A two-stage stochastic programming model for perishable goods allocation under LIFO customer preferences and diverse demand patterns

Multi-period fourth-party logistics network design with the temporary outsourcing service under demand uncertainty

This paper aims to optimise healthcare resource pre-positioning, patient scheduling, and patient transferring under uncertain demands and stochastic resource consumption. We propose a two-stage stochastic programming model that formulates the patient scheduling problem as a Markov decision process. To address complexities and uncertainties, we use Artificial Intelligence (AI) techniques to improve both model formulation and algorithmic performance. To tackle the limited data challenge, we introduce a Wasserstein distance-based ambiguity set and propose a two-stage distributionally robust optimisation (DRO) model, which derives a deterministic equivalent using the Lagrangian dual of non-anticipativity constraints. The solution process is accelerated with a scenario decomposition approach and the K-means clustering method. Both theoretical and numerical results demonstrate the consistency of the two-stage DRO model with the sample average approximation (SAA) method. The potential of AI to improve the model's performance is evident through the significant reduction in computation time achieved with the K-means clustering approach without compromising solution quality. Compared to the SAA method, the DRO model exhibits a considerable reduction in the waiting penalty cost for out-of-sample cases, ranging from 43.06 % to 81.23 % . Numerical results show that the proposed algorithm outperforms SAA methods and several benchmark policies in terms of computational efficiency and solutions.

Closed-loop supply chain network design for hazardous products with uncertain demands and returns

This paper investigates a stochastic Returnable Transport Items (RTI) distribution network design problem for an RTI service provider responsible for managing RTI flows under uncertain demand and returns. The objective is to determine the optimal number and location of intermediate facilities to accommodate RTI flows while making repositioning decisions across the network. A two-stage stochastic programming model is developed to minimise costs associated with network design, RTI storage, the choice of direct or indirect shipping, and the allocation of multiple transportation modes in both forward and reverse flow channels. To address this complex problem, a robust sample average approximation method is proposed, with its efficiency evaluated through statistical validation. Additionally, the stochastic model’s effectiveness is assessed by computing the expected value of perfect information and the value of stochastic solutions. Comprehensive numerical experiments, including 64 instances with varying cost and robustness coefficients, provide managerial insights. The results demonstrate that efficient RTI network design significantly impacts multi-period operational decisions and highlights the critical role of RTI depots in mitigating demand uncertainty. Furthermore, the robust stochastic optimisation approach employed delivers high-quality solutions with an optimality gap of less than 1% within reasonable computational times. This study highlights the importance of integrating robust decision-making in RTI network design, offering practical insights for managing uncertainty and optimising cost efficiency across complex RTI networks.

It is difficult to clearly estimate CO2 emissions because CO2 is emitted from various sources according to changing environments. Any approach relating to CO2 disposal has to deal with the presence of such uncertainty in CO2 disposal demand. A two-stage stochastic programming model is developed for planning CO2 emissions disposal networks by taking into account this uncertainty. The proposed CO2 disposal network model allows us to determine where and how much captured CO2 can be held for storage, and where to sequester the given amount of CO2 among multiple potential candidates for the purpose of minimizing the total cost of handling in the face of uncertainty in CO2 sequestration demand. The proposed model is applied to a case study that examines the robustness of the model in terms of handling changing environments in the context of CO2 emissions and disposal targets in Korea. The results gained aid in determining policy to plan the budget for the disposal of CO2.

A two-stage stochastic model for an industrial symbiosis network under uncertain demand

Robust optimization and stochastic programming approaches for medium-term production scheduling of a large-scale steelmaking continuous casting process under demand uncertainty

Uncertainties in the day-ahead forecasts for load and wind energy availability are considered in a reliability unit commitment problem. A two-stage stochastic program is formulated to minimize total expected cost, where commitments of thermal units are viewed as first-stage decisions and dispatch is relegated to the second stage. Scenario paths of hourly loads are generated according to a weather forecast-based load model. Wind energy scenarios are obtained by identifying analogue historical days. Net load scenarios are then created by crossing scenarios from each set and subtracting wind energy from load. A new heuristic scenario reduction method termed forward selection in recourse clusters (FSRC) is customized to alleviate the computational burden. Results of applying FSRC are compared with those of a classical scenario reduction method, fast forward selection (FFS) by evaluating the expected dispatch costs when the commitment decisions derived from each subset of scenarios are applied to the whole scenario set. In an instance down-sampled from data of an Independent System Operator in the U.S., the expected dispatch costs for both scenario reduction methods are similar, but FSRC improves reliability.

The main objective of this study is to develop a two-stage stochastic programming framework for lot-sizing and scheduling the production activities at a kitting facility to support a manufacturing plant. The novelty of the study lies in modeling multiple uncertainties using two-stage stochastic programming to solve a kitting specific production planning problem in a manufacturing setting. The demand for the kits and the yield of the kitting workers are the two sources of uncertainties considered in this study. The first-stage decisions include the baseline production schedule and the workforce requirement, while the second stage makes recourse decisions on overtime production. The proposed decision-making framework is validated on a multi-period, multi-product case study involving a kitting facility supporting a manufacturing plant producing braking equipment. The main conclusion of the study is that uncertainties have significant impacts on kitting planning decisions and that the proposed two-stage stochastic programming model was robust in determining optimal production plans under uncertainty.

One of the most critical challenges for modern power systems is to reliably supply electricity to its consumers during and in the aftermath of natural disasters. As our dependence on electrical power has increased over the years, long-term power outages can lead to devastating impacts on affected communities. Furthermore, power outages can halt the operation of water treatment plants, leading to shortages in clean water, which is essential during post-disaster recovery. One way to address this is to temporarily reconfigure power and water networks into localized networks, i.e., electric microgrids and water micro-nets, that utilize local resources to supply local demand independently of the main power grid and/or water network. Utilizing distributed energy resources such as wind and solar and treating wastewater locally for potable reuse can provide the operational flexibility for such systems to operate sustainably. However, due to uncertainties in both renewable energy generation and electric/water consumption, ensuring sustainable operation is a challenging task. In this paper, an optimal operational strategy is proposed for an islanded microgrid/micro-net, considering the stochastic nature of renewable energy resources, electric demand, and water demand. An energy storage system is modeled to address the uncertainty in power generation and demand, in conjunction with local water storage and wastewater treatment to accommodate variable water demands. A two-stage stochastic programming model is formulated and solved to determine an optimal operation strategy for the combined system.

Transportation network design for maximizing flow-based accessibility

In this paper, the uncertain duration of the section blockade is considered for the high-speed railway train timetable rescheduling problem with the entire section blockade. The objective is to minimize the conditional value-at-risk of the train delay by retiming, reordering, etc. A two-stage stochastic mixed-integer linear programming model is established for the train rescheduling problem in a macroscopic view. Some model approximation methods with scenarios are applied to speed up the solution process. The proposed models are solved by the GUROBI commercial solver. Numerical experiments are performed based on the Beijing-Tianjin intercity railway to show the effectiveness of the proposed stochastic programming model. The experimental results showed that the proposed model with scenario reduction and order scenario reduction could get a high- quality solution with a shorter computation time.

This thesis provides tools for decision support systems for perishable goods logistics. It also provides an approach for enterprises to estimate the benefit of adopting the proposed logistic systems. Perishable products are produced, transported, and consumed all over the world. Thanks to perishable goods supply chains, we can enjoy safe, fresh, and affordable products. Nevertheless, it is estimated that one third of the agricultural products produced globally for human consumption end up wasted, which amounts to 1.3 billion tonnes per year. This also means that one third of the resources and greenhouse gas emissions for producing and transporting these products are in vein. The wastage of these fresh products happens throughout the supply chains, which are often caused by the perishing nature and inefficiencies of supply chain planning. For instance, congestions at a certain location or over supply at a retailer may result in spoilage. Disruptions such as malfunctioning of cooling equipment can also contribute to wastage in supply chains. Recent technological developments provide new insights into supply chain management, allowing further waste reduction. With sensors and communication technologies, information of products such as location and freshness can be made known to supply chain planners in real-time. Thus the research question of this thesis is given real-time information of perishable goods logistics, in what ways can perishable goods supply chain players better control and coordinate logistic processes to reduce loss of perishable products? Traditional mathematical methods cannot capture enough details when describing perishable goods in their supply chains. Therefore, this thesis proposes a general framework in a system and control fashion to describe and control logistic operations. This general framework consists of a quality-aware modeling method and a model predictive control strategy. The quality-aware modeling method considers the perishable goods in the supply chain as a system, with quality and logistic features. The model predictive control strategy observes the system and steers the system in a manner that the wastage can be minimized. In this thesis, the proposed method is used in case studies of supply chains with three different commodities, namely bananas, starch potatoes, and cut roses. In each case study, because each commodity is unique in its physiological nature, it perishes in a unique way. Therefore, the supply chain takes care of the commodity in a way that may not suit other commodities. As a result, the logistic features of the supply chains are also different from each other. This requires the systems to be described differently, and control architectures can also vary. Results from the case studies show that the general framework can improve the effectiveness of supply chain logistic planning, and to reduce wastage. The improvements are quantified to illustrate the benefit of making full use of the information on perishable goods in supply chains.

The emergence of the circular economy concept has pushed industry owners towards the green supply chain and waste reduction. The closed-loop supply chain originates from the concept of the circular economy and its purpose is to increase efficiency and profitability by reducing waste and energy consumption. Hence, in this research, an integrated bi-objective mixed-integer linear programming model is developed with the aim of optimizing both operational and strategic decisions in a closed-loop supply chain network. The proposed model benefits the location-inventory-routing problem to structure the network, and it applies a carbon tax policy and vehicle scheduling problem to reduce emissions and vehicle waiting time, respectively. Considering problems such as split-delivery, storage possibility, shortage, supplier selection, order allocation, heterogenous vehicles, and uncertain demand has led to the development of a comprehensive model. A stochastic scenario-based approach is used to deal with demand uncertainty, and an augmented epsilon-constraint method is employed to solve the proposed bi-objective model. The applicability of the proposed model and the effectiveness of the bi-objective solution approach for achieving circular economy are examined through its implementation in a cable and wire industry in Iran.

Improving the sustainability of a reverse supply chain system under demand uncertainty by using postponement strategies.