This paper investigates competition in dynamic product markets from combined resource base and strategic flexibility perspectives. A concept of strategic flexibility in product competition is developed in which flexibility depends jointly on (1) the resource flexibility of the product creation resources avaialble to a firm and (2) the coordination flexibility of the firm in using its available resources in product markets. Two recent technological innovations affecting product creation processes—CADD/CIM systems and modular product design—are argued to have greatly increased the potential flexibilities of key product creation resources. Managerial innovations in the use of these technologies have also led to important new coordination flexibilities. The combination of recently achievable resource and coordination flexibilities is argued to have transformed the competitive environments of many product markets, leading to new kinds of product strategies, new organizational forms, and a new dominant logic for competing in dynamic product markets.

Strategic flexibility in product competition

Computational Grid (Grid Computing) is a new paradigm that will drive the computing arena in the new millennium. Unification of globally remote and diverse resources, coupled with the increasing computational needs for Grand Challenge Applications (GCA) and accelerated growth of the Internet and communication technology will further fuel the development of global computational power grids. In this paper, we attempt to address the scheduling of jobs to the geographically distributed computing resources. Conventional wisdom in the field of scheduling is that scheduling problems exhibit such richness and variety that no single scheduling method is sufficient. Heuristics derived from the nature has demonstrated a surprising degree of effectiveness and generality for handling combinatorial optimization problems. This paper begins with an introduction of computational grids followed by a brief description of the three nature's heuristics namely Genetic Algorithm (GA), Simulated Annealing (SA) and Tabu Search (TS). Experimental results using GA are included. We further demonstrate the hybridized usage of the above algorithms that can be applied in a computational grid environment for job scheduling.

Nature's heuristics for scheduling jobs on Computational Grids

https://riuma.uma.es/xmlui/bitstream/handle/10630/8145/scheduling-ins.pdf?sequence=1

Software project management with GAs

https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=3087&context=usdaarsfacpub

Review: Development of soft computing and applications in agricultural and biological engineering

A fundamental problem in managing product development is the optimal timing, frequency, and fidelity of sequential testing activities that are carried out to evaluate novel product concepts and designs. In this paper, we develop a mathematical model that treats testing as an activity that generates information about technical and customer-need related problems. An analysis of the model results in several important findings. First, optimal testing strategies need to balance the tension between several variables, including the increasing cost of redesign, the cost of a test as function of fidelity, and the correlation between sequential tests. Second, a simple form of our model results in an EOQ-like result: The optimal number of tests called the Economic Testing Frequency or ETF is the square root of the ratio of avoidable cost and the cost of a test. Third, the relationship between sequential tests can have an impact on optimal testing strategies. If sequential tests are increasing refinements of one another, managers should invest their budgets in a few high-fidelity tests, whereas if the tests identify problems independently of one another it may be more effective if developers carry out a higher number of lower-fidelity tests. Using examples, the implications for managerial practice are discussed and suggestions for further research undertakings are provided.

Sequential Testing in Product Development

This work describes a genetic algorithm approach to resource-constrained scheduling using a direct, time-based representation. Whereas traditional solution methods are typically sequencebased, this representation encodes schedule information as a dual array of relative delay times and integer execution modes. The representation supports multiple execution modes, preemption, non-uniform resource availability/usage, a variety of resource types, probabilistic resource performance models, overlapping precedence relationships, and temporal constraints on both tasks and resources. In addition, the representation includes time-varying resource availabilities and requirements. Many objective measures can be defined such as minimization of makespan, maximization of net present value, or minimization of average tardiness. Multiple, possibly conflicting objectives are supported. The genetic algorithm adapts to dynamic factors such as changes to the project plan or disturbances in the schedule execution. In addition to the scheduling representation, this thesis presents a structured method for defining and evaluating multiple constraints and objectives. The genetic algorithm was applied to over 1000 small job shop and project scheduling problems (10-300 activities, 3-10 resource types). Although computationally expensive, the algorithm performed fairly well on a wide variety of problems. With little attention given to its parameters, the algorithm found solutions within 2% of published best in 60% of the project scheduling problems. Performance on the jobshop problems was less encouraging; in a set of 82 jobshop problems with makespan as the single performance measure, the algorithm found solutions with makespan 2 to 3 times the published best. On project scheduling problems with multiple execution modes, the genetic algorithm performed better than deterministic, bounded enumerative search methods for 10% of the 538 problems tested. The test runs were performed with minimal attention to tuning of the genetic algorithm parameters. In most cases, better performance is possible simply by running the algorithm longer or by varying the selection method, population size or mutation rate. However, the results show the flexibility and robustness of a direct representation and hint at the possibilities of integrating the genetic algorithm approach with other methods. Thesis Committee: Mark Jakiela  , Associate Professor of Mechanical Engineering, MIT Woodie Flowers, Pappalardo Professor of Mechanical Engineering, MIT Stephen Graves, Professor of Management Science, Sloan School of Management Karl Ulrich, Associate Professor of Operations and Information Management, The Wharton School This document is available from ftp://lancet.mit.edu/pub/mbwall/phd/thesis.ps.gz effective 1 August 1996, Hunter Associate Professor of Mechanical Design and Manufacturing, Mechanical Engineering, Washington University, St. Louis.

/pdf/a-genetic-algorithm-for-resource-constrained-scheduling-34onuih1am.pdf

A genetic algorithm for resource-constrained scheduling

Firms that design mechanical and electromechanical products confront a variety of difficult issues in their prototyping activities. For a given part, how can a choice among fabrication technologies be made? Where should investments in new prototyping technology be focused? How can new and existing prototyping technologies be evaluated? Our primary goal has been to develop a systematic method of evaluating prototyping processes in order to determine the best process for a given situation. A secondary goal has been to map the “space” of prototyping processes in order to determine future process development needs. Using data from a field study at the Kodak Apparatus Division, we have developed a systematic method for evaluating and selecting prototyping processes. Our data are drawn from (1) a user survey of prototyping perceptions and needs, (2) a survey to determine the importance of various prototype part performance attributes, and (3) estimates of the fabrication time, cost, and part performance for 104 parts and four prototyping processes.

Evaluating Prototyping Technologies, for Product Design

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, and Sloan School of Management, 1991.

/pdf/making-sense-of-prototyping-technologies-for-product-design-1d4vz8ui1o.pdf

Matthew Bartschi Wall

Papers

A genetic algorithm for resource-constrained scheduling

Evaluating Prototyping Technologies, for Product Design

Making sense of prototyping technologies for product design