A bi-level programming model for supply chain network optimization with assembly line balancing and push–pull strategy

The design of robust value-creating supply chain networks: A critical review

World Health Organization (WHO) declared COVID-19 as a pandemic On March 12, 2020. Up to January 13, 2022, 320,944,953 cases of infection and 5,539,160 deaths have been reported worldwide. COVID-19 has negatively impacted the blood supply chain by drastically reducing blood donation. Therefore, developing models to design effective blood supply chains in emergencies is essential. This research offers a novel multi-objective Transportation-Location-Inventory-Routing (TLIR) formulation for an emergency blood supply chain network design problem. We answer questions regarding strategic, operational, and tactical decisions considering disruption in the network and blood shelf-life. Since, in real-world applications, the parameters of the proposed mathematical formulation are uncertain, two flexible uncertain models are proposed to provide risk-averse and robust solutions for the problem. We applied the proposed formulations in a case study. Under various scenarios and realizations, we show that the offered robust model handles uncertainties more efficiently and finds solutions that have significantly lower costs and delivery time. To make a reliable conclusion, we performed extensive worst-case analyses to demonstrate the robustness of the results. In the end, we provide critical managerial insights to enhance the effectiveness of the supply chain.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10479-022-04673-9.

(English) Speculation and Postponement are opposite supply chain strategies intended to either advance or postpone the necessary production processes to transform raw materials into finished goods. A Customer Order Decoupling Point, or CODP, is a logistic point of the chain where the speculative production is stored until the placement of demand orders, so the CODP positioning characterizes the associated strategy of the supply chain. Two optimization models are presented to decide the optimal Supply Chain Network Design and its Speculation – Postponement strategy through a two-stage stochastic optimization approach: a first one called (OSCS) that assigns strategic and speculation decisions to first-stage variables, and postponement and recourse values to second-stage variables; and a second one called (OSCST) with the same strategic decisions as (OSCS) where speculation and postponement concepts are explicitly excluded of the modelling, so all production flow decisions are left to second-stage variables. The analysis of a study case of (OSCS) shows clear combined Speculation – Postponement strategies for different products joint along with tactical criterions to fulfill demand orders in decreasing order of inventory production added value, as well as significant strategic differences between the original problem and its deterministic equivalent. The solutions of the study cases of (OSCST) keep the observed tactical criterions and show Speculation – Postponement strategies that lay outside of the classical scope. Finally, an extension of the Speculation – Postponement strategy framework is proposed joint along with two measures, the Absolute and Relative Degree of Speculation, to quantify the Speculation – Postponement strategy of any supply chain problem with inventory levels. This PhD thesis is the result of a collaboration between GNOM-UPC, Accenture Technology Labs, Accenture Analytics Innovation Center and CIM-UPC. (Español) Speculation y Postponement son estrategias opuestas de cadena de suministro dirigidas a avanzar o posponer los procesos de producción que transforman materias primas en productos acabados. Un Punto de Desacople de Órdenes de Consumidor, o CODP, es un punto logístico de la cadena donde la producción especulativa es almacenada hasta la llegada de órdenes de demanda, de manera que el posicionamiento de CODPs caracteriza la estrategia asociada de la cadena de suministro. Dos modelos de optimización se presentan para decidir el Diseño en Red de Cadena de Suministro óptimo y su estrategia Speculation – Postponement asociada a través de un enfoque de optimización estocástica de dos etapas: el primero llamado (OSCS) que asigna decisiones estratégicas y de speculation a variables de primera etapa, y decisiones de postponement y valores de recurso a variables de segunda etapa; y un segundo llamado (OSCST) con las mismas decisiones estratégicas que (OSCS) donde los conceptos speculation y postponement son explícitamente excluidos de la modelización, de manera que todas las decisiones de flujos de producción quedan asignados a variables de segunda etapa. El análisis del caso de estudio de (OSCS) muestra estrategias combinadas de Speculation – Postponement claras para diferentes productos junto a criterios tácticos para satisfacer las órdenes de demanda en orden decreciente de valor añadido de la producción almacenada, así como diferencias significantes entre el problema original y su equivalente determinista. Las soluciones de los casos de estudio de (OSCST) mantienen los criterios tácticos observados y muestran estrategias Speculation – Postponement que quedan fuera del alcance clásico. Finalmente, se propone una extensión del marco de estrategias Speculation – Postponement junto a dos medidas, el Grado Absoluto y Relativo de Speculation, para cuantificar la estrategia Speculation – Postponement de todo problema de cadena de suministro con niveles de inventario. Esta tesis doctoral es el resultado de una colaboración entre GNOM-UPC, Accenture Technology Labs, Accenture Analytics Innovation Center y CIM-UPC.

A stochastic programming approach for supply chain network design under uncertainty

This article integrates the company operations decisions (i.e. location, production, inventory, distribution, and transportation) and finance decisions (i.e. cash, accounts payable and receivable, debt, securities, payment delays, and discounts) in which the demands and return rate are uncertain, defined by a set of scenarios. The cash flow and budgeting model will be coupled with supply chain network design using a mixed integer linear programming formulation. The article evaluates two financial criteria, that is, the change in equity and the profit as objective functions. The results indicate that objective functions are partially interdependent, that is, they conflict in certain parts. This fact illustrates the inadequacy of treating process operations and finances in isolated environments and pursuing objective myopic performance indicators such as profit or cost. Due to the importance of the supply chain network design problem, a multi-objective robust optimization with the max–min version is extended to cope with the uncertainty. A solution approach integrating Benders’ decomposition method with the scenario relaxation algorithm is also proposed in this research. The improved algorithm has been applied to solve a number of numerical experiments. All results illustrate significant improvement in computation time of the improved algorithm over existing approaches. For a problem, the proposed algorithm shows a significant reduction in computational time compared with the Benders’ decomposition and scenario relaxation that shows the efficiency of the proposed solution method.

A stochastic programming approach for supply chain network design under uncertainty

Designing a closed-loop supply chain network for citrus fruits crates considering environmental and economic issues

Supply chain network design is one of the most important strategic issues in operations management. The main objective in designing a supply chain is to keep the cost as low as possible. However, the modelling of a supply chain requires more than single-objective such as lead-time minimization, service level maximization, and environmental impact maximization among others. Usually these objectives may cause conflicts such as increasing the service level usually causes a growth in costs. Therefore, the aim should be to find trade-off solutions to satisfy the conflicting objectives. The aim of this chapter is to propose a new method based on a hybrid version of the Bees Algorithm with Slope Angle Computation and Hill Climbing Algorithm to solve a multi-objective supply chain network design problem. A real case from the literature has been selected and solved in order to show the potentiality of the proposed method in solving a large scale combinatorial problem.

Three-phase unbalance could have significant impacts on the secure and economic operation of a low-voltage distribution system. With the deployment of smart meters and the emergence of phase switch devices (PSDs), more efficient schemes for mitigating three-phase unbalance are expected by utilizing numerous measurement data and the installed PSDs in low-voltage areas. Therefore, a bi-level optimal phase switch device placement model for mitigating three-phase unbalance is proposed in this work. In the upper-level model, the PSD placement strategy is optimized by minimizing the total costs of PSD installation and operation costs of low-voltage areas determined in the lower-level model. In the lower-level model, the operation costs of low-voltage areas for typical days are minimized based on the PSDs and their locations determined in the upper-level model. The proposed bi-level model is tested by an actual low-voltage area of Zhejiang province, China, and the effectiveness of the optimal PSD placement model for reducing the total investment and operation costs of the low-voltage area demonstrated.

Nowadays, the design of supply chain networks should be based on environmental issues as well as the needs of customers since the main driver of a supply chain network is customers. Continuous innovation of products requires understanding the features that are most important to customers, and product pricing should be carried out in a way that includes the satisfaction of both customers and manufacturers. This study uses the Kano model to classify product features into different categories. The design of the green supply chain network based on the Kano model has not been investigated in the literature so far. This study examines a green supply chain network including multiple manufacturers, product types, distributors, and carriers that is designed based on Kano’s conceptual model of multiple needs. In the proposed mathematical model of this paper, customer demand is a function of the selling price of the product, transportation pollution is minimized, and a solution based on the Cooperative Game Theory approach is used to solve the mathematical model using the GAMS software. One of the advantages of the proposed mathematical model in this research compared to other supply chain models is that the design needs of the supply chain network based on the Kano model (“must-be”, “one-dimensional”, “attractive” and “indifferent”) can be determined based on customer satisfaction. In addition, the price of the product can be determined according to the satisfaction of both customers and the manufacturers.

This study aims to investigate the closed-loop supply chain network design problem considering the environmental and responsiveness features. For this purpose, a multi-objective mathematical model is suggested that minimizes the carbon emissions and the total costs and maximizes the responsiveness of the system. Due to the dynamic space of the business environment, uncertainty is an integral part of the supply chain problem. Therefore, this research applies the robust possibilistic programming method to cope with uncertainty. Afterwards, since the research problem has a high level of the complexity, a hybrid solution approach based on a heuristic method and the meta-goal programming method is developed to solve the research problem in a reasonable time. Then, due to the importance of the ventilator device during the recent pandemic (COVID-19), this study considers this product as a case study. The main contribution of the current study is to design a green-responsive closed-loop supply chain network under uncertainty using a multi-objective robust possibilistic programming model, for the first time in the literature, especially in the medical devices industry. On the other side, the other contribution of this study is to develop an efficient hybrid solution method. The achieved results demonstrate the efficiency of the offered model and the developed hybrid method. Eventually, by carrying out sensitivity analysis, the impact of some of the critical parameters on the model is investigated. Based on the obtained results, an increase in the demand sizes leads to increasing the environmental damages and the total costs while reducing the responsiveness level. On the other side, an increase in the rate of return leads to an increase in all of the objective functions. Also, the achieved results show that when the capacity parameter is increased, the total costs are decreased, but the responsiveness and environmental impacts are increased.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11356-022-19105-1.

To enhance the resilience of urban rail transit networks in dealing with interference events and facilitating rapid network recovery, this paper focuses on studying damaged urban rail transit networks and proposes comprehensive resilience evaluation indexes for urban rail transit networks that take into account two dimensions: network topology and passenger travel path selection. A bi-level programming model is constructed to maximize the comprehensive toughness, where the upper-level model is an integer planning model for determining the optimal recovery sequence of the affected stations under interference events that result in station closure or inoperability. The lower-level model is a passenger flow allocation model aiming to minimize travelers’ impedance. A genetic algorithm and Dijkstra’s labeling algorithm are used to solve the upper model as well as the shortest path of the lower model, respectively. Using a real-world urban rail transit network as an example, this research applies different recovery strategies, random recovery, node importance-based recovery, and comprehensive toughness-based recovery, across five common interference scenarios to analyze the recovery sequence of stations in each scenario. The modeling results show that the comprehensive toughness-based restoration strategy yields the most favorable results for the rail transportation network, followed by the node importance-based restoration strategy. In addition, the network’s toughness varies more significantly when employing different restoration strategies during target interference, as compared to the random and range interference scenarios.

A multi-stage supply chain network design problem with in-house production and partial product outsourcing

The problem of network design of blood supply chains is traditionally studied considering a maximum of three objective functions. In the real world, however, there are always many conflicting objectives for different stakeholders. This paper addresses a blood supply chain (BSC) network design problem to optimize the costs of blood shortage and substitution in addition to other common objective functions. To this end, four important objectives that decision makers are always faced with in disaster are considered: (1) minimizing the total cost, (2) minimizing transportation time, (3) minimizing total unsatisfied demand, and (4) maximizing the total reliability. A mixed-integer linear programming (MIP) model is proposed to formulate the problem at hand. Since this problem is known to be strongly NP-hard, the intelligent NSGA-II algorithm is applied to solve it in a reasonable time. Data from a real case study is used to evaluate the performance of the proposed solution method. The comparison of the results of the proposed algorithm with the mathematical model confirms the accuracy of the proposed method. Furthermore, the analysis of the results indicates the superiority of the proposed model over previous studies. Moreover, the proposed algorithm provides a wide range of suitable solutions. Therefore, different alternatives are presented to the decision makers to make a trade-off according to their preferences.

Integration of sale and leaseback in the optimal design of supply chain networks