Great Price "Modelling and heuristics of FMS scheduling with multiple objectives [An article from: Computers and Operations Research]" for $5.95 Today

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Modelling and heuristics of FMS scheduling with multiple objectives [An article from: Computers and Operations Research] Overviews

This digital document is a journal article from Computers and Operations Research, published by Elsevier in . The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

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The performance of a scheduling system, in practice, is not evaluated to satisfy a single objective, but to obtain a trade-off schedule regarding multiple objectives. Therefore, in this research, we make use of one of the multiple objective decision-making methods, a global criterion approach, to develop a multi-objective model for solving FMS scheduling problems with consideration of three performance measures, namely minimum mean job flow time, mean job tardiness, and minimum mean machine idle time, simultaneously. In addition, hybrid heuristics, which are a combination of two common local search methods, simulated annealing and tabu search, are also proposed for solving the addressed FMS scheduling problems. The feasibility and adaptability of the proposed heuristics are investigated through experimental results.

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Great Price "For many North Carolina start-ups, biology is destiny: the Biotechnology Center has provided .5 million to 86 companies for product research and development.(N.C. ... An article from: Business North Carolina" for $5.95 Today

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For many North Carolina start-ups, biology is destiny: the Biotechnology Center has provided .5 million to 86 companies for product research and development.(N.C. ... An article from: Business North Carolina Overviews

This digital document is an article from Business North Carolina, published by Business North Carolina on September 1, 2004. The length of the article is 1037 words. The page length shown above is based on a typical 300-word page. The article is delivered in HTML format and is available in your Amazon.com Digital Locker immediately after purchase. You can view it with any web browser.

Citation Details
Title: For many North Carolina start-ups, biology is destiny: the Biotechnology Center has provided .5 million to 86 companies for product research and development.(N.C. Biotechnology Center)(North Carolina)
Publication:Business North Carolina (Magazine/Journal)
Date: September 1, 2004
Publisher: Business North Carolina
Volume: 24 Issue: 9 Page: S12(2)

Distributed by Thomson Gale

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Great Price "Heuristic approaches to determine base-stock levels in a serial supply chain with a single objective and with multiple objectives [An article from: European Journal of Operational Research]" for $7.95 Today

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Heuristic approaches to determine base-stock levels in a serial supply chain with a single objective and with multiple objectives [An article from: European Journal of Operational Research] Overviews

This digital document is a journal article from European Journal of Operational Research, published by Elsevier in 2006. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

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This paper deals with the problem of determination of installation base-stock levels in a serial supply chain. The problem is treated first as a single-objective inventory-cost optimization problem, and subsequently as a multi-objective optimization problem by considering two cost components, namely, holding costs and shortage costs. Variants of genetic algorithms are proposed to determine the best base-stock levels in the single-objective case. All variants, especially random-key gene-wise genetic algorithm (RKGGA), show an excellent performance, in terms of convergence to the best base-stock levels across a variety of supply chain settings, with minimum computational effort. Heuristics to obtain base-stock levels are proposed, and heuristic solutions are introduced in the initial population of the RKGGA to expedite the convergence of the genetic search process. To deal with the multi-objective supply-chain inventory optimization problem, a simple multi-objective genetic algorithm is proposed to obtain a set of non-dominated solutions.

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A heuristic algorithm for master planning that satisfies multiple objectives [An article from: Computers and Operations Research] Review

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This digital document is a journal article from Computers and Operations Research, published by Elsevier in 2007. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

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This study proposes a heuristic algorithm, called the multi-objective master planning algorithm (MOMPA), to solve master planning (MP) problems for a supply chain network with multiple finished products. MOMPA has three objectives: to minimize delay penalties, to minimize use of outsourcing capacity, and to minimize the costs of materials, production, processing, transportation, and inventory holding-all while respecting the capacity limitations and the demand deadlines of all those involved in a given supply chain network. MOMPA plans each demand, one by one, without backtracking, and sorts those demands using a sorting mechanism that is part of the algorithm. For each demand, the minimum production cost tree is determined within the limits of the time bucket for the demand deadline. The maximum available capacity of this tree is then computed for the ''no delay'' case. Following this calculation, the delay-or-not criterion is evaluated to determine whether or not further delay is necessary. MOMPA compares the results of these two procedures and allocates the appropriate capacities to the demand for all the nodes on the selected tree. If the minimum cost production tree has no available capacity, MOMPA adjusts the network and looks for a new tree. With complexity and computational analysis, MOMPA is shown to be very efficient in solving MP problems, sometimes generating the same optimal solution as the LP model.

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An iterative sequential heuristic procedure to a real-life 1.5-dimensional cutting stock problem [An article from: European Journal of Operational Research]

An iterative sequential heuristic procedure to a real-life 1.5-dimensional cutting stock problem [An article from: European Journal of Operational Research] Review

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This digital document is a journal article from European Journal of Operational Research, published by Elsevier in 2006. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

Description:
This paper addresses a real-life 1.5D cutting stock problem, which arises in a make-to-order plastic company. The problem is to choose a subset from the set of stock rectangles to be used for cutting into a number of smaller rectangular pieces so as to minimize total production cost and meet orders. The total production cost includes not only material wastage, as in traditional cutting stock problems, but also production time. A variety of factors are taken into account, like cutter knife changes, machine restrictions, due dates and other work in progress limitations. These restrictions make the combinatorial structure of the problem more complex. As a result, existing algorithms and mathematical models are no longer appropriate. Thus we developed a new 1.5D cutting stock model with multiple objectives and multi-constraints and solve this problem in an incomplete enumerative way. The computational results show that the solution procedure is easy to implement and works very well.

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