A stochastic programming approach for supply chain network design under uncertainty

A stochastic programming approach for supply chain network design under uncertainty

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

In recent years, various kinds of carbon dioxide capture, utilization and storage supply chain network design (CCUS SCND) problems have been extensively studied by scholars from the supply chain management community and other fields. The existing works mainly focus on the various deterministic or uncertainty problems; few works consider the CCUS SCND resilience problem in the context of utilization/storage facility disruptions due to unexpected natural disasters or other geological anomaly events. This paper aims to study the CCUS SCND resilience problem under utilization/storage facility capacity disruption risk. We propose a stochastic mixed-integer linear programming model for the considered problem. In the considered problem, the main decisions related to the following areas are taken into account: supply chain design and planning; facility disruption risk handling, including the optimal determination of facility locations and the matching of carbon dioxide emission sources and utilization/storage facilities; carbon dioxide normal transportation planning; and transshipment planning for various disruption scenarios. Finally, an experimental study comprising a case study from China is conducted to validate the effectiveness and performance of our proposed model. The obtained results show that the supply chain networks for the case study obtained by our proposed model are efficient, cost-effective and resilient in mitigating various kinds of utilization/storage facility disruption scenarios, showing the model can be applied to large-scale CCUS projects to help managers effectively deal with disruption risks. Future research should consider multiple disruption events and propose multiple effective resilience strategies.

A tabu search based algorithm for the optimal design of multi-objective multi-product supply chain networks

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.

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

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.

(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.

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.

This article investigates how to simultaneously optimize both strategic and tactical decisions in the supply chain network design. For this purpose, a bi-level programming model is developed in which supply chain network design problem is considered as a strategic decision in the upper-level model, while the lower-level model contains the assembly line balancing as a tactical decision. In addition, the problem is extended to include push–pull strategy where decisions such as production amount and inventory level of each component in manufacturers are made. Based on the special structure of the model, a heuristic method is proposed to solve the developed bi-level model. A numerical example is employed to show the performance of proposed method in terms of feasibility and convergence. Finally, computational experiments on several problem instances are presented to demonstrate the applications of developed model and the solution method.

Green supply chain network design to reduce carbon emissions

In this paper, the classical problem of supply chain network design is reconsidered to emphasize the role of contracts in uncertain environments. The supply chain addressed consists of four layers: suppliers, manufacturers, warehouses, and customers acting within a single period. The single owner of the manufacturing plants signs a contract with each of the suppliers to satisfy demand from downstream. Available contracts consist of long-term and option contracts, and unmet demand is satisfied by purchasing from the spot market. In this supply chain, customer demand, supplier capacity, plants and warehouses, transportation costs, and spot prices are uncertain. Two models are proposed here: a risk-neutral two-stage stochastic model and a risk-averse model that considers risk measures. A solution strategy based on sample average approximation is then proposed to handle large scale problems. Extensive computational studies prove the important role of contracts in the design process, especially a portfolio of contracts. For instance, we show that long-term contract alone has similar impacts to having no contracts, and that option contract alone gives inferior results to a combination of option and long-term contracts. We also show that the proposed solution methodology is able to obtain good quality solutions for large scale problems.

In the lack of historical data for an uncertain event, the belief degree-based uncertainty becomes more applicable than other types of uncertainty like fuzzy theory, stochastic programming, etc. This study focuses on an uncertain bi-objective two-stage supply chain network design problem. The problem consists of plants, depots, and customers with cost and environmental impacts (CO2 emission) where direct shipment between plants and customers is allowed. As such network could be designed for the first time in a geographical region, such problem is modeled in a belief degree-based uncertain environment. This is almost the first study on belief degree-based uncertain supply chain network design problem with environmental impacts and direct shipment. Three approaches of expected value model, chance-constrained model, and their combination are applied to convert the proposed uncertain problem to its crisp form. The obtained crisp forms are solved by two multi-objective optimization approaches of goal programming (GP) and global criterion method (GCM). An extensive computational study with various test problems is performed to study the performance of the crisp models and the solution approaches. As result, the obtained crisp formulations are highly sensitive to the changes in the cost parameters’ values, and the GP performs better than the GCM from the solution quality point of view.

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.

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