Rajeshwar Kadadevaramath, Siddaganga Institute of Technology, India, rskmutt@yahoo.com

K.M.Mohanasundaram, PSG College of Technology, India

K.Rameshkumar, Amrita School of Engineering, India
B. Latha Shankar, Siddaganga Institute of Technology, India

T.B.U.ShankarAradya, Siddaganga Institute of Technology, India
 

Ensuring competitiveness in today’s globally connected marketplace is very demanding and calls for different business strategies than what were employed by businesses in the past. Today’s businesses have to be more adaptive to change. In order to stay competitive and continue to subsist they need to be better suited to handle fluctuations in an ever-changing market than their competitors. Supply chains encompass a series of steps that add value through time, place, and material transformation. Each manufacturer or distributor has some subset of the supply chain that it must manage and run profitably and efficiently to survive and grow. Optimization is no longer a luxury but has become the order of the day. Given the nature of the complex network problem and inherent complexity associated with it, it is surprising that very little work has been done in this area. This paper specifically deals with the modeling and optimization of a four-stage supply chain using the Particle Swarm Optimization algorithm.
In this work, the four stage supply chain includes vendors, plants, distribution centers and retailers; problem is solved for optimal distribution of components and product made by them in various stages of supply chain by using Particle Swarm Optimization (PSO) algorithm, invented by Kennedy and Eberhart in 1995. PSO is motivated by the social behavior of organisms, such as bird flocking and fish schooling. And it was found that the PSO algorithm gives quality results in significantly fewer fitness and constraint evaluations. The algorithm is intuitive and does not need specific domain knowledge to solve the problem.

Amin Chaabane, École de Technologie Supérieure, amin.chaabane.1@ens.etsmtl.ca

Global competition and rapid market changes force several companies to rethink their strategies in order to achieve more agility and efficiency. Organizations migrate toward inter-organizational relationships as a way to adapt to this new environment. Advanced planning and optimization of supply chains is one of the most important decisions that firms have to use in this context. The supply chain configuration is a dynamic process. Indeed, in the early stage of a market, it is often the lean paradigm that prevails. As the market evolves and demands for higher levels of variety grow, the agile paradigm replaces it to offer more flexibility and reactivity. “Leagile” supply chains are able to capture this dynamic behaviour, and offer much more flexibility. In this research we propose a multi-product, multi-echelon, and multi-period tactical planning model for managing “leagile” supply chains. The optimization program is constructed as a multiobjective mixed-integer linear model in order to satisfy two objectives. The first one minimizes the total logistics cost. The second objective minimises the total amount of products delivered in advance or backordered during the planning horizon. A simple illustrative example, solved with the “ε-constraint” method, demonstrates the trade-off between the two objectives and their impact on supply chain performance.