Solving closed-loop supply chain problems using game theoretic particle swarm optimisation

© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group. In this paper, we propose a closed-loop supply chain network configuration model and a solution methodology that aim to address several research gaps in the literature. The proposed solution methodology employs a novel metaheuristic algorithm, along with the popular gradient descent search method, to aid location-allocation and pricing-inventory decisions in a two-stage process. In the first stage, we use an improved version of the particle swarm optimisation (PSO) algorithm, which we call improved PSO (IPSO), to solve the location-allocation problem (LAP). The IPSO algorithm is developed by introducing mutation to avoid premature convergence and embedding an evolutionary game-based procedure known as replicator dynamics to increase the rate of convergence. The results obtained through the application of IPSO are used as input in the second stage to solve the inventory-pricing problem. In this stage, we use the gradient descent search method to determine the selling price of new products and the buy-back price of returned products, as well as inventory cycle times for both product types. Numerical evaluations undertaken using problem instances of different scales confirm that the proposed IPSO algorithm performs better than the comparable traditional PSO, simulated annealing (SA) and genetic algorithm (GA) methods

Optimal Battery Energy Storage Placement for Transient Voltage Stability Enhancement

A placement problem for multiple Battery Energy Storage System (BESS) units is formulated towards power system transient voltage stability enhancement in this paper. The problem is solved by the Cross-Entropy (CE) optimization method. A simulation-based approach is adopted to incorporate higher-order dynamics and nonlinearities of generators and loads. The objective is to maximize the voltage stability index, which is set up based on certain grid-codes. Formulations of the optimization problem are then discussed. Finally, the proposed approach is implemented in MATLAB/DIgSILENT and tested on the New England 39-Bus system. Results indicate that installing BESS units at the optimized location can alleviate transient voltage instability issue compared with the original system with no BESS. The CE placement algorithm is also compared with the classic PSO (Particle Swarm Optimization) method, and its superiority is demonstrated in terms of fewer iterations for convergence with better solution qualities.Comment: This paper has been accepted by the 2019 IEEE PES General Meeting at Atlanta, GA in August 201

Meta-heuristic based Construction Supply Chain Modelling and Optimization

Driven by the severe competition within the construction industry, the necessity of improving and optimizing the performance of construction supply chain has been aroused. This thesis proposes three problems with regard to the construction supply chain optimization from three perspectives, namely, deterministic single objective optimization, stochastic optimization and multi-objective optimization respectively. Mathematical models for each problem are constructed accordingly and meta-heuristic algorithms are developed and applied for resolving these three problems

AN INDUSTRIAL APPLICATION OF IMPROVED PARTICLE SWARM OPTIMIZATION: AVAILABILITY ASSESSMENT OF ELECTROSTATIC PRECIPITATOR

The Electrostatic Precipitator (ESP) is common equipment used in thermal power plants and industrial mining plants such as steel, copper, and cement. ESP is installed to capture the dust in the exhaust gas of boilers or furnaces. The availability of ESP is vital for plants since any interruption in this device causes serious process problems and environmental pollution. As a result, the availability of ESP is crucial, and a comprehensive study in this area must be performed for maintenance activities. This paper presents a novel method for assessing complex equipment availability, such as ESP, based on improved dynamic particle swarm optimization (IDPSO). To evaluate the availability of ESP, all related systems, sub-systems, and all components of ESP must be considered. Availability assessment of ESP, consisting of many series-parallel sections and components, can be challenging and time-consuming. An IDPSO is used to search for the most probable states among numerous possible states. In addition, IDPSO overcomes shortcomings of standard PSO, such as falling into local optimums. The proposed method is applied to the actual data of an ESP installed at a copper factory. The results show the proposed method achieved an accuracy of 99.54 % in availability assessment

Multi-agent knowledge integration mechanism using particle swarm optimization

This is the post-print version of the final paper published in Technological Forecasting and Social Change. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2011 Elsevier B.V.Unstructured group decision-making is burdened with several central difficulties: unifying the knowledge of multiple experts in an unbiased manner and computational inefficiencies. In addition, a proper means of storing such unified knowledge for later use has not yet been established. Storage difficulties stem from of the integration of the logic underlying multiple experts' decision-making processes and the structured quantification of the impact of each opinion on the final product. To address these difficulties, this paper proposes a novel approach called the multiple agent-based knowledge integration mechanism (MAKIM), in which a fuzzy cognitive map (FCM) is used as a knowledge representation and storage vehicle. In this approach, we use particle swarm optimization (PSO) to adjust causal relationships and causality coefficients from the perspective of global optimization. Once an optimized FCM is constructed an agent based model (ABM) is applied to the inference of the FCM to solve real world problem. The final aggregate knowledge is stored in FCM form and is used to produce proper inference results for other target problems. To test the validity of our approach, we applied MAKIM to a real-world group decision-making problem, an IT project risk assessment, and found MAKIM to be statistically robust.Ministry of Education, Science and Technology (Korea

Particle swarm optimization for bi-level pricing problems in supply chains

With rapid technological innovation and strong competition in hi-tech industries such as computer and communication organizations, the upstream component price and the downstream product cost usually decline significantly with time. As a result, an effective pricing supply chain model is very important. This paper first establishes two bi-level pricing models for pricing problems with the buyer and the vendor in a supply chain designated as the leader and the follower, respectively. A particle swarm optimization (PSO) based algorithm is developed to solve problems defined by these bi-level pricing models. Experiments illustrate that this PSO based algorithm can achieve a profit increase for buyers or vendors if they are treated as the leaders under some situations, compared with the existing methods. © 2010 Springer Science+Business Media, LLC