Resolving forward-reverse logistics multi-period model using evolutionary algorithms

© 2016 Elsevier Ltd In the changing competitive landscape and with growing environmental awareness, reverse logistics issues have become prominent in manufacturing organizations. As a result there is an increasing focus on green aspects of the supply chain to reduce environmental impacts and ensure environmental efficiency. This is largely driven by changes made in government rules and regulations with which organizations must comply in order to successfully operate in different regions of the world. Therefore, manufacturing organizations are striving hard to implement environmentally efficient supply chains while simultaneously maximizing their profit to compete in the market. To address the issue, this research studies a forward-reverse logistics model. This paper puts forward a model of a multi-period, multi-echelon, vehicle routing, forward-reverse logistics system. The network considered in the model assumes a fixed number of suppliers, facilities, distributors, customer zones, disassembly locations, re-distributors and second customer zones. The demand levels at customer zones are assumed to be deterministic. The objective of the paper is to maximize the total expected profit and also to obtain an efficient route for the vehicle corresponding to an optimal/near optimal solution. The proposed model is resolved using Artificial Immune System (AIS) and Particle Swarm Optimization (PSO) algorithms. The findings show that for the considered model, AIS works better than the PSO. This information is important for a manufacturing organization engaged in reverse logistics programs and in running units efficiently. This paper also contributes to the limited literature on reverse logistics that considers costs and profit as well as vehicle route management

Optimization of system reliability using chaos-embedded self-organizing hierarchical particle swarm optimization

This paper addresses a reliability optimization problem, where the motive is to select the best components for series and series-parallel systems such that system reliability becomes maximized while simultaneously minimizing the cost, weight, and volume. Previous formulation of the problem has implicit restrictions, i.e. it either maximizes system reliability or minimizes the cost. Thus, in order to give a realistic view to the model, a comprehensive objective function has been formulated by combining the normalized values of reliability, cost, weight, and volume. In this paper, a chaos-embedded hierarchical particle swarm optimization (CE-HPSO) algorithm has been proposed to solve the problems arising in the optimization of system reliability using redundancy. The salient features of the proposed algorithm are the use of chaotic sequences and time-varying acceleration coefficients which are responsible for diversifying the search space. Moreover, to restrict the premature convergence, a hierarchical particle swarm optimizer has been used in the proposed algorithm. The performance of the CE-HPSO algorithm has been tested on three benchmark problems and the comparisons are made with genetic algorithm results. In order to check the scalability of the proposed solution methodology, small and large problems are also considered. The results demonstrate the benefits of the proposed algorithm for solving this type of problem