Multi-echelon Supply Chain Research Articles

A multi-level multi-product supply chain network design of vegetables products considering costs of quality: A case study.

Effective logistics management is crucial for the distribution of perishable agricultural products to ensure they reach customers in high-quality condition. This research examines an integrated, multi-echelon supply chain for perishable agricultural goods. The supply chain consists of four stages: supply, processing, storage, and customers. This study investigates the quality-related costs associated with product perishability to maximize supply chain profitability. Key factors considered include the network design, location of processing and distribution centers, the ability to process raw products to minimize post-harvest quality degradation, the option to sell the excess produce to a secondary market due to unpredictable yields, and the decision not to fulfill demand from distant customers where significant quality loss and price drops would be involved, instead diverting those products to the aforementioned secondary market. Quantitative methods and linear mathematical programming are employed to model and validate the proposed supply chain using actual data from a real-world case study on vegetable supply chains. The main contribution of this research is the incorporation of quality costs into the objective function, which allows the supply chain to prioritize meeting nearby customers' demands with minimal quality loss over serving distant customers where high quality loss is unavoidable. Additionally, deploying a faster transportation fleet can significantly improve the overall profitability of the perishable product supply chain.

Distribution and Inventory Planning in Multi-Echelon Supply Chains Under Demand Uncertainty

Distribution and Inventory Planning in Multi-Echelon Supply Chains Under Demand Uncertainty

Increasing willingness to pay in the food supply chain: a blockchain-oriented trust approach

Food products' quality information is advertised on labels but do customers trust them? This study investigates how the consumers' Willingness-To-Pay (WTP) for food products can be increased by deploying managerial effort and advanced technologies, such as the Blockchain Technology (BT). Our model explains how revealing verified information about product quality throughout the supply chain will generate optimal consumers' WTP and maximise profit. At each echelon of a multi-echelon supply chain, a buyer holds a Bayesian belief about the quality of the input to be procured. This belief is shaped by the accuracy and veracity of the information about this quality. Managerial effort is required both to enhance quality as well as ensure full and verified information. We show why this effort must be made across the chain and how opportunistic behaviour may be circumscribed. Using empirically grounded analytics and real prices of olive oil intermediate produce from various official bodies, we show how the application of BT may be financially justified. This research shows how trust and WTP can be further enhanced through the use of BT and additional smart technologies in a supply chain, which may be projected on other supply chains of organic and sustainable food products.

Solving the guaranteed-service time inventory model for real-world supply chains by implementing it in commercial optimisers

The guaranteed service time inventory (GSTI) model aims to satisfy customers’ demand in an assured time. As multi-echelon supply chains (SC) grow in size and complexity, there is a need to develop exact algorithms to compute the amount of safety stock to deliver products to customers in the guaranteed service time. This work proposed an algorithm to minimise the safety stock in real–world SCs in minutes. The application reads the data set provided in [31]; then a continuous initial solution is computed in seconds using the software library (IPOpt); finally, that solution is used as the initial solution to run the branch–and–cut (B&C) algorithm implemented in a state-of–the–art commercial solver. In the proposed application, the GSTI model is modified to be implemented in commercial solvers when the demand is normally distributed. The reader can download the application’s source code from [24] to analyse the results of the 38 instances. The application computes an integer solution to each of the 38 SCs; in the worst case, it takes 150 min to find the solution. Seventeen instances are solved optimally, and the worst gap between the actual and best solution is about 24% in three instances. The biggest instance is solved in less than 10 min, but other smaller ones are solved in a longer time because of the echelons’ structure (degree and depth). Thus, the time to compute a solution does not depend on the size of the SC but on its structure. The findings indicate that by using a state-of–the–art commercial solver (based on the B&C method) and running it with powerful computer capabilities, real–world SCs can be solved with techniques that return optimal solutions.

A tabu search heuristic for the outsourcing risk management problem in multi-echelon supply chains

A tabu search heuristic for the outsourcing risk management problem in multi-echelon supply chains

A quantitative analysis of simultaneous supply and demand disruptions on a multi-echelon supply chain

A quantitative analysis of simultaneous supply and demand disruptions on a multi-echelon supply chain

An optimization model for sustainable multi-product multi-echelon supply chain networks with U-shaped assembly line balancing under uncertainty

This study presents an integrated supply chain network with suppliers, manufacturers, assemblers, and customers. The proposed model considers a U-shaped assembly line with three sustainability objective functions. We consider assumptions considering different types of raw materials, multiple products, location selection of manufacturers, location selection of assemblers, and capacity of suppliers. The problem is formulated non-linear and then linearized as a multi-objective model. Some cost and demand parameters are considered uncertain and are represented by fuzzy sets and theory. The proposed uncertain model is first converted to a multi-objective crisp model by applying the modified robust possibilistic programming approach. Then, the obtained crisp multi-objective model is solved by an interactive-fuzzy optimization approach in the literature. For computational study, some test problems are generated and solved using an original deterministic formulation and the crisp form of the uncertain formulation. The obtained results are analyzed and compared according to the objective function values. Finally, an extensive sensitivity analysis is performed on the parameters of the models.

Medical Equipment Supply Chain Optimization and Stability Study using Deep Reinforcement Learning

Medical equipment is a critical resource during the COVID-19 pandemic. An efficient and stable supply chain of medical equipment (masks, goggles, protective coveralls, etc.) enables medical workers and first responders to effectively and safely fight against this highly contagious disease. In my research, I design and investigate two agents based on the traditional (s,Q) policy and the Deep Reinforcement Learning (DRL) algorithm and apply them respectively to optimize a two-echelon medical equipment supply chain where one distribution center and multiple retailers are involved. To my knowledge, this is the first implementation of DRL algorithm for medical supply chain optimization. I implement the DRL algorithm in Python using Ray and RLlib packages and conduct experiments using Google Colab with GPU support. To maximize the DRL algorithm’s potential, I optimize the reward function and the hyperparameters of this algorithm. By testing the agents in different environment initializations, I find that the DRL algorithm outperforms the static (s, Q) agent, which is one of the most commonly used methods in many inventory optimization systems, by returning a 17.33% greater cumulative reward on average. Additionally, the relative standard deviation of baseline (s,Q) Policy is 1.97% and that of DRL is 2.49% based on ten repeated trials. Thus, the DRL approach is not only stable but can also significantly improve the retailer’s profit. My DRL model can be further applied to more complicated multi-echelon supply chain systems and lays a solid foundation for optimizing large-scale medical supply chains [TF18].

A signomial programming-based approach for multi-echelon supply chain disruption risk assessment with robust dynamic Bayesian network

Disruption risk assessment is a primary and crucial step before taking measures to mitigate the negative impact of disruptions propagating along supply chains (SCs). Recently, robust dynamic Bayesian network (DBN) provides a valid tool for disruption risk estimation under the ripple effect in a data-scarce environment. However, existing literature has not considered such disruption risk assessment for multi-echelon SCs that are usually structurally complicated and thus vulnerable to disruptions with ripple effects. Motivated by this fact, we study the disruption risk assessment problem under the ripple effect for a multi-echelon SC with several suppliers and one manufacturer, in which only probability intervals of the suppliers’ states and those of the related disruption propagations are known. The aim is to acquire a robust risk estimation, measured by the worst-case total weighted probabilities for the manufacturer in the disrupted state over a time horizon. For the problem, a nonconvex nonlinear programming model is established to obtain the worst-case risk estimation. To efficiently solve the problem, a novel signomial programming (SP)-based approach is developed for finding near-optimal solutions. Numerical experiments on instances in the literature and our randomly generated instances are conducted to evaluate the efficiency of the proposed method. Besides, managerial insights are drawn.

Designing a Tri-Objective, Sustainable, Closed-Loop, and Multi-Echelon Supply Chain During the COVID-19 and Lockdowns

Abstract This paper proposes a mathematical model of Sustainable Closed-Loop Supply Chain Networks (SCLSCNs). When an outbreak occurs, environmental, economic, and social aspects can be traded off. A novelty aspect of this paper is its emphasis on hygiene costs. As well as healthcare education, prevention, and control of COVID-19, this model offers job opportunities related to COVID-19 pandemic. COVID-19 damages lead to lost days each year, which is one of the negative social aspects of this model. COVID-19 was associated with two environmental novelties in this study. positive and negative effects of COVID-19 can be observed in the environmental context. As a result, there has been an increase in medical waste disposal and plastic waste disposal. Multi-objective mathematical modeling whit Weighted Tchebycheff method scalarization. In this process, the software Lingo is used. The COVID-19 pandemic still has a lot of research gaps because it’s a new disease. An SC model that is sustainable and hygienic will be developed to fill this gap in the COVID-19 condition disaster. Our new indicator of sustainability is demonstrated using a mixed-integer programming model with COVID-19-related issues in a Closed-Loop Supply Chain (CLSC) overview.

A Simulation Optimization Method for Coordination of Production, Transportation and Sales

This study considers a problem of coordinating production, transportation and sales in a multi-echelon supply chain network. A simulation model is built to generate the random customer demands at different locations, which are affected by a marketing strategy. Customer demands need to be satisfied by the supply chain through production, transportation and distribution. The optimization problem for coordination of production, transportation and distribution is first formulated as a linear programming with demands as input parameters in the constraint. Our objective is to maximize the expectation of the optimal profit of the supply chain given random demands by selecting an optimal marketing strategy. A simulation optimization technique is proposed to control the generation of random demands and solve the linear programming for efficiently learning the optimal marketing strategy. Numerical results show that our method can significantly improve the expected profit of the supply chain and reduce the computational burden of solving linear programming for achieving a given level of probability of correct selection of the optimal marketing strategy. Furthermore, we extend the optimization problem to a mixed integer programming and also demonstrate the computational efficiency of our proposed method.

A meta-heuristic-based algorithm for designing multi-objective multi-echelon supply chain network

Recently, practitioners and researchers have concentrated on the use of meta-heuristics in the design of multi-objective supply chain networks. The number of publications in this field reflects this interest. The purpose of this paper is to propose a novel metaheuristic approach for designing supply chain network problems in the case of multi-objective supply chains. The proposed algorithm hybridizes three meta-heuristic approaches; simulated annealing, tabu search, and variable neighborhood algorithms. The proposed algorithm is also combined with a linear programming approach. The purpose of hybridization is to aggregate the approaches in order to benefit from the advantages of these solution algorithms. The features of these algorithms are investigated in this paper for the first time. The most useful characteristics of each algorithm are then utilized for developing the new algorithm. The performance of the proposed algorithm is compared with an exact algorithm, simulated annealing, and tabu search algorithms. The results indicated that the proposed algorithm is comparable to the exact in the case of small and medium-sized supply chain problems. The findings revealed that the proposed algorithm takes on an average of 6.3 min to solve small and medium problems, while the exact algorithm takes 412 min. Moreover, the proposed algorithm outperforms other algorithms with a mean ideal distance (MID) of 13,606,871, a percent of domination (POD) of 0.84, and a computational time of 15.64 min compared to the tabu search algorithm’s MID of 15,574,523, a POD of 0.55, and a computational time of 21.46 min. The simulated annealing algorithm, on the other hand, achieved an average MID of 18,145,931, a POD of 0.40, and a computational time of 30 min. Furthermore, the findings showed that the proposed algorithm is capable of solving complex and large supply chain problems in a reasonable time when exact approaches fail.

Sustainability and robust decision-support strategy for multi-echelon supply chain system against disruptions

ABSTRACT Due to the higher nonlinearity and parametric sensitivity of multi-echelon supply chains, minor changes may result in a completely different evolution of supply chain capabilities. Realizing resilient and sustainable development goals for enterprise logistics requires great insights into management schemes in a volatile market. This paper deals with dynamic characteristics and resilience issues of managing multi-echelon supply chain systems against disruptions. Discrete-time nonlinear systems with time delay are extensively evaluated via computation of Lyapunov exponent, bifurcation diagram, permutation entropy, and Poincaré map. For time-series evolution, forecasting and predictability are assessed by the Hurst exponent. An echo state network (ESN) will be utilized to predict the future states of the supply chain dynamics. Based on a decision-support strategy with an ESN model, a resilience strategy concerning disturbing factors is implemented to ensure a sustainable supply chain network. Numerical simulations are demonstrated to validate dynamical behaviors and verify the efficiency of control theory with machine learning, guaranteeing the resilience and sustainability of a supply chain against market volatility. The test results demonstrate how machine learning algorithms can be integrated with management schemes. Finally, the presented methodology can ensure more supply chain visibility and traceability under highly uncertain markets.

An Integrated Optimization Model of Green Supply Chain Network Design with Inventory Management

Supply chain network design and inventory management are both significant for improving the core competitiveness of enterprises. This study investigates the joint optimization problem of facility locations and inventory for assembly manufacturing enterprises’ multi-echelon supply chain networks, considering the locations of facilities, the selection of suppliers, transport mode choices, and inventory decisions simultaneously. A corresponding integrated optimization model is proposed, which aims to minimize the total cost, consisting of the fixed open cost of facilities, the inventory cost of the open plants and distribution centers, and the transportation cost of vehicles in the entire supply chain network as well as the cost of CO2 emissions. Based on the characteristics of the proposed optimization model, a hybrid genetic algorithm embedded with a local search is developed to solve the proposed model. Numerical examples and a case study are provided to illustrate the effectiveness of the proposed model and the corresponding algorithm. The findings show that the model is reasonable and applicable, and hybrid genetic algorithm (HGA) is more efficient than the standard genetic algorithm (SGA). In addition, plants’ maximum lead-time has a significant impact on the total cost of the supply chain.

An integrated decision support framework for resilient vaccine supply chain network design

Designing resilient supply chain networks for vaccine development and distribution requires reliable and robust infrastructure. This stud develops a novel two-stage decision support framework for configuring multi-echelon Supply Chain Networks (SCNs), resilient supplier selection, and order allocation under uncertainty. Resilient supplier selection is done using a hybrid Multi-Criteria Decision-Making (MCDM) approach based on Best-Worst Method (BWM), Weighted Aggregated Sum Product Assessment (WASPAS), and Type-2 Neutrosophic Fuzzy Numbers (T2NN). A robust multi-objective optimization model is then built for order allocation considering resiliency scores, reliability of facilities, and uncertain supply and demand. The objectives are to minimize the total cost of SCN design, maximize the resiliency score, and maximize the reliability of SC, respectively. A Non-dominated Sorting Genetic Algorithm II (NSGA-II) is developed to tackle the problem on large scales, tuned by the Taguchi design technique. The NSGA-II solution is compared to the ε-constraint and Multi-objective Particle Swarm Optimization (MOPSO) solutions using test problems. We demonstrate the superiority of the suggested NSGA-II method over the two competing methods according to five performance metrics. A case study is then investigated to illustrate the applicability and effectiveness of the offered methodology for COVID-19 vaccine distribution in a developing country. It is revealed that the models and algorithms can treat the problem optimally, such that Germany is the main source (approximately 25.61%) while India does not contribute to the supply of vaccines.

Data-driven evolutionary computation for service constrained inventory optimization in multi-echelon supply chains

Supply chain digital twin has emerged as a powerful tool in studying the behavior of an actual supply chain. However, most studies in the field of supply chain digital twin have only focused on what-if analysis that compares several different scenarios. This study proposes a data-driven evolutionary algorithm to efficiently solve the service constrained inventory optimization problem using historical data that generated by supply chain digital twins. The objective is to minimize the total costs while satisfying the required service level for a supply chain. The random forest algorithm is used to build surrogate models which can be used to estimate the total costs and service level in a supply chain. The surrogate models are optimized by an ensemble approach-based differential evolution algorithm which can adaptively use different search strategies to improve the performance during the computation process. A three-echelon supply chain digital twin on the geographic information system (GIS) map in real-time is used to examine the efficiency of the proposed method. The experimental results indicate that the data-driven evolutionary algorithm can reduce the total costs and maintain the required service level. The finding suggests that our proposed method can learn from the historical data and generate better inventory policies for a supply chain digital twin.

Extensions to the guaranteed service model for industrial applications of multi-echelon inventory optimization

Multi-echelon inventory optimization (MEIO) plays a key role in a supply chain seeking to achieve specified customer service levels with a minimum capital in inventory. In this work, we propose a generalized MEIO model based on the Guaranteed Service approach to allocate safety stock levels across the network at the lowest holding cost. This model integrates several existing and some novel features that are usually present in pharmaceutical multi-echelon supply chains into a single model: review periods, manufacturing facilities, hybrid nodes (nodes with both internal and external demand), minimum order quantities (MOQ), and different service level performance indicators (fill rate and cycle service levels). We include a polynomial regression to approximate fill rates as a possible target measure to set safety stocks. To improve efficiency, we propose a nonlinear programming model to support decision making, which can be reformulated as a Quadratically Constrained Program (QCP), which leads to order of magnitude reductions in computational time. The performance of the model is evaluated by solving illustrative and real-world cases, and is validated with simulation.

An inventory analysis in a multi-echelon supply chain system under asymmetry fuzzy demand: a fmincon optimization

In this article, the three-echelon supply chain model is developed with a single supplier, a single vendor and a single buyer in a fuzzy environment. The three-stage inspection technique is considered at the end of the vendor's process to supply high-quality products to the buyer. Moreover, the demand rate of the product in the market is often assumed to be uncertain, and accordingly, in this paper, the uncertainty is handled using fuzzy numbers. The primary goal of this model is to minimize the overall inventory cost relative to the asymmetry hexagonal fuzzy demand. Additionally, a backorder strategy is incorporated to gain customer satisfaction and reduce lost sales. To validate this model, numerical examples are tested and constrained non-linear optimization (i.e. fmincon solver) is performed to derive the optimal solution for the decision variables. Finally, conclusions with potential future directions are provided.

Sustainable Green Economy for a Supply Chain with Remanufacturing by Both the Supplier and Manufacturer in a Varying Market

Background: In a typical multiechelon supply chain, the supplier makes semifinished items, from which the manufacturer produces finished products to eventually get sold at retailers. However, the majority of existing supply chain models consider the remanufacturing of defective products by solely one organization, despite the fact that both the supplier and manufacturer can produce defective products. This study considers the remanufacturing of defective products with fresh materials and additional expenses by both the supplier and manufacturer. Contrary to well-established articles that hold major partners to be accountable for reducing carbon emissions under a carbon cap-and-trade policy, the proposed model presumes an initial green technological investment by each chain partner. Methods: This study represents a varying market with fuzzy cost components that are then defuzzified with λ-integral method. This study determines the critical values of three discrete and four other continuous decision variables that globally maximize the profitability of the proposed model. Results: Slower production with a longer cycle boosts profitability in a developing market. To increase profit, a case study on the oil and natural gas business suggested to reduce the production of defective items and cutting emission through green investments. Conclusions: Managers can sustainably boost profit via careful production, modern machinery, and slightly longer cycles.

Performance evaluation of meta-heuristic algorithms for designing multi-objective multi-product multi-echelon supply chain network

Performance evaluation of meta-heuristic algorithms for designing multi-objective multi-product multi-echelon supply chain network