Supply Chain Logistics Network Research Articles

A Multi-Objective Model for Designing a Sustainable Closed-Loop Supply Chain Logistics Network

Background: The growing concern for environmental and social issues has led to a focus on designing sustainable supply chains and increasing industrial responsibility towards society. In this paper, a multi-objective mixed-integer programming model is presented for designing a sustainable closed-loop supply chain. The model is aimed at the minimization of the total cost with the total used facilities, the negative environmental impacts, and the maximization of the positive social impacts. Methods: The epsilon-constraint method is utilized for solving the model and further extracting the Pareto solutions. Results: The result of the research clearly shows an optimal trade-off between the conflicting objectives, where, by paying more attention to the social and environmental aspects of sustainability, the total costs are increased or by optimizing the number of facilities, a better balance between the dynamics associated with the short-term and long-term goals is reached. The results of the sensitivity analysis also show that increasing the demand of the supply chain has the greatest impact on the supply chain costs compared to other objectives. Conclusions: Consequently, investigating such comprehensive sustainable objectives provides better insights into the impact of design variables on the expectations of stakeholders.

Optimizing COVID-19 medical waste management using goal and robust possibilistic programming

During the global Coronavirus Disease (COVID-19) pandemic, the exponential rise in Hazardous Medical Waste (HMW) due to increased demand for personal protective equipment and heightened medical requirements posed significant threats to public health. This study proposes an innovative approach using a reverse logistics supply chain network that comprehensively integrates sustainability factors (e.g., cost, working conditions, exposure risks, and environmental impact) to manage the risks associated with medical waste effectively amid the pandemic. This research focuses on employing a guideline-based allocation of medical waste to specific technologies, leveraging the Torabi-Hassini (TH), Lp-metric (Lebesgue metric), and Goal Attainment (GA) approaches and robust possibilistic programming to address uncertainties. A real-case study validates the proposed model, demonstrating its ability to balance multiple objectives by optimizing the flow among treatment centers and introducing new Temporary Treatment Centers (TTCs). Also, we analyze broad sensitivity through weights assigned to the objective functions to obtain Pareto solutions. The convexity of the Pareto front confirms the conflict among the objective functions. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach specifies that the Lp-metric approach outperforms the others, and the TH approach is regarded as the second rank. The study's findings highlight the model's efficacy and provide crucial managerial insights for health organization administrators in efficiently managing the HMW supply chain network.

Logistics Supply Chain Network Risk Prediction Model Based on Intelligent Random Forest Model

Logistics Supply Chain Network Risk Prediction Model Based on Intelligent Random Forest Model

Presenting a Fuzzy Multiobjective Mathematical Model of the Reverse Logistics Supply Chain Network in the Automotive Industry to Reduce Time and Energy

The current research aims to design a fuzzy multiobjective model of the reverse logistics supply chain network in the automotive industry, taking into account the energy and time reduction approach. The automobile industry is one of the industries with a high demand worldwide. To continue the competition, the manufacturers of the leading car equipment should strive for better product quality by continuously improving their production processes, directing the production of greenhouse gases with low carbon levels, and increasing sustainability. In this regard, reverse supply chain networks and closed-loop chains have unique features that are very useful in the industry under review. The goal is to transform this model into a supply chain of a secure link in the automotive industry. Deterministic methods, genetic algorithm, particle swarm algorithm, and several scenarios with different aspects have been used to solve the model. The results show that the effectiveness of the three ways in terms of solution time is higher in the deterministic solution method. Proper use of the proposed process can help managers effectively manage the flow of recycled products concerning environmental considerations, and this process provides a sustainable competitive advantage for companies.

On the value of shipment consolidation and machine learning techniques for the optimal design of a multimodal logistics network

We study a multimodal logistics network for a multi-echelon supply chain (SC) with multiple products, considering economic and environmental sustainability and shipment consolidation (ShC). The SC logistics network is modelled as a Mixed Integer Linear Program (MILP) and then tested on randomly generated but realistic test instances. The effects of ShC in SC network design on economic and environmental costs are analyzed, showing that consolidation decreases the SC cost, especially when the distance between the shipper and receiver is significant. Moreover, machine learning (ML) approaches for predicting stochastic parameters using historical data are evaluated compared to the more traditional stochastic programming approaches over multiple prediction periods. The three ML models utilized; namely, Attention CNN-LSTM, Attention ConvLSTM and an ensemble of both models using Support Vector Regression, performed significantly better than the stochastic programming approaches considered (simple recourse and chance-constrained) in all scenarios. The numerical examples show that the MILP models using the predictions from the ML algorithms provide the highest value of the stochastic solution and the lowest expected value of perfect information. This study makes a case for the continued integration of ML prediction methodologies into stochastic optimization modelling in the setting of sustainable SC logistics design problems.

Multi-objective optimization of recycling and remanufacturing supply chain logistics network with scalable facility under uncertainty

ABSTRACT Recycling and remanufacturing logistics network affects the scale and efficiency of sustainable development of the manufacturing industry. This paper designs a multi-level closed-loop supply chain network with supplier, manufacturer, recycling centers, preprocessing centers and processing centers. An improved nonlinear grey Bernoulli-Markov model is developed to predict the recycled quantity. The capacity of recycling center and preprocessing center, the demand of manufacturer and the inventory of preprocessing center are formulated as constraints. A dynamic multi-objective model, which is based on scalable logistics facilities, takes into account the minimization of system operating costs and minimization of time costs related to the out-of-stock and inventory in each operating cycle. This model realizes the dynamic selection of the scale of facilities. Objective weighted genetic algorithm is adopted to transform multi-objective optimization problem into a single-objective. A scrap automobile products calculations are analyzed to verify the effectiveness and practicability of this model.

Expression of concern: “A novel multi-objective scenario-based optimization model for sustainable reverse logistics supply chain network redesign considering facility reconstruction” [Journal of Cleaner Production, Volume 270, 10 October 2020, 122405

Expression of concern: “A novel multi-objective scenario-based optimization model for sustainable reverse logistics supply chain network redesign considering facility reconstruction” [Journal of Cleaner Production, Volume 270, 10 October 2020, 122405

Supply chain network design considering customer psychological behavior-a 4PL perspective

From the perspective of Fourth Party Logistics, a novel supply chain network design problem considering customer psychological behavior is proposed in this paper. First, we calculate the minimum cost of the supply chain network design in Fourth Party Logistics when the demand of customers is fully satisfied. In the shortage of investment in the process of supply chain network design, the demand of customers can be partially satisfied. Then, customer psychological behavior is considered to maximize the value function of customer satisfaction under cost constraint. After introducing the definitions of the psychological reference point and the service level for customers based on the prospect theory, we formulate a non-linear integer programming model for Fourth Party Logistics network design problem. Since the objective function of the proposed model is non-linear, an approximation linearization method is introduced to adjust the proposed model to be an equivalent linear model. Numerical experiments are designed to justify the effectiveness of the proposed method. Through a thorough analysis on customer psychological behavior, it can be seen that customers tend to be more risk-averse for gains than risk-seeking for losses in Fourth Party Logistics supply chain network design. Moreover, valuable investment insights are recapitulated. This proposed method provides an effective tool for a Fourth Party Logistics provider to give reasonable suggestions to an investor in Fourth Party Logistics supply chain network design. Furthermore, we extend the basic model of 4PL supply chain network design considering customer satisfaction with multiple commodities and provid a solution method based on greedy adding heuristic to further reduce the calculation time for large scale cases.

Research on the invulnerability of cross-border bidirectional logistics network in e-commerce supply chain

In order to improve the security of cross-border bidirectional logistics network in e-commerce supply chain, this paper proposes a research method for the invulnerability of cross-border bidirectional logistics network in e-commerce supply chain. This paper builds a cross-border two-way electricity supply chain logistics network model, that analyses the cross-border two-way electricity supply chain logistics network's anti-destroying ability of network security, network loss degree and average benefit evaluation indexes through the index variation characteristics that describe the network invulnerability, and cross-border development of two-way logistics network attack strategy on the impact of network invulnerability. Through experimental analysis, it is found that the anti-destruction analysis time of the method in this paper is always lower than 5.83 s, with the shortest time, and the analysis accuracy is always above 98.26%, with high accuracy, which can effectively resist the cross-border two-way logistics network attack of the e-commerce supply chain and improve the network security.

Optimization of the cold supply chain logistics network with an environmental dimension

This article is the result of generalizing a real practical problem of optimizing the operation of a cold supply logistics network. A small (up to 100 clients) logistics company solves the problem of operational planning: during the working day (fixed time window) to provide the supply of cold (food) products from distribution centers to end consumers along well-defined routes (i.e., the logistic network graph is fixed). The company has set the following conditions for the authors. In addition to minimizing the usual transport costs, take into account also the environmental costs (associated with negative consequences for the environment) that arise during the operation of the logistics supply network. And also when deciding to use software available to the company. The case is considered when the transport tariff on each route is a linearly increasing function of the total volume of all traffic on this route. When the Nash equilibrium conditions are met, the mathematical model for optimizing the total costs of the logistics network is reduced to a convex (quadratic) programming problem. Computer implementation in MS Excel environment has been carried out. This makes it possible to carry out simulation calculations for various options for transport costs on routes, taking into account environmental costs.

Reliable Fourth Party Logistics Location-Routing Problem Under the Risk of Disruptions

A smooth operation of the fourth party logistics supply chain network requires the cooperation of multiple distribution centers and third party logistics. Any unexpected disruption caused by a certain distribution center may lead to serious consequences to the whole fourth party logistics supply chain. Therefore, we study the reliable fourth party logistics location-routing problem in the network design considering disruption risk of distribution centers. In addition, to minimize the total cost of the proposed problem, a scenario-based integer programming model is established to locate distribution centers, to select proper third party logistic providers, and to accomplish the vehicle routing from distribution centers to customers. Then, a two-level heuristic framework is proposed to handle this NP-hard problem through dividing the original problem into two sub-problems. One is the reliable fourth party logistics location assignment problem, and the other is the fourth party logistics vehicle routing problem. These two sub-problems can be independently solved and iteratively composed to get to the solution of the original problem. Furthermore, numerical experiments are designed, and the effectiveness of the heuristic framework is tested by comparing the quality of solutions and running time with those calculated by using CPLEX. Managerial insights are also provided based on the calculation results.

A novel multi-objective scenario-based optimization model for sustainable reverse logistics supply chain network redesign considering facility reconstruction

With the increase of concern from environmental pollution and waste of resources, manufacturing firms have realized that the recovery of used products becomes an environment-friendly way to support sustainable development. However, many enterprises face a problem that is how to implement the recovery of used products into their existing forward logistics networks. In this case, it is necessary to redesign a novel sustainable reverse logistics supply chain network by reconstructing the existing facilities into hybrid processing facilities. To handle the foregoing problem, a multi-objective scenario-based optimization model is proposed. It aims to maximize the expected total monetary profits, minimize the expected total carbon emission costs, and maximize the expected total created job opportunities. To solve the proposed model, we develop a method to linearize the model and then apply the weighted-sum method to reformulate it as a single-objective one. In addition to the weighted-sum method, an augmented E-constraint method is used to obtain a set of efficient Pareto-optimal solutions and the trade-off among three conflicting objectives is presented. Finally, several numerical instances from a tire supply chain are performed to validate the proposed model and some managerial implications are also concluded.

Two-stage efficiency-based approach to biofuel supply chain logistics network design under the risk of disruptions

This paper deals with a design problem for the biofuel supply chain logistics network (BSCLN) under the risk of disruptions. Under a circumstance of disruptions, the BSCLN design problem should deal with multiple goals. This paper proposes an innovative procedure for combining a goal programming (GP) approach and the two-stage network data envelopment analysis (NDEA). The GP model will generate various BSCLN configurations that depend on the weights assigned to the goals. Then, the two-stage NDEA is applied to find the overall efficiency of BSCLN and to identify most efficient BSCLN schemes. Through a case study using the real data on the disaster events in South Carolina, the applicability of the proposed procedure is demonstrated. It is observed that the proposed procedure performs well with respect to designing and identifying the efficient and robust BSCLN schemes, which would be able to attract potential investors who are interested in investing in the biofuel industry.

Two-stage efficiency-based approach to biofuel supply chain logistics network design under the risk of disruptions

This paper deals with a design problem for the biofuel supply chain logistics network (BSCLN) under the risk of disruptions. Under a circumstance of disruptions, the BSCLN design problem should deal with multiple goals. This paper proposes an innovative procedure for combining a goal programming (GP) approach and the two-stage network data envelopment analysis (NDEA). The GP model will generate various BSCLN configurations that depend on the weights assigned to the goals. Then, the two-stage NDEA is applied to find the overall efficiency of BSCLN and to identify most efficient BSCLN schemes. Through a case study using the real data on the disaster events in South Carolina, the applicability of the proposed procedure is demonstrated. It is observed that the proposed procedure performs well with respect to designing and identifying the efficient and robust BSCLN schemes, which would be able to attract potential investors who are interested in investing in the biofuel industry.

Benefit Distribution Method of Coastal Port Intelligent Logistics Supply Chain under Cloud Computing

Li, S.; Sun, Q., and Wu, W., 2019. Benefit distribution method of coastal port intelligent logistics supply chain under cloud computing. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 1041–1046. Coconut Creek (Florida), ISSN 0749-0208.In order to improve the benefit distribution planning ability of coastal port intelligent logistics supply chain under cloud computing, a benefit distribution model of coastal port intelligent logistics supply chain based on intelligent bionic group evolutionary optimization control is proposed. The distributed node deployment model of coastal port intelligent logistics supply chain under cloud computing is constructed, and the optimal path planning and design of coastal port intelligent logistics supply chain network is carried out by using the least square programming method. The fuzzy decision statistical characteristics of coastal port intelligent logistics supply chain path output under cloud computing are extracted, and the benefit distribution model of coastal port intelligent logistics supply chain under cloud computing is constructed by combining adaptive decision optimization control method. The intelligent bionic group evolutionary optimization algorithm is used to realize the adaptive planning and design of coastal port intelligent logistics supply chain under cloud computing, and the optimal benefit distribution and control of coastal port intelligent logistics supply chain under cloud computing are realized. The simulation results show that the resource consumption of coastal port intelligent logistics supply chain planning under cloud computing is small and the spatial planning ability is strong, which improves the scheduling path optimization and intelligent control ability of coastal port intelligent logistics supply chain.

A Bee Evolutionary Algorithm for Multiobjective Vehicle Routing Problem with Simultaneous Pickup and Delivery

A new closed-loop supply chain logistics network of vehicle routing problem with simultaneous pickups and deliveries (VRPSPD) dominated by remanufacturer is constructed, in which the customers are originally divided into three types: distributors, recyclers, and suppliers. Furthermore, the fuel consumption is originally added to the optimization objectives of the proposed VRPSPD. In addition, a bee evolutionary algorithm guiding nondominated sorting genetic algorithm II (BEG-NSGA-II) with a two-stage optimization mechanism is originally designed to solve the proposed VRPSPD model with three optimization objectives: minimum fuel consumption, minimum waiting time, and the shortest delivery distance. The proposed BEG-NSGA-II could conquer the disadvantages of traditional nondominated sorting genetic algorithm II (NSGA-II) and algorithms with a two-stage optimization mechanism. Finally, the validity and feasibility of the proposed model and algorithm are verified by simulating an engineering machinery remanufacturing company’s reverse logistics and another three test examples.

Multi-objective Remanufacturing Supply Chain Optimization Problem with Dual Stochastic Programming

The successful implementation of remanufacturing supply chain not only needs foundation engineering technology, but also needs the efficient supply chain model to support logistics network. But till now, the optimization problems of supply chain logistics network focus on the determination for the number and location of facilities and logistics distribution between the various facilities. Except for the decisive factors, the optimization problem of remanufacturing supply chain logistics network considers the environmental pollution factors the waste product returns and transportation. The market demand and waste product returns are actually uncertain in remanufacturing supply chain. There is few papers focus on dual uncertain factors although there are lots of studies on the problems. Therefore, based on dual stochastic programming, the optimization model of multi-phase multi-objective remanufacturing supply chain is established with maximum profit in the remanufacturing supply chain, maximum rapid response customer satisfaction and minimum environmental pollution. Dual-layer genetic algorithm mechanism was brought up. The first layer algorithm is responsible for supply chain logistics network structure. The second layer algorithm determines specific distributions for remanufacturing supply chain, based on optimized logistics network structure mechanism in the first layer algorithm. Finally, numerical examples demonstrate the validity of the model and algorithm for the optimization problem.

The Optimization Model and Algorithm of Remanufacturing Supply Chain Logistics Network with Option Contracts

By the point of view of remanufacturing enterprise, the studies of supply chain logistics network optimization problem focused on network optimization and logistics distribution between manufacturing enterprise and customer area. The present business environment is full of uncertainties. To compete effectively in such an environment, remanufacturing enterprises need to develop the capability of responding flexibly to changing market conditions and optimize effectively logistics networks. The mechanism of option contracts has effectively coordinated the relationship of remanufacturing enterprise and retailer and hedged against the risks of over- and under-production. There is few papers considers the mechanism of option contracts although there are lots of studies on supply chain logistics networks optimization problem. Therefore, the mechanism of option contracts is led into the study of remanufacturing supply chain logistics networks optimization problem. Considering environmental pollution influence factors in the remanufacturing process, a multi-objective optimization model of supply chain is developed. Dual-layer genetic algorithm mechanism was brought up. The first layer is responsible for supply chain logistics network structure with genetic algorithm. The second layer answers for specific supply chain logistics distribution with adaptive immune genetic algorithm. Finally, numerical examples demonstrate the validity of the model and algorithm for the optimization problem.

Optimization Model and Algorithm for Responsive Closed-Loop Supply Chain Logistics Network with Direct Shipment under E-Commerce

In view of the characteristic of goods return in electronic commerce which some returned goods can be redistributed directly to customers after handling simply, a responsive closed-loop supply chain logistics network with integrating forward and reverse flow is researched. The returning feature of online commodity and economy of scale of return goods at initial collection point are based, and the optimization design model of closed-loop supply chain network aiming at determining the number and location of hybrid centers and initial collection points is presented, and a hybrid genetic algorithm is devised to solve the closed-loop supply chain logistics network problem. The usefulness of the proposed model and algorithm is validated by its application to an illustrative example.

Invulnerability Analysis of Large Logistics Supply Chain Network in Complex Environment Based on the Internet of Things

The network cascade topology structure of large logistics supply chain and its invulnerability were researched. Logistics supply chain is the combination set of entities such as warehouse, highway, railway, aviation, etc. And its stability of network structure determined the survival and development of the user in the network and supply chain. In order to improve the stability and robustness of logistics supply chain network, an improved cascaded topology structure of large logistics supply chain network with Internet of things in complex environment was proposed based on metric uniform random distribution, and cascading invulnerability of mathematical analysis model was proposed. The probability distribution and critical value of logistics and supply chain network cascading failure was solved in complex environment, and the logistics supply chain network paralysis and damage problems were analyzed from the view of probability and critical value analysis. Take the intelligent car supply chain as the example, simulation result shows that the invulnerability analysis effect is perfect, and the cascading invulnerability critical value is solved precisely, research results provide important theoretical support to realize the intelligent logistic management, it has good application value in the research of logistics supply chain network security and stability.