C Rajendran

India has a large network of roads of length over 5 million kilometres, consisting of different categories of roads. The maintenance management of such large road network with limited resources is a demanding task in emerging economics like India. The road users expect the performance of these road infrastructures to be above the desired level during the service life. The road authorities expect this performance to be achieved within the limited resources. This contradicting inevitability makes the problem quite interesting and challenging. In the present study, a framework for the maintenance management of road network through multi-criteria approach is proposed by considering the (performance) deterioration level as an objective function which is to be minimized. For this purpose, two distress criteria are considered, one reflecting functional condition of pavement quantified in terms of roughness and the other reflecting structural condition of pavement quantified in terms of rebound deflection. Development of a decision support system minimizing the roughness and the deflection by selection of appropriate maintenance strategies at the optimal time for different categories of roads carrying different volumes of traffic, within available resources (budget) is a challenging task. A multi-objective optimization model is developed using mixed integer linear programming (MILP). The ϵ-constraint method is adopted by considering one of the distresses as constraint to generate non-dominated solutions. A knee point is also identified as the best available solution among the Pareto-optimal solutions. The framework can be adopted for preparation of an annual budget or a block year budget within the maintenance horizon.

In service oriented architecture, workflow discovery and web service composition (WSC) are two major challenges and many works have been carried out in the recent years. In this work, we address the problem of WSC in a multi-cloud environment. All existing methods assume that a feasible solution exists and solves the WSC problem, however when user constraints are hard, a feasible solution might not exist and models fail to return an optimal solution. To address this problem, we propose an Integer Linear programming model where violations in global constraints are permitted but with an associated penalty. The results show that our method performs significantly better than the standard method when user constraints are hard.

In this work, a discrete version of PSO algorithm is proposed to minimize the makespan of a job-shop. A novel schedule builder has been utilized to generate active schedules. The discrete PSO is tested using well known benchmark problems available in the literature. The solution produced by the proposed algorithms is compared with best known solution published in the literature and also compared with hybrid particle swarm algorithm and variable neighborhood search PSO algorithm. The solution construction methodology adopted in this study is found to be effective in producing good quality solutions for the various benchmark job-shop scheduling problems.

Inventory Routing Problem arises in a Vendor Managed Inventory setting where the supplier manages the inventory at the retailers. We study a finite horizon (multi-period) Inventory Routing Problem where a single supplier serves multiple retailers. The demands incurred at the retailers are deterministic and dynamic (time-varying). Back-orders are allowed at the retailers either to leverage on savings in transportation costs or because of capacity constraints. The supplier makes the decision on delivery schedules, delivery quantities, and delivery routes, with an objective of minimizing the total cost. The total cost comprises of inventory holding cost, back-ordering cost, and vehicle routing cost (fixed and variable transportation cost). The supplier benefits by coordinating orders from different retailers and the retailers benefit by not allocating resources for inventory management. We propose two Mixed Integer Linear Programming Models to solve two variants of the Inventory Routing Problem, with and without split deliveries. We compare the performance of the proposed models with an existing model using a set of randomly generated data instances. Both the proposed models perform better than the existing model in terms of CPU time. We also propose three math-model based methods to find lower bounds for the problem. These models are also tested on the same data instances. The models reported good lower bounds within a few minutes of CPU time. Furthermore, feasible solutions to the IRP can be derived from the lower bound solutions by using simple heuristics to solve the Travelling Salesman sub-problem. The models can be incorporated in decision-making tools to help supply chain managers make quick tactical level decisions in vendor managed supply chains.

A novel genetic algorithm (GA) is proposed in this paper for solving the machine-part cell formation problem in the presence of alternative process plans. Parent chromosomes with number of genes equal to the number of parts are generated based on the correlation value calculated using a ranking index. Crossover is carried out on the 60% of the parent chromosomes and mutation is carried out at the weakly correlated part (gene) in the chromosomes. Performance of the algorithm was tested using 20 test instances from the literature. The proposed genetic algorithm is superior in terms of solution quality for 10% of the total test instances and equal to the best solution achieved by the other algorithms for the remaining 90% of the test instances.

This paper aims to demystify the possibility of using Hamann, Yule (value ranges from-1 to +1) and Jaccard (value ranges from 0 to +1) similarity measures for the machine-part cell formation (MPCF) using the CARI heuristic[17] that uses correlation coefficient (value ranges from-1 to +1) as the similarity measure. It has been found that grouping efficacy (GE) achieved by CARI heuristic while using Hamann and Yule as similarity measure is less for 71.42% and 51% of the dataset respectively compared to the GE achieved while using correlation coefficient as similarity measure. Jaccard similarity measure has been found unsuitable for MPCF using CARI heuristic due to the formation of single large cluster for all the benchmark dataset. CARI heuristic in its current version produces machine-part cells with high GE, only while using correlation coefficient as similarity measure whereas while using the other similarity measures, it produces machine-part cells with low GE or single large cluster is created. To overcome this issue, a normalizing procedure is appended to the current version of CARI heuristic, thus making it capable of producing machine-part cells with any of the similarity measures. Computational performance of the proposed version of CARI heuristic was tested using 35 dataset. The proposed method resulted in increasing GE for 42.84% and 74.27% of the dataset for Hamann and Jaccard similarity measures respectively

Globalization has led to a new class of service apart from the services that are offered offline and semionline (in which case part of the service transactions are online and part of it offline) as a result of collaboration of static entities resulting in static service supply chains. The advent of enablers like Service Oriented Architecture and development of web service applications has enabled online / dynamic service supply chain networks (SSCNs) formed by dynamic collaboration of many serving entities. The entities in web SSCNs are interdependent and the performance of one entity impacts the performance of other entities as well as overall performance of service network. It is important to study the relationship and dependency between each entity of web SSCNs. Once the relationship is identified, it will help in devising some composite performance indicator for the entire service supply chain considering the interests of service providers and clients. We take a scenario based illustration of such online service supply chains to show the feasibility of the concept© 2011 IEEE.