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To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ijpe.2008.11.015 Byline: Satyaveer S. Chauhan (a)(b), Alexandre Dolgui (c), Jean-Marie Proth (d) Keywords: Assembly-to-order; Random lead-times; Supply planning; Optimization; Simulated annealing Abstract: This paper studies a customized product assembly scenario where some components cannot be stocked due to high component cost and risk. We consider the case where these key components are ordered after a demand has been registered with a promised delivery date. Component lead-times are stochastic and associated distribution function is known in advance. The objective is to determine the ordering time for each component such as to minimize the sum of expected holding and backlogging costs. An approach to solve this problem is proposed and the algorithm is tested on a randomly generated data set. Author Affiliation: (a) John Molson School of Business, Concordia University, 1455 de Maisonneuve Boulevard West, Montreal, Quebec, Canada H3G 1M8 (b) Centre Interuniversitaire de Recherche sur les Reseaux d'Entreprise, la Logistique et le Transport (CIRRELT), Montreal, Canada (c) Centre for Industrial Engineering and Computer Science, Ecole des Mines de Saint Etienne, 158, cours Fauriel 42023 Saint Etienne, France (d) INRIA-SAGEP, 22 rue du Colombier, 57420 Pouilly, France Article History: Received 1 May 2007; Accepted 7 November 2008

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ijpe.2008.10.012 Byline: A. Awasthi (a), S.S. Chauhan (b), S.K. Goyal (b), Jean-Marie Proth (c) Abstract: In this paper we consider a supplier selection problem for a single manufacturer/retailer who faces a random demand. All the available suppliers may quote different prices and may have restrictions on minimum and maximum order sizes. The objective is to find a low-cost assortment of suppliers which is capable of satisfying the demand. Properties of the problem are explored and a solution algorithm is proposed. Numerical experimentation is presented for evaluating the performance of the algorithm. Author Affiliation: (a) CIISE, Concordia University, Montreal, Canada (b) JMSB, Concordia University, Montreal, Canada (c) INRIA Lorraine, Metz, France Article History: Received 24 February 2008; Accepted 31 October 2008

In this paper we consider a supplier selection problem for a single manufacturer/retailer who faces a random demand. All the available suppliers may quote different prices and may have restrictions on minimum and maximum order sizes. The objective is to find a low-cost assortment of suppliers which is capable of satisfying the demand. Properties of the problem are explored and a solution algorithm is proposed. Numerical experimentation is presented for evaluating the performance of the algorithm. Keywords: Supply chain Sourcing Supplier selection Heuristics

The profit margins of retailers and providers are becoming smaller as a greater numbers of players are joining the market. The customer generally switches brands/retailers as the price increases. Since provider's revenue, retailer's revenue and the customer demand are interrelated; it is necessary to have a business partnership to win the market. This business partnership requires a mutual cooperation and coordination among partners to decide the retail and the wholesale price, profit margins and inventory level in stock in order to get a greater market share and hence higher revenues. In this paper, we present a provider-retailer partnership model based on profit sharing. We assume that customer demand depends upon the retail price and tends to zero as the price of commodity tends to infinity. We propose an approach to maximize the combined profit and sharing the profit among partners proportional to their risk. Keywords: Supply chain; Revenue sharing and partnership

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ijpe.2004.05.006 Byline: Satyaveer S. Chauhan, Jean-Marie Proth Abstract: The profit margins of retailers and providers are becoming smaller as a greater numbers of players are joining the market. The customer generally switches brands/retailers as the price increases. Since provider's revenue, retailer's revenue and the customer demand are interrelated; it is necessary to have a business partnership to win the market. This business partnership requires a mutual cooperation and coordination among partners to decide the retail and the wholesale price, profit margins and inventory level in stock in order to get a greater market share and hence higher revenues. In this paper, we present a provider-retailer partnership model based on profit sharing. We assume that customer demand depends upon the retail price and tends to zero as the price of commodity tends to infinity. We propose an approach to maximize the combined profit and sharing the profit among partners proportional to their risk. Author Affiliation: UFR Scientifique, INRIA-SAGEP, Universite de Metz, Ile du Saulcy - Metz 57045, France

A general predictive replacement model reduces the maintenance policy in one-unit systems. The model, considered as optimal maintenance policy or control limit policy is based on dynamic programming systems and is not fully constrained by the properties of the probability rules. It is an effective predictive tool for one-unit systems where the state of deterioration is less than a breakdown limit.

A study considers the n-job two-machine flow shop scheduling problem to minimize the sum of job completion times. A mathematical model of the flow shop scheduling problem was constructed, and three heuristic schemes were developed to ferret out near optimal schedules that provide an upper bound for a branch-and-bound algorithm. The algorithm was proven to be extremely powerful and capable of solving n-job two-machine flow shop scheduling problems.

Inventory control of repairable tools with incomplete information can be made more efficient through the use of a simulation program that would help managers define the number of tools to keep in order to meet a given service rate. The global behavior of the repairable tools are determined first. Then, the paramaters of the global behavior is fixed by using global information. The resulting program is very useful in the inventory management of repairable tools with incomplete information.