Decision support for build-to-order supply chain management through multiobjective optimization

This is the post-print version of the final paper published in International Journal of Production Economics. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.This paper aims to identify the gaps in decision-making support based on multiobjective optimization (MOO) for build-to-order supply chain management (BTO-SCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting MOO techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted. Future research directions to better exploit the decision support capabilities of MOO are proposed. These include: reformulation of the extant optimization models with a MOO perspective, development of decision supports for interfaces not involving manufacturers, development of scenarios around service-based objectives, development of efficient solution tools, considering the interests of each supply chain party as a separate objective to account for fair treatment of their requirements, and applying the existing methodologies on real-life data sets.Brunel Research Initiative and Enterprise Fund (BRIEF

Effectiveness of R&D project selection in uncertain environment: An empirical study in the German automotive supplier industry

This paper presents results of an empirical large-scale study on uncertainty reduction of R&D projects and R&D project selection. The empirical field is the German automotive supplier industry. We explore R&D project selection practices in this specific industry and briefly contrast our findings with the academic research and management literature in this field. We concentrate on answering three research questions (with focus on questions no. 1 and 2): I. Which information and related uncertainties are crucial for the product selection decision to the R&D decision makers? II. How do R&D decision makers today cope with typical challenges related to reducing uncertainty? Where do they face major problems and how effective are they? III. What are major implications for managing the Fuzzy Front End (FFE) of innovation process in industry practice and respectively for further academic research in this field? Key findings are that on the one hand certainty about fields of product applications, target markets and production feasibility are most important criteria for initial product selection decisions. On the other hand market and cost related uncertainties (e.g. sales volume, product price, cost per unit) cannot be satisfyingly reduced in practice before project approval for development or definite termination of projects. Although different uncertainty profiles exist within the process of project evaluation, most companies do not systematically choose available product selection methods and tools according to specific uncertainty situations. Intuition still plays a major role in R&D product selection. Some first conclusion drawn from this research are: A sufficient level of resources (including financial and methodological know-how), a systematic use of suitable project selection instruments, and a fit with the company specific as well as the OEMs' product/brand strategies can be potential levers for more effective uncertainty reduction before product decision. --

A fuzzy decision tool to evaluate the sustainable performance of suppliers in an agrifood value chain

Sustainable supply chain management has received much attention from both academia and industry due to various issues such as economic stability, environment conservation, and social ethics. To improve the sustainable performance of a value chain, its members need to carefully select their suppliers in relation to their own strategy. Thus, an effective tool for sustainable supplier selection and evaluation is essential, which considers the triple bottom line (TBL) of economic, environmental and social aspects by means of criteria adapted to the situation analysed. This paper develops a fuzzy decision tool to evaluate the sustainable performance of suppliers according to TBL. Sustainability criteria are identified to take into account the real hotspots in a food value chain. The proposed model integrates triangular fuzzy numbers (TFN), AHP (Analytic Hierarchy Process) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) in a novel way to consider quantitative and qualitative criteria as well as objective and subjective data. This is missing in most existing research when building their fuzzy models for supplier selection, but critical in dealing with the heterogeneous data available for TBL assessment. The application in a sustainable agrifood value chain illustrates the effectiveness of the proposed tool

A methodology for the selection of new technologies in the aviation industry

The purpose of this report is to present a technology selection methodology to quantify both tangible and intangible benefits of certain technology alternatives within a fuzzy environment. Specifically, it describes an application of the theory of fuzzy sets to hierarchical structural analysis and economic evaluations for utilisation in the industry. The report proposes a complete methodology to accurately select new technologies. A computer based prototype model has been developed to handle the more complex fuzzy calculations. Decision-makers are only required to express their opinions on comparative importance of various factors in linguistic terms rather than exact numerical values. These linguistic variable scales, such as ‘very high’, ‘high’, ‘medium’, ‘low’ and ‘very low’, are then converted into fuzzy numbers, since it becomes more meaningful to quantify a subjective measurement into a range rather than in an exact value. By aggregating the hierarchy, the preferential weight of each alternative technology is found, which is called fuzzy appropriate index. The fuzzy appropriate indices of different technologies are then ranked and preferential ranking orders of technologies are found. From the economic evaluation perspective, a fuzzy cash flow analysis is employed. This deals quantitatively with imprecision or uncertainties, as the cash flows are modelled as triangular fuzzy numbers which represent ‘the most likely possible value’, ‘the most pessimistic value’ and ‘the most optimistic value’. By using this methodology, the ambiguities involved in the assessment data can be effectively represented and processed to assure a more convincing and effective decision- making process when selecting new technologies in which to invest. The prototype model was validated with a case study within the aviation industry that ensured it was properly configured to meet the

Decision support for build-to-order supply chain management through multiobjective optimization

This paper aims to identify the gaps in decision-making support based on multiobjective optimization for build-to-order supply chain management (BTOSCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting multiobjective optimization (MOO) techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted and future research directions to better exploit the decision support capabilities of MOO are proposed

airline revenue management

With the increasing interest in decision support systems and the continuous advance of computer science, revenue management is a discipline which has received a great deal of interest in recent years. Although revenue management has seen many new applications throughout the years, the main focus of research continues to be the airline industry. Ever since Littlewood (1972) first proposed a solution method for the airline revenue management problem, a variety of solution methods have been introduced. In this paper we will give an overview of the solution methods presented throughout the literature.revenue management;seat inventory control;OR techniques;mathematical programming

Towards A Comprehensive Framework For Technology Selection And Capacity Planning For Sustainable Manufacturing

Technology mixed perspective, as a combination of two functions of ‘Technology Selection’ and ‘Capacity Planning’, is not usually addressed in the research literature. Yet, the importance of integrated decisions at such strategic level is evident. The overall aim of this paper is to develop a framework for combined ‘technology selection’ and ‘capacity planning’ in manufacturing sector. The approach will also incorporate the multiperspective concept of sustainability, while taking uncertainties into account

Application of Artificial Intelligence (AI) methods for designing and analysis of Reconfigurable Cellular Manufacturing System (RCMS)

This work focuses on the design and control of a novel hybrId manufacturing system: Reconfigurable Cellular Manufacturing System (RCMS) by using Artificial Intelligence (AI) approach. It is hybrid as it combines the advantages of Cellular Manufacturing System (CMS) and Reconfigurable Manufacturing System (RMS). In addition to inheriting desirable properties from CMS and RMS, RCMS provides additional benefits including flexibility and the ability to respond to changing products, product mix and market conditions during its useful life, avoiding premature obsolescence of the manufacturing system. The emphasis of this research is the formation of Reconfigurable Manufacturing Cell (RMC) which is the dynamic and logical clustering of some manufacturing resources, driven by specific customer orders, aiming at optimally fulfilling customers' orders along with other RMCs in the RCMS