Multimodal distribution network design problem for returnable transport items with uncertainty

Transportation network design for maximizing flow-based accessibility

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

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

Integrated strategic and operational planning of dry port container networks in a stochastic environment

Efficient inventory management for perishable goods is a critical operational challenge for retailers due to the complexities introduced by limited shelf-life and uncertain demand. This paper presents a two-stage stochastic programming model tailored for optimising perishable goods distribution in retail networks, explicitly addressing last-in-first-out customer preferences, demand uncertainty and fixed shelf-life. The model integrates operational objectives including cost minimisation, demand satisfaction, and waste reduction through scenario-based stochastic optimisation. Due to computational complexity in solving large-scale stochastic problems, we introduce heuristic and approximation methodologies for improved scalability. Specifically, we develop three approaches: a Demand-Based Fractional Allocation heuristic fairly distributing goods among locations, a Ranked Greedy Allocation heuristic prioritising stores based on profitability, and a Scenario Reduction method using Weighted K-means clustering to manage scenario complexity efficiently. These heuristics are evaluated using synthetic datasets and real-world-inspired instances from the M5 forecasting competition dataset, covering various demand patterns including intermittent and smooth series. Results indicate that scenario reduction via Weighted K-means significantly improves computational efficiency (up to 90 % reduction in computation time for short shelf-lives) while maintaining high solution accuracy for smooth demands (average optimality gap 1.37 % ). The greedy heuristic effectively minimises waste for intermittent demands, highlighting the need for testing general allocation approaches considering diverse demand patterns.

In a telecommunication network, routing and wavelength assignment (RWA) is the problem of finding lightpaths for incoming connection requests. When facing a dynamic traffic, greedy assignment of lightpaths to incoming requests based on predefined deterministic policies leads to a fragmented network that cannot make use of its full capacity because of stranded bandwidth. At this point, service providers try to recover the capacity via a defragmentation process. We study this setting from two perspectives: (i) while granting the connection requests via the RWA problem and (ii) during the defragmentation process by lightpath rerouting. For both problems, we present the first two-stage stochastic integer programming model incorporating incoming request uncertainty to maximize the expected grade of service. We develop a decomposition-based solution approach, which uses various relaxations of the problem and a newly developed problem-specific cut family. Simulation of two-stage policies for a variety of instances in a rolling-horizon framework of 52 stages shows that our stochastic models provide high-quality solutions when compared with traditionally used deterministic ones. Specifically, the proposed provisioning policies yield improvements of up to 19% in overall grade of service and 20% in spectrum saving, while the stochastic lightpath rerouting policies grant up to 36% more requests, using up to just 4% more bandwidth spectrum.Summary of Contribution: For handling the intrinsic uncertainty of demand in the telecommunications industry, this paper proposes novel stochastic models and solution methodology for two fundamental problems in telecommunications at operational level: (i) routing and wavelength assignment (RWA) and (ii) lightpath rerouting problem. Despite the vast literature on the RWA problem, stochastic optimization has not been considered as a viable solution for resource allocation in optical networks. We propose two-stage stochastic programming models for both problems and design efficient decomposition-based solution methods that use various relaxations of the models and a new family of cutting planes. Our extensive and rigorous numerical experiments show the significant merit of incorporating uncertainty into decision making, as well as the effectiveness of the decomposition framework and our newly designed family of cuts in enhancing the solvability of both models. This work opens new avenues to explore where the powerful stochastic programming literature can be leveraged to make operational decisions in telecommunications problems, a field that currently relies mostly on deterministic and heuristic solution methods.

Toward circular economy for pomegranate fruit supply chain under dynamic uncertainty: A case study

In this paper, a Casualty Collection Points (CCPs) location problem is formulated as a two-stage robust stochastic optimization model in an uncertain environment. In this modelling approach, the network design decisions are integrated with the multi-period response operational decisions where the number of casualties with different levels of injuries coming from the affected areas is uncertain. Furthermore, the transportation capacity for the evacuation of casualties to CCPs and hospitals is also uncertain. To solve this complex problem, a robust sample average approximation method with the feasibility restoration technique is proposed, and its efficiency is examined through a statistical validation procedure. We then evaluate the proposed methodology in the backdrop of a hypothetical case of Bhopal gas tragedy (with the same hazard propagation profile) at the present day. We also report the solution robustness and model robustness of 144 instances of the case-study to show the proficiency of our proposed solution approach. Results analysis reveals that our modelling approach enables the decision makers to design a humanitarian logistic network in which not only the proximity and accessibility to CCPs are improved, but also the number of lives lost is decreased. Moreover, it is shown that the proposed robust stochastic optimization approach converges rapidly and more efficiently. We hope that our methodology will encourage urban city planners to pre-identify CCP locations, and, in the event of a disaster, help them decide on the subset of these CCPs that could be rapidly mobilised for disaster response.

In the design and planning of vehicular ad-hoc networks, road-side infrastructure nodes are commonly used to improve the overall connectivity and communication capability of the networks, however, to determine the locations to install the infrastructure nodes for optimal performance based on the ever-changing density and connectivity dynamics of moving vehicles remains to be a challenging issue. In this paper, we introduce a stochastic traffic model to capture the space and time dynamics of vehicles in signalized urban road systems to identify poorly-connected regions for infrastructure node placements. To closely approximate the practical road conditions, we propose a density-dependent velocity profile to approximate vehicle interactions and capture platoons formation and dissipation at traffic signals. Numerical results are presented to evaluate the stochastic traffic model. In general, we show that the fluid model can adequately describe the mean behavior of the traffic stream, while the stochastic model can approximate the probability distribution well even when vehicles interact with each other as their movement is controlled by traffic lights. With the understandings of the vehicular density dynamics from the proposed model, we illustrate that connectivity dynamics of vehicles can be determined and consequent system engineering and planning can be carried out.

We consider a new biannual liquefied natural gas (LNG) ship routing and scheduling problem and a stochastic extension under boil-off gas (BOG) uncertainty while serving geographically dispersed multiple customers using a fleet of heterogeneous vessels. We are motivated not only by contract trend changes to shorter ones but also by technological advancesin LNG vessel design. The mutual coincidence of both transitions enables developing a new LNG shipping strategy to keep up with emerging market trend. We fi rst propose a deterministic LNG scheduling model formulated as a multiple vehicle routing problem (VRP). The model is then extended to consider BOG using a two-stage stochastic modeling approach in which BOG is a random variable. Since the VRP is typically a combinatorial optimization problem, its stochastic extension is much harder to solve. In order to overcome this computational burden, a Monte Carlo sampling optimization is used to reduce the number of scenarios in the stochastic model while ensuring ggood quality of solutions. The solutions are evaluated using expected value of perfect information (EVPI) and value of stochastic solution (VSS). The result shows that our proposed model yields more stable solutions than the deterministic model. The study was made possible by the NPRP award [NPRP 4-1249-2-492] from the Qatar National Research Fund (a member of the Qatar Foundation).

ABSTRACTThis paper introduces a two-stage stochastic integer linear programming model to improve phlebotomist scheduling in laboratory facilities of healthcare delivery systems. The model developed enables laboratory management to determine optimal scheduling policies that minimize work overload. The stochastic programming model considers the uncertainty associated with the blood collection demand in laboratory environments when optimizing phlebotomist scheduling. The paper presents an application of the model to a hospital laboratory in urban North Carolina as a case study discussing the implications for hospital laboratory management.

We propose a two-stage stochastic model for optimizing the operation of energy storage. The model captures two important features: uncertain real-time prices when day-ahead operational commitments are made; and the price impact of charging and discharging energy storage. We demonstrate that if energy storage has full flexibility to make real-time adjustments to its day-ahead commitment and market prices do not respond to charging and discharging decisions, there is no value in using a stochastic modeling framework, i.e., the value of stochastic solution is always zero. This is because in such a case the energy storage behaves purely as a financial arbitrageur day ahead, which can be captured using a deterministic model. We show also that prices responding to its operation can make it profitable for energy storage to “waste” energy, for instance by charging and discharging simultaneously, which is normally sub-optimal. We demonstrate our model and how to calibrate the price-response functions from historical data with a practical case study.

In this paper, we introduce a shipping logistics network optimization method for construction waste recycling. In our case, construction waste is transported by a relay mode integrating land transportation, shipping transportation, and land transportation. Under the influence of urban economic life, the quantity (demand) of construction waste is uncertain and stochastic. Considering the randomness of construction waste generation, a two-stage stochastic integer programming model for the design of a shipping logistics network for construction waste recycling is proposed, and an accurate algorithm based on Benders decomposition is presented. Based on an actual case in Shanghai, numerical experiments are carried out to evaluate the efficacy of the proposed model and algorithm. Based on an actual case study in Shanghai, numerical experiments demonstrate that the proposed model can help to reduce transportation costs of construction waste. Sensitivity analysis highlights that factors like the penalty for untransported waste and capacity constraints play a crucial role in network optimization decisions. The findings provide valuable theoretical support for developing more efficient and sustainable logistics networks for construction waste recycling.

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.

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.