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Emergency medical services (EMS) is a system that provides emergency medical care for incidents involving serious illness or injury. The location of EMS stations plays an essential role in delivering effective and efficient medical services. Numerous location models have been developed for locating and sizing EMS stations. However, it remains challenging to satisfy all EMS planning criteria within a single location model. In this study, a multi-period capacitated facility location problem with maximum travel time and backup service (EMSLSP) is proposed for locating and sizing EMS stations. The most important criteria for EMS planning are taken into account in EMSLSP: the demand changes due to population mobility, the maximum service capacity of an ambulance, the maximum number of ambulances at each EMS station, the maximum travel time from each EMS station to the locations it serves, the full coverage of dynamic demand, the minimum percent of population covered by EMS service in a specific travel time, and a backup station for each demand location in case of need. A case study in Zhengzhou, a large city in China, demonstrates that effective and efficient locations and sizes of EMS stations can be determined by solving the EMSLSP with various planning parameters. Compared with the existing EMS systems, the average ambulance travel time and the percentage of the population served are significantly improved. Simulations of ambulance scheduling confirm that the relocated and resized EMS stations perform better than those in the existing system. The evaluation-optimization-simulation method outlined in this paper provides a comprehensive and effective approach for EMS station planning. [ABSTRACT FROM AUTHOR]

This article investigates the mechanisms that underlie the biases in price elasticities of demand in applying static demand models under dynamic demand, which have been pointed out by previous empirical studies. It studies three sources of biases: disregard of state variables (affecting short-run elasticity); inconsistent utility parameter estimates; and changing expectations of consumers (affecting long-run elasticity). Disregard of state variables, such as durable product holdings, which is not negligible but not paid much attention to in the literature, leads to an overestimate of short-run own elasticities. Inconsistent utility parameter estimates arises due to the failure to account for consumers' future expectations and unobserved state variables. Changing expectations of consumers are not explicitly specified in the static model, and this also leads to biased results when applying static models. Regarding the magnitude of the biases, the first and the third sources of biases might induce large biases in price elasticities, especially when the focus is on the large conditional choice probability products. Possible remedies for the use of static demand models are also discussed. [ABSTRACT FROM AUTHOR]

Abstract Emergency medical services (EMS) is a system that provides emergency medical care for incidents involving serious illness or injury. The location of EMS stations plays an essential role in delivering effective and efficient medical services. Numerous location models have been developed for locating and sizing EMS stations. However, it remains challenging to satisfy all EMS planning criteria within a single location model. In this study, a multi-period capacitated facility location problem with maximum travel time and backup service (EMSLSP) is proposed for locating and sizing EMS stations. The most important criteria for EMS planning are taken into account in EMSLSP: the demand changes due to population mobility, the maximum service capacity of an ambulance, the maximum number of ambulances at each EMS station, the maximum travel time from each EMS station to the locations it serves, the full coverage of dynamic demand, the minimum percent of population covered by EMS service in a specific travel time, and a backup station for each demand location in case of need. A case study in Zhengzhou, a large city in China, demonstrates that effective and efficient locations and sizes of EMS stations can be determined by solving the EMSLSP with various planning parameters. Compared with the existing EMS systems, the average ambulance travel time and the percentage of the population served are significantly improved. Simulations of ambulance scheduling confirm that the relocated and resized EMS stations perform better than those in the existing system. The evaluation-optimization-simulation method outlined in this paper provides a comprehensive and effective approach for EMS station planning.

For the dynamic takeout delivery vehicle routing problem, which faces fluctuating order demand and time-varying speeds, this study presents a novel approach. We analyze the time distribution of takeout orders and apply a Receding Horizon Control (RHC) strategy to convert the dynamic challenge into a static one. The driving speed of delivery vehicles on different roads at different times is determined based on the subdivision criteria of the urban road network and a traffic congestion measurement method. We propose a dynamic takeout delivery vehicle routing optimization model and a time-varying subdivision road network is established to minimize the total delivery cost. We validated the model through simulation examples. The optimization results show that the total distribution cost is reduced after considering the time-varying subdivision road network, with the penalty cost decreasing by 39%. It is evident that considering the subdivision of the road network can enhance order delivery efficiency and optimize the overall dining experience. The sensitivity analysis of various parameters reveals that the delivery platform must appropriately determine the time domain and allocate the number of delivery personnel based on order scale to avoid escalating delivery costs. These findings provide theoretical guidance for vehicle routing planning in the context of delivery platforms. [ABSTRACT FROM AUTHOR]

Cold chain logistics, with its high carbon emissions and energy consumption, contradicts the current advocacy for a "low-carbon economy". Additionally, in the real delivery process, customers often generate dynamic demand, which has the characteristic of being sudden. Therefore, to help cold chain distribution companies achieve energy-saving and emission-reduction goals while also being able to respond quickly to customer needs, this article starts from a low-carbon perspective and constructs a two-stage vehicle distribution route optimization model that minimizes transportation costs and refrigeration costs, alongside carbon emissions costs. This research serves to minimize the above-mentioned costs while also ensuring a quick response to customer demands and achieving the goals of energy conservation and emission reduction. During the static stage, in order to determine the vehicle distribution scheme, an enhanced genetic algorithm is adopted. During the dynamic optimization stage, a strategy of updating key time points is employed to address the dynamic demand from customers. By comparing the dynamic optimization strategy with the strategy of dispatching additional vehicles, it is demonstrated that the presented model is capable of achieving an overall cost reduction of approximately 17.13%. Notably, carbon emission costs can be reduced by around 17.11%. This demonstrates that the dynamic optimization strategy effectively reduces the usage of distribution vehicles and lowers distribution costs. [ABSTRACT FROM AUTHOR]

The increasing integration of renewable energy sources and the growing complexity of modern power grids demand innovative solutions for efficient energy management. This paper introduces a novel dynamic demand-aware Power Grid Intelligent Pricing (PGIP) algorithm based on Deep Reinforcement Learning (DRL). The proposed PGIP algorithm aims to optimize energy consumption and pricing in real time by leveraging the capabilities of DRL to adapt to dynamic demand patterns and evolving grid conditions. PGIP employs a sophisticated neural network architecture to model the intricate relationships between various grid parameters, user demand, and pricing strategies. Through continuous learning and adaptation, the algorithm dynamically adjusts pricing structures to incentivize demand-side flexibility while ensuring grid stability. The reinforcement learning framework enables the algorithm to discover optimal policies for pricing in response to changing environmental factors and user behaviors. We used real-world data sets to assess its performance in diverse scenarios. Results demonstrate the algorithm’s ability to optimize energy consumption, reduce peak demand, and enhance overall grid efficiency. Moreover, comparisons with traditional pricing models highlight the superior adaptability and responsiveness of PGIP in addressing the challenges posed by the evolving landscape of power grids. PGIP presents a promising approach to address the dynamic nature of power grids and the increasing demand for efficient energy management.

With the rapid growth of the national economy, people's demand for transportation is becoming increasingly strong. The rail transit business is booming in large and medium-sized cities, and the education management of urban rail transit students needs further reform. At the same time, digital information technology is widely used in various fields, and digital management of education has become one of the major development directions of education reform. The study proposes a specific construction path based on the analysis of the necessity of digital management of education for urban rail transportation majors, and then optimizes the K-medoids algorithm in the clustering algorithm and validates its education digital management effect. The outcomes show that the clustering precision of the upgraded K-medoids algorithm in the selected dataset is up to 92.68%, and the running time is all below 5s, with the lowest value being 3.9s; In the digital management of urban rail transit majors in universities, the precision obtained by the algorithm is all maintained at around 95%, and the satisfaction rate is all higher than 90%. The effectiveness of the proposed method has been verified, providing a new method for the management of digital education systems for urban rail transit students. It can better understand the needs and characteristics of students, help improve their learning effectiveness and educational quality, and achieve more targeted allocation of educational resources. [ABSTRACT FROM AUTHOR]

The traditional applied demand system estimation assumes that when consumer income and commodity prices change, the consumers instantaneously fully adjust to a new consumption equilibrium level and use a static demand system such as Static Linear Almost Ideal Demand System (Static LA-AIDS) for estimation. However, in real-life situations, such an assumption does not hold as consumers take time to settle to a new consumption equilibrium level. Hence, dynamic demand system estimations generate more nuanced insights into the short-run dynamics of consumer demand. This paper estimates two forms of dynamic versions of AIDS, Dynamic LA-AIDS and the error-corrected LA-AIDS, and compares the results with the Static LA-AIDS. The paper also models the consumption patterns of consumers in developed and developing countries and provides a comparative analysis of implied elasticities using recent data. The results show that the dynamic models support demand theory hypotheses—demand homogeneity and Slutsky symmetry—more than the static model. The estimated mean own-price elasticities reveal that the demand in the short-run and long-run is price inelastic across all countries. Long-run and short-run income elasticities demonstrated notable variation across various commodity groups and country groups. For example, the restaurant meal is a luxury in the short-run for all countries. In the long-run, it is a necessity in developed countries and a luxury in developing countries. Food and housing are necessities; durables, transport and recreation are luxuries in developed and developing countries in the long-run and short-run. [ABSTRACT FROM AUTHOR]

As a new delivery mode, the collaborative delivery of packages using trucks and drones has been proven to reduce delivery costs and delivery time. To cope with the huge cost challenges brought by strict time constraints and ever-changing customer orders in the actual delivery process, we established a two-stage optimization model based on different demand response strategies with the goal of minimizing delivery costs. To solve this problem, we designed a simulated annealing chimp optimization algorithm with a sine–cosine operator. The performance of this algorithm is improved by designing a variable-dimensional matrix encode to generate an initial solution, incorporating a sine–cosine operator and a simulated annealing mechanism to avoid falling into a local optimum. Numerical experiments verify the effectiveness of the proposed algorithm and strategy. Finally, we analyze the impact of dynamic degree on delivery cost. The proposed model and algorithm extend the theory of the vehicle routing problem with drones and also provide a feasible solution for route planning, taking into account dynamic demands and time windows. [ABSTRACT FROM AUTHOR]

In the urban rail transit system, train timetabling according to the dynamic passenger demand is important to offer high-quality service. Timetable optimization with the predetermined train route plan cannot synchronously consider the spatial and temporal characteristics of demand. Based on the Automated Fare Collection (AFC) data, this study proposes the integrated optimization (IO) to make route plan and timetable. A mixed-integer nonlinear programming (MINLP) model is formulated with the aim of reducing passenger penalty travel time and operation penalty cost. An improved non-dominated sorted genetic algorithm-II (Improved NSGA-II) is applied to deal with the bi-objective problem. A numerical experiment is used to prove the effectiveness and efficiency of the model and the algorithm. Then, the IO approach is applied in a bi-directional urban rail transit line. The results show that the IO performs the best compared with two other feasible strategies, namely the current optimization (CO) and phased optimization (PO). [ABSTRACT FROM AUTHOR]

With the change in shopping methods, people have higher and higher requirements for e-commerce logistics transportation. In this paper, we first briefly study the dynamic vehicle path problem under pickup and delivery mixture and determine the modeling idea of regular path adjustment strategy. Then, based on the constraints of vehicle access rules, travel time, vehicle continuity, loading capacity ceiling, dynamic vehicle path adjustment, etc., a mathematical model to minimize the total cost consisting of vehicle call cost, travel cost, and time window penalty cost together is constructed. Finally, a single-objective optimization with the total vehicle distance as the objective is performed on the original data, and the interrelationship between the objectives in the score MOMDVRP algorithm is solved for the optimal route scheme. In the route of the optimal compromise scheme, the distance of each vehicle, although not reaching the equilibrium degree in the social scheme, is much better than the economic scheme and the environmental scheme, and the difference between the longest drive and the shortest drive is 1.47km, which is about 28.79% and 69.9% less than the difference between the economic scheme and the environmental scheme of 2.171km and 495km, and each objective has reached a more balanced state. It is of great practical significance to study the optimization of the shared path of e-commerce logistics transportation resources.

The main dynamic truck routing problem also presents a significant difficulty in the logistics sector, which is an unavoidable development trend of the contemporary technological changing society. A dynamic vehicle routing problem with time window model is suggested by the study in order to establish an effective and low-energy dynamic response method. The fundamental concept is to disrupt the conventional strategy of static dynamic consumers responding in time slots by dividing the dynamic time window into a static time window with several time slice intervals. The study makes use of cutting-edge ideas including dynamic attitude, before-and-after time slicing, and continuous optimisation while proposing a new method for model solution to optimise dynamic vehicle route issues effectively and affordably. The study employs the Solomon optimisation dataset and runs simulation studies on the Java platform to confirm its efficacy. The experimental findings demonstrated that the optimisation technique employed in the study reduced the cost of travelling by 83.8 miles while also considerably increasing the average vehicle utilisation by 3.6%. Because driving distance cost and vehicle number cost are typically positively connected with dynamic attitude, the study employs solutions that can increase dynamic response efficiency and save money. As a result, their robustness is higher. [ABSTRACT FROM AUTHOR]

For the dynamic takeout delivery vehicle routing problem, which faces fluctuating order demand and time-varying speeds, this study presents a novel approach. We analyze the time distribution of takeout orders and apply a Receding Horizon Control (RHC) strategy to convert the dynamic challenge into a static one. The driving speed of delivery vehicles on different roads at different times is determined based on the subdivision criteria of the urban road network and a traffic congestion measurement method. We propose a dynamic takeout delivery vehicle routing optimization model and a time-varying subdivision road network is established to minimize the total delivery cost. We validated the model through simulation examples. The optimization results show that the total distribution cost is reduced after considering the time-varying subdivision road network, with the penalty cost decreasing by 39%. It is evident that considering the subdivision of the road network can enhance order delivery efficiency and optimize the overall dining experience. The sensitivity analysis of various parameters reveals that the delivery platform must appropriately determine the time domain and allocate the number of delivery personnel based on order scale to avoid escalating delivery costs. These findings provide theoretical guidance for vehicle routing planning in the context of delivery platforms.

In the operational, strategic and tactical decision-making problems of the agri-food supply chain, the perishable nature of the commodities can represent a particular complexity problem. It is, therefore, appropriate to consider decision support tools that take into account the characteristics of the products, the needs and the requirements of producers, sellers and consumers. This paper presents a green vehicle routing model for fresh agricultural product distribution and designs an adaptive hybrid nutcracker optimization algorithm (AH-NOA) based on k-means clustering to solve the problem. In the process, the AH-NOA uses the CW algorithm to increase population diversity and adds genetic operators and local search operators to enhance the global search ability for nutcracker optimization. Finally, the experimental data show that the proposed approaches effectively avoid local optima, promote population diversity and reduce total costs and carbon emission costs. [ABSTRACT FROM AUTHOR]

Copyright of Journal Of Sichuan University (Natural Sciences Division) / Sichuan Daxue Xuebao-Ziran Kexueban is the property of Editorial Department of Journal of Sichuan University Natural Science Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As a new delivery mode, the collaborative delivery of packages using trucks and drones has been proven to reduce delivery costs and delivery time. To cope with the huge cost challenges brought by strict time constraints and ever-changing customer orders in the actual delivery process, we established a two-stage optimization model based on different demand response strategies with the goal of minimizing delivery costs. To solve this problem, we designed a simulated annealing chimp optimization algorithm with a sine–cosine operator. The performance of this algorithm is improved by designing a variable-dimensional matrix encode to generate an initial solution, incorporating a sine–cosine operator and a simulated annealing mechanism to avoid falling into a local optimum. Numerical experiments verify the effectiveness of the proposed algorithm and strategy. Finally, we analyze the impact of dynamic degree on delivery cost. The proposed model and algorithm extend the theory of the vehicle routing problem with drones and also provide a feasible solution for route planning, taking into account dynamic demands and time windows.

To explore the impact of dynamic changes in tourist demand on the digital transformation of tourism enterprises, this paper starts with demand-side changes, environmental influences, and technical support, based on theories of supply and demand equilibrium, regional innovation environment, technology gaps, and comparative advantage, and conducts a multiple regression analysis using a two-way fixed effects model on data from China's Shanghai, Shenzhen and New Third Board-listed tourism companies, as well as 31 provincial administrative regions. The study found that dynamic changes in tourist demand significantly impacted the digital transformation of tourism enterprises. In addition, the regional innovation environment and the level of big data also contributed positively to the relationship between tourist demand and the digital transformation of enterprises. For regions with different tourism resource endowments, there is heterogeneity in the impact of dynamic changes in tourist demand on the digital transformation of enterprises. Finally, recommendations for practitioners regarding the digital transformation of enterprises are provided based on this. We suggest that enterprises should pay attention to the changing needs of travelers. They should also consider the regional innovation environment and the level of big data as key resources to facilitate transformation. This will help accelerate digital transformation in tourism. [ABSTRACT FROM AUTHOR]

The paper presents an inventory routing problem for perishable products using dynamic demand and spoilage rate data. This problem has three main costs for the simulation criteria: holding inventory cost, routing shipment cost, and perishable product cost with a single supplier and many retailers. Instead of the problem, there are 2 set decision variables inventory level range and shipping route. The simulation uses a Genetic Algorithm method to find the minimum cost. It shows that the dynamic data demand can be optimized, and the dynamic demand data have bigger cost consequences than the static demand data

In the operational, strategic and tactical decision-making problems of the agri-food supply chain, the perishable nature of the commodities can represent a particular complexity problem. It is, therefore, appropriate to consider decision support tools that take into account the characteristics of the products, the needs and the requirements of producers, sellers and consumers. This paper presents a green vehicle routing model for fresh agricultural product distribution and designs an adaptive hybrid nutcracker optimization algorithm (AH-NOA) based on k-means clustering to solve the problem. In the process, the AH-NOA uses the CW algorithm to increase population diversity and adds genetic operators and local search operators to enhance the global search ability for nutcracker optimization. Finally, the experimental data show that the proposed approaches effectively avoid local optima, promote population diversity and reduce total costs and carbon emission costs.

Presently in the commercial environment, because of the high level of market globalization and rapid increase in industrialization, supply chain synchronization is playing an increasingly significant role in the proper management of the whole system including several factors at the same time. In real business world, both manufacturer and retailer accept credit to make their business position strong, as credit not only strengthens their business relationships but also increases the scale of their profits. The long period of credit may increase the demand ratebut simultaneously it can also increase the credit risk. We investigate a two-layer supply chain model under dynamic demand with a manufacturer and a retailer maintaining decaying items with controllable deterioration rates under two levels of trade credit policies. For the time of trade credit granted to the retailer, the manufacturer bears opportunity costs. To promote sales and optimize sales volume, both supply chain participants give trade credit periods to downstream members and due to the credit period, both of them are facing default risk. Both members work together to invest in preservation technologies to abate the rate of degradation. The proposed models are developed for both the centralized and the decentralized scenarios. A closed form model having profit maximization problem is developed for both the centralized and the decentralized scenarios. The focus of this study is to obtain the optimal selling price, replenishment cycle time, preservation technology cost, upstream and downstream trade credit period to optimize supply chain profit. The paper's novelty lies in introducing two level trade credit with default risk considering decaying items with controllable deterioration and price and credit sensitive customer's demand in a dual channel supply chain inventory policy. It is found that joint supply chain model can be able to enhance the total profit of the whole supply chain. Lastly, sensitivity analysis highlights the influence of major model parameters using numerical examples. [ABSTRACT FROM AUTHOR]

The emergence of new information technologies, market globalization, climate change, labor shortages, and changing consumer habits have led to dynamic demand and the need for customized mass production systems. This has forced companies, especially small- and medium-sized enterprises (SME), to rethink their product design, production, and marketing models to remain competitive by increasing their agility in the face of growing and changing demand. It becomes relevant to investigate how to move efficiently towards customized mass production in an Industry 4.0 (I4.0) environment. The objective of this research is to develop a strategy for implementing I4.0 in manufacturing SME, based on lean, agility, and intelligent modular product design. A literature review made it possible to target the following performance measures: reducing inventory, minimizing makespan, and reducing time to consumer (reaction time). A case study was conducted in an SME in the agri-food sector to validate the proposed strategy. Inventory levels were reduced by more than 70% and time was cut by almost 65%. [ABSTRACT FROM AUTHOR]

When should manufacturers and retailers change their prices? Should they change prices based on the phases of the product life cycle (PLC) that are affected by advertising or without its effect? To answer these questions, we develop dynamic models for analyzing market demand, pricing decisions, and advertising cost according to the properties of PLC phases. Our contributions extend beyond conventional analyses. For the first time, we introduce dynamic mathematical models that capture market demand, pricing decisions, and advertising costs, intricately aligning them with the unique attributes of each PLC phase. Furthermore, this study pioneers the consideration of the effect of advertising on demand but also examining its influence on the duration of PLC phases, shedding light on a previously unexplored facet of market behavior. Furthermore, our research presents a groundbreaking perspective on the relationship between advertising, pricing, and the PLC phases. Based on the PLC perspective, product prices, especially for electronic products, decrease over time. Therefore, manufacturers and retailers should adopt appropriate price-switching policies to remain competitive. This paper examines a dynamic supply chain consisting of one manufacturer and one retailer, that demand changes over time. The price switching policies including: 1) the first policy: at the beginning of the advertising-influenced PLC phases, and 2) the second policy: at the beginning of without advertising-influenced PLC phases, are investigated. The results indicate that the wholesale and retail prices under the second policy are always lower than the first policy. In the second policy, the retailer's profit is higher, if the price reduction rate is not too low, and the other hand the manufacturer's profit is higher than other policy. [ABSTRACT FROM AUTHOR]

The operating condition of bus transit system has not been efficient in most cities of Iran, and many management methods such as regular bus scheduling, assigning exclusive bus lanes, etc., which are necessary for increasing the efficiency of this system, were not regarded enough. Thus, achieving a method for locating the bus stops and optimizing the number of such stops based on a non-homogeneous spatial and temporal distribution of passengers as well as the local traffic patterns are important to be investigated. As such, the present study aims to investigate the modeling of a bus transit system corridor according to the non-homogeneous spatial and temporal distribution of passengers throughout the route aiming at optimization of the number of attracted passengers to the bus. For this purpose, the 8-km route from Vali-e-asr roundabout to Gas roundabout in the city of Rasht in the north of Iran is selected for modeling. Hammersley sampling method, as well as two heuristic optimization techniques, including a Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm, are used for generating a non-uniform population and solving the optimization model. Therefore, the results of this analysis are compared to the optimization results by using the probabilistic analysis without considering the reference uncertainty. Finally, the PSO is selected as the superior algorithm for modeling and locating the bus stops due to its results in less travel time, and the validity of robust optimization model is shown due to its higher accuracy and adaptation to the real-world environment. Overall, although the optimization results based on indeterminate analysis in comparison to determinate analysis brought about more average travel time, more population sets were covered by the new introduced stops during 18 active hours of the bus transit system.

Inventory routing problems (IRP) are among important tools to be used for implementing vendor manage inventory. Many researchers try to develop methods for solving inventory routing problem, however, only a few developed methods for inventory routing problems for spoilage items. In reality, many items are deteriorated and spoiled during transportation and storage period. In this paper, we developed a model and methods to solve the inventory routing problem for deteriorating items with dynamic demand and spoilage rate, i.e., demand varies and items spoil during planning periods. Those cases are more realistic since many commodities such as fruits and vegetables have dynamic demand and spoilage rate. A Genetic Algorithm and Particle Swarm Optimization are developed to solve the problem with various demands in a specific planning period since the problem is Np-hard. A numerical example and sensitivity analysis are conducted to verify the model, and to get management insight it. The result is interesting and support general hypothesis that dynamic demands result in higher inventory cost than the static demands, and the increasing demand results in increasing inventory cost. Also, the results show that increasing demand and deteriorating rates significantly affect the total cost, therefore, the developed model is important and significantly useful to be used for solving IRP with dynamic demand and spoilage items

Bike-and-Ride (B&R) has long been considered as an effective way to deal with urbanization-related issues such as traffic congestion, emissions, equality, etc. Although there are some studies focused on the B&R demand forecast, the influencing factors from previous studies have been excluded from those forecasting methods. To fill this gap, this paper proposes a new B&R demand forecast model considering the influencing factors as dynamic rather than fixed ones to reach higher forecasting accuracy. This model is tested in a theoretical network to validate the feasibility and effectiveness and the results show that the generalised cost does have an effect on the demand for the B&R system.

Integrated inventory and transportation decisions are critical in the supply chain, providing significant gains for all parties. In this paper, we present a mathematical formulation for the dynamic demand multi-item single source replenishment problem with a piecewise linear transportation cost. Through an extensive experimental study, we find that the new formulation provides a tighter LP relaxation of the problem, while requiring fewer computational resources to optimally solve the problem when compared with existing model in the literature. We also present a new metaheuristic for this general class of coordinated capacitated replenishment problems. On average, the solutions from heuristics are within 1.23% of the optimal solution for the comprehensive set of test problems. [ABSTRACT FROM AUTHOR]

Due to the globalization of cruise tourism, in the early stage of cruise liner operation, contracting purchases with suppliers in advance incurs cost savings due to price reduction for the first replenishment of food and beverage (F&B) items. To meet the onboard customer demand of a cruise liner and deal with uncertainty in demand, the second replenishment is conducted from a spot market at an intermediate port if needed. Applying Bayesian updating theory to cases of incomplete information, this article studies the optimal strategy for two-stage replenishment in cruise liner operations, based on updating demand information under dynamic demand, and establishes a model with two-stage profit maximization as the objective function. Then, incomplete information updating theory is applied to assist in solving this problem. Finally, a numerical example is given to analyse the influence of parameter variation on the sensitivity of the results. The results show that an effective replenishment strategy for cruise ships must take into consideration the trade-offs between both replenishment approaches under different parameters. [ABSTRACT FROM AUTHOR]

Nowadays, use of various types of hybrid metaheuristic algorithms attracts the researchers to optimize the average profit or cost of an inventory system to avoid the local optimality due to high nonlinearity of the corresponding optimization problem. This paper deals with an application of binary tournament-based quantum-behaved particle swarm optimization algorithms on an imperfect production inventory problem with shortages. In order to reduce the production of defective items, modern/improvement technology has been incorporated in the production system. Also, the demand of the product is assumed to be dependent on its warranty period and selling price. The main objective of this study is to optimize the production rate, production period, selling price of the product, manufacturer's improvement technology level and maximum shortage level as well as maximize the average profit of the production system. For this purpose, three hybrid metaheuristic algorithms based on binary tournamenting and different variants of quantum-behaved PSO techniques have been developed. Then to examine the validity of the proposed model, three numerical examples have been solved. Considering each example, nonparametric statistical tests have been performed by using four different methods to analyze the performance of the used algorithms. Finally, sensitivity analyses have been performed to investigate the effects of different parameters on optimal policy. [ABSTRACT FROM AUTHOR]

• This paper studies an application of the DVRPTW for IoT customers' dynamic demands. • This paper develops a method of re-optimization based on time slices. • This paper constructs a mathematical model with the goal of minimizing distance cost. Developing a more convenient and sustainable logistics delivery operation model has become a consensus need for both academics and practitioners. However, the existing literature has focused on the impact of dynamic features of IoT customers on the distance cost of vehicles (DCV) and quantity cost of vehicles (QCV) in the field of the vehicle routing problem with time windows (VRPTW). Moreover, only a few application scenarios have been considered. This paper focuses on the perspective of intelligent logistics development to research the impact of IoT customers' dynamic demand on the VRPTW. A mathematical model is established to respond to the dynamic demands of IoT customers with the goal of minimizing the distance cost. A strategy for solving the dynamic VRPTW (DVRPTW) based on time slices is developed, and an improved tabu search (I-TS) optimization algorithm is proposed to solve a given delivery business case. In simulation experiments, the proposed I-TS solution and another known best solution (namely, Solomon) are compared, and the results show the superior performance of the I-TS algorithm in reducing the DCV and QCV. Furthermore, the case study explores the relationship between the degree of dynamism (DoD), DCV and QCV under two scenarios (responding and not responding to dynamic demands of IoT customers). It is concluded that fluctuations of the DoD in a certain range affect only the DCV and not the QCV. Large-capacity vehicles can improve the robustness of the route scheme to dynamic demands. In addition, decomposing dynamic issues in space through time slicing effectively reduces the complexity of the DVRPTW solution. This research also aims to assist practitioners in better formulating dynamic delivery routes as well as policy makers in developing intelligent delivery operation models. Finally, limitations and future research directions are discussed. [ABSTRACT FROM AUTHOR]

Urban rail transit (URT) plays a pivotal role in facilitating human mobility within urban environments. It is significant to understand its vulnerability, i.e., the variation in capacity and demand when confronted with unexpected events, particularly operational disruptions. Although the network topology is generally fixed, the hourly-changing travel demand greatly impacts the actual vulnerability of a URT system. Unfortunately, few existing studies consider the combined influence of dynamic travel demand and network topology. To fill the gap, this paper proposes a network vulnerability assessment method with the joint consideration of static network topology and dynamic travel demand. This method includes a defined reasonable path, an accessibility-based identification of station importance with time-varying passenger demand, and a new dynamic vulnerability delay index considering affected travel demand. An empirical analysis was carried out by taking the URT system of Beijing, China as an example, and the impact of the more realistic multiple consecutive station failures in a URT system is also examined. Results show that the distribution of high-importance stations indeed varies with the time of day, affected by both static topology and hourly-changing passenger flow. When the disturbance of operation delay occurs, the impact of high-importance stations on the network vulnerability changes nonlinearly with the increase of delayed travel demand. Some stations that serve as bridges and are visited by large passenger flows have the greatest impact on network vulnerability. Network performance degradation is obviously segmented and stratified in the case of interval continuous failure. The disruption between different lines is the main cause of network performance degradation, and some high-importance stations within the lines act as catalysts to accelerate the performance degradation. The proposed method not only offers a valuable reference for quantifying network vulnerability arising from fluctuations in passenger mobility but also introduces a novel vulnerability evaluation index to the URT system. [ABSTRACT FROM AUTHOR]

With the development of e-commerce platforms, the logistics industry is also booming. The transportation process is at the core of the logistics industry, which influences the timeliness and rationality of logistics distribution. In the real world, there are many dynamic demands in logistics distribution, so logistics distribution should deal with dynamic demands quickly to realize more effective route planning. This paper proposes an adaptive genetic algorithm (AGA) with elastic strategy (AGA-ES) to solve the dynamic capacitated vehicle routing problems (DCVRPs). Firstly, AGA designs an adaptive local search operator, which can adaptively adjust the quantity of nodes that need local search, according to the fitness value of different individuals. At the same time, AGA develops adaptive crossover and mutation operators, which can automatically adjust the crossover and mutation probability based on the different fitness values. The experimental results verify the better performance of AGA on the traditional capacitated vehicle routing problems (CVRPs). After that, AGA combines elastic strategy (AGA-ES) to solve DCVRPs. This paper mainly concerns three types of actual demand information in DCVRPs: distribution nodes increase, distribution nodes decrease, and distribution road conditions change, according to the actual background. AGA-ES could interact with demand information and judge the types of different demands, and then adaptively plan new and reasonable routes after receiving one or more of the above demand information. The verification of benchmark data sets shows that AGA-ES can effectively deal with DCVRPs. Furthermore, this paper collects the longitude and latitude coordinates of 100 SF express stations in Xi'an, constructs the distribution information of these express stations, and verifies that AGA-ES can solve the DCVRPs under the actual background. • AGA designs an adaptive local search operator, which could improve the local search ability of GA. • AGA devises adaptive crossover and mutation operators. • AGA combines elastic strategy (AGA-ES) to handle DCVRPs. • AGA-ES could judge the type of dynamic demand according to the matching operator. • AGA-ES could adopt different measures to handle the different dynamic demands. [ABSTRACT FROM AUTHOR]

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Purpose: The purpose of this study is to propose a new coordinated dynamic demand lot-size and delivery planning problem (CDLSDP), in which the delivery policy is integrated into the coordinated dynamic demand lot-size problem (CDLSP). Design/methodology/approach: As a non-deterministic polynomial complete (NP-complete) problem, this CDLSDP seems difficult to be solved by a polynomial-time method. To handle this problem effectively and efficiently, a four-phase heuristic that balances the setup and inventory costs in the coordinating and delivery stages is designed to find near-optimal solutions. Findings: Numerous computational experiments show that the proposed four-phase heuristic is effective and efficient. For 1,800 experiments with different scales, and different joint setup costs, solutions by the proposed heuristic have an average gap no more than 1.34% from the optimal solution. Research limitations/implications: To decrease total system cost, the CDLSDP optimizes the time-phased replenishment and delivery schedule, which includes joint setup cost, item setup, delivery and inventory cost, for each period. An effective and efficient four-phase heuristic is designed to solve the CDLSDP. Originality/value: Compared with the traditional CDLSP, the delivery policy is considered by the new CDLSDP. Moreover, the proposed four-phase heuristic is a good candidate for solving the CDLSDP. [ABSTRACT FROM AUTHOR]

With the increasing number of people starting to attach importance to urban green space (UGS), estimating urban residents’ demand for UGS has become critical. Previous studies have estimated residents’ demands for UGS in different cities using the Rosen–Roback model. However, there has been little analysis of the renters’ requirements for UGS or the dynamic and heterogeneous demands for UGS by residents in cities with varying characteristics. In this study, the prefecture-level cities in China were selected as the object of study for their wide representation of various city characteristics, and the Rosen–Roback model was used to address the abovementioned issues. Residents’ demand for UGS in 285 prefecture-level cities was assessed from 2010 to 2017. The results confirm our hypothesis that when UGS increases by 1%, house prices will increase by about 3% and rents will increase by about 7%, and the demand continues to grow. We also analyzed the heterogeneous demands for UGS by urban residents in different types of cities from three aspects: population density, economic level, and the effects of the National Forest City policy. Finally, we provided suggestions based on empirical results for planning and managing UGS. [ABSTRACT FROM AUTHOR]

An agent-based simulation framework is extended to design efficient large-scale public transport networks. It goes beyond existing approaches by incorporating a dynamic demand response towards both changes in the network and exogenous factors. The framework is based on an agent-based (MATSim) simulation and tested for the city of Zurich. In contrast to most existing public transport network optimization approaches, this framework is part of a city-level, multi-modal transport simulation tool. Compared to the existing public transport system, it proposes a sparser network with smaller vehicles and substantially higher frequencies. By doing so, the approach predicts a higher transit ridership at a lower level of subsidies. Moreover, it reliably identifies corridors for potential capacity upgrades. The method may help transport planners to assess their existing public transport networks and to plan public transport infrastructure for the era of automated vehicles. [ABSTRACT FROM AUTHOR]

Purpose: Hub location problems are getting more and more attention today due to their widespread application in designing product transportation systems and communication networks. In these systems, products (including data transmission, passenger transportation, freight forwarding and logistics, postal services, etc.) are shipped from multiple sources to multiple destinations. The performance of these systems can be improved by using hub points. In fact, instead of just deciding on a single period, a planning horizon with multiple time periods is considered. The necessity of doing this research is that decisions are made in each period according to the costs and flows of the same period. In the real world, facilities are usually depreciated after a certain period of time and must be closed or rebuilt if possible. Design/methodology/approach: In this research, hubs reconstruction is allowed, meaning it can be reconstructed to reduce costs rather than startups, to operate for a specified lifetime and to cover network demands. It is also selected to operate hubs from different contractors. In this study, there are also several levels of capacity to set up that can be selected with respect to the demand of the period, which is both sufficient and cheaper to meet the demands. Genetic algorithm has been used to solve and evaluate the model performance. In general, this study has attempted to provide comprehensive modeling for the multi-period hub location problem with multiple capacities. The investigated problem considered the hubs lifetime as well as the possibility of reconstruction at the end of lifetime. It has also been attempted to design the model to suit real-world conditions. Findings: Investigations in this study showed that the proposed model is more efficient than the previous models in terms of coordination and compliance with real-world conditions. Further,the validity of the proposed model was also evaluated by performing a variety of sensitivity analyzes. Solving the various numerical examples by the genetic algorithm and comparing them with the exact method demonstrates the acceptable performance of the meta heuristic method. Research limitations/implications: For future research and improvement of proposed model the following recommendations are suggested: Consider different capacity levels for nodes so that the capacity of each node can be changed at different times depending on the problems conditions. Assume that the hubs can be closed before the expiration date depending on the problems conditions. Consider case studies in various industries and model development with conditions much closer to real situations, for example in the field of urban transport, perishable products, emergency services, and so on. Using uncertain programming such as probabilistic and fuzzy programming to predict and plan the model and its parameters in successive periods. Providing solution methods with better performance and solving larger problems in rational times. For example, heuristic methods can reduce the complexity of the problem and thus solve larger samples in shorter times, so they will be useful. Practical implications: The main advantage of a multi-period problem over a single period problem is making the best decision in the time frame. In the single-period model, once the decision is made for all time periods, it is obvious that this decision will not be optimal because the parameters and conditions governing the problem are not the same over successive periods, so the result will not be global optimal. Another advantage is that the single-period model optimizes the cost of each period separately and it does not consider the relationship between the network structure of the nodes in different periods. Therefore, in this study, a multi-period model was proposed to obtain the global optimal solutions. Originality/value: The main difference between this study and previous work is the following: Providing a hub location model in which it is possible to select a contractor for each hub set up. In this research, the hubs have a known lifespan and this lifespan is determined after the contractor selection. Providing a new model that enables the choice between reconstructing and closing at the end of each hub's life by considering network costs. [ABSTRACT FROM AUTHOR]

While the literature primarily addresses MPS design from the manufacturer's perspective, this research considers MPS policy design in a two-stage rolling schedule environment with a particular focus on the policy governing schedule flexibility in the non-frozen time interval (i.e. liquid versus slushy orders). Using computer simulation, we experimentally evaluate the impact of four MPS design factors (non-frozen interval policy, planning horizon length, frozen interval length and re-planning frequency) and four environmental factors (natural order cycle length, vendor flexibility, demand range and demand lumpiness) on MPS schedule cost and instability. The experimental design considers the often-conflicting impact of MPS policy on the channel members by capturing performance metrics at the manufacturer, vendor and system level. The research findings indicate that moving from a liquid to a slushy non-interval strategy increases the manufacturer's costs, but may result in an even greater cost reduction for the vendor resulting in lower system costs. The economic benefit of the slushy strategy is directly tied to the vendor's relative flexibility in responding to the manufacturer's orders on a lot-for-lot basis. High vendor flexibility favours the liquid strategy, while low vendor flexibility favours the slushy strategy. [ABSTRACT FROM AUTHOR]

This paper presents tight mathematical programming formulations for the dynamic demand joint replenishment problem (DJRP). Experimental studies using general-purpose software document the improved computational efficiency of the new formulations versus the earlier models of Boctor et al. (Boctor, F.F., Laporte, G. and Renaud, J., Models and algorithms for the dynamic-demand joint replenishment problem. Int. J. Prod. Res. , 2004, 42, 2667–2678.) and Robinson and Gao (Robinson, E.P. and Gao, L., A dual-ascent procedure for multi-product dynamic demand coordinated replenishment with backlogging. Manag. Sci. , 1996, 42, 1556–1564.). The findings encourage the development of specialized algorithms for their solution. The paper also evaluates the performance of the perturbation and dual-ascent heuristics for solving DJRP finding the perturbation heuristic is superior at relatively low set-up cost ratios and high joint set-up cost levels, while the dual-ascent heuristic strongly dominates at relatively high set-up cost ratios and low joint set-up costs. Considering that both heuristics are computationally efficient, the best application strategy is to solve the problem with both heuristics and implement the best found solution. [ABSTRACT FROM AUTHOR]