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As firms and consumers engage with environmental issues, decisions for inventory control need to entail this perspective of sustainability. Most green inventory models employ methods such as carbon caps or taxes for dealing with environmental sustainability. This problem can be more generally tackled via an explicit estimation of the environmental drivers of maintaining inventory in a warehouse, paired with the economic perspective within a transparent multi-objective optimization framework. With this goal, this paper builds on a detailed estimation of environmental and cost factors for a continuous-review inventory policy. The bi-objective problem is tackled by keeping the objective functions separate. In particular, the modeling of greenhouse gas emission or cost performance factors for the inventory encompasses factors that can depend on the decision variables, taking into account aspects such as warehouse location, building characterization, energy usage, and transport requirements. The effects of the emission drivers on the multi-objective optimization decisions are analyzed, considering that the problem can be constrained by multiple service level measures. Stockout response can be multifaceted and different service level measures capture different aspects of inventory shortages, affecting the resulting efficient solutions differently. The results highlight the impact of aspects such as warehouse location and supply capacity on solutions for the multi-objective inventory problem. Managerial decisions are thus influenced by warehousing and supply attributes via a traceable link to specific cost and emission determinants. [ABSTRACT FROM AUTHOR]

With the rapid development of e-commerce, the online channels encroaching on the offline sales market are becoming more serious, which will definitely harm the offline market. Moreover, there exists a certain percentage of consumers (mostly elderly people) who are not able to purchase online because they lack digital skills. Therefore, understanding the impact of the purchase channel preference and service level on pricing decisions is vital for the dual-channel supply chain management. Focusing on the channel preference and service level, we first develop an optimal pricing model containing centralized and decentralized decision-making for an online and offline retailer by deploying the Stackelberg game. We first develop a Stackleberg game to capture such a dual-channel supply chain with the offline channel preference and service level. Secondly, under centralized decision-making, we derive the optimal retail prices and obtain the optimal total profit. Thirdly, under decentralized decision-making, we obtain the optimal retail prices and optimal total profit as well. Moreover, extensive monotonicity properties when system parameters change are obtained. Relying on the theoretical results, firstly, we show that the improvement of the offline service level would lead to higher pricing of the commodities for both online and offline channels. From our numerical results, when the service level is improved, the offline and online optimal pricing increases by 47.5% and 31.1%, respectively, which may contradict the conventional belief that the improvement of one channel would harm another one. Secondly, we demonstrate that the benefit of improving the offline service level has a diminishing marginal effect. The numerical results show that when the current service level is low, the effectives of improving the service level is roughly five times that when the service level is high. This indicates that the investment in improving the offline service level should not be unlimited. Thirdly, we show that the pricing decision under centralized decision-making should be adopted with the existence of both the offline channel preference and offline service. [ABSTRACT FROM AUTHOR]

Ride-hailing services have grown rapidly, presenting challenges such as increased traffic congestion, inefficient driver workload distribution, and environmental concerns like higher fuel consumption and emissions. This study develops a non-linear ride-hailing assignment model addressing these issues by considering service level, driver workload, and fuel consumption. A piecewise linear method was employed to handle a non-linear programming model, and the method was modified to function autonomously without operator intervention. The model's performance was evaluated using a publicly accessible dataset of taxi trips in Manhattan, focusing on indicators such as passenger waiting time, driver workload distribution, and fuel consumption. Numerical simulations demonstrated significant improvements: a 15% reduction in average passenger waiting time, a 20% improvement in balancing driver workloads, and a 10% decrease in overall fuel consumption, contributing to reduced emissions and environmental impact. The modified piecewise linear method proved effective in optimizing ride-hailing assignments, providing a more efficient and sustainable solution. The model also showed robustness in handling large datasets, ensuring scalability and applicability to various urban settings. These findings highlight the model's potential to enhance operational efficiency and promote sustainability in ride-hailing services. By integrating considerations for service level, driver workload, and fuel consumption, the model offers a holistic approach to addressing the key challenges faced by the ride-hailing industry. This study provides valuable insights for future ride-hailing development and implementations of ride-hailing systems, promoting practices that are both efficient and environmentally friendly. [ABSTRACT FROM AUTHOR]

Warehouses recently face increasing stress imposed by a volatile customer demand and increasing customer expectations in terms of ever shorter order response times. In that respect, warehouses more and more offer same-day and next-day shipment conditions. However, same-day shipment promises are challenging to fulfil, especially as the order fulfilment process operates against fixed deadlines imposed by the predefined truck departure times. As a natural mitigation strategy, warehouses set a cutoff point and offer same-day shipment only to customers that order until the cutoff point, but next-day shipment to all customers ordering thereafter. Setting an appropriate cutoff point is challenging as it affects multiple facets of the service quality, such as the order response time and the service level. In this paper, we study the design of cutoff-based shipment promises for stochastic deadline-oriented order fulfilment processes in warehouses. We present a discrete-time Markov chain model for exact steady-state performance analysis and propose two novel performance measures – α - and β - cutoff service level – for service level measurement in these systems. We numerically show the benefit of cutoff-based shipment promises. Even with a late cutoff point, there is a significant gain in the system performance. Furthermore, we find that warehouses should set the cutoff point such that it balances customer expectations in terms of service level and order response time. Finally, warehouses can improve their shipment promises when referring to β - instead of α - cutoff service level and by implementing measures reducing the utilisation and the variabilities of the order fulfilment process. [ABSTRACT FROM AUTHOR]

Due to safety perceptions, Colombian banking clients prefer to visit bank branch offices instead of other channels. Thus, there are long waiting for lines at branch offices. Considering the need for more tools for strengthening and streamlining client service, the number of financial clients tends to diminish. In this context, this paper aims to measure the impact of technological innovation on the clients' waiting time and the attraction of new customers. We propose a simulation-based methodology to analyze customer behaviors and forecast the diffusion effect on mobile app adoption. Furthermore, our study provides managerial insights and future research lines. [ABSTRACT FROM AUTHOR]

AbstractFor many service systems, waiting time is a critical performance indicator of customer experience. This study addresses service-level computation techniques in a time-varying mixed-preemptive priorities queueing system. This queueing system has two distinguishing characteristics. First, it has time-varying arrival rates and service capacities. Second, and more importantly, there are multiple classes of customers, and both preemptive and non-preemptive priorities are considered in this system. Combining these two characteristics in a queueing system makes it difficult to compute the service level for each customer class. We derive the exact computation methods for pointwise and piecewise service levels. Furthermore, we propose closed-form expressions to approximate the service levels. Lastly, we show how to apply these service-level computation techniques to a real-life physician staffing problem in the emergency department, in which the service levels of each class of patients are considered as constraints. The computational tests are performed using data generated based on real data from a large hospital. The results show that our staffing solutions are better than the hospital’s current staffing solution, guaranteeing patients’ service levels and reducing physicians’ working times. [ABSTRACT FROM AUTHOR]

A common practice in inventory systems with several customers requiring differentiated service levels is to group them into two or three classes, where a customer class is a group of customers with the same preset service level in terms of product availability. However, there is no evidence that grouping customers into two or three classes is optimal in terms of the ordering policy parameters. This paper studies the effect of the number of customer classes on the inventory level of a single-period inventory system with stochastic demand and individual service-level requirements from multiple customer classes. Using a Sample Average Approximation approach, we formulate computationally tractable multi-class service level models, under responsive and anticipative priority policies in cases of shortage, as mixed integer linear problems (MIPs). The effect of the number of classes on the inventory level is determined using a round-up aggregation scheme; i.e., given a sufficiently large initial number of classes, it is reduced by adding the lower service level classes to the next higher class. We analytically characterize the optimal inventory level under responsive and anticipative priority policies as a function of the initial number of classes and the number of classes grouped based on the round-up aggregation scheme. Under a responsive priority policy, we show that there is an optimal number of classes, while under an anticipative priority policy, the optimal number of classes is equal to the initial number of classes. The effect of free-riders resulting from the round-up aggregation scheme on the optimal inventory level is studied through numerical experiments. [ABSTRACT FROM AUTHOR]

The main objective of this article is to implement business intelligence (BI) technology to improve incident management in the control center of a security company; making use of the Ralph Kimball method for the development of the data mart, contemplating the phases: planning of projects, definition of requirements, design of technological architecture, performing a dimensional modeling a star with 9 dimensions and 1 table made, also used the structured query language (MySQL) database, extract, transform, and load (ETL). Development the pentaho data integration, which allowed the migration, transformation and cleaning of the data from the online transaction processing (OLTP) and online analytical processing (OLAP) database, obtaining as a result that the BI managed to increase the level of the service by 28.42% and reducing the frequency index by 25%. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic and the Russian–Ukrainian war have significantly impacted global supply chains, including the food supply chain, in numerous countries. As one of the leading wheat importers, Egypt has been adversely affected by the simultaneous occurrence of these two events. Baladi bread is an integral part of the daily diet in Egypt, so any disruption affecting its availability can have a severe impact on the country's food security. This study aims to simulate the causes and effects of potential disruptions that could occur, such as increased transportation time, unavailability of sourcing, and surge in demand due to lockdowns and panic buying. The East Cairo region was chosen as a case study to model the Baladi bread supply chain. A discrete-event simulation model was developed using anyLogistix software (version 2.15.1) for this study. Five key performance indicators were selected to evaluate, analyze, and compare the outcomes of each scenario in terms of the performance and operation of the food supply chain: service level by product, lead time, demand backlog, average daily available inventory in the mills, and on-hand inventory of wheat in the silos. The results indicate that the supply chain has been significantly impacted by the disruptions caused by these two events, leading to decreased availability of Baladi bread, unmet demand, extended lead times, and high backlogs. By utilizing the research findings, proactive strategies can be developed to minimize the impact of such disruptions in the future and maximize food security and supply chain resilience. [ABSTRACT FROM AUTHOR]

The service level is the quality of traffic conditions in the road network, which includes comfort, safety, freedom of maneuvering and so on. There are different service level depending on the conditions and circumstances of the roundabout construction. The purpose of this paper is to highlight the geometric elements as highly important parameters in the uninterrupted traffic flow in roundabouts, as well as the safety elements for an uninterrupted and efficient traffic flow. [ABSTRACT FROM AUTHOR]

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) 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 the demand for cultural and recreational services grows, the mismatch between the supply and demand of park services significantly affects residents' well-being. Optimizing the spatial layout of park services is a focal point of urban park and green space research. Taking Hangzhou, Zhejiang Province, as a case study, this research analyzes the spatial patterns and balance of park service supply and demand. Utilizing the Grey Wolf Optimization Model optimized by the K-Nearest Neighbor Model (GWO-KNN), this study proposes construction objectives for optimizing park services. The results indicate the following: (1) significant differences exist in the park service demands of residents in different residential environments; (2) there is a noticeable spatial disparity in park service supply among various residential areas with an overall positive correlation between park service supply levels and resident demands, yet an imbalance exists; (3) this study categorizes spatial types into low-service coordination, high-service coordination, low-service imbalance, and high-service imbalance; (4) the GWO-KNN Model is applied with optimization objectives being the innovative aspect of this study. Strategies for each park category are proposed: emphasizing suburban park construction by utilizing surrounding green resources and adding diverse facilities; introducing facilities friendly to vulnerable groups to meet the needs of diverse populations; enhancing the complementary advantages between "new" and "old" cities by moderately increasing park sizes and improving cultural and facility development levels; optimizing spatial structure with limited land resources to construct an urban park network system. This study aims to provide theoretical and technical support for optimizing urban park and green space systems. [ABSTRACT FROM AUTHOR]

After the COVID-19 pandemic has subsided, the catering industry has returned to its previous thriving scene. Many restaurants have launched group buying services and collaborate with third-party online platforms to attract more customers. For the optimal decisions and maximum profits of restaurants and online platforms, a two-layer supply chain model is designed to investigate and study the price strategies and service strategies. Considering the service levels of restaurants and platforms, we construct the mathematical expressions of the offline retail model, online retail model, and online group buying model, respectively, and solve the game equilibrium. The results are shown as follows: (1) With cooperation between the restaurant and platform, both the restaurant and platform can obtain their maximum profits when they apply optimal pricing strategies. (2) The optimal pricing strategies of the restaurant are influenced by the sensitivity coefficients of customers to the restaurant and platform's service levels, and the cost coefficients of the restaurant and platform's service levels. (3) The maximum profit of the restaurant is directly proportional to the number of new potential consumers who are attracted. The more potential consumers who are attracted, the higher the profit the restaurant earns. The research results provide a reference for restaurants and platforms to formulate price strategies and service strategies, to help restaurants and platforms achieve more profits. [ABSTRACT FROM AUTHOR]

In the context of regular epidemic prevention and control, this paper considers a two-stage tourism supply chain consisting of a scenic spot that attracts tourists through advertising and a travel agency that invests in service improvement and epidemic prevention. By establishing theoretical game models of a tourism supply chain, we investigate how the service level and advertising level can affect the retail price, product service level, and profits of the supply chain. The results show that the service level of travel agencies could improve consumers’ preferences, expand the market demand for tourism products, and improve the efficiency of the supply chain to achieve a win-win situation and increase the profits of the scenic spot and the travel agency. The retailer price, service level, promotion level, and supply chain profit all increase as the service coefficient and advertising coefficient increase, and the speed of the increase is higher for the centralized model than for other models. Some valuable information could be provided for supply chain enterprises to develop collaborative strategies and promote tourism supply chain management practices.

Locating facilities such as factories or warehouses is an important and strategic decision for any organization. Transportation costs, which often form a significant part of the price of goods offered, are a function of the location of the plans. To determine the optimal location of these designs, various methods have been proposed so far, which are generally definite (non-random). The main aim of the study, while introducing these specific algorithms, is to suggest a stochastic model of the location problem based on the existing models, in which random programming, as well as programming with random constraints are utilized. To do so, utilizing programming with random constraints, the stochastic model is transformed into a specific model that can be solved by using the latest algorithms or standard programming methods. Based on the results acquired, this proposed model permits us to attain more realistic solutions considering the random nature of demand. Furthermore, it helps attain this aim by considering other characteristics of the environment and the feedback between them.

With the rapid development of e-commerce, the online channels encroaching on the offline sales market are becoming more serious, which will definitely harm the offline market. Moreover, there exists a certain percentage of consumers (mostly elderly people) who are not able to purchase online because they lack digital skills. Therefore, understanding the impact of the purchase channel preference and service level on pricing decisions is vital for the dual-channel supply chain management. Focusing on the channel preference and service level, we first develop an optimal pricing model containing centralized and decentralized decision-making for an online and offline retailer by deploying the Stackelberg game. We first develop a Stackleberg game to capture such a dual-channel supply chain with the offline channel preference and service level. Secondly, under centralized decision-making, we derive the optimal retail prices and obtain the optimal total profit. Thirdly, under decentralized decision-making, we obtain the optimal retail prices and optimal total profit as well. Moreover, extensive monotonicity properties when system parameters change are obtained. Relying on the theoretical results, firstly, we show that the improvement of the offline service level would lead to higher pricing of the commodities for both online and offline channels. From our numerical results, when the service level is improved, the offline and online optimal pricing increases by 47.5% and 31.1%, respectively, which may contradict the conventional belief that the improvement of one channel would harm another one. Secondly, we demonstrate that the benefit of improving the offline service level has a diminishing marginal effect. The numerical results show that when the current service level is low, the effectives of improving the service level is roughly five times that when the service level is high. This indicates that the investment in improving the offline service level should not be unlimited. Thirdly, we show that the pricing decision under centralized decision-making should be adopted with the existence of both the offline channel preference and offline service.

Ride-hailing services have grown rapidly, presenting challenges such as increased traffic congestion, inefficient driver workload distribution, and environmental concerns like higher fuel consumption and emissions. This study develops a non-linear ride-hailing assignment model addressing these issues by considering service level, driver workload, and fuel consumption. A piecewise linear method was employed to handle a non-linear programming model, and the method was modified to function autonomously without operator intervention. The model’s performance was evaluated using a publicly accessible dataset of taxi trips in Manhattan, focusing on indicators such as passenger waiting time, driver workload distribution, and fuel consumption. Numerical simulations demonstrated significant improvements: a 15% reduction in average passenger waiting time, a 20% improvement in balancing driver workloads, and a 10% decrease in overall fuel consumption, contributing to reduced emissions and environmental impact. The modified piecewise linear method proved effective in optimizing ride-hailing assignments, providing a more efficient and sustainable solution. The model also showed robustness in handling large datasets, ensuring scalability and applicability to various urban settings. These findings highlight the model’s potential to enhance operational efficiency and promote sustainability in ride-hailing services. By integrating considerations for service level, driver workload, and fuel consumption, the model offers a holistic approach to addressing the key challenges faced by the ride-hailing industry. This study provides valuable insights for future ride-hailing development and implementations of ride-hailing systems, promoting practices that are both efficient and environmentally friendly.

The urban and economic developments of recent years have generated changes in the development of road traffic, determined by the continuous growth of the vehicle fleet, the increase in the mobility index of the existing vehicle fleet and the increase in the number of vehicles transiting the main cities of the country, this having as consequences the decrease in the traffic capacity of the streets. Through the application of innovative technologies, which allow the anticipation and control of road traffic, traffic jams are avoided, this leads to an increase in the quality of life. In this article, a case study was analyzed, in which an artificial neural network was implemented within a traffic model, with the help of which predictions were made on traffic light times. The results obtained are satisfactory, and are highlighted by the performance parameters exported from the traffic models.

The city of Bucharest together with Ilfov county form an urban agglomeration with over 2.2 million inhabitants. Currently, due to exceeding the traffic capacity of the capital’s ring road, the only possibility to travel North - South or East - West is through the city centre. The public transport network in the city of Bucharest lacks lanes dedicated to public transport, the lack of an extensive network of adaptive traffic lights and traffic lights that give priority to public transport. Also, the high degree of vehicle loading of the street network leads to a low level of service both in the morning peak hour and in the afternoon rush hour, leading to low transport speeds of surface public transport. In this article, the possibilities of introducing dedicated public transport lanes in the city of Bucharest are identified, quantifying the reduction in travel times. In addition, some technical solutions regarding the necessary infrastructure for the introduction of lanes dedicated to public transport vehicles and possible traffic regulation measures are presented.

The purpose of this study, which was started in response to the growth of business process outsourcing (BPO) in the Philippines, is to determine how real-time analyst work affects KPIs and service levels. This study utilizes secondary data from the Realtime Analyst's Reports for the period of June 2022 to June 2023. Shrinkage, attrition, and absenteeism data were the top three aggregated Key Performance Indicators that were statistically examined. As a result, it was highlighted that RealTime Analyst's work constitutes a foundation for how aggregated Key Performance Indicators affected Service Level. [ABSTRACT FROM AUTHOR]

Waste bin monitoring solutions are an essential step towards smart cities. This study presents an exploratory analysis of two waste bin monitoring approaches: (1) ultrasonic sensors installed in the bins and (2) visual observations (VO) of the waste collection truck drivers. Bin fill level data was collected from a Portuguese waste management company. A comparative statistical analysis of the two datasets (VO and sensor observations) was performed and a predictive model based on Gaussian processes was applied to enable a trade-off analysis of the number of collections versus the number of overflows for each monitoring approach. The results demonstrate that the VO are valuable and reveal that significant improvements can be achieved for either of the monitoring approaches in relation to the current situation. A monitoring approach based on VO combined with a predictive model is shown to be viable and leads to a considerable reduction in the number of collections and overflows. This approach can enable waste collection companies to improve their collection operations with minimal investment costs during their transition to fully sensorized bins. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Industrial Engineering Research in Production Systems is the property of Bu-Ali Sina University 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.)

This paper studies a two-machine flow shop scheduling problem with availability constraints due to a breakdown on the first machine. The starting time of the breakdown is considered stochastic and follows a known probability distribution. A service-level constraint is introduced to model the guarantee with which the obtained schedule takes into account the stochastic nature of the breakdown's starting time. The objective is to find a solution that minimizes the makespan while satisfying the desired service level. The studied problem is strongly NP-hard. We develop two mixed integer linear models that linearize the non-linear model. Using interval modeling of the breakdown, we propose lower bounds and a valid inequality that are used to strengthen both models. When the lower bounds and the valid inequality are applied, the performance of both models is greatly improved by reducing the gap and reaching optimality for more instances. We also introduced two heuristics that exploit the proposed interval modeling. The computational results indicate that both models are able to reach optimality for the 10-job instances. Moreover, the comparisons results between both models with the two heuristics showed their effectiveness. [ABSTRACT FROM AUTHOR]

Considering the impact of emission on environment, over the last few decades, reduction of emission during production process (or emission free production process) appears to be one of the most growing preferences for every manufacturing firm. Again, manufacturing companies of different products are bounded to offer various facilities to their consumers in order to survive in the current highly competitive market situation. Primarily considering these two factors taken in to account, this study addresses an imperfect manufacturing inventory model in which customers' demand increases linearly w. r. to emission reduction level, service level from the manufacturer and decreases nonlinearly w. r. to price of the items. The analytical expressions of production rates, emission levels and service levels are derived by solving a number of optimum control problems using Hamiltonian maximum principle with a values for the business period and selling price. The basic goal is to maximize the average profit of the system. In addition, a number of numerical examples are considered to elaborate four distinct instances involved in the proposed model and these are solved numerically using teaching learning based optimization algorithm. Finally, sensitivity experiment is performed to investigate the influence of various system factors on the best‐found policies. The sensitivity analysis indicates that service rate provided by the manufacturer and selling price of the items impose a huge impact on the average profit of the system. Further, the carbon reduction affects the revenue of the system less significantly. [ABSTRACT FROM AUTHOR]

Due to gradual disappearance of global demographic dividend, the improvement of workforce efficiency becomes increasingly important. It has been commonly recognised that workforce motivation can largely stimulate the workers, especially in manufacturing systems. This paper investigates a worker scheduling problem under given work shifts with workforce motivation effects, where motivation effects are characterised by motivation coefficients of job processing times. We focus on the situation where motivation coefficients are uncertain due to various factors, and only the mean vector and covariance matrix are known. The objective is to maximise the service level, measured by the probability of ensuring no tardy jobs. We first propose a distributionally robust chance constrained formulation with a probabilistic objective function. Then an adapted sample average approximation (SAA) method and a heuristic, based on an approximated mixed integer second-order cone programming (MI-SOCP) model and the idea of problem decomposition, is developed. Numerical results show that the decomposition-based heuristic is more efficient. We also draw some managerial insights. [ABSTRACT FROM AUTHOR]

The article is devoted to the study of the system of long-term care for senior citizens. The purpose of the article is to analyze the current state of the sphere of social services of the population, as well as to consider the essence, tasks, advantages and disadvantages of the introduction of a long–term care system on the territory of Russian Federation. According to the study results, the authors conclude that the introduction and development of a system of longterm care for senior citizens is one of the foreground and socially significant tasks of Russian Federation. The authors propose to use public-private partnership as one of the tools to provide senior citizens with the necessary level of availability of qualified and permanent assistance in the long-term care system. In the process of writing the article, the authors used such research methods as analysis, synthesis, a systematic approach, methods of displaying and processing the information received.

A common practice in inventory systems with several customers requiring differentiated service levels is to group them into two or three classes, where a customer class is a group of customers with the same preset service level in terms of product availability. However, there is no evidence that grouping customers into two or three classes is optimal in terms of the ordering policy parameters. This paper studies the effect of the number of customer classes on the inventory level of a single-period inventory system with stochastic demand and individual service-level requirements from multiple customer classes. Using a Sample Average Approximation approach, we formulate computationally tractable multi-class service level models, under responsive and anticipative priority policies in cases of shortage, as mixed integer linear problems (MIPs). The effect of the number of classes on the inventory level is determined using a round-up aggregation scheme; i.e., given a sufficiently large initial number of classes, it is reduced by adding the lower service level classes to the next higher class. We analytically characterize the optimal inventory level under responsive and anticipative priority policies as a function of the initial number of classes and the number of classes grouped based on the round-up aggregation scheme. Under a responsive priority policy, we show that there is an optimal number of classes, while under an anticipative priority policy, the optimal number of classes is equal to the initial number of classes. The effect of free-riders resulting from the round-up aggregation scheme on the optimal inventory level is studied through numerical experiments.

The spatial-temporal imbalance of demand will affect the formulation of urban rail transit public welfare fare schemes, and then affect the passenger benefits and the traffic structure. A bi-level programming model considering multi-period and different direction demands is established. The upper model takes the maximization of social welfare as the objective function, and considers public welfare constraints; the lower model is a stochastic user equilibrium model. A heuristic hybrid algorithm which combines multi-population genetic algorithm, continuous average method and parallel computation is designed. The results show that: compared with for-profit pricing scheme, when public welfare pricing scheme is adopted, ticket price is reduced by 43%; the generalized travel cost per passenger is decreased by 19% and 4% in peak and off-peak periods respectively in up direction, 16% and 6% in down direction; the consumer surplus is increased by 15.2% and 5.7% in peak and off-peak periods respectively in up direction, 12% and 7% in down direction; the urban rail transit sharing rate is increased by 40% and 71% in peak and off-peak periods respectively in up direction, 36% and 66% in down direction. This model provides some reference for government to formulate the fare and management strategy. [ABSTRACT FROM AUTHOR]

Demand planning is informed by demand forecasts, service level requirements, replenishment constraints, and revenue projections. "Demand forecasts" differ from "demand plans" in that forecasts only represent the distribution (or the most likely value) of product demand. Motivated by common forecasting practices in industry, our research examines whether forecasters recognize this difference between demand forecasts and demand plans. Based on a lab experiment informed by data from two large FMCG companies, we found that forecasters factor service levels into their demand forecasts, even when they are clearly instructed to predict the most likely demand and incentivized to minimize the forecast error. We establish that this result holds for students and practitioners alike, and show that this behavior is driven by the service level information, and not some other anchor. We use data from a recent industry survey to support the external validity of our key findings. Highlights: Service level targets can bias the behavior of demand forecasters resulting in excessive inventory in the supply chain.Forecasts are way above the most likely value of demand, if forecasters are aware of high service level targets.The service level bias persists even for more experienced forecasters, or when forecasters are clearly instructed to avoid service level targets, or when forecasters receive feedback on forecast errors. [ABSTRACT FROM AUTHOR]

We study the optimal retail price and service level in a dual-channel supply chain in which the manufacturer may sell products through its online channel and offline retailer. Consumers with channel preference make their purchasing decision based on retail price, service level and channel valuation difference. We consider the reference price effect into a Hotelling utility function to formulate the competition of retail price and service level. We analytically derive the unique equilibrium separately in three power structures: (1) Manufacturer-led Stackelberg game (MS), (2) Retailer-led Stackelberg game (RS) and (3) Nash game (N). Particularly, we characterize the impacts of channel valuation difference and reference price effect intensity on the optimal decision and expected profit. In contrast to the previous literature on the dominance of the supply chain in that the dominance always can raise the dominant enterprise's profit, our findings indicate that supply chain members with channel advantages and channel disadvantages have different preferences for power structure. The member with channel disadvantages should give up dominance. Only when channel valuation difference is significant or reference price effect intensity is weak, the member with channel advantages will fight for dominance. Nash game is highly competitive in that the members only earn less profit. Numerical examples verified theoretical results. [ABSTRACT FROM AUTHOR]

This paper constructs a two-level closed-loop supply chain system consisting of original parts manufacturers and parts distributors. Based on the different preferences of consumers for remanufactured parts and new parts, four combination models of patent protection and service improvement are constructed. Through comparative analysis, the impact of implementing patent protection policies by original parts manufacturers and improving service levels by parts distributors on the pricing decisions of the closed-loop supply chain is explored.Through the comparison between related models and the verification of calculation examples, it is found that (1) a manufacturer prevents the price of new products from being affected by the price of remanufactured products and upgrading of service level by introducing royalties, which reduces its loss of profit; (2) in the absence of patent protection, the manufacturerꞌs profit decreases as the level of service increases; in the presence of patent protection, the manufacturerꞌs profit increases as the level of service increases; (3) retailersꞌ profits decrease after the manufacturer introduces royalties, which discourages them to improve service levels for remanufactured products; (4) as retailers raise the service level of the remanufactured products, the profits of the manufacturer and third-party manufacturers keep increasing, while the profits of the retailers first increase and then decrease.

Many manufacturers and retailers have already opened online stores to sell their products. Thus, manufacturers are competing as sellers, and retailers are transforming into dual-channel retailers (DCRs). Such an expansion in business scope and the wide spread of lenient return policies trigger tremendous return volume that requires great deal of logistical efforts. The service levels offered within online stores greatly affect channels’ demand. However, they also influence the channel choice of return for online customers, if applicable, when their purchases are unsatisfactory. Therefore, this paper studies the optimal service level for a centralized DCR. In addition, it examines the optimal levels for a decentralized two-echelon system through the implementation of Nash and Stackelberg theoretical frameworks. Important properties of optimal solutions and vital managerial insights have been devised through analytical and sensitivity analysis. It is found that a DCR may have the following tradeoff: decrease service level to increase the reward from the physical store or increase service level to enhance competitiveness of the online store. The optimal decision depends greatly on how sensitive the customers’ return behavior is to service level. In addition, as the exogenous price increases, the change in the retailer’s offered level depends significantly on the different rates of return.

Energy efficiency plays an increasingly important role not only in supply chains, but also in in-plant supply systems. Manufacturing companies are increasingly using energy-efficient material handling equipment to solve their in-plant material handling tasks. A new example of this effort is the use of drones for in-plant transportation of small components. Within the frame of this article, a new AGV-drone joint in-plant supply model is described. The joint service of AGV-based milkrun trolleys and drones makes it possible to optimize the in-plant supply in production lines. This article discusses the mathematical description of AGV-drone joint in-plant supply solutions. The numerical analysis of the different AGV-drone joint in-plant supply solutions shows that this new approach can lead to an energy consumption reduction of about 30%, which also has a significant impact on GHG emission. [ABSTRACT FROM AUTHOR]

We study an inventory control problem of a perishable product with a fixed short shelf life in Dutch retail practice. The demand is non-stationary during the week but stationary over the weeks, with mixed LIFO and FIFO withdrawal. The supermarket uses a service level requirement. A difficulty is that the age-distribution of products in stock is not always known. Hence, the challenge is to derive practical and efficient order policies that deal with situations where this information is either available or lacking. We present the optimal policy in case the age distribution is known, and compare it with benchmarks from literature. Three heuristics have been developed that do not require product age information, to align with the situation in practice. Subsequently, the performance of the heuristics is evaluated using demand patterns from practice. It appears that the so-called STIP heuristic (S for Total estimated Inventory of Perishables) provides the lowest cost and waste levels. [ABSTRACT FROM AUTHOR]

The study aims to uncover the service level of major cellular providers in Indonesia. The stages of the research included the determination of point where cellular towers are built as well as the density of the telecommunication traffic in particular regions in Indonesia. The users of the cellular towers are representatives of the companies from certain logistics establishment that utilizing the existence of the towers. Twelve spots of cellular towers were taken as research objects with 155 users representing their companies who uses and utilizing the cellular towers for various of businesses dominantly in logistics businesses. Structural equation model analysis were implemented to seek the affects of each variables. The result showed that all the data collected were supporting the hypotheses and this result were also showed that loyalty towards service delivered and complaints might occur parallelly. [ABSTRACT FROM AUTHOR]

As the demand for cultural and recreational services grows, the mismatch between the supply and demand of park services significantly affects residents’ well-being. Optimizing the spatial layout of park services is a focal point of urban park and green space research. Taking Hangzhou, Zhejiang Province, as a case study, this research analyzes the spatial patterns and balance of park service supply and demand. Utilizing the Grey Wolf Optimization Model optimized by the K-Nearest Neighbor Model (GWO-KNN), this study proposes construction objectives for optimizing park services. The results indicate the following: (1) significant differences exist in the park service demands of residents in different residential environments; (2) there is a noticeable spatial disparity in park service supply among various residential areas with an overall positive correlation between park service supply levels and resident demands, yet an imbalance exists; (3) this study categorizes spatial types into low-service coordination, high-service coordination, low-service imbalance, and high-service imbalance; (4) the GWO-KNN Model is applied with optimization objectives being the innovative aspect of this study. Strategies for each park category are proposed: emphasizing suburban park construction by utilizing surrounding green resources and adding diverse facilities; introducing facilities friendly to vulnerable groups to meet the needs of diverse populations; enhancing the complementary advantages between “new” and “old” cities by moderately increasing park sizes and improving cultural and facility development levels; optimizing spatial structure with limited land resources to construct an urban park network system. This study aims to provide theoretical and technical support for optimizing urban park and green space systems.

Stochastic scheduling optimisation is a hot and challenging research topic with wide applications. Most existing works on stochastic parallel machine scheduling address uncertain processing time, and assume that its probability distribution is known or can be correctly estimated. This paper investigates a stochastic parallel machine scheduling problem, and assumes that only the mean and covariance matrix of the processing times are known, due to the lack of historical data. The objective is to maximise the service level, which measures the probability of all jobs jointly completed before or at their due dates. For the problem, a new distributionally robust formulation is proposed, and two model-based approaches are developed: (1) a sample average approximation method is adapted, (2) a hierarchical approach based on mixed integer second-order cone programming (MI-SOCP) formulation is designed. To evaluate and compare the performance of the two approaches, randomly generated instances are tested. Computational results show that our proposed MI-SOCP-based hierarchical approach can obtain higher solution quality with less computational effect. [ABSTRACT FROM AUTHOR]

Due to fierce competition in today's global market, businesses are forced to provide customers with high service levels. Typically, vendors produce or order sufficient quantities at the beginning of a selling season to ensure reasonable service levels for the whole season. However, due to the probabilistic nature of demand, high service levels at the beginning of a selling season does not guarantee appropriate service levels during the course of consuming the item. Thus, revision of service levels during a selling season is important and ignoring such revision may lead to serious consequences for businesses like profit loss due to cancelled orders and reduction of the market share of the company. In this paper, we propose a model for a newsvendor supply chain with single vendor and multiple retailers where the vendor has two-ordering opportunities. At the beginning of a selling season, the retailer orders from a vendor a quantity such that a predetermined service level is achieved. At the second-ordering instant, the retailer learns more about the demand pattern and uses the new available demand data to update the coming demand using Bayesian approach. Based on the updated demand, the retailer evaluates the new service level for the remaining portion of the selling season. If this service level is lower than a specific value, a second batch is ordered. We develop the model for general demand distribution and determine the optimal quantities at the beginning of the selling season and at the second-ordering opportunity. [ABSTRACT FROM AUTHOR]