Designing a supply chain network under a dynamic discounting-based credit payment program

The electric power supply chain network plays an important role in the world economy. It powers our homes, offices, and industries and runs various forms of transportation. This paper considers an electric power supply chain network design problem featuring differential pricing and preventive maintenance. We demonstrate that this general model can be formulated as the centralized and decentralized supply chain models. A continuous approximation approach is used to model the problems. The objective of these models is to determine the optimal power plants’ service area, electricity price, and preventive maintenance budget while maximizing the total network profit or the own organization’s benefits. Our model is applied to the case of a power company in northern Vietnam. We show that the proposed approach can be used to address real-world cases effectively. The results demonstrate that the use of differential pricing policy and preventive maintenance could much enhance power company profit.

ABSTRACTIn this study, the application of type-II fuzzy sets is addressed to design a multi-product, multi-level, multi-period supply chain networks. The proposed model provides integrated approach to make optimal decisions such as location–allocation, production, procurement and distribution subject to operational and tactical constraints. In the context of fuzzy linear programming, this study involves type-II fuzzy numbers for the right-hand side of constraints regarding three sources of uncertainty: demand, manufacturing and supply. According to fuzzy components considered, a type-II fuzzy mixed-integer linear programming is converted into an equivalent auxiliary crisp model using linear fuzzy type-reducer models. The final models are linear and the global optimum solutions can be achieved using commercial OR softwares. The contributions of this study are three folds: (1) introducing a new integrated supply chain network design problem; (2) considering a solution procedure based on type-II fuzzy sets and (3) presenting a linear fuzzy type-II reducer. Finally, the proposed model and solution approach are illustrated through a numerical example to demonstrate the significance.

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.

Retailers must routinely contend with difficulties in managing the supply of their products due to disruptions in the supply chain. For new retailers, the challenge is how to design a resilient supply chain network; for existing retailers, the challenge is to adapt the supply chain network to satisfy customer demands. Generally, global retailers set up retail outlets in different areas to support specific customer zones and distribution centers (DCs) to manage the inventory for a cluster of retail shops. The supply chain network is usually highly exposed to external uncertainties like human-made and natural disasters. Therefore, disruptions to any of the supply chain nodes may result in the disconnection of the network and, ultimately, an interruption in the regular operations of the retailer. In this chapter, we consider an extended four-echelon centralized supply chain (CSC) consisting of the following echelons: (I) raw-materials suppliers, (II) manufacturers, (III) DCs, and (IV) retailers and their customer zones. We consider the retail business organization as the key stakeholder in this supply chain, as it owns the commercial outlets and the DCs, in addition to centrally controlling the supply operations of the four echelons. In developing our CSC, we review several resilience strategies that can be used to design or redesign a resilient retail supply chain. In doing so, we present the definition, advantages, and shortcomings of each strategy in the surveyed literature, and we also review the models that have been developed utilizing these strategies. Finally, we propose additional resilience strategies that can be adopted to improve supply chain operations when faced with major events or uncertainties, such as the disruptions caused by the COVID-19 pandemic.

Credit payment strategies have been implemented widely in the online retail industry. This work studies an online-retail supply chain involving credit period and selling price-dependent demands. The participants of the supply chain form a Stackelberg game where the supplier as a follower sells products to the customers through an online platform provider, who as a leader provides a credit period to customers and charges the supplier based on the quantity of goods sold. We study and compare the supply chains when the online platform provider adopts the cash payment and credit payment strategies, respectively, to investigate the effects of the credit period, the selling price and the default risk on supply chain system performance. We also investigate these supply chains under both the centralized and decentralized settings and provide an example to illustrate a simple allocation mechanism to coordinate the decentralized supply chain. Finally, an extension of the supply chain with credit payment is given.

Supply chain network design problem is increasingly showing its importance, especially the perishable supply chain. This research develops a multi-objective mathematical model to design four-echelon intermodal multi-product perishable supply chain configuration in order to ensure a balance of the three pillars of sustainable development: economy, environment, and society. The optimization objective functions of the model are, respectively, minimizing costs, delivery time, emissions, and the supply-demand mismatch in time. The model addresses particular problems in the supply chain of fresh fruits, which is more challenging compared to other types of perishable products due to its seasonal characteristics. The study proposes a new approach that combines and standardizes the above objective functions into a single weighted objective function. The solution from the model supports the decision-making process at both strategic and tactical levels. Strategically, the model supports decisions about the location, size of facilities, product flows, and workforce level. Tactically, the decision variables provide information on harvest time, delivery time, the delivery route, and mode of transport. To demonstrate its practical applicability, the model is applied to Mekong Delta region, Vietnam, where a variety of fruit types, large yields, and high distribution demand in this region make designing a shared supply chain desirable for its overall economic, environmental, and social concerns. Moreover, sensitivity analysis regarding weights of different objectives is performed to assess possible changes in supply chain configurations. Application of this model to other perishable products, the addition of modes of transport, social policy, and uncertainty parameters may be suggested for future research.

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

PT Asagri Selaras Asia is an agribusiness company in Jakarta, Indonesia that provides food supply for entrepreneurs in the tourism sector, such as hotels and restaurants. In running their business, the company has a supply chain network that effectively integrates suppliers, manufacturers, warehouses, and stores. Supply chain management optimization is deemed necessary to maximize efficiency in every process carried out at PT Asagri Selaras Asia. One of the processes that can be optimized is the determination of facility location. The right facility location can minimize distance, delivery time, and operational costs and optimize the company's profit. Currently, the contract period for the distribution center (DC) used by PT Asagri Selaras Asia is about to expire therefore a decision is needed regarding the determination of the DC location for the next period, either extending the current DC contract or moving to a new location. This study aims to maximize the company's profit by comparing the profit obtained under existing and alternative DC, and the proposed DC from the experimental results using the Green Field Analysis (GFA) method. This study also aims to determine the feasibility of the three DC locations using the Network Optimization (NO) method with Anylogistix software. Based on the data analysis, the new DC is recommended to be located in the West Pejaten area, South Jakarta with latitude -6.2729150574 and longitude 106.8230798418 which will give an increase in profit of 80.53% compared to the existing DC.

Hybrid adaptive simplified human learning optimization algorithms for supply chain network design problem with possibility of direct shipment

A stochastic programming approach for supply chain network design under uncertainty

A stochastic programming approach for supply chain network design under uncertainty

The supply chain design problem is one of the important problems in supply chain management. In this problem, the goal is to locate several facilities for distribution centers in different geographical areas to cover customer demands. In the model presented in this paper, customer demand is met through distribution centers, while these distribution centers supply products from manufacturing plants. In this paper, a mathematical model of integer linear programming is presented that, in addition to the optimal supply chain network design, considers the facility's location. This model aims to minimize the costs of sending products from the factory to distribution centers, from distribution centers to customers, and establishing distribution centers. To evaluate the proposed model, different network samples were created randomly on small, medium and large scales for experiments. In order to perform the experiments, a linear programming solver (LP Solver) and an iterated local search algorithm were used to compare the performance of each of these methods to find the optimal solution. These experiments show the superiority of the iterated local search algorithm compared to LP Solver in achieving the optimal solution in the shortest runtime for all instances.

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.

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

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.