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This article develops a decision model which enables service firms to optimize their productivity. Companies must efficiently determine the necessary resource input to increase service productivity to meet customer demand. In so doing, managers face service-specific challenges: They must select the appropriate type and quantity of limited resources to deliver services efficiently, consider the volatility of demand to provide services effectively, and integrate the interaction effects of resources in terms of substitution to utilize constraint resources optimally. In addressing these challenges, we develop an interdisciplinary approach by combining insights from service research and operations research to create a decision model that helps managers select the optimal type and quantity of resources available to overcome the abovementioned challenges. We validate our model in several case studies and further generalize our findings by applying it to different data settings. Ultimately, we prove that productivity can be increased significantly if firms optimize resource selection by considering stochastic demand, the effects of substitution among resources, and resource constraints. [ABSTRACT FROM AUTHOR]

Abstract In the context of extremely uneven sources and loads in China, several large‐scale power transmission systems have been established. Affected by the small equivalent impedance of the system, the increase of negative sequence currents during open‐phase operation has seriously threatened the stable operation of synchronous generators. The necessity of negative sequence current suppression is particularly prominent. To address the above issues, this paper analyzes the feasible negative sequence suppression capacity of the grid‐connected inverter under the principle of positive sequence priority. Using the geometric analysis method to determine the optimal suppression capacity. The none‐phase‐locked loop (PLL) control method is used to improve the response speed and form the negative sequence current suppression strategy adapted to the open‐phase operation period. A simulation model was built in PSCAD to verify the effectiveness of the proposed strategy. The result shows that the suppression strategy has a faster response than the conventional PLL control. In scenarios with large negative sequence currents to be suppressed, a maximum suppression effect of approximately 63% can be achieved. Compared with the traditional suppression capacity selection, the strategy in this paper can reduce the residual of negative sequence current by about 10% at most. The negative sequence currents intruding into the generator can be effectively suppressed without additional devices.

A number of studies have examined a variety of solutions for overcoming master production schedule nervousness which is a major cause of cost increase and material flow disruption throughout the supply chain. The study reported in this paper provides a comprehensive review of the literature and provides insights on the effect of capacity constraints on the effectiveness of policies for dampening schedule nervousness. The insights on the impact of capacity constraints are then confirmed via a simulation study investigating the effectiveness of three nervousness reduction policies. The relative effectiveness of the policies (freezing a portion of the schedule, using safety stock at the end item level, and a combination policy of freezing and safety stock) are examined under a variety of conditions characterised by variations in the level of demand uncertainty faced by the system and product structure complexity. [ABSTRACT FROM AUTHOR]

In the healthcare industry, to reduce the waiting time for patients, policy makers may allow private hospitals (or called the toll system) to enter the market. However, when the total healthcare capacity in the market is limited (e.g. the number of medical staff or equipment is limited), the entrance of the toll system may offer higher salaries to attract medical staff from public hospitals (or called the free system) and reduce its capacity. Then whether or not introducing toll system in the system can reduce the waiting time becomes an issue. In this paper, we investigate the impact of capacity constraint on a two-tier healthcare system. The results show that when the total capacity is tight enough, the two-tier healthcare system often yields less social welfare than the one-tier free system; and when the total capacity is sufficient (the demand does not exceed the total capacity), the two-tier healthcare system improves the social welfare. Specially, we find under certain conditions the capacity constraint can improve social welfare. In addition, if the capacity constraint has a negative effect on the two-tier system's performance, the government can set an appropriate upper limit for the toll system's capacity to remove the negative effect. [ABSTRACT FROM AUTHOR]

In today's hypercompetitive marketing environment in order to significantly boost sales revenue, supplier frequently offers allowable credit duration to the retailer for payment (also known as "trade credit"). Additionally, warehouse capacity constraints can create logistical challenges when orders exceed a retailer's own storage space, requiring additional costs for rented warehouse. In addition, due to globalization in marketing strategy, supplier is now offering to the retailers' order volume discount linked with price and trade credit. In addition to this, this study considers carbon emissions that occur during the production and storage of goods at the supplier inventory system, and the retailer is responsible for covering the costs associated with each emission that was generated during product transportation and storage. The unit production cost, which is reliant on the production rate, and shortages of the commodities are permissible which is partially backordered. The optimum production and replenishment practices for the both supplier and the retailer are determined after the establishment of a novel algorithm. Eventually, several numerical examples are provided to demonstrate the theoretical findings. The sensitivity analysis subject to the primary parameters is incorporated to verify the reliability of the proposed study which leads to some intriguing managerial insights that will help the manager or decision-maker to make some suitable and qualitative decisions. [ABSTRACT FROM AUTHOR]

We consider a two-echelon supply chain consisting of one capacity-constrained supplier and two Cournot-competitive retailers. The incumbent retailer has private demand information and takes strategic information sharing to maximize its expected profit. The supplier carries out capacity allocation strategy when the capacity cannot satisfy both retailers' total order quantity. We model three scenarios of information sharing: (1) no member is informed; (2) only the entrant retailer is informed; and (3) both the supplier and entrant retailer are informed. We characterize the conditions under which information sharing may benefit or hurt the supply chain and different members and meaningful information sharing is achieved via "cheap talk". Compared with no information sharing, when only the entrant retailer is informed, the incumbent retailer always has incentive to deflate its demand information to make the entrant retailer order less. Both retailers cannot achieve meaningful information sharing. When the supplier and entrant retailer both are informed, the incumbent retailer faces a trade-off between the desire to receive more products and the fear of intense competition and high wholesale price. The meaningful information sharing is achieved and all members are benefitted. The numerical examples verify theoretical results. [ABSTRACT FROM AUTHOR]

In the context of extremely uneven sources and loads in China, several large‐scale power transmission systems have been established. Affected by the small equivalent impedance of the system, the increase of negative sequence currents during open‐phase operation has seriously threatened the stable operation of synchronous generators. The necessity of negative sequence current suppression is particularly prominent. To address the above issues, this paper analyzes the feasible negative sequence suppression capacity of the grid‐connected inverter under the principle of positive sequence priority. Using the geometric analysis method to determine the optimal suppression capacity. The none‐phase‐locked loop (PLL) control method is used to improve the response speed and form the negative sequence current suppression strategy adapted to the open‐phase operation period. A simulation model was built in PSCAD to verify the effectiveness of the proposed strategy. The result shows that the suppression strategy has a faster response than the conventional PLL control. In scenarios with large negative sequence currents to be suppressed, a maximum suppression effect of approximately 63% can be achieved. Compared with the traditional suppression capacity selection, the strategy in this paper can reduce the residual of negative sequence current by about 10% at most. The negative sequence currents intruding into the generator can be effectively suppressed without additional devices. [ABSTRACT FROM AUTHOR]

When the number of tasks is large, how should a firm design reward and penalty schemes to incentivize its employees? In "Dynamic Mechanism Design with Capacity Constraint," He studies the role of capacity constraint in a project assignment problem, where a principal needs to assign multiple tasks to an agent. The author fully characterizes the optimal mechanism via a sequence of deadlines. This characterization is used to show that the presence of the capacity constraint reduces the principal's payoff and delays the completion of projects. It further illustrates that the widely adopted no-capacity constraint framework may provide inaccurate results in various dynamic problems. We study a project assignment problem, where a principal needs to assign multiple projects to an agent. The agent is privately informed about the cost, which could be high or low. The agent's type evolves stochastically over time. We fully characterize the optimal mechanism via a sequence of deadlines and show that the presence of the capacity constraint reduces the principal's payoff and delays the assignment of projects. In particular, as the number of projects increases, the limit optimal contract may be strictly bounded away from the optimal contract when there are infinitely many projects, and the principal's payoff may be strictly below that in the setting without the capacity constraint. Funding: This work was supported by the National Natural Science Foundation of China [Grant 72122023]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2023.2449. [ABSTRACT FROM AUTHOR]

In today's competitive market environment, providing different sorts of concessions are often observed from the suppliers/manufacturers to their customers for escalating the number of order quantity, as well as work mutually to control the rising level of carbon emission along with long-lasting financial benefits. So, it is challenging for researchers and industry to develop low carbon inventory models that can meet emission reduction targets while maintaining company's profit. The credit function plays an important role within the organization. Furthermore, with the globalization of the marketing policy, the supplier may provide the retailer a discounted price if the quantity of purchase is large enough, and green inventory management reduces the environmental impact of a business without affecting its profitability. In this paper, we study a profit maximizing green economic order quantity (EOQ) model by considering capacity constraints under order-size dependent trade credit and all-units discount, along with minimizing carbon emissions for a cleaner environment. Shortages are allowed and partially backordered. The paper discusses all the potential cases, which may occur in green inventory models with carbon emission costs under different allowable delay-in-payments. We find that if retailers' own warehouse capacity is relatively small, they always benefit from enlarging order quantities and renting an extra warehouse. Finally, some numerical examples are presented to illustrate the applicability of the proposed model. Sensitivity analysis of the major parameters is performed and some insights are obtained. [ABSTRACT FROM AUTHOR]

There is ample empirical evidence documenting that large firms set significantly lower prices than smaller, capacity-constrained, firms. This is paradoxical in light of the standard theoretical result that large firms charge higher prices than small firms in models of price competition with capacity constraints. We argue that private information about capacity constraints can account for this puzzle. We provide concavity conditions on the demand and on the type distribution under which there exists a unique, monotone decreasing price equilibrium. Solving the model requires a novel approach of studying several different regions of pricing incentives depending on the realized capacity levels. We show that firms with intermediate capacities compete in an auction type interaction, while firms with low or high capacity levels compete less vigorously on the margin. [ABSTRACT FROM AUTHOR]

Recent technological advances and substantial cost reductions have made the genomic surveillance of pathogens during pandemics feasible. Our paper focuses on full genome sequencing as a tool that can serve two goals: the estimation of variant prevalences, and the identification of new variants. Assuming that capacity constraints limit the number of samples that can be sequenced, we solve for the optimal distribution of these capacities among countries. Our results show that if the principal goal of sequencing is prevalence estimation, then the optimal capacity distribution is less than proportional to the weights (e.g., sizes) of countries. If, however, the main aim of sequencing is the detection of new variants, capacities should be allocated to countries or regions that have the most infections. Applying our results to the sequencing of SARS-CoV-2 in 2021, we provide a comparison between the observed and a suggested optimal capacity distribution worldwide and in the EU. We believe that following such quantifiable guidance will increase the efficiency of genomic surveillance for pandemics.

• First solution algorithm to solve the static link transmission model (sLTM) on general networks. • Improved solution algorithm for point queue model of Bliemer et al. (2014). • Calibration of user configurable parameters. • Comparison with existing static network loading models. Most static traffic assignment models, both in the literature and in practice, are neither capacity constrained nor storage constrained. They allow flows to exceed the link capacity and/or queues to exceed the link length. Recent studies in this area have resulted in novel approaches that do consider capacity constraints, resulting in residual queues, and sometimes even storage constraints, resulting in possible queue spillback. We build upon the results of these works and in particular on the model formulated in Bliemer and Raadsen (2020) that introduced a static network loading model formulation that is both capacity constrained as well as storage constrained. Their static network loading model formulation is derived from – and consistent with - the link transmission model, a well-established dynamic network loading model. It is referred to as the static link transmission model (sLTM) in this paper. This model considers a general concave fundamental diagram for each link and a general first order node model. It is well known that ignoring queue spillback can result in significant underestimation of path travel times. This is especially true for paths that do not traverse any of the bottleneck(s) directly, but that are affected by space occupied by queues that are spilling back. The prospect of being able to capture queuing and spillback effects in static assignment provides new opportunities for improving the modelling capabilities of this paradigm. In this paper, we propose a solution scheme to sLTM capable of finding a solution on large scale networks. This is the first time that an algorithm is proposed for solving an analytical static model with queue spillback. The inclusion of a node model in a static context - while enhancing the model's capabilities – generally results in the absence of a guaranteed convergent algorithm, and introducing spillback exacerbates the issue. Given the challenges of finding a stable solution, we discuss a base solution scheme and three (configurable) extensions. Further, we investigate algorithmic settings with respect to convergence and its impact on computational cost. A large-scale case study demonstrates the feasibility of the proposed scheme by finding solutions under the most challenging of conditions in a real-world setting. We show that improving convergence capabilities negatively affects computational efficiency. To this end, several potential improvements based on our findings are discussed. Lastly, we discuss the potential of this line of research in more general terms, highlighting strengths and weaknesses following our experiences. [ABSTRACT FROM AUTHOR]

One of the most fundamental and striking limitations of hu-man cognitive function is the constraint on the number ofcontrol-dependent processes that can be executed simultane-ously. However, the sources of this capacity constraint re-main largely unexplored. Previous work has attributed the con-straints on control-dependent processing to the sharing of rep-resentations between tasks in neural systems. Here, we exam-ine how shared representations interact with two other factorsin producing constraints on control-dependent processing. Wefirst demonstrate that the detrimental effects of shared repre-sentations on multitasking performance are contingent on theamount of conflict that is induced by the tasks that share rep-resentations. We then examine how the persistence of sharedrepresentations between tasks affects processing interferenceduring serial task execution. Finally, we discuss how this set ofmechanisms can account for various phenomena in neural ar-chitectures, including the psychological refractory period, taskswitch costs, as well as constraints on cognitive control.

We consider the problem of patent licensing in a Cournot duopoly in which the innovator (patentee) is one of the firms and it is capacity constrained. We show that when the patentee can produce a relatively small (relatively large) quantity, it prefers licensing by means of a fixed fee (unit royalty). When the patentee can set two-part tariffs in the form of combinations of fixed fees and unit royalties, it charges a positive fixed fee if and only if it is limited to producing a relatively small quantity. We also show that with combinations of fixed fees and royalties, the royalty rate is lower than is true for the standard case. [ABSTRACT FROM AUTHOR]

Because of incomplete information on pre-sold products, consumers face uncertainty about the value of what they have purchased, which leads to a mismatch between supply and demand and a large number of returns. By developing appropriate return policies and effectively managing and handling consumer returns, retailers can not only reduce waste but also ensure better resource utilization, which is essential for sustainable development. In this paper, we analyze the full-refund and full-and-freight-refund policies of retailers and develop a game model based on binary competition for selecting the optimal return policy for the pre-sold products of two retailers. The study shows that when both retailers have low capacity, there is no pre-sale stage. However, when their combined capacity is high and exceeds the demand of non-strategic consumers, equilibrium depends on their combined capacity and the proportion of strategic consumers who choose to keep the pre-purchased product under both return policies. When the number of strategic consumers who retain pre-order products is low under the full-refund policy and both retailers have moderate capacity, equilibrium is achieved when an asymmetric return policy is followed rather than a symmetric return policy. Specifically, when the percentage of strategic consumers who keep their reserved products under the product return strategy is small and the capacity of the two retailers is moderate, the maximum benefit is achieved if one of the retailers adopts the policy of a full refund and the other adopts the policy of a full-and-freight refund. Otherwise, when one retailer adopts the policy of a full refund or a full-and-freight refund, its competitor should adopt the same strategy to gain maximum revenue. The research on retailers' pre-sale and return strategies in this paper helps to optimize the operational strategies and operational processes of e-retailers, further improve the management and decision making of their joint pre-sale and return strategies, and help optimize retailers' profits. [ABSTRACT FROM AUTHOR]

• Propose a booking cum rationing strategy for equitable travel demand management; • Establish a user equilibrium model to characterize the booking cum rationing strategy. • Formulate the problem as a linear programming problem in a general network. • Use a few simple examples to investigate the benefits of the strategy. • The strategy can maintain fairness, efficiency and flexibility of individual choices. Trip booking and traffic rationing have been proposed as two alternative travel demand management (TDM) strategies over the last two decades. Through artificially restricting demand (vehicle travel) by booking or rationing the scarce road capacity during the peak periods, the negative externalities generated by travel demand over available supply or road capacity can be reduced. In many cases, the two strategies also have the main goal of reduction of air pollution. Trip booking system allows vehicles/drivers to reserve prescribed areas or some lanes/segments of freeways/roads for their use during specific time periods, thereby maintaining a certain level of service of the roadway space. It is often in the form of a permit to control the number of reservations issued. On the other hand, traffic rationing is often achieved in reality through restricting access into an urban cordoned-off area or city center based on the last digits of the license number on pre-established days and during certain periods, usually the peak hours. However, theoretical studies and practical implementation of the two strategies have been conducted separately. Taking their advantages, this paper proposes and demonstrates a novel hybrid strategy of booking cum rationing for efficient and equitable TDM. We show that the problem of interest can be simply formulated as a convenient linear programming problem in a general network. Simple examples are provided to elucidate how this hybrid strategy can achieve a traffic flow distribution pattern prescribed by the traffic planner while maintaining the fairness, efficiency, and flexibility of individual choices. [ABSTRACT FROM AUTHOR]

Due to the highly competitive marketing economy, for different kinds of products’ related suppliers confer various incentives to their respective retailers with certain terms and conditions. Even though suppliers may require advance payments before delivering their products, they incentivize the scheme by offering instant price-discounts, maybe with some other additional benefits and we have constructed an inventory-model consisting of two-warehouses mathematically with deteriorating products. In this proposed model, the suppliers offer some price-discounts for advance payments made by their retailers. As advance payments put a constraint on the capital position of the retailers, the retailers meanwhile, enjoy some delay in the final payment of the rest amount which acts as a booster for their business. A partially backlogged shortage is allowed and its rate is considered to be dependent on the duration of waiting time from the replenishment of a lot to the arrival of the next lot. Finally, a numerical example is performed for validation of the suggested model.

Traditional hub location problems are usually based on deterministic circumstances. However, many uncertain factors can cause demand to vary in the long run, which increases the difficulty of strategic hub location planning. The hub location problem for fresh agricultural products is studied considering the perishability of the products and the uncertainty of customer demands. An uncertain demand variable is described by an affine function of the nominal mean and several independent uncertainty sources and is further adjusted by the deterioration rate. An uncapacitated robust hub location model for fresh agricultural products with uncertain demand is first established and solved using the Lagrangian relaxation approach. Then, a robust optimization model for the corresponding capacitated hub location problem is given. A numerical study based on the Australian Post data set (AP20) shows that the deterioration rate of fresh agricultural products, the uncertainty of demand and the degree of conservatism of decision makers all have significant impacts on the total transportation profit. Furthermore, the capacitated model yields more profit than the uncapacitated model because it allows the effects of the deterioration rate and uncertainty to be moderated through flow reallocation. The proposed models are useful for helping decision makers determine the locations and capacities of hubs for fresh agricultural products in accordance with different risk preferences.

Büyük veri, işletmelerin iç yapısında biriken veya çeşitli dış kaynaklardan toplanan verilerin derlenmesi, depolanması, düzenlenmesi ve analiz edilmesiyle anlamlı bilgiler ortaya çıkararak, işletmeler için fayda yaratabilmektedir. İşletmenin fonksiyonlarından biri olan üretim alanında veri derlemek bu verileri yapısal hale getirerek değer yaratmak büyük avantaj yaratmaktadır. Büyük veri setlerinin analiz edilerek üretim planlamasında kullanılması hammaddelerin başarılı bir şekilde çıktılara dönüşebilmesinde önemli rol oynamaktadır. Madencilik sektörü içinde birçok belirsizliği barındırması, sermaye yoğun bir iş olması nedeniyle uzun vadeli üretim planlamasına ihtiyaç duyan sektörlerden biridir. Öte yandan gelişen teknolojiyle beraber maden sahaları hakkında büyük veri setlerinin derlenebilmesi mümkün hale gelmektedir. Bu çalışma kapsamında açık ocak madenciliği yapan orta büyüklükte bir firmanın sahip olduğu kömür ocağında yaşanan sorunların çözülebilmesi amacıyla maden sahasından gerekli veri toplanarak uzun vadeli üretim planlaması ve çizelgelemesi yapılmıştır. Firmanın üretimini etkileyen kaynak ve kapasite kısıtları ve sahip olduğu cevherin özellikleri, literatürde yer alan tam sayılı üretim planlanma ve çizelgeleme matematiksel modellerine uyarlanmıştır. Kurulan matematiksel model Python programlama diliyle çözülmüştür. Çalışmanın sonucunda firmanın ilgili kömür ocağındaki 5 yıllık üretiminin çizelgesine ve toplamda elde edeceği kârın net bugünkü değerine ulaşılmıştır. Çalışma yararlandığı büyük veri setleri ve matematiksel model sayesinde ortaya çıkardığı plan ile firmaya gelecek için politikalar geliştirebilme imkânı sunmuştur.

One of the most compelling characteristics of controlled pro-cessing is our limitation to exercise it. Theories of control allo-cation account for such limitations by assuming a cost of con-trol that constrains how much cognitive control is allocated toa task. However, this leaves open the question of why sucha cost would exist in the first place. Here, we use neural net-work simulations to test the hypothesis that constraints on cog-nitive control may reflect an optimal solution to the stability-flexibility dilemma: allocating more control to a task results ingreater activation of its neural representation but also in greaterpersistence of this activity upon switching to a new task, yield-ing switch costs. We demonstrate that constraints on controlimpair performance of any given task but reduce performancecosts associated with task switches. Critically, we show thatoptimal control constraints are higher in environments with ahigher probability of task switches.

In today's competitive business situation, the supplier frequently offers his or her retailers a permissible delay period (i.e., trade credit) to stimulate sales. In addition, the capacity of any warehouse is limited in practice, thus the retailer needs an additional rented warehouse (RW) to store the excess units when the order quantity exceeds the capacity of the own warehouse (OW). Furthermore, with the globalization of the marketing policy, the supplier may provide the retailer with a discounted price if the quantity of purchase is large enough. Considering all of the factors mentioned above, in this paper we study an integrated inventory model with capacity constraint under order-size dependent trade credit and all-units discount. Shortages are allowed and partially backordered. In addition, the unit production cost, which is a function of the production rate, is considered. An algorithm is developed to determine the optimal production and replenishment policies for both the supplier and the retailer. Finally, numerical examples are presented to illustrate theoretical results. Sensitivity analysis of the major parameters are performed and some insights are obtained. [ABSTRACT FROM AUTHOR]

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In recent years, numerous studies have been conducted regarding inventory control of deteriorating items. However, due to the complexity of the solution methods, various real assumptions such as discrete variables and capacity constraints were neglected. In this study, we presented a multi-item inventory model for deteriorating items with limited carrier capacity. The proposed research considered the carrier, which transports the order has limited capacity and the quantity of orders cannot be infinite. Dynamic programming is used for problem optimization. The results show that the proposed solution method can solve the mixed-integer problem, and it can provide the global optimum solution. [ABSTRACT FROM AUTHOR]

Motivated by the increasing number of drones used for package delivery, we first study the problem of Multiple drOne collaborative Routing dEsign (MORE) in this article. That is, given a fixed number of drones and customers, determining the delivery trip for drones under capacity constraint with stochastic demand for customers such that the overall expected traveling cost is minimized. To address the MORE problem, we first prove that MORE falls into the realm of the classical vehicle routing problem with stochastic demand and then propose an effective algorithm for MORE. Next, we have a scheme of resplitting customers into different individual delivery trips while the stochastic demands are determined. Moreover, we consider a variety of MORE, MORE-TW, and design an effective algorithm to address it. We conduct simulation experiments for MORE to verify our theoretical findings. The results show that our algorithm outperforms other comparison algorithms by at least 79.60%.

Due to the highly competitive marketing economy, for different kinds of products' related suppliers confer various incentives to their respective retailers with certain terms and conditions. Even though suppliers may require advance payments before delivering their products, they incentivize the scheme by offering instant price-discounts, maybe with some other additional benefits and we have constructed an inventory-model consisting of two-warehouses mathematically with deteriorating products. In this proposed model, the suppliers offer some price-discounts for advance payments made by their retailers. As advance payments put a constraint on the capital position of the retailers, the retailers meanwhile, enjoy some delay in the final payment of the rest amount which acts as a booster for their business. A partially backlogged shortage is allowed and its rate is considered to be dependent on the duration of waiting time from the replenishment of a lot to the arrival of the next lot. Finally, a numerical example is performed for validation of the suggested model. [ABSTRACT FROM AUTHOR]

We propose a prototype model of market dynamics in which all functional relationships are linear. We take into account three borders, defined by linear functions, that are intrinsic to the economic reasoning: non-negativity of prices; downward rigidity of capacity (depreciation); and a capacity constraint for the production decision. Given the linear specification, the borders are the only source for the emerging of cyclical and more complex dynamics. In particular, we discuss centre bifurcations, border collision bifurcations and degenerate flip bifurcations—dynamic phenomena the occurrence of which are intimately related to the existence of borders. [ABSTRACT FROM AUTHOR]

In the course of recommending locations for establishing new facilities on urban planning or commercial programming, the location prediction offers the optimal candidates, which maximizes the number of served customers or minimize customer inconvenience, therefore brings the maximum profits. In most existing studies, only the spatial-temporal features are recognized to evaluate the location popularity, where social relationships of customers, which are significant factors for popularity assessing, have been ignored. Additionally, current researches also fail to take capacities and categories of the facilities into consideration. To overcome the drawbacks, we introduce a novel model of Multi-characteristic Information based Top- k Location Prediction (MITLP), it captures the spatio-temporal behaviors of customers based on historical trajectories, exploits the social relevancy from their friend relationships, as well as examines the category competitiveness of specific facilities thoroughly. Subsequently, by drawing on the feature evaluation and popularity quantization, MITLP will be implemented within a hybrid B-tree-liked recommending framework, Constrained Location and Social-Trajectory Clustered forest (CLSTC-forest), which can not only produce better performance in practice but also address the facility service constraints. Finally, extensive experiments conducted on real-world datasets demonstrate the higher efficiency and effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

This study considers a dual sourcing problem with multiple products in which each product can be procured from two capacitated suppliers, one with low production costs but high unreliability, and the other with high production costs but high reliability. The unreliable supplier cannot deliver any products if there is even one disruption. The objective of the retailer is to determine what order quantities for all of the products from the two suppliers, subject to the supplier capacity constraints, will maximize the expected profit. We use an extended newsvendor model to characterize the problem. A two-tier bi-section search method is developed for seeking the optimal solution to the problem. Numerical results are used to assess the effectiveness of the proposed method and the value of dual sourcing. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to explore the impact of supply capacity constraint, water delivery loss and fairness concern on the operational decisions/efficiency of the IBWT supply chain under the random precipitation. Design/methodology/approach – Two game-theoretic decision models for the IBWT supply chain coordination considering water delivery loss without/with fairness concern under the supply capacity constraint and random precipitation are developed, analyzed and compared. On this basis, the corresponding numerical analyses are conducted and compared to derive the corresponding management insights and policy implications. Findings – The research results indicate that the two-part tariff contract could effectively coordinate the IBWT supply chain and achieve operational performance improvement; the binding supply capacity constraint makes the water capacity to be allocated among IBWT distributors in accordance with fair shortage allocation rule and reduces the profit (or utility) of the IBWT supply chain and its members; the existence of fairness concern reduces the utility of the IBWT supply chain and its members; a lower precipitation utilization factor in the case with non-binding capacity constraint is beneficial for improving the profit/utility of the IBWT supply chain while a higher precipitation utilization factor in the case with binding capacity constraint is beneficial for improving the profit/utility of the IBWT supply chain; and reducing the water delivery loss rate, the mainline transfer cost, the branch-line transfer cost, the holding cost and the shortage cost and setting a higher retail price are beneficial for improving the profit/utility of the IBWT supply chain. Originality/value – Two innovative coordination decision models under random precipitation are developed, analyzed and compared through game-theoretic approaches to investigate the impact of supply capacity constraint, water delivery loss and fairness concern on the operational decisions/efficiency of the IBWT supply chain, which have enhanced the optimization decision theory for the operations management of IBWT projects and provided a better decision support for the IBWT stakeholders to make better operations strategies.

The proven benefits of limited-stop services have captured the attention of researchers, especially during the last decade. However, to solve the limited-stop service design problem many existing works directly impose a capacity constraint to a total social cost objective function. This "naïve approach" implicitly assumes that passengers behave altruistically, basing their decisions on what is best for the whole system. Although this issue has been identified in earlier works, the magnitude of the error induced by this simplification has not been studied yet. The objective of this work is to measure this error and to understand how it misrepresents passenger flows and bus occupation rates. To measure this error gap, we optimize a set of test scenarios by applying a naïve approach, and then take the resulting design and obtain a benchmark passenger assignment using a simple behavioral model. We propose two main indicators to compare both passenger assignment: the total passenger deviation, and the total capacity deficit. This comparison reveals that the assignment of the naïve approach may indeed be unrealistic, and raises concerns that a network design based on the naïve approach might have severe problems when implemented. Thus, the work highlights the importance of taking the results of the naïve approach with caution and verify them with a passenger assignment model before their implementation. [ABSTRACT FROM AUTHOR]

We extend the classical asset-selling problem to include debt repayment obligation, selling capacity constraint, and Markov price evolution. Specifically, we consider the problem of selling a divisible asset that is acquired through debt financing. The amount of asset that can be sold per period may be limited by physical constraints. The seller uses part of the sales revenue to repay the debt. If unable to pay off the debt, the seller must go bankrupt and liquidate the remaining asset. Our analysis reveals that in the presence of debt, the optimal asset-selling policy must take into account two opposing forces: an incentive to sell part of the asset early to secure debt payment and an incentive to delay selling the asset to capture revenue potential under limited liability. We analyze how these two forces, originating from debt financing, will distort the seller's optimal policy. [ABSTRACT FROM AUTHOR]

High-tech manufacturers frequently suffer from supply risk and capacity constraint. This paper considers a manufacturer who procures a type of high-tech component from two suppliers with asymmetric costs, capacities, and reliabilities, where the low-cost supplier has low reliability. One supplier is selected as a primary supplier and another as a backup supplier. The manufacturer places regular order to the primary supplier and determines backup order quantity from the backup supplier after random yield realization of the primary supplier. By developing a dynamic game-theoretical model, we obtain the optimal wholesale prices for the primary supplier and backup supplier, the optimal order quantity and supplier arrangement decisions for the manufacturer. We show that the manufacturer selects the low-cost supplier as a primary supplier and the high-cost supplier as a backup supplier (namely, LH policy) when the reliabilities of the two suppliers are close. When these reliabilities are not so close, the manufacturer chooses the high-cost supplier as a primary supplier and the low-cost supplier as a backup supplier (namely, HL policy). In addition, the manufacturer in the industry with scale diseconomies is more likely to select HL policy than that in the industry with scale economies. Compared with centralized decision making scheme, decentralized scheme induces the manufacturer more likely to select HL policy in the industry without scale economies, whereas in the industry with scale economies, decentralized scheme induces the manufacturer more likely to choose either HL policy when the capacity advantage of the low-cost supplier is weak, or LH policy when this advantage is significant.

Since Kreps and Scheinkman's seminal article (1983) a large number of papers have analyzed capacity constraints' potential to relax price competition. However, the majority of the ensuing literature has assumed that products are either perfect or very close substitutes. Therefore very little is known about the interaction between capacity constraints and local monopoly power. The aim of the present paper is to shed light on this question using a standard Hotelling setup. The high level of product differentiation results in a variety of equilibrium firm behavior and it generates at least one pure-strategy equilibrium for any capacity level. • The aim of the present paper is to shed light on the interaction between capacity constraints and local monopoly power using a standard Hotelling setup. • Substantial horizontal product differentiation results in a variety of equilibrium firm behavior and it generates at least one pure-strategy equilibrium for any capacity level. • The existence of pure-strategy equilibria for every capacity pair is in stark contrast with most of the literature on capacity-constrained pricing. [ABSTRACT FROM AUTHOR]

We propose a general model of oligopoly with firms relying on a two factor production function. In a first stage, firms choose a certain fixed factor level. In the second stage, firms compete on price and adjust the variable factor to satisfy all the demand. When the factors are substitutable, the capacity constraint is "soft," implying a convex cost function in the second stage. We show that there exists a continuum of subgame perfect equilibria in pure strategies, whatever the returns to scale. Among them, a payoff dominant one can always be selected. The equilibrium price may increase with the number of firms. [ABSTRACT FROM AUTHOR]

• Retailer with stock-dependent demand, two warehouses, and deterioration. • Continuous resupply from RW to OW proposed to keep stock in OW high. • Net present value functions to maximise profits derived. • Integrated distribution pattern achieved across retailer and wholesaler. • Continuous resupply better than common OW/RW policy for some situations. This paper expands previous work on stock-dependent demand for a retailer with a two-warehouse (OW/RW) situation to the case of deteriorating items and where the retailer seeks to obtain the integrated optimal distribution policy from collaboration with a supplier. Motivated by practical applications and recent literature, a policy is considered whereby products in good order from the retailer's back-room (RW) are frequently transferred to its capacitated main store OW. Because the demand depends on the stock of good products in the OW, the aim is to keep this stock at its full capacity with products in good condition, and this can be done for as long as the RW stock of good products is positive. A firm's objective function is the Net Present Value (NPV) of the firm's future cash-flows. The profit functions are developed for both this continuous resupply policy and the commonly used policy in the OW/RW literature. Numerical examples are included and have been solved with grid search methods. The examples illustrate the benefits of adopting the continuous resupply policy, and also collaboration between the retailer and the wholesaler. Moreover, it is shown how these benefits can be shared by small adjustments to the product's unit price between the firms. [ABSTRACT FROM AUTHOR]