Design of a Multiobjective Reverse Logistics Network Considering the Cost and Service Level

Reverse logistics, which is induced by various forms of used products and materials, has received growing attention throughout this decade. In a highly competitive environment, the service level is an important criterion for reverse logistics network design. However, most previous studies about product returns only focused on the total cost of the reverse logistics and neglected the service level. To help a manufacturer of electronic products provide quality postsale repair service for their consumer, this paper proposes a multiobjective reverse logistics network optimisation model that considers the objectives of the cost, the total tardiness of the cycle time, and the coverage of customer zones. The Nondominated Sorting Genetic Algorithm II (NSGA-II) is employed for solving this multiobjective optimisation model. To evaluate the performance of NSGA-II, a genetic algorithm based on weighted sum approach and Multiobjective Simulated Annealing (MOSA) are also applied. The performance of these three heuristic algorithms is compared using numerical examples. The computational results show that NSGA-II outperforms MOSA and the genetic algorithm based on weighted sum approach. Furthermore, the key parameters of the model are tested, and some conclusions are drawn

Decision Model Research on Engineering Project Arrangement of Power Grid Enterprise Group Based on Input and Output Efficiency Evaluation

The engineering project arrangement of a large-scale Power Grid Enterprise Group confronts optimization problems on how to achieve efficient investment, how to achieve overall balance. This paper presents a general analytical framework, and establishes appropriate quantitative analysis models from the development of general control scale, input and output efficiency evaluation, index optimization of engineering project input of a single subsidiary, and balance optimization of engineering project arrangement of subordinate units of the Group, and so on. Simulated calculation examples show that this method is more practical, which can flexibly solve such problems

Analysis of the bullwhip effect in two parallel supply chains with interacting price-sensitive demands

This paper offers insights into how the bullwhip effect in two parallel supply chains with interacting price-sensitive demands is affected in contrast to the situation of a single product in a serial supply chain. In particular, this research studies two parallel supply chains, each consisting of a manufacturer and a retailer, and the external demand for a single product depends on its price and the other\u27s price in a situation in which each price follows a first-order autoregressive process. In this paper, we propose an analytical framework that incorporates two parallel supply chains, and we explore their interactions to determine the bullwhip effect. We identify the conditions under which the bullwhip effect is amplified or lessened with interacting price-sensitive demands relative to the situation without interaction

The bullwhip effect on inventory under different information sharing settings based on price-sensitive demand

Information sharing (IS) is proved to be a valid method to counter demand variability amplification along the supply chain, or bullwhip effect (BWE). Different from the traditional way of measuring the BWE based on order quantity, we measure the BWE on inventory in different IS settings and try to find the best IS approach. In this paper, the retailer will face the market demand which is price-sensitive, and the price follows a first-order autoregressive process. This demand model includes some indexes that can provide more useful managerial insights than previously studied parameters. Our study identifies the best IS setting under any conditions, and clarifies that the benefits of IS will be evident when the overall market product pricing process is highly correlated over time, the demand shocks to the retailer are high, the price sensitivity coefficient is small, the overall market shocks are low, the retailer’s lead-time is long and the manufacturer’s lead-time is short