Multi-echelon Supply Chain Research Articles

Tactical supply chain planning under uncertainty with an application in the wind turbines industry

With today's rapidly changing global market place, it is essential to explicitly include uncertainty in supply chain planning models. In this paper, we develop a two-stage stochastic programming model for the comprehensive tactical planning of supply chains under demand and supply uncertainty. The model handles multi-period, multi-product, and multi-echelon supply chains. It incorporates backorder penalties with general cost structures. The considered supply uncertainty combines supplier random yield and stochastic lead times, and is thus the most general form of such uncertainty to date. We illustrate how our model directly applies to the planning of the supply chain of one of the world's biggest manufacturers of wind turbines. We illustrate theoretical and numerical results that show the impact of supplier uncertainty/unreliability on optimal procurement decisions. We also quantify the value of modeling uncertainty versus deterministic planning.

A bi-objective model for a multi-echelon supply chain design considering efficiency and customer satisfaction: a case study in plastic parts industry

One of the fundamental challenges of today’s manufacturing systems is the contradiction between cost efficiency and customer satisfaction. Finding a good balance between good customer satisfaction and supply chain efficiency is a critical problem in the supply chain management. To achieve this goal, a bi-objective mathematical model is suggested in this paper to maximize the efficiency of network and also customer satisfaction. This multi-period and multi-product supply chain network design model consists of suppliers, factories, distribution centers (DCs), and customers. The proposed bi-objective mixed-integer non-linear programming (MINLP) model is a member of the NP-hard class of optimization problems. Hence, two well-known multi-objective metaheuristic algorithms namely, Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Non-dominated Ranked Genetic Algorithm (NRGA) are employed to solve the proposed model. The author uses Taguchi method for tuning the parameters of algorithms in order to achieve better performances. Moreover, a case study in the plastic industry is performed to collect data from the north region of Iran. Some well-known multi-objective metrics such as analysis of variance (ANOVA) is used to measure the performance of the proposed framework. Finally, results demonstrate the efficiency of the proposed framework.

Optimal design of a multi-echelon supply chain in a system thinking framework: An integrated financial-operational approach

Abstract The purpose of this study is to design a four-echelon supply chain network in which operational and financial dimensions have been considered by a holistic, comprehensive and rigorous viewpoint within tactical and strategic decision-making levels. In this process, the aforementioned dimensions are extended and integrated in mathematical modeling framework. The research modeling is compared with traditional approaches, which merely rely on operational dimension to optimize profit and due to problems in profit, the research objectives changed to the multiple objectives of corporate value, change in equity and economic value added. Within this framework, a comparison is primarily made between profit and each of corporate value, change in equity and economic value added two by two (in the form of traditional approach (scenario A) and new approach (scenario B)). Then the objectives are simultaneously assessed and a compromise is gained among them through fuzzy goal programming. In this process, the effectiveness and efficiency of the scenario B is analyzed and assessed. Furthermore, a sensitivity analysis is performed on such factors as demand, return of equity rate, tax rate, production capacity and supplier capacity while the impacts of their changes on multi-objective function and satisfaction coefficient are simultaneously calculated. The mathematical model in the study is multi-product and multi-period while the values of parameters are determined under definite circumstances. For modeling and solving the mathematical model, GAMS 24 software and CPLEX solver are employed. To test the model, data of an Iranian petrochemical company are used. Besides highlighting the significance of the financial dimension and its integration with the operational dimension in gaining sustainable competitive advantage, the research results revealed that CVM -in contrast to change in equity and EVA- is much more favorable than the other objectives. Similarly, changes in demand had more favorable effects on multi-objective analysis.

Multi-Objective Artificial Bee Colony Algorithm for Multi-Echelon Supply Chain Optimization Problem

Supply Chain Management has become an integrated part of today's industries. Advancement in technology in this field is the key to the successful operation of businesses. Many techniques and algorithms have a risen dealing with the challenging problems of present industry. In this paper, we have deployed an Artificial Bee Colony algorithm to solve a multi-echelon, multi-objective supply chain problem. Also, we have explained the working of the algorithm while applying it to our problem through various mathematical formulations to get a set of results.

Fair profit distribution in multi-echelon supply chains via transfer prices

The total profit maximisation of a supply chain may result in an uneven and impractical profit distribution among the members. This work addresses the fair profit distribution within a multi-echelon supply chain using transfer prices. A mixed integer linear programming (MILP) model framework is proposed for the optimal production, distribution and capacity planning of a supply chain of an active ingredient (AI), consisting of AI plants, formulation plants and markets. The transfer prices of the AI from AI plants to formulation plants, and those of products from formulation plants to markets are to be optimised. The proportional and max–min fairness criteria are adopted to define fair profit distributions. Considering bargaining powers of supply chain members, game theoretic solution approaches are developed for fair solutions using Nash bargaining and lexicographic maximin principles. Especially, a hierarchical approach is developed to obtain an approximate optimal fair solution efficiently. The applicability and efficiency of the proposed approaches are demonstrated by two examples, including a real world agrochemical supply chain.

The multi-sourcing location inventory problem with stochastic demand

This paper deals with a multi-period location-inventory optimization problem in a multi-echelon supply chain network characterized by an uncertain demand and a multi-sourcing feature. The aim of the paper is to propose a generic modeling approach to integrate key features of the inventory planning decisions, made under a reorder point order-up-to-level (s, S) policy, with the location-allocation design decisions to cope with demand uncertainty. Given the hierarchical structure of the problem, a two-stage stochastic mathematical model that maximizes the total expected supply chain network profit is proposed. This optimization model is intractable due to its non-linearity. Therefore, a linear approximation is proposed and a sample average approximation approach is used to produce near-optimal solutions. Numerical experiments are conducted to validate the proposed modeling and solution approaches. The results show the efficiency of the linear approximation of the (s, S) policy at the strategic level to produce robust design solutions under uncertainty. They underline the sensitivity of the design solution to the demand type and the impact of the inventory holding costs and backorder costs, especially under non-stationary processes.

An Integrated Multi-Echelon Supply Chain Network Design Considering Stochastic Demand: A Genetic Algorithm Based Solution

The growing trend of natural resources consumption has caused irreparable losses to the environment. The scientists believe that if environmental degradation continues at its current pace, the prospect of human life will be shrouded in mystery. One of the most effective ways to deal with the environmental adverse effects is by implementing green supply chains. In this study a multilevel mathematical model including supply, production, distribution and customer levels has been presented for routing–location–inventoryin green supply chain. Vehicle routing between distribution centres and customers has been considered in the model. Establishment place of distribution centres among potential places is determined by the model. The distributors use continuous review policy (r, Q) to control the inventory. The proposed model object is to find an optimal supply chain with minimum costs. To validate the proposed model and measure its compliance with real world problems, GAMS IDE/Cplex has been used. In order to measure the efficiency of the proposed model in large scale problems, a genetic algorithm has been used. The results confirm the efficiency of the proposed model as a practical tool for decision makers to solve location-inventory-routing problems in green supply chain. The proposed GA could reduce the solving time by 85% while reaching on the average 97% of optimal solution compared with exact method.

Effective bullwhip metrics for multi-echelon distribution systems under order batching policies with cyclic demand

A large number of problems in a distribution supply chain require that decisions are made in the presence of the bullwhip effect phenomenon. The impact of the order batching policies on the bullwhip effect is analysed in this paper, when cycle demand on a multi-echelon supply chain operating is considered. While investigating which bullwhip effect metrics are more adequate to measure the bullwhip effect in these type of systems, the optimal reordering plan that minimises the operation costs of the overall system is calculated. A Mixed Integer Linear Programming (MILP) model is developed that takes into account an inventory and distribution system formed by multiple warehouses and retailers with lateral transshipments. The bullwhip effect is measured through four metrics: the echelon average inventory; the echelon inventory variance ratio; the echelon average order; and the echelon order rate variance ratio. As conclusion the inventory metrics suggest that (i) using batching policy reduces instability; (ii) batching may reduce in general order variance if using larger batches and (iii) cycle demand length has no major impact in the bullwhip effect. A motivational example and a real word case study are used and tested.

Stochastic multi-period multi-product multi-objective Aggregate Production Planning model in multi-echelon supply chain

<p>In this paper a multi-period multi-product multi-objective aggregate production planning (APP) model is proposed for an uncertain multi-echelon supply chain considering financial risk, customer satisfaction, and human resource training. Three conflictive objective functions and several sets of real constraints are considered concurrently in the proposed APP model. Some parameters of the proposed model are assumed to be uncertain and handled through a two-stage stochastic programming (TSSP) approach. The proposed TSSP is solved using three multi-objective solution procedures, i.e., the goal attainment technique, the modified ε-constraint method, and STEM method. The whole procedure is applied in an automotive resin and oil supply chain as a real case study wherein the efficacy and applicability of the proposed approaches are illustrated in comparison with existing experimental production planning method.</p>

Equilibrium analysis in multi-echelon supply chain with multi-dimensional utilities of inertial players

In a supply chain, the importance of information elicitation from the supply chain players is vital to design supply chain network. The rationality and self-centredness of these players causes the information asymmetry in the supply chain and thus situation of conflict and non-participation of the players in the network design process. In such situations, it is required to analyse the supply chain players’ behaviour in order to explore potential for coordination through incentives. In this paper, a novel approach of social utility analysis is proposed to elicit the information for supply chain coordination among the supply chain players in a dyadic relationship – supplier and buyer. In principal, we consider a monopsony situation where buyer is a dominant player. With the objective of maximizing the social utility, efforts have been made to value behavioural issues in supply chain. On the other hand, the reluctance of player due to the information asymmetry is measured in the form of inertia – an offset to the supply chain profit. The suppliers’ behaviour is analysed with three distinct level of risk for two types of the product in procurement process. The useful insight from this paper is in supplier selection process where the reluctance of supplier offsets supply chain profit. The paper provides recommendations to supply chain managers for efficient decision-making ability in supplier selection process.

Bi-objective intelligent water drops algorithm to a practical multi-echelon supply chain optimization problem

Industrial Clusters (ICs) are defined as geographically adjacent interconnected companies working together within the same commercial section which enjoy unusual competitive success in their field. The relationships between Supply Chain Management (SCM) and ICs have been imperfectly mathematically investigated, in spite of their intrinsic correlation. To bridge this gap, a bi-objective multi-echelon supply-distribution model is firstly proposed in this paper to optimize collaborations of different echelons. The considered problem is then solved using a recently introduced metaheuristic algorithm, the intelligent water drops (IWD) algorithm in terms of a multi-objective approach. The IWD is then compared with two well-known algorithms, reference-point based non-dominated sorting genetic algorithm (NSGA-III) and non-dominated ranking genetic algorithm (NRGA). The two considered objectives are 1) minimizing the total incurred logistics costs, and 2) maximizing the service level of customers. The small-and-medium sized enterprises (SMEs) positioned in IC as manufacturers profit from using a 3PL-managed supply-demand hub in industrial cluster (SDHIC) as a public provider of warehousing and logistics services. The validity of the proposed approach is illustrated through experimental results including comprehensive statistical analysis on the three used measurement metrics.

Competition in Multi-Echelon Systems

Around the start of this new millennium, scholars in the operations management/operations research field started to make important contributions to the study of price competition models. In this tutorial, we review these contributions, and partition them into five broad areas. Most of the tutorial is devoted to the last and most recent category: price competition models for multi-echelon supply chains with an arbitrary number of competing firms and products at each echelon.

Simulation of multi-echelon supply chain inventory transshipment models at different levels

An inventory transshipment model comprising manufacturers, distributors, and retailers based on multi-echelon supply chains is established using the system dynamics method. Thereafter, the single-, double-, three-, and four-chain inventory transshipment models at different levels are comparatively analyzed through simulation. Studies indicated that from the single- to four-chain inventory transshipment models, the average stock level minimally changed and declined. Meanwhile, the stability of the supply chain inventory improved continuously. The average customer requirement satisfaction rate of the inventory transshipment system consistently increases as the number of chains added to the transshipment system increases. However, the growth range of the average customer satisfaction rate continuously decreases with the increasing transshipment costs.

A global simulation-optimisation approach for inventory management in a decentralised supply chain

ABSTRACTThe optimal calibration of inventory management policies in a multi-echelon linear supply chain (SC) constitutes a complex problem. The difficulty to provide a clear and unique answer leads to frustration in using serious games for educational purposes. In this work, a simulation-based optimisation approach, using a dynamic tabu search metaheuristic, is developed and applied to a specific case of a four-echelon linear SC. A stochastic demand with constant mean is used and different service levels to the market are considered. The results indicate significant differences of the required on-hand inventory, cost contributions and cost element importance between the different echelons. The non-linear influence of the service level on the various parameters and costs is also observed. The obtained results are consistent and provide a clear answer to the stated problem. The methodology developed can be easily generalised to other cases.

A multi-objective multi-echelon green supply chain network design problem with risk-averse retailers in an uncertain environment

In this paper, the multi-objective multi-echelon supply chain network design problem is investigated. The resulted problem considers uncertainty of single product demand and the downstream risk attitude simultaneously and integrate it into the greenness concept. That is, makes the model more realistic in comparison with the others. According to the related literature study, there is no specific study to design the green supply chain network based on the risk attitude of the retailer. This paper aims to deal with this research gap by formulating robust counterpart of the main proposed uncertain problem. To do this, the conditional value at risk (CVaR) method through the data-driven approach is applied to deal with demand uncertainty to makes the model to be convex and sensitive to the risk averseness level in a robust manner. In addition, uncertainty sets approach is employed to deal with the uncertainty of other three parameters according to CO2 emission. The augmented e-constraint method is used to transform the resulted multi-objective mathematical programming problem into the single objective one, to obtain the global optimal solution through the exact mathematical solution method. Finally, the model is successfully simulated and its results are illustrated through a numerical example.

Material flow optimisation in a multi-echelon and multi-product supply chain

This paper addresses a distribution problem in a multi-product, multi-echelon supply chain. We develop a mixed integer linear programming formulation that considers allocation of vehicles with varying capacities for the transportation of products between the stages of the supply chain. The cost of transportation includes the fixed and variable costs of using the vehicles. The proposed formulations are solved optimally up to certain sizes, and we propose a heuristic based on total opportunity penalty cost method to solve the large sized problems. These proposed solution procedure is tested over a set of hypothetical problem sets. The results indicate that the proposed heuristic algorithm yields solutions within 5% from the optimal solutions.

Multi-echelon closed-loop supply chain network design and configuration under supply risks and logistics risks

Present day supply chains are dynamically expanding with the increase in globalisation but susceptible towards a number of risks too. The risks cause disturbances in supply chain and hinder the movement of products across their stage which ultimately leads to overall increases in supply chain costs. In this paper, a multi-echelon closed-loop supply chain model is considered, where supplier's quality risks (production issues), logistics risks (transportation issues) are mathematically modelled using mixed integer linear programming (MILP) formulation. The paper highlights benefits of adopting the supply chain network design under risk. The comparison of risk and no risk situation is illustrated for a generic closed-loop supply chain network. The findings suggest that risk consideration in supply chain network design is very useful, and its cost is not much higher to the organisation too. The supply chain resilience can be achieved with a moderate risk premium. The firms which can pay 10-13% more will be able to sustain in risk events and their network configuration will be robust under supply and logistics risks happenings.

Material flow optimisation in a multi-echelon and multi-product supply chain

This paper addresses a distribution problem in a multi-product, multi-echelon supply chain. We develop a mixed integer linear programming formulation that considers allocation of vehicles with varying capacities for the transportation of products between the stages of the supply chain. The cost of transportation includes the fixed and variable costs of using the vehicles. The proposed formulations are solved optimally up to certain sizes, and we propose a heuristic based on total opportunity penalty cost method to solve the large sized problems. These proposed solution procedure is tested over a set of hypothetical problem sets. The results indicate that the proposed heuristic algorithm yields solutions within 5% from the optimal solutions.

Applying a hybrid transportation system to design an agile multi-product and multi-echelon dynamic supply chain

Making optimum decisions under complicated circumstances, requires using appropriate mathematical modelling techniques and optimisation methods. In many cases, supply and demand are different in principal. These major differences force producers to make various decisions for supplying required raw materials and meeting demands. Respecting the low operational and high strategic costs of applying the rail transport, it is highly recommended to use this method of transportation for the purpose of transporting huge load of materials/products in a centralised network. However, using vehicles and road transport is more suitable for small loads of materials/products in widespread networks. Current study is devoted to design an efficient hybrid transportation system and an agile multi-product supply chain. A mathematical model is presented in order to formulate the problem at hand. Numerical results demonstrate the validity of the presented model and the applied optimisation procedure in making optimum and applicable decisions. In fact, numerical results reveal the promising ability of the applied optimisation procedure confronting real-world situations.

Applying a hybrid transportation system to design an agile multi-product and multi-echelon dynamic supply chain

Applying a hybrid transportation system to design an agile multi-product and multi-echelon dynamic supply chain