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To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2005.04.039 Byline: J. Sudhir Ryan Daniel, Chandrasekharan Rajendran Abstract: 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. Author Affiliation: Department of Management Studies, Indian Institute of Technology Madras, Chennai 600 036, India Article History: Received 26 August 2003; Accepted 14 April 2005

Byline: J. Sudhir Ryan Daniel, Chandrasekharan Rajendran This paper presents simulation-based heuristic methodologies to compute installation base-stock levels in a serial supply chain so as to minimise the total supply chain cost. A simulated annealing heuristic is proposed to compute the installation base-stock levels for different installations in a serial supply chain. In addition, a simple heuristic to determine the installation base-stock levels is also presented. Simulation is used to evaluate the installation base-stock levels yielded by the proposed simulated annealing heuristic and the simple heuristic. A best-move local search is also used as a benchmark procedure. The solution constructed by the simple heuristic is initialised as the seed solution to both simulated annealing heuristic and the best-move local search, and their performances are studied. All the solution methodologies are extensively evaluated with different supply chain test problems. The solutions yielded by the proposed solution methodologies is compared against the optimal installation base-stock level which is obtained through a complete enumeration of the solution space for each of the supply chain test problems. A comparison of the performance of all the solution methodologies shows that the proposed simulated annealing heuristic initialised with the heuristic solution as the initial seed solution performs the best by consistently producing good-quality solutions with little computational effort.