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As a result of a wide variety of risks, diverse in scale, complexity, and consequences, countries must generate various strategies through which they can face them, protecting the population, the ecosystem, and the economy. The objective of the document to be presented is to help health organizations in decision-making in logistical aspects, specifically, in the location of facilities and distribution of supplies. Thiswork proposes a logistic model that allows the location of a feasible municipality through the integration of the classic p-median problem and the Multiple Vehicle Routing Problem (MVRP). The goal is to determine a feasible location to establish a warehouse and the routes to supply Personal protection supplements for the health sector personnel to municipalities that host hospitals of different public institutions with COVID-19 patients. The model is evaluated in one of the states belonging to Mexico, making a typification of its municipalities. The results are obtained in four scenarios, showing both the host municipalities and the delivery routes. The results showed the municipalities of Cuitláhuac, Huiloapan de Cuauhtémoc, Huatusco, and Tlalixcoyan as feasible locations for the warehouse. From the information provided and through the vehicle routing problem with time windows (VRPTW), new delivery routes are established, showing a comparison of results. The total of established routes for delivery is seven. Due to the characteristics of the content, this research falls into the classification of case studies. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
ABSTRACT
The outbreak of major public health emergencies such as the coronavirus epidemic has put forward new requirements for urban emergency management procedures. Accuracy and effective distribution model of emergency support materials, as an effective tool to inhibit the deterioration of the public health sector, have gradually become a research hotspot. The distribution of urban emergency support devices, under the secondary supply chain structure of "material transfer center-demand point," which may involve confusing demands, is studied to determine the actual situation of fuzzy requests under the impact of an epidemic outbreak. An optimization model of urban emergency support material distribution, based on Credibility theory, is first constructed. Then an improved sparrow search algorithm, ISSA, was designed by introducing Sobol sequence, Cauchy variation and bird swarm algorithm into the structure of the classical SSA. In addition, numerical validation and standard test set validation were carried out and the experimental results showed that the introduced improved strategy effectively improved the global search capability of the algorithm. Furthermore, simulation experiments are conducted, based on Shanghai, and the comparison with existing cutting-edge algorithms shows that the designed algorithm has stronger superiority and robustness. And the simulation results show that the designed algorithm can reduce vehicle cost by 4.83%, time cost by 13.80%, etc. compared to other algorithms. Finally, the impact of preference value on the distribution of emergency support materials is analyzed to help decision-makers to develop reasonable and effective distribution strategies according to the impact of major public health emergencies. The results of the study provide a practical reference for the solution of urban emergency support materials distribution problems.
Subject(s)
Emergencies , Public Health , Humans , China/epidemiology , Algorithms , Computer SimulationABSTRACT
The demand for same-day delivery (SDD) has increased rapidly in the last few years and has particu-larly boomed during the COVID-19 pandemic. The fast growth is not without its challenge. In 2016, due to low concentrations of memberships and far distance from the depot, certain minority neighborhoods were excluded from receiving Amazon's SDD service, raising concerns about fairness. In this paper, we study the problem of offering fair SDD service to customers. The service area is partitioned into differ-ent regions. Over the course of a day, customers request for SDD service, and the timing of requests and delivery locations are not known in advance. The dispatcher dynamically assigns vehicles to make de-liveries to accepted customers before their delivery deadline. In addition to overall service rate ( utility ), we maximize the minimal regional service rate across all regions ( fairness ). We model the problem as a multi-objective Markov decision process and develop a deep Q-learning solution approach. We introduce a novel transformation of learning from rates to actual services, which creates a stable and efficient learn-ing process. Computational results demonstrate the effectiveness of our approach in alleviating unfairness both spatially and temporally in different customer geographies. We show this effectiveness is valid with different depot locations, providing businesses with an opportunity to achieve better fairness from any location. We also show that the proposed approach performs efficiently when serving heterogeneously wealthy districts in the city.(c) 2022 Elsevier B.V. All rights reserved.
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Vaccination uptake has become the key factor that will determine our success in containing the coronavirus pneumonia (COVID-19) pandemic. Efficient distribution of vaccines to inoculation spots is crucial to curtailing the spread of the novel COVID-19 pandemic. Normally, in a big city, a huge number of vaccines need to be transported from central depot(s) through a set of satellites to widely scattered inoculation spots by special-purpose vehicles every day. Such a large two-echelon vehicle routing problem is computationally difficult. Moreover, the demands for vaccines evolve with the epidemic spread over time, and the actual demands are hard to determine early and exactly, which not only increases the problem difficulty but also prolongs the distribution time. Based on our practical experience of COVID-19 vaccine distribution in China, we present a hybrid machine learning and evolutionary computation method, which first uses a fuzzy deep learning model to forecast the demands for vaccines for each next day, such that we can predistribute the forecasted number of vaccines to the satellites in advance;after obtaining the actual demands, it uses an evolutionary algorithm (EA) to route vehicles to distribute vaccines from the satellites/depots to the inoculation spots on each day. The EA saves historical problem instances and their high-quality solutions in a knowledge base, so as to capture inherent relationship between evolving problem inputs to solutions;when solving a new problem instance on each day, the EA utilizes historical solutions that perform well on the similar instances to improve initial solution quality and, hence, accelerate convergence. Computational results on real-world instances of vaccine distribution demonstrate that the proposed method can produce solutions with significantly shorter distribution time compared to state-of-the-arts and, hence, contribute to accelerating the achievement of herd immunity.
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Determining the optimal vehicle routing of emergency material distribution (VREMD) is one of the core issues of emergency management, which is strategically important to improve the effectiveness of emergency response and thus reduce the negative impact of large-scale emergency events. To summarize the latest research progress, we collected 511 VREMD-related articles published from 2010 to the present from the Scopus database and conducted a bibliometric analysis using VOSviewer software. Subsequently, we cautiously selected 49 articles from these publications for system review;sorted out the latest research progress in model construction and solution algorithms;and summarized the evolution trend of keywords, research gaps, and future works. The results show that do -mestic scholars and research organizations held an unqualified advantage regarding the number of published papers. However, these organizations with the most publications performed poorly regarding the number of literature citations. China and the US have contributed the vast majority of the literature, and there are close collaborations between researchers from both countries. The optimization model of VREMD can be divided into single-, multi-, and joint-objective models. The shortest travel time is the most common optimization objective in the single-objective optimization model. Several scholars focus on multiobjective optimization models to consider conflicting objectives simultaneously. In recent literature, scholars have focused on the impact of uncertainty and special events (e.g., COVID-19) on VREMD. Moreover, some scholars focus on joint optimization models to optimize vehicle routes and central locations (or material allocation) simultaneously. So-lution algorithms can be divided into two primary categories, i.e., mathematical planning methods and intelligent evolutionary algorithms. The branch and bound algorithm is the most dominant mathematical planning algorithm, while genetic algorithms and their enhancements are the most commonly used intelligent evolutionary algorithms. It is shown that the nondominated sorting genetic algorithm II (NSGA-II) can effectively solve the multiobjective model of VREMD. To further improve the algorithm's performance, re-searchers have proposed improved hybrid intelligent algorithms that combine the ad-vantages of NSGA-II and certain other algorithms. Scholars have also proposed a series of optimization algorithms for specific scenarios. With the development of new technologies and computation methods, it will be exciting to construct optimization models that consider uncertainty, heterogeneity, and temporality for large-scale real-world issues and develop generalized solution approaches rather than those applicable to specific scenarios.(c) 2022 Periodical Offices of Chang'an University. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC -ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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Harris Hawks Optimization (HHO) is a Swarm Intelligence (SI) algorithm that is inspired by the cooperative behavior and hunting style of Harris Hawks in the nature. Researchers' interest in HHO is increasing day by day because it has global search capability, fast convergence speed and strong robustness. On the other hand, Emergency Vehicle Dispatching (EVD) is a complex task that requires exponential time to choose the right emergency vehicles to deploy, especially during pandemics like COVID-19. Therefore, in this work we propose to model the EVD problem as a multi-objective optimization problem where a potential solution is an allocation of patients to ambulances and the objective is to minimize the travelling cost while maximizing early treatment of critical patients. We also propose to use HHO to determine the best allocation within a reasonable amount of time. We evaluate our proposed HHO for EVD using 2 synthetic datasets. We compare the results of the proposed approach with those obtained using a modified version of Particle Swarm Optimization (PSO). The experimental analysis shows that the proposed multi-objective HHO for EVD is very competitive and gives a substantial improvement over the enhanced PSO algorithm in terms of performance. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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In this paper, we propose a solution for optimizing the routes of Mobile Medical Units (MMUs) in the domain of vehicle routing and scheduling. The generic objective is to optimize the distance traveled by the MMUs as well as optimizing the associated cost. These MMUs are located at a central depot. The idea is to provide improved healthcare to the rural people of India. The solution is obtained in two stages: preparing a mathematical model with the most suitable parameters, and then in the second phase, implementing an algorithm to obtain an optimized solution. The solution is focused on multiple parameters, including the number of vans, number of specialists, total distance, total travel time, and others. The solution is further supported by Reinforcement Learning, explaining the best possible optimized route and total distance traveled.
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Purpose: This paper aims to propose an operation policy of multi-capacity room service robots traveling within a hotel. As multi-capacity robots can serve many requests in a single trip, improved operation policy can reduce the investment cost of robots. Design/methodology/approach: Using a mathematical model-based optimization technique, an optimal set of robots with minimum installation cost is derived while serving the entire room service demands. Through testing a variety of scenarios by changing the price and function of robots to be installed, insights that consider the various situations are offered. Findings: Though the increase in capacity saves much time for room service at a lower capacity level, the amount of time saved gradually decreases as the capacity increases. Besides, the installation strategy is divided into two cases depending on the purchase cost of robots. Research limitations/implications: Currently, the studies focusing on the adoption of service robots from an operations view are rarely be found. To reduce the burden of investment cost, this study takes the unique approach to improve the operation policy of service robots by using the multi-capacity robots. Practical implications: This study guides the hotel to install an adequate set of robots. The result confirms that the optimal installation set of robots is affected by various factors, such as the room service information, the hotel structure and the unit execution cycle. Originality/value: After the outbreak of COVID-19, people avoid face-to-face contact and interest in non-contact service is growing. This paper deals with the efficient way to implement non-contact delivery through logistic robots, a timely and important topic. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
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The rapid outbreak of COVID-19 pandemic invoked scientists and researchers to prepare the world for future disasters. During the pandemic, global authorities on healthcare urged the importance of disinfection of objects and surfaces. To implement efficient and safe disinfection services during the pandemic, robots have been utilized for indoor assets. In this paper, we envision the use of drones for disinfection of outdoor assets in hospitals and other facilities. Such heterogeneous assets may have different service demands (e.g., service time, quantity of the disinfectant material etc.), whereas drones have typically limited capacity (i.e., travel time, disinfectant carrying capacity). To serve all the facility assets in an efficient manner, the drone to assets allocation and drone travel routes must be optimized. In this paper, we formulate the capacitated vehicle routing problem (CVRP) to find optimal route for each drone such that the total service time is minimized, while simultaneously the drones meet the demands of each asset allocated to it. The problem is solved using mixed integer programming (MIP). As CVRP is an NP-hard problem, we propose a lightweight heuristic to achieve sub-optimal performance while reducing the time complexity in solving the problem involving a large number of assets. © 2022 IEEE.
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This article develops a stochastic programming framework for multiagent systems, where task decomposition, assignment, and scheduling problems are simultaneously optimized. The framework can be applied to heterogeneous mobile robot teams with distributed subtasks. Examples include pandemic robotic service coordination, explore and rescue, and delivery systems with heterogeneous vehicles. Owing to their inherent flexibility and robustness, multiagent systems are applied in a growing range of real-world problems that involve heterogeneous tasks and uncertain information. Most previous works assume one fixed way to decompose a task into roles that can later be assigned to the agents. This assumption is not valid for a complex task where the roles can vary and multiple decomposition structures exist. Meanwhile, it is unclear how uncertainties in task requirements and agent capabilities can be systematically quantified and optimized under a multiagent system setting. A representation for complex tasks is proposed: agent capabilities are represented as a vector of random distributions, and task requirements are verified by a generalizable binary function. The conditional value at risk is chosen as a metric in the objective function to generate robust plans. An efficient algorithm is described to solve the model, and the whole framework is evaluated in two different practical test cases: capture-the-flag and robotic service coordination during a pandemic (e.g., COVID-19). Results demonstrate that the framework is generalizable, is scalable up to 140 agents and 40 tasks for the example test cases, and provides low-cost plans that ensure a high probability of success.
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The novel coronavirus pandemic is a major global public health emergency, and has presented new challenges and requirements for the timely response and operational stability of emergency logistics that were required to address the major public health events outbreak in China. Based on the problems of insufficient timeliness and high total system cost of emergency logistics distribution in major epidemic situations, this paper takes the minimum vehicle distribution travel cost, time cost, early/late punishment cost, and fixed cost of the vehicle as the target, the soft time window for receiving goods at each demand point, the rated load of the vehicle, the volume, maximum travel of the vehicle in a single delivery as constraints, and an emergency logistics vehicle routing problem optimization model for major epidemics was constructed. The convergence speed improvement strategy, particle search improvement strategy, and elite retention improvement strategy were introduced to improve the particle swarm optimization (PSO) algorithm for it to be suitable for solving global optimization problems. The simulation results prove that the improved PSO algorithm required to solve the emergency medical supplies logistics vehicle routing problem for the major emergency can reach optimal results. Compared with the basic PSO algorithm, the total cost was reduced by 20.09%. © 2023 by the authors.
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Due to the global outbreak of COVID-19, the perishable product supply chains have been impacted in different ways, and consequently, the risks of food insecurity have been increased in many affected countries. The uncertainty in supply and demand of perishable products, are among the most influential factors impacting the supply chain networks. Accordingly, the provision and distribution of food and other perishable commodities have become much more important than in the past. In this study, a bi-objective optimization model is proposed for a three-echelon perishable food supply chain (PFSC) network with multiple products to formulate an integrated supplier selection, production scheduling, and vehicle routing problem. The proposed model aims to mitigate the risks of demand and supply uncertainties and reinforce the distribution-related decisions by simultaneously optimizing the total network costs and suppliers' reliability. Using the distributionally robust modeling paradigm, the probability distribution of uncertain demand is assumed to belong to an ambiguity set with given moment information. Accordingly, distributionally robust chance-constrained approach is applied to ensure that the demands of retailers and capacity of vehicles are satisfied with high probability. Leveraging duality and linearization techniques, the proposed model is reformulated as a mixed-integer linear program. Then, the weighted goal programming approach is adopted to address the multi-objectiveness of the proposed optimization model. To certify the performance and applicability of the model, a real-world case study in the poultry industry is investigated. Finally, the sensitivity analysis is conducted to evaluate the impacts of influential parameters on the objective functions and optimal decisions, and then some managerial insights are provided based on the obtained results. © 2022 Elsevier Ltd
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In recent years, some phenomena such as the COVID-19 pandemic have caused the autonomous vehicle (AV) to attract much attention in theoretical and applied research. This paper addresses the optimization problem of a heterogeneous fleet that consists of autonomous electric vehicles (AEVs) and conventional vehicles (CVs) in a Business-to-Consumer (B2C) distribution system. The absence of the driver in AEVs results in the necessity of studying two factors in modeling the problem, namely time windows in the routing plan and different compartments in the loading space of AEVs. We developed a mathematical model based on these properties, that was NP-hard. Then we proposed a hybrid algorithm, including variable neighborhood search (VNS) via neighborhood structure of large neighborhood search (LNS), namely the VLNS algorithm. The numerical results shed light on the proficiency of the algorithm in terms of solution time and solution quality. In addition, employing AEVs in the mixed fleet is considered to be desirable based on the operational cost of the fleet. The numerical results show the operational cost in the mixed fleet decreases on average by 57.22% compared with the homogeneous fleet.
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In recent years, some phenomena such as the COVID-19 pandemic have caused the autonomous vehicle (AV) to attract much attention in theoretical and applied research. This paper addresses the optimization problem of a heterogeneous fleet that consists of autonomous electric vehicles (AEVs) and conventional vehicles (CVs) in a Business-to-Consumer (B2C) distribution system. The absence of the driver in AEVs results in the necessity of studying two factors in modeling the problem, namely time windows in the routing plan and different compartments in the loading space of AEVs. The arrival and departure times of the AEV at the customer’s location must be pre-planned, because, the AEV is not able to decide what to do if the customer is late at this point. Also, due to increasing the security of the loads inside the AEVs and the lack of control of the driver during the delivery of the goods, each customer should only have access to his/her orders. Therefore, the compartmentation of the AEV’s loading area has been proposed in its conceptual model. We developed a mathematical model based on these properties and proposed a hybrid algorithm, including variable neighborhood search (VNS) via neighborhood structure of large neighborhood search (LNS), namely the VLNS algorithm. The numerical results shed light on the proficiency of the algorithm in terms of solution time and solution quality. In addition, employing AEVs in the mixed fleet is considered to be desirable based on the operational cost of the fleet. Author
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Garbage disposal and collection is an ongoing global crisis amplified by the increasing world population, lack of funds and public awareness, and recently because of the Covid-19 pandemic. Information Technology can be utilized as a solution for the existing garbage collection methods that are old-fashioned, time-consuming, and energy-consuming due to the lack of a unified and consistent system that incorporates all the parties involved in garbage production and collection. A mobile-based garbage collection system is proposed to overcome the issues aforementioned through route and schedule optimization, AI chatbot, and optimized GPS tracking. The route and schedule optimization is achieved through vehicle routing problem with time windows(VRPTW) with synchronization and precedence that was optimized using LNS;the total travel cost went from 172 minutes to 144 minutes. The AI chatbot feature facilitates reporting garbage collection issues and complaints and enquiring about waste management tips (reduce, recycle, and reuse tips) to be used at home. The most prominent role of developing this AI chatbot is replacing the manual process of reporting garbage collection issues in Sri Lanka with an efficient and interactive way. The chatbot has waste management tips Q and A. In Optimized GPS Tracking, the user can use the map to find the nearest garbage disposal place based on the type of rubbish they generate. The truck driver can find the optimal path to the closest current garbage disposal centres and public trash bins and view the location of Homeowners on the map. The optimized path between two points is displayed based on distance, time, and fuel consumption. The main goal of the component is to show the location of garbage disposal bins and the optimal paths for truck drivers using Linear regression and the Node2vec algorithm. © 2022 IEEE.
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As transportation system plays a vastly important role in combatting newly-emerging and severe epidemics like the coronavirus disease 2019 (COVID-19), the vehicle routing problem (VRP) in epidemics has become an emerging topic that has attracted increasing attention worldwide. However, most existing VRP models are not suitable for epidemic situations, because they do not consider the prevention cost caused by issues such as viral tests and quarantine during the traveling. Therefore, this paper proposes a multi-objective VRP model for epidemic situations, named VRP4E, which considers not only the traditional travel cost but also the prevention cost of the VRP in epidemic situations. To efficiently solve the VRP4E, this paper further proposes a novel algorithm named multi-objective ant colony system algorithm for epidemic situations, termed MOACS4E, together with three novel designs. First, by extending the efficient “multiple populations for multiple objectives” framework, the MOACS4E adopts two ant colonies to optimize the travel and prevention costs respectively, so as to improve the search efficiency. Second, a pheromone fusion-based solution generation method is proposed to fuse the pheromones from different colonies to increase solution diversity effectively. Third, a solution quality improvement method is further proposed to improve the solutions for the prevention cost objective. The effectiveness of the MOACS4E is verified in experiments on 25 generated benchmarks by comparison with six state-of-the-art and modern algorithms. Moreover, the VRP4E in different epidemic situations and a real-world case in the Beijing-Tianjin-Hebei region, China, are further studied to provide helpful insights for combatting COVID-19-like epidemics.
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During natural disasters or accidents, an emergency logistics network aims to ensure the distribution of relief supplies to victims in time and efficiently. When the coronavirus disease 2019 (COVID-19) emerged, the government closed the outbreak areas to control the risk of transmission. The closed areas were divided into high-risk and middle-/low-risk areas, and travel restrictions were enforced in the different risk areas. The distribution of daily essential supplies to residents in the closed areas became a major challenge for the government. This study introduces a new variant of the vehicle routing problem with travel restrictions in closed areas called the two-echelon emergency vehicle routing problem with time window assignment (2E-EVRPTWA). 2E-EVRPTWA involves transporting goods from distribution centers (DCs) to satellites in high-risk areas in the first echelon and delivering goods from DCs or satellites to customers in the second echelon. Vehicle sharing and time window assignment (TWA) strategies are applied to optimize the transportation resource configuration and improve the operational efficiency of the emergency logistics network. A tri-objective mathematical model for 2E-EVRPTWA is also constructed to minimize the total operating cost, total delivery time, and number of vehicles. A multi-objective adaptive large neighborhood search with split algorithm (MOALNS-SA) is proposed to obtain the Pareto optimal solution for 2E-EVRPTWA. The split algorithm (SA) calculates the objective values associated with each solution and assigns multiple trips to shared vehicles. A non-dominated sorting strategy is used to retain the optimal labels obtained with the SA algorithm and evaluate the quality of the multi-objective solution. The TWA strategy embedded in MOALNS-SA assigns appropriate candidate time windows to customers. The proposed MOALNS-SA produces results that are comparable with the CPLEX solver and those of the self-learning non-dominated sorting genetic algorithm-II, multi-objective ant colony algorithm, and multi-objective particle swarm optimization algorithm for 2E-EVRPTWA. A real-world COVID-19 case study from Chongqing City, China, is performed to test the performance of the proposed model and algorithm. This study helps the government and logistics enterprises design an efficient, collaborative, emergency logistics network, and promote the healthy and sustainable development of cities.
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This article develops a stochastic programming framework for multiagent systems, where task decomposition, assignment, and scheduling problems are simultaneously optimized. The framework can be applied to heterogeneous mobile robot teams with distributed subtasks. Examples include pandemic robotic service coordination, explore and rescue, and delivery systems with heterogeneous vehicles. Owing to their inherent flexibility and robustness, multiagent systems are applied in a growing range of real-world problems that involve heterogeneous tasks and uncertain information. Most previous works assume one fixed way to decompose a task into roles that can later be assigned to the agents. This assumption is not valid for a complex task where the roles can vary and multiple decomposition structures exist. Meanwhile, it is unclear how uncertainties in task requirements and agent capabilities can be systematically quantified and optimized under a multiagent system setting. A representation for complex tasks is proposed: agent capabilities are represented as a vector of random distributions, and task requirements are verified by a generalizable binary function. The conditional value at risk is chosen as a metric in the objective function to generate robust plans. An efficient algorithm is described to solve the model, and the whole framework is evaluated in two different practical test cases: capture-the-flag and robotic service coordination during a pandemic (e.g., COVID-19). Results demonstrate that the framework is generalizable, is scalable up to 140 agents and 40 tasks for the example test cases, and provides low-cost plans that ensure a high probability of success.
ABSTRACT
Due to the global outbreak of COVID-19, the perishable product supply chains have been impacted in different ways, and consequently, the risks of food insecurity have been increased in many affected countries. The uncertainty in supply and demand of perishable products, are among the most influential factors impacting the supply chain networks. Accordingly, the provision and distribution of food and other perishable commodities have become much more important than in the past. In this study, a bi-objective optimization model is proposed for a three-echelon perishable food supply chain (PFSC) network with multiple products to formulate an integrated supplier selection, production scheduling, and vehicle routing problem. The proposed model aims to mitigate the risks of demand and supply uncertainties and reinforce the distribution-related decisions by simultaneously optimizing the total network costs and suppliers’ reliability. Using the distributionally robust modeling paradigm, the probability distribution of uncertain demand is assumed to belong to an ambiguity set with given moment information. Accordingly, distributionally robust chance-constrained approach is applied to ensure that the demands of retailers and capacity of vehicles are satisfied with high probability. Leveraging duality and linearization techniques, the proposed model is reformulated as a mixed-integer linear program. Then, the weighted goal programming approach is adopted to address the multi-objectiveness of the proposed optimization model. To certify the performance and applicability of the model, a real-world case study in the poultry industry is investigated. Finally, the sensitivity analysis is conducted to evaluate the impacts of influential parameters on the objective functions and optimal decisions, and then some managerial insights are provided based on the obtained results.
ABSTRACT
The novel coronavirus pneumonia (COVID-19) has created huge demands for medical masks that need to be delivered to a lot of demand points to protect citizens. The efficiency of delivery is critical to the prevention and control of the epidemic. However, the huge demands for masks and massive number of demand points scattered make the problem highly complex. Moreover, the actual demands are often obtained late, and hence the time duration for solution calculation and mask delivery is often very limited. Based on our practical experience of medical mask delivery in response to COVID-19 in China, we present a hybrid machine learning and heuristic optimization method, which uses a deep learning model to predict the demand of each region, schedules first-echelon vehicles to pre-distribute the predicted number of masks from depot(s) to regional facilities in advance, reassigns demand points among different regions to balance the deviations of predicted demands from actual demands, and finally routes second-echelon vehicles to efficiently deliver masks to the demand points in each region. For the subproblems of demand point reassignment and two-batch routing whose complexities are significantly lower, we propose variable neighborhood tabu search heuristics to efficiently solve them. Application of the proposed method in emergency mask delivery in three megacities in China during the peak of COVID-19 demonstrated its significant performance advantages over other methods without pre-distribution or reassignment. We also discuss key success factors and lessons learned to facilitate the extension of our method to a wider range of problems.