Optimization of supply chain management scheduling using hybrid adaptive NSGA-II genetic algorithm

Background: The costs incurred in the provision of products and services across the supply chain are on the rise in many industries, including the mobile phone industry. Despite this, there is limited information in South Africa on the perceptions of supply chain players regarding cost reduction in the mobile phone industry. Furthermore, there is currently no framework for reducing supply chain costs in the South African mobile phone industry.Aim: The purpose of this study is to explore supply chain costs in the South African mobile phone industry, and to develop a supply chain cost-reduction framework for the South African mobile phone industry.Setting: This study explores supply chain costs in four mobile phone companies operating in the South African mobile phone industry, of which three mobile network operators and one mobile retailing group. It uses semi-structured interview data collected in 2011.Method: This study adopted a qualitative case study design to understand supply chain costs and develop a supply chain cost-reduction framework for the South African mobile phone industry. Eight semi-structured interviews with managers of mobile phone companies were conducted. The data were analysed with the help of Atlas.ti, using an adapted three-phased analytical framework as suggested by Miles and Huberman (1994) and O’ Dwyer (2004).Results: The study found that consolidation of strategic relationships through collaboration and strategic alliances between MNOs and other supply chain players is one of the ways to drive costs down across the supply chain. Outsourcing of some of the support activities and retailers’ direct purchasing transactions from device manufacturers were also found to be other avenues for reducing supply chain costs in the industry.Conclusion: The study suggests that mobile network operators (MNOs) need to consolidate their strategic relationships by increasing the share of the network infrastructure, and emphasising the need to strive for operational efficiencies. This combined effort should result in significant cost reductions across the supply chain. The findings of this study provided some avenues that managers of mobile-phone companies could consider to drive costs down supply chain-wide and service end-users at lower rates. The findings of this study could also help regulating authorities to get insights into supply chain cost reduction and develop appropriate mobile phone policies in South Africa.

Solving a tri-objective supply chain problem with modified NSGA-II algorithm

Problem definition: We study the bullwhip effect and analyze the impact of human behavior. We separate rational ordering in response to increasing incoming orders from irrational ordering. Academic/practical relevance: Prior research has shown that the bullwhip effect occurs in about two-thirds of firms and impacts profitability by 10%–30%. Most bullwhip mitigation efforts emphasize processes such as information sharing, collaboration, and coordination. Previous work has not been able to separate the impact of behavioral ordering from rational increases in order quantities. Methodology: Using data from a laboratory experiment, we estimate behavioral parameters from three ordering models. We use a simulation to evaluate the cost impact of bullwhip behavior on the supply chain and by echelon. Results: We find that cost increases are not equally shared. Human biases (behavioral ordering) at the retailer results in higher relative costs elsewhere in the supply chain, even as similar ordering by a wholesaler, distributor, or factory results in increased costs within that echelon. These results are consistent regardless of the behavioral models that we consider. The cognitive profile of the decision maker impacts both echelon and supply chain costs. We show that the cost impact is higher as more decision makers enter a supply chain. Managerial implications: The cost of behavioral ordering is not consistent across the supply chain. Managers can use the estimation/simulation framework to analyze the impact of human behavior in their supply chains and evaluate improvement efforts such as coordination or information sharing. Our results show that behavioral ordering by a retailer has an out-sized impact on supply chain costs, which suggests that upstream echelons are better placed to make forecasting and replenishment decisions.

Collaboration among supply chain actors leads to improved market access and reduced logistics costs. However, in practice, collaboration initiatives are challenging to implement due to barriers such as sharing sensitive information, coordination complexity, and lack of operational visibility. The impact of such barriers may be mitigated by a third‐party facilitator—an organization that facilitates coordination among actors and safeguards sensitive information. We assess the impact of such a facilitator on supply chain collaboration. We further investigate whether the facilitator can induce a positive second‐order effect on environmental impact and logistics costs through improved collaboration. We tested our hypotheses using survey interviews in member organizations of Connekt, a logistics facilitator. Our results suggest that the facilitator increases the number of collaborations, leading to a reduction in supply chain costs and a positive environmental impact. Lastly, we demonstrate that larger firms further improve collaborations, in the presence of the facilitator.

Photocatalytic coating material is antifouling in dim lighting

This study discusses the integration model of production and distribution planning in the shrimp agroindustry supply chain, consisting of four echelons: shrimp suppliers, shrimp agroindustry, logistics provider companies, and buyers. The shrimp agroindustry supply chain is an essential part of the supply chain of processed product food, which transforms raw shrimp into various processed shrimp frozen products. One form of collaboration between supply chain actors is the integration of production and distribution planning activities. A model is developed to determine the flow of goods from each echelon, the number of processed shrimp products in the agroindustry, and the supplies of processed shrimp products. The bi-objective mixed-integer linear programming is proposed to describe the characteristics of the problem to minimize the total supply chain costs and maximize service level. Non-dominated sorting genetic algorithm II (NSGA-II) is designed to solve the shrimp agroindustry supply chain problem. The sample problem from the shrimp agroindustry in East Java, Indonesia, is applied to exhibit an algorithm’s efficiency. The result shows the best solution for the total supply chain is 1.75 trillion, and the service level is 1,502,264.5.

Biomass supply chain (BSC) activities have caused social and environmental disruptions, such as climate change, energy security issues, high energy demand, and job opportunities, especially in rural areas. Moreover, different economic problems have arisen globally in recent years (e.g., the high costs of BSC logistics and the inefficiency of generating bioenergy from low-energy-density biomass). As a result, numerous researchers in this field have focused on modeling and optimizing sustainable BSC. To this end, this study aims to develop a multi-objective mathematical model by addressing three sustainability pillars (economic cost, environmental emission, and job creation) and three decision levels (i.e., strategic (location of facilities), tactical (type of transportation and routing), and operational (vehicle planning). A palm oil BSC case study was selected in the context of Malaysia in which two advanced evolutionary algorithms, i.e., non-dominated sorting genetic algorithm II (NSGA-II) and Multiple Objective Particle Swarm Optimization (MOPSO), were implemented. The study results showed that the highest amounts of profit obtained from the proposed supply chain (SC) design were equal to $13,500 million and $7000 million for two selected examples with maximum emissions. A better target value was achieved in the extended example when 40% profit was reduced, and the minimum emissions from production and transportation in the BSC were attained. In addition, the results demonstrate that more Pareto solutions can be obtained using the NSGA-II algorithm. Finally, the technique for order of preference by similarity to the ideal solution (TOPSIS) was adopted to balance the optimum design points obtained from the optimization algorithm solutions through two-objective problems. The results indicated that MOPSO worked more efficiently than NSGA-II, although the NSGA-II algorithm succeeded in generating more Pareto solutions.

Purpose Triggered by the measures taken to manage Covid-19, the increase in supply uncertainty and demand distortion in the market caused information asymmetry that led to the bullwhip effect of supply chain. We study the factors that affect the bullwhip effect caused by Covid-19 as well as its aftermaths. Methods We designed a stock flow diagram to analyze the bullwhip effect based on a supply chain simulation using system dynamics and also perform a causal analysis on variables affecting the supply. Results The results show that the increase in safty inventory, reduction of ship delays, and information sharing can reduce the bullwhip effect while dampening the amplification of orders in the global supply chain consisting of a retailer, a RDC (Regional Distribution Center) and a manufacturer. Conclusion A research model that analyzes the bullwhip effect of the covid-19 logistic crisis using system dynamics is proposed. The model is expected to serve as a useful tool to analyze and manage potentially uncertain global supply chain risk scenarios caused by disruptive events such as Covid-19.

A cooperative negotiation embedded NSGA-II for solving an integrated product family and supply chain design problem with remanufacturing consideration

For supply chain optimization, as a key determinant of strategic resources mobility along the value-added chain, simulation is widely used to test the impact on supply chain performance for the strategic level decisions, such as the number of plants, the modes of transport, or the relocation of warehouses. Traditionally, a single centralized model that encompasses multiple participants in the supply chain is built when optimization of the supply chain through simulation is required. However, due to the heterogeneity of supply chain, a centralized simulation model has a limit to reflect the dynamics of each supply chain participant. This research focuses on the conceptual and the technical issues about the supply chain simulation with a parallel and distributed simulation (PADS) concept. In particular, to ensure the decomposition of supply chain simulation model by each supply chain participant, the simulation framework is proposed using Service Oriented Architecture (SOA) in e-collaboration environment. For a clear proof of concept, the virtual supply chain simulation model is implemented and experienced using business process modeling tools in SOA. Due to the reusable, interoperable, and scalable characteristics of the proposed supply chain simulation framework, various alternatives for supply chin optimization can be generated and evaluated.

Contributors

At most time, supply chain is a hybrid dynamic system composed of continuous material flow, discrete information flow and their interaction. Traditional method of modeling and simulation of supply chain looking supply chain as discrete event system can’t reflect systemic dynamic complexity. In this paper, two kinds of order decision-making simulation model of supply chain, discrete model and discrete-continuous hybrid model are built with system dynamics and discrete state chart using the software AnyLogic. Inventory levels of manufacturer and distributor under traditional inventory strategy and VMI strategy are studied. Simulation results show that hybrid model describes bullwhip effect of supply chain different from discrete model, that is the bullwhip effect exhibiting higher inventory level of supply chain upstream enterprise in discrete-continuous combined modeling while exhibiting higher inventory fluctuation of supply chain upstream enterprise in discrete model and can simulates the process of system evolvement caused by adaptive agent’s evolutionary learning greatly.

Increasing environmental concerns require businesses to become more responsive to products that either have been returned or that are at the end of their useful lives. Organization's responsiveness and their reactions toward life cycles of products are critical to achieve sustained success once fluctuations are recurrent and the business environments are turbulent. Life cycles are getting shorter, and efficient handling can save large amounts of cash since many materials can be extracted, reused, and redistributed. Reverse Logistics (RL) and Closed Loop Supply Chains (CLSC) have garnered growing interest as a way to manage this reverse flow of products in a cost effective way. It is discernible that the degree of complexity in closed loop networks is usually higher than open networks. In a closed loop supply chain network, the attempt is to ensure a smooth flow of materials as well as extracting the maximum value from returning and end-of-life goods. Reduction of waste and generating profit for enterprises are two paramount achievements through integrating forward and reverse logistics. By considering various proposed conceptual models for CLSC, in this study at first we present a comprehensive applicable conceptual model. Then, we specifically focus on a CLSC network, which includes multiple plants, collection centers, demand markets, and products. To this aim, a generalized proposed mixed-integer linear programming model is presented that minimizes the total cost. The objective is to know how many, which production plants and Return Processors (Collection sites) should be open, which products, and in which quantities should have stuck. The goal of this model is to minimize the supply chain waste and reduce supply chain costs. In such a way, the supply chain system can be agile, integrated, robust, and lean. The proposed model can be extended to consider more environmental factors. Moreover, the efficacy and efficiency of that have been validated by case study and numerical example.

Two parameter tuned multi-objective evolutionary algorithms for a bi-objective vendor managed inventory model with trapezoidal fuzzy demand

Commonly, an upstream member in a supply chain provides productions or resources for many downstream members. When the sum of retailers order quantities is beyond suppliers' inventories, suppliers might apply order allocation mechanism to solve the difficulty. The allocation mechanism is not considered in most of previous studies on the bullwhip effect. This paper develops the supply chain simulation software based browser/server technology, which can build tree-shape supply chain structure considering allocation mechanism. Two experiments are designed and implemented in order to isolate the effect of different order allocation mechanisms on bullwhip effect. The studies show that BWV differs from allocation mechanisms. For three types of supply chain members, the change tendencies of BWV with two allocation mechanism are similar. The paper also demonstrates that distributors have effect more obviously on bullwhip effect than other two roles with different allocation mechanism.