences to achieve a set of goals is a computationally difficult task. When multiple goals are present, the problem is even worse. Although many solutions to this problem have been discussed in the literature, practical solutions focus on the use of restricted mechanisms for planning or the application of domain dependent heuristics for providing rapid solutions (i.e., domain-dependent planning). One previously proposed technique for handling multiple goals efficiently is to design a planner or even a set of planners (usually domain-dependent) that can be used to generate separate plans for each goal. The outputs are typically either restricted to be independent and then concatenated into a single global plan, or else they are merged together using complex heuristic techniques. In this paper we explore a set of limitations, less restrictive than the assumption of independence, that still allow for the efficient merging of separate plans using straightforward algorithmic techniques. In particular, we demonstrate that for cases where separate plans can be individually generated, we can define a set of limitations on the allowable interactions between goals that allow efficient plan merging to occur. We propose a set of restrictions that are satisfied across a significant class of planning domains. We present algorithms that are efficient for special cases of multiple plan merging, propose a heuristic search algorithm that performs well in a more general case (where alternative partially ordered plans have been generated for each goal), and describe an empirical study that demonstrates the efficiency of this search algorithm.