C Rajendran

In this work we address the problem of transforming a jobshop layout into a flowshop layout with the objective of minimizing the length of the resulting flowline. This problem is a special case of the well-known classical Shortest Common Supersequence (SCS) stringology problem. In view of the problem being NP-hard, an ant-colony algorithm, called PACO-SFR, is proposed. A new scheme of forming an initial supersequence of machines (i.e., flowline) is derived from a permutation of jobs, followed by the reduction in the length of the flowline by using a concatenation of forward reduction and inverse reduction techniques, machine elimination technique and finally an adjacent pair-wise interchange of machines in the flowline. The proposed ant-colony algorithm's performance is relatively evaluated against the best known results from the existing methods by considering many benchmark jobshop scheduling problem instances. © 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering.

The problem of scheduling in permutation flowshops is considered with the objectives of minimizing the makespan and total flowtime of jobs. A multi-objective ant-colony algorithm (MOACA) is proposed. The salient features of the proposed multi-objective ant-colony algorithm include the consideration of two ants (corresponding to the number of objectives considered) that make use of the same pheromone values in a given iteration; use of a compromise objective function that incorporates a heuristic solution's makespan and total flowtime of jobs as well as an up per bound on the makespan and an upper bound on total flowtime of jobs, coupled with weights that vary uniformly in the range [0, 1]; increase in pheromone intensity of trails by reckoning with the best solution with respect to the compromise objective function; and updating of pheromone trail intensities being done only when the ant-sequence's compromise objective function value is within a dynamically updated threshold level with respect to the best-known compromise objective function value obtained in the search process. In addition, every generated ant sequence is subjected to a concatenation of improvement schemes that act as local search schemes so that the resultant compromise objective function is improved upon. A sequence generated in the course of the ant-search process is con sidered for updating the set of heuristically non-dominated solutions. We consider the benchmark flowshop scheduling problems proposed by Taillard (1993), and solve them by using twenty variants of the MOACA. These variants of the MOACA are obtained by varying the values of parameters in the MOACA and also by changing the concatenation of improvement schemes. In order to benchmark the proposed MOACA, we rely on two recent research reports: one by Minella et al. (2008) that re ported an extensive computational evaluation of more than twenty existing multi-objective algorithms available up to 2007; and a study by Framinan and Leisten (2007) involving a multi-objective iterated greedy search algorithm, called MOIGS, for flowshop scheduling. The work by Minella concluded that the multi-objective simulated annealing algorithm by Varadharajan and Rajendran (2005), called MOSA, is the best performing multi-objective algorithm for permutation flowshop scheduling. Framinan and Leisten found that their MOIGS performed better than the MOSA in terms of generating more heuristically non-dominated solutions. They also obtained a set of heuristically non-dominated solutions for every benchmark problem instance provided by Taillard (1993) by consolidating the solutions obtained by them and the solutions reported by Varadharajan and Rajendran. This set of heuristically non-dominated solutions (for every problem instance, up to 100 jobs, of Taillard's benchmark flowshop scheduling problems) forms the reference or benchmark for the present study. By considering this set of heuristically non-dominated solutions with the solutions given by the twenty variants of the MOACA, we form the net heuristically non-dominated solutions. It is found that most of the non-dominated solutions on the net non-dominated front are yielded by the variants of the MOACA, and that in most problem instances (especially in problem instances exceeding 20 jobs), the variants of the MOACA con tribute more solutions to the net non-dominated front than the corresponding solutions evolved as benchmark solutions by Framinan and Leisten, thereby proving the effectiveness of the MOACA. We also pro vide the complete set of heuristically non-dominated solutions for the ninety problem instances of Taillard (by consolidating the solutions obtained by us and the solutions obtained by Framinan and Leisten) so that researchers can use them as benchmarks for such research attempts. © 2009 Springer-Verlag Berlin Heidelberg.

This study considers a scheduling model for a supply chain system that can choose a set of subcontractors from the available subcontractors (non-identical manufacturing facilities) to fulfill a part of its orders/jobs in order to maximize the supply chain profitability. Further, this study aims to reduce carbon emissions from transportation activities in the supply chain. The orders/jobs to be processed have different processing times on different manufacturing facilities and due dates. All the completed jobs at the outsourced centers (sub-contractors) need to be transported back to the central manufacturing facility. The present study integrates three issues: (1) selection of subcontractors; (2) scheduling of jobs, and (3) logistic scheduling with carbon emission consideration; all these decisions in a supply chain have not been considered together in the existing literature. We present this problem with the objective of minimizing the total costs associated with production and logistic decisions, and propose a mixed-integer linear programming model.

New variant of PSO algorithm called Neighborhood search assisted Particle Swarm Optimization (NPSO) algorithm for data clustering problems has been proposed in this paper. We have proposed two neighborhood search schemes and a centroid updating scheme to improve the performance of the PSO algorithm. NPSO algorithm has been applied to solve the data clustering problems by considering three performance metrics, such as TRace Within criteria (TRW), Variance Ratio Criteria (VRC) and Marriott Criteria (MC). The results obtained by the proposed algorithm have been compared with the published results of basic PSO algorithm, Combinatorial Particle Swarm Optimization (CPSO) algorithm, Genetic Algorithm (GA) and Differential Evolution (DE) algorithm. The performance analysis demonstrates the effectiveness of the proposed algorithm in solving the partitional data clustering problems. © 2011 Springer-Verlag.

Risks are inevitable in supply chains, and they need to be detected early and appropriately addressed. This chapter primarily attempts to identify early warning signals and implement suitable mitigation decisions to meet exigencies related to the management of supply chain risks. Further, the chapter also presents an approach that addresses the risks in an integrated manner. First, a framework is essential to understand the relationships between the independent and dependent variables. An empirical study is undertaken by developing a questionnaire that captures the perceptions of the supply chain practitioners on risks perceived in their supply chains, and the framework is subjected to validity tests. Secondly, the data obtained from these surveys is utilized to develop a fuzzy model for identifying and predicting all plausible risks based on the instantaneous risk vector. Fuzzy Cognitive Map (FCM) is used to represent the overall behavior of the dynamical system of the supply chain. The instantaneous risk vector is passed on to the dynamical system to identify all plausible risks that may appear in near future. The resultant vector obtained suggests that ignoring the initially perceived risks eventually lead to possible disruptions in the supply chain. The resultant vector thus obtained is useful for decision making to alleviate the impact of various types of risks. Finally, the relative comparison of the mitigation strategies’ ranking was made for the results obtained from regression, FCM, and Fuzzy TOPSIS.