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The dependence of Islamic bank rates on the conventional bank rate violates the religiosity principle and the fundamentals of an efficient market due to the possibility of arbitrage profit from the rate differences. This study tests such dependency in a dual banking system by considering monthly data from January 2009 to April 2018 on Malaysian banks using several ARDL tests, supplemented by robustness tests using a 12-month correlation of the rolling standard deviation and causality models based on the Toda-Yamamoto approach to investigate the short- and long-run dependency of rates. The study finds that Malaysia's Islamic bank deposit and financing rates are influenced by both the conventional and Bank Negara Malaysia's policy rates. Results imply that Islamic banks do serve profit-driven customers. We suggest that Islamic banks are forced to benchmark their rates to conventional rates because of the trade-off between religious and economic fundamentals by profit-driven customers. The study demonstrates that the question of Islamic legitimacy of Islamic banks needs to be addressed considering the correct profiling of the customer-base and the regulatory environment in which Islamic banks operate. We discuss implications for Islamic rate-setting behavior in the presence of monetary shocks in a dual banking system.

“Retail Link” initially developed by Walmart enables the tier-1 supply chain partners to access the sales big data, and hence achieve demand information sharing. Doing so helps improve supplier relationships, but the supplier can make better wholesale price decisions based on accurate demand information, which might hurt the profit of the multinational firm (MNF) such as Walmart. Therefore, it is observed that some MNFs outsource both procurement and logistics functions to a logistics service provider (LSP) for the benefits of “one-stop-shopping,” and meanwhile, cut the information link with the supplier. This significantly changes their decision interactions and the resulted social welfare. In this article, we build a co-opetition model and study an MNF's two typical outsourcing strategies, where the MNF either outsources the logistics or the bundled functions of procurement and logistics to an LSP. We find that, when the market demand uncertainty is low, the MNF prefers bundled outsourcing given its significant brand image advantage. When the market demand uncertainty is high, the MNF prefers bundled outsourcing regardless of its brand image advantage. Interestingly, when market demand uncertainty is moderate, the MNF prefers bundled outsourcing when its brand image advantage is either not significant or very significant. We further compare the social welfare levels under two outsourcing strategies and find that win–win situations in the senses of the MNF's profit and social welfare can be identified.

We undertake the first empirical analysis of profit shifting by U.S. firms during foreign tax holidays. We show that foreign tax holidays have become a prevalent and powerful tax planning strategy among U.S. firms. We find that U.S. firms significantly increase their outbound profit shifting while participating in foreign tax holidays. However, we also find that profit shifting associated with tax holidays comes at the cost of increased tax uncertainty. Our results have important implications for policy making and for understanding firm behavior.

One-level optimization methods have been proposed to optimize a single user’s load profile or a cluster of users in the smart grids. In this work, two two-level optimization methods are studied, one case considering technical requirements (case 1) and another considering economic criterion (case 2). In the upper level, the supplier optimizes the objective function. Meanwhile, at the lower level, users optimize their electrical costs. The proposed methods are based on Genetic Algorithm methods. In this sense, an indirect control is established in which users react to a price signal. Simulation results illustrate that both cases improve the demand profile and increase the retailer profit concerning an unscheduled case. However, when the supplier tries to maximize the profit, some users receive benefits to detriment of others, concluding that the technical approach is preferable to the economic one.

Innovative edge devices (e.g., smartphones, IoT devices) are becoming much more pervasive in our daily lives. With powerful sensing and computing capabilities, users can generate massive amounts of data. A new business model has emerged where data producers can sell their data to consumers directly to make money. However, how to protect the profit of the data producer from rogue consumers that may resell without authorization remains challenging. In this paper, we propose a smart-contract based protocol to protect the profit of the data producer while allowing consumers to resell the data legitimately. The protocol ensures the revenue is shared with the data producer over authorized reselling, and detects any unauthorized reselling. We also introduce a data relay process that can enhance data accessibility in wireless edge networks. We formulate a revenue sharing problem to maximize the profit of both the data producer and resellers/relayers. We formulate the problem into a two-stage Stackelberg game and determine a ratio to share the reselling revenue between the data producer and resellers/relayers. Extensive simulations show that with resellers and relayers, our mechanism can achieve up to 49.5% higher profit for the data producer and resellers/relayers.

In this article, we study a multiproduct multiperiod production planning problem faced by a cash-strapped manufacturer, who utilizes inventory-based financing (IBF) to capture additional demand during peak season, which otherwise will not be met due to cash shortage. Each product has a unique selling season and its production incurs a significant setup cost. We formulate the problem as an integer program and show the special case with one product is NP-hard. By exploiting the inherent structure of three distinct but related decisions, i.e., the production, financing, and working capital allocation decisions, we develop a Lagrangian relaxation-based branch-and-bound procedure to solve the model. Through a series of numerical experiments using real data, this article presents managerial insights into how the manufacturer's optimal production plan could differ with and without IBF. In an optimal plan, we show the profit maximizing strategy for the cash-strapped manufacturer is to overstock and pledge inventories to secure more loans prior to peak demand periods.

This paper studies the impact of foreign labour on the competitiveness of Italian dairy farms relying on the theory of foreign labour in profit functions. Application of an endogenous switching regression model identifies the drivers of adoption of the immigrant workforce. A counterfactual analysis performed on unit labour costs between farms employing and non-employing immigrants suggests the essential role of foreign farmworkers on dairy farms’ competitiveness, which provide a cheaper source of labour. The lower unit labour cost for immigrant workers resembles staunch support to the newly introduced ‘social conditionality’, for a CAP delivering also for farmworkers.

The use of recycled plastics is critical in the transition to a circular economy. However, for certain types of plastics, the recycling process is economically unviable. Government-driven incentives, such as a policy imposing a minimum fraction of recycled plastics to be used in production processes of plastic goods, offer an exit from this impasse. In this paper, we study how a firm's investment behavior is affected by policy uncertainty governing the introduction of such a regulatory measure. Specifically, we adopt a real option approach to study the two-step investment of a firm in its transition to the use of recycled plastics. A clear trade-off can be distinguished. On the one hand, investing early causes unnecessary profit losses before the policy implementation. On the other hand, a lack of investment leads to market exclusion after the policy implementation. For our case study on the use of recycled polyethylene, we find that firms plan their first investment step, so that the timing of the second investment step approximates their projection on the policy implementation time. Moreover, we find that the firm's value is maximized when the capacity of the first investment is smaller than the capacity of the second investment.

This paper introduces the joint problem of locating hubs on a network and determining transportation prices between the hubs. Two levels of decision makers are present in the problem acting non-cooperatively: hub transportation provider and customers. The objective of the hub transportation provider is to locate hubs and to set the prices (per unit of commodity) of crossing the hub arcs maximizing its profit, whereas the customers aim is to send their commodities, in the cheapest way, having the possibility of using the hub arcs at the price set by the hub transportation provider or using the existing network at a predefined tariff. The problem is modeled as a nonlinear bilevel programming formulation, which is in turn linearized, and strengthened through variable reductions as well as valid inequalities. The case in which the price of each hub arc is determined by applying a common discount factor to the predefined tariff in the existing network is also studied. Computational results of mixed integer programming models and a metaheuristic on instances adapted from the literature are presented.

Product returns cost retailers billions of dollars annually. We analyze return policies (RPs) with cash refunds in the presence of wardrobers. We then analyze RPs with store credit or gift cards (SC/GC) refunds. We also analyze two-option RPs in which consumers choose between cash and SC/GC refunds. We find that for cash-only RPs, the retailer uses price to attract consumers while using the refund fraction to discourage wardrobers. SC/GC can be a good alternative to cash when consumers have high valuation for SC/GC and some SC/GC are not redeemed. With high redemption rate, SC/GC can be a good alternative when consumers make hedonic purchases and the retailer has a reasonable profit margin. We also find that single-option RPs dominate two-option RPs when genuine consumers and wardrobers have the same valuation for SC/GC, and two-option RPs can dominate when the two consumer segments have heterogeneous valuation for SC/GC. When two-option RPs dominate, the retailer gives lenient SC/GC option to genuine consumers who have a high valuation for SC/GC, and uses the cash option to decrease the cost or increase the profit from wardrobers who have a low valuation for SC/GC for some ranges of problem parameters. Moreover, when consumers have continuous valuation for SC/GC, two-option RPs dominate single-option RPs in genuine-only consumer markets. With two-option RPs, the retailer uses cash to increase profit from consumers with a low valuation for SC/GC and uses SC/GC to increase profit from consumers with a high valuation for SC/GC.

In supply chain (SC) systems, channel coordination can be achieved by incentive alignment contracts. The contracting mechanisms reported in the literature are usually “short-term-based” and the respective decisions are assumed to be static. However, real world supply chains (SCs) operate in the long term and decisions may evolve when time passes. As a result, this paper analytically explores the instability of the short-term coordination strategies and examines the evolutionary behaviors of SC members towards SC coordination. Moreover, it incorporates the effects of SC members’ evolutionary strategy adoption into profit surplus distribution among them. To be specific, an evolutionary game theory-based model, namely the “profit surplus distribution (PSD) mechanism for supply chain coordination”, is proposed to investigate how long-term behaviors of SC members will affect the coordination decisions (i.e., choosing to collaborate) and their share of coordination profit surplus. Based on a real case in consumer electronics, we analytically investigate a two-level SC consisting of a retailer investing in sales-effort and a population of manufacturers investing in innovation effort. Using the evolutionary game theory, we examine the relationship between the evolutionary strategies of SC members towards channel coordination and the profit surplus distribution among them. The results reveal that the coordination strategy is preferred by a great percentage of manufacturers in the long term and their share of coordination profit surplus plays a critical role in affecting their evolutionary preference (and vice versa).

Many companies are considering “social recommendation” for their businesses, e.g., “rms are offering rewards to customers who recommend the firms” products/services in online social networks (OSNs). However, the pros and cons of such social recommendation scheme are still unclear. Thus, it is difficult for firms to design rewarding schemes, and for OSN platforms to design regulating policies. Via empirical analysis of data, we first identify key factors that affect the spreading of a firm's product in OSNs. These findings enable us to develop an accurate (i.e., with a high validation accuracy) mathematical model on social recommendations. Our model captures how users decide whether to recommend an item, which is a key factor but often ignored by previous social recommendation models. We also design algorithms to infer model parameters. Using our model, we uncover conditions when social recommendation improves a firm's profit and users’ utilities, as well as when it cannot improve the profit or hurts users’ utilities. These conditions help the design of both rewarding schemes and regulating policies. Last but not least, we extend our model to an dynamic setting, so that a firm can improve its profit by dynamically optimizing its rewarding schemes.

The aggressive goal of having a 100% renewable energy system requires preparing an appropriate infrastructure for deploying as much distributed generation (DG) as possible into the electricity network. This article aims at proposing a new framework to maximize the hosting capacity (HC) of DGs in a distribution network. This operation-planning framework provides a suitable and interactive environment for the electricity market, distribution company (DISCO), electric vehicle (EV) aggregator, and rival DISCOs to increase the HC. The problem is modeled via a bilevel conditional-value-at-risk-constrained stochastic programming approach. The DISCO, in the upper level (UL), tries to maximize the HC and minimize the operating costs while interacting with a passive electricity market and an EV aggregator. The aggregator, in the lower level, aims to maximize their profit by interacting with the primary DISCO in the UL and the rival DISCO, to satisfy the EV owners. The Karush–Kuhn–Tucker conditions are used to recast the model into an equivalent single level. The proposed framework is tested on a 33-node distribution network. Results show how an interactive-based framework can contribute to maximizing the HC of DGs.

This study attempts to examine whether it is beneficial to introduce the buy online and return to store (BORS) strategy in a competitive market. We consider two competing dual-channel retailers, and investigate conditions under which it is optimal for one or both of the retailers to offer the BORS strategy. We first assume that a partial refund is offered for online returns and then consider the important case of a full refund. We show that whether both retailers adopt this strategy significantly depends on the return rate and cross-selling profit. Specifically, if the return rate is sufficiently low (high), at least one retailer (both retailers) will adopt this strategy when cross-selling profit is relatively high. Nonetheless, both retailers will fall into the prisoner's dilemma under certain conditions. Interestingly, the return rate and proportion of consumers with a high hassle cost associated with BORS returns significantly influence the equilibrium channel strategy and the existence of the prisoner's dilemma. We further find that, when both retailers implement the BORS strategy, they both benefit from offering a full refund return policy when the number of online-type consumers is large enough and the unit cross-selling profit is relatively small, but should provide a partial refund otherwise. When only one retailer adopts the BORS strategy, a partial refund return policy is preferable.

In this research, using a capability perspective, we first define innovation based, strategic flexibility (ISF) as consisting of three lower-level capabilities- market sensing,resource reconfiguration, and proactiveness; then link it to new-product/innovation outcomes, and develop a new metric of ISF that is input-output based. This metric captures a firm’s realized flexibility in translating market opportunities into innovation output. We then examine the role of CEO ties with marketing and R&D in driving a firm's ISF. Combining survey data of 191 CEOs with objective performance data, we find that (a) ISF is positively influenced by the frequency and duration of the CEO's relationship with the two functional units, but negatively influenced by CEO's relationship closeness, and (b) ISF contributes to future profit growth of firms. We test the robustness of our findings to the potential violation of sequential-ignorability assumption as defined in the causal mediation literature and find the results to be robust. The results provide insights into the role of CEO characteristics in driving innovation, and upper echelon’s contributions to marketing.

Electric vehicles (EVs) are now widely acknowledged as ideal means of transportation in future, in terms of energy conservation and environmental protection for urban governance. EV lease service is an effective measure to promote the use of EVs and has gained the support of many countries. In this article, a comprehensive pricing scheme is developed to optimize the annual operations profit for EV lease service based on mileage-based pricing (MBP), where the profit is defined as the revenue subtracted by the maintenance cost. Different from the traditional MBP for vehicle lease service, our model characterizes how the grade level of EVs affects the maintenance cost after each failure. Moreover, the lease service is modeled as a queuing system where each consumer has the probability choosing the MBP by comparing the service value and his expense. An enumerative algorithm is developed to find the optimal pricing as well as analyze the influence of parameters on the optimal profit. The algorithm is implemented in the pricing of an EV lease company in China. The results show that the optimal pricing will increase with service rate, consumer arrival rate, and the service value from lessor, but decrease with waiting cost of consumers. Differential pricing strategies are also proposed to resolve the conflict goals of lessors and consumers, which can further boost lessors’ profit and raise consumers’ satisfaction level. The results show that MBP with differential pricing strategies will increase profit between 2.3% and 69.8%, comparing with cases without differential pricing strategies.

Modern digital technology has enabled the emergence of the hybrid workforce in service organizations, where a firm uses on-demand freelancers to augment its traditional labor supply of employees. Freelancers are typically supplied by an electronic platform. How should demand be allocated between employees and freelancers? Under what conditions is the system (comprising the firm and its platform) sustainable in the long run? We investigate these questions in the context of last-mile delivery. We develop a discrete-time, stochastic dynamic program that captures the system’s profit from serving demand and the platform’s growth dynamics. The dynamic model incorporates a service constraint for the platform and a simple version of a stochastic network effect. We find that the answers to our research questions critically depend on two key parameters: the mean and variance of the cross-network effect. We conduct a numerical study with data from a last-mile delivery firm in Vietnam to illustrate our findings. This paper was accepted by Vishal Gaur, operations management.

In many supply chains, suppliers that provide a key component to retailers in the wholesale market also have their own retail divisions in the retail market and form vertically integrated producers (VIPs) with these divisions. A VIP’s profit comes from two sources: (i) the wholesale profit from supplying the key component to retailers and (ii) the retail profit from its own retail division. In this situation, the VIP’s intra-company discount is often restricted by the arm’s length regulation (ALR) to induce fair competition in the retail market and improve the consumer benefit. However, the ALR may not always result in the desired outcome, especially when the retailers are asymmetric. Thus motivated, we investigate the impact of the ALR on the supply chain profit and the consumer benefit. Our analysis incorporates two types of asymmetry commonly observed among retailers: (i) retailer-specific selling costs and (ii) private information regarding the intra-company discount. Our results have several important implications. First, the ALR improves the supply chain profit only when the rival retailer is more competitive than the VIP’s retailer. Also, the ALR benefits consumers when the rival retailer is more competitive and the competition intensity is low. Second, under information asymmetry, while the rival retailer would not benefit from the VIP’s private information, the VIP’s gain does not always outweigh the rival retailer’s loss. Hence, the total supply chain profit may increase with the ALR. In addition, the consumer surplus can increase under information asymmetry unless the VIP’s intra-company discount is extremely limited.

P2P lending is an increasingly prosperous financial market, where lenders can directly bid and invest on the loans posted by borrowers. However, when facing massive loan requests, it is very difficult and also boring for lenders to choose loan portfolios meeting their ideal expectations. Actually, when choosing loans, most lenders pursue the highest profit with the lowest risk as well as satisfying their hobbies. In this paper, we formalize a multi-objective optimization problem to help lenders select loan portfolios. Specifically, the recommending scenario in P2P lending is formulated as a multi-objective optimization problem, where two objective functions are designed for capturing lenders' multiple demands. On this basis, a multi-objective evolutionary algorithm based on return-risk management named MOEA-RRM is then proposed for the multi-objective optimization problem, which can help lenders choose loan portfolios to meet lenders' multiple demands. Furthermore, in MOEA-RRM, a decision space dimensionality reduction strategy and an initialization strategy are proposed to improve the performance of algorithm. Finally, experimental results on a real-world P2P lending data set demonstrate the effectiveness of our proposed MOEA-RRM, i.e., the proposed approach can recommend the loan portfolio with a good trade-off between risk and return as well as satisfying the hobbies of lenders.

The rapid development of information technologies enables recycling companies to purchase and collect used products from consumers through both traditional and Internet-based online channels. Because an online channel transmits price information instantly to consumers, choosing the best time to announce the recycling price (i.e., acquisition price) of used products to consumers has become a critical problem for recycling companies. This paper seeks to solve this problem by developing a game-theoretic model describing a dual-channel reverse supply chain consisting of a recycling company and a third-party collector in which the recycling company purchases products not only through a third-party collector, but also directly from consumers online. We derive two major results by solving the model. The first is that first-mover advantage arises, which indicates that each firm constituting a dual-channel reverse supply chain should announce its own recycling price before the other. This first result is notable because it is exactly opposite to conventional wisdom that the second-mover advantage of pricing usually emerges when price competition occurs among firms in a horizontal relationship, which is well known in noncooperative game theory. The second result is that the recycling company can maximize its own profit and consumers' surplus by announcing its recycling price in the online channel before or upon, but not after, determining the transfer price paid to the collector for products collected in the offline channel. Both results can be used as practical decision-making guidelines in dual-recycling channel reverse supply chain management.

Based on the research and development (R&D) state of prefabricated buildings, this paper establishes an evolutionary game model involving such three parties as latecomer prefabricated building firm (LPBF), related firm, and university-research institute, and simulates the evolutionary results of the game to determine the evolutionarily stable strategy (ESS) of the game. The results show that: In the tripartite system evolution, the evolution and trend of each party are closely related with the strategies chosen by the other two parties; eventually, the LPBF strategy always converges to R&D, while the strategies of related firm, and university-research institute always converge to cooperation in R&D. Whether the game system converges to the desired equilibrium state hinges on the R&D profit and cost of, and opportunistic profit from subsequent cooperation with the LPBF. During the development of prefabricated buildings, the game parties should promote the high-quality development of the industry by stepping up technological R&D, forging a complete industrial chain, and implementing industry-university-research cooperation.

This paper sheds the light on the future of green hydrogen in Tunisia. So, a detailed economic assessment and evaluation of the Levelized Hydrogen Cost (LHC) and the Net Profit (NP) of a Photovoltaic (PV) Hydrogen Refueling Station (HRS) are presented and discussed. Tunisia is characterized by its high PV potential which makes the production of electricity from solar energy an effective alternative source. However, due to the regulations and issues related to the connection of medium PV scale to the power grid, the energy produced from renewable sources (RS) is still less than 3% of the total produced electricity. On the other hand, the price of hydrocarbon fuels is still increasing. The gap between production and total demand in hydrocarbons has created a deficit in the primary energy balance. Therefore, the production of hydrogen from solar energy for refueling Fuel Cell Vehicles (FCV)s consists of a promising solution to boost the development of the country, reduce hydrocarbon fuels consumption, and protect the environment. The sizing of a small PV-HRS to produce 150 kg of hydrogen per day shows the necessity to install PV systems with a total Direct Current (DC) capacity of 1.89 MWp. The Initial Cost (IC) analysis shows that while the PV system cost represents 48.5% of the total IC, the IC of electrolysers represents 41%. The storage system cost is approximately equal to 3.2% of the total IC. The LHC is equal to 3.32€/kg with a total IC of 2.34 million €.

This paper proposes a mixed-integer program (MIP) to address a challenge issue of vehicle upgrade policy in current electric car rental market, i.e., idle luxury and high-end vehicles can be used as ordinary vehicles when the demand for ordinary vehicles is high. This model essentially is to maximize the total profit through balancing the demand and supply with a comprehensive consideration of revenue of rental operation, cost of potential demand loss, dispatching cost, energy consumption per kilometer, mileage limitation of electric vehicles (EVs), and the uncertainty of user demand for multi-type EVs. The proposed MIP model can be solved by CPLEX or Gurobi to search for the global optimal solution. Finally, real data from an electric carsharing system (ECS) with three types of EVs and 20 carsharing stations was used to validate the model. As a result, the daily increase of profit could reach 11.09% with an average of 8.08%.

In an environment with multiple heterogeneous multiservers across multiple clouds and data centers, optimal workload management and server speed setting makes strong impact on the aggregated performance, cost, and profit of a cloud service provider. Such a situation provides us a more challenging opportunity to address and discuss profit maximization of a service provider in a wider scale and to generate more significant influence. In this paper, we investigate profit maximization by optimal workload management and server speed setting for multiple heterogeneous multiservers in a federated cloud or a geo-distributed data center. The heterogeneous multiservers have different sizes, speeds, power consumption models, workload, performance, costs, and profit. To conduct rigorous study, each multiserver system is modeled by an M/M/m queueing system, such that the profit of a multiserver system can be characterized analytically. We address two problems, i.e., workload management without server speed setting and workload management with server speed setting. Both problems are formulated as multi-variable optimization problems. We develop numerical algorithms to solve these problems. We also provide numerical data for the purpose of illustration. This is the first paper which analytically discusses profit maximization for multiple heterogeneous multiservers in a federated cloud or a geo-distributed data center.

In recent years, an increasing number of competing firms have started to integrate their product attribute development and advertising strategies by targeted advertising of the best-developed attribute of their product. With a focus on such integration and its potential effects, we establish an analytical model in which two competing firms sell multi-attribute products. Each firm can choose which attribute to focus on in its development strategy and which attribute to emphasize in its advertising strategy. By optimizing the development and advertising strategies of these firms, we first verify the integration of these two strategies as each firm advertises only its developed attribute. Second, we find that advertising, according to the firms’ development choices, plays distinctive roles on profit generation: the difference-alleviating or difference-strengthening effect. Therefore, when determined endogenously, the advertising intensities show qualitatively different changing trends: a monotonous curve in the same-attribute-development case but an inverted-U shaped curve in the different-attribute-development case. Third, we find advertising establishes a connection between firms’ attribute development choices, making them depending not only on their own development cost. A smaller difference between the development costs of these firms or a higher advertising efficiency increases the firms’ willingness to develop a same attribute. We also extend our analysis to the flexible quality level of the non-developed attribute, asymmetric substitution degrees between attributes and prices, or the endogenous attribute development levels, and study how the interactions between the product development and advertising are affected. This paper highlights the importance of making development and advertising strategies jointly.

In the Make-To-Order (MTO) supply chain, for enticing the retailer to choose a shorter delivery lead-time, which improves the manufacturer's wholesale prices, the manufacturer adopts the Production Time Hedging (PTH) strategy as an incentive. Since the profit of the retailer depends not only on the Quoted Delivery Lead-Time (QDLT) but also on the Realized Delivery Lead-Time (RDLT), effectively hedging production time uncertainty reduces tardy delivery. We build an analytical model to investigate how PTH affects the retailer's QDLT decision and the supply chain performance. We consider models for four scenarios, namely (I) the centralized model, (II) the Nash model, (III) the manufacturer-led Stackelberg model, and (IV) the retailer-led Stackelberg model. We derive close-form results on the optimal hedging and QDLT decisions. In models II and III, PTH reduces the QDLT under all the conditions. However, in model IV, PTH can only coordinate the retailer when the hedging cost is low, or the holding cost/tardiness penalty is high. Besides, we find that model III is the dominant scenario for both players. Extending model IV to consider the case where the retailer sources from multiple manufacturers, we show that multiple sourcing is the dominant strategy when a manufacturer's hedging cost is high, or its holding cost/lead-time sensitivity factor is small. From the numerical study, we are interesting to find that the retailer's profit increases with the manufacturer's holding cost/lead-time sensitivity factor.

With the rapid development of information and communications technology and high penetration of renewable energy, the role of an aggregator in a smart grid has emerged to better coordinate power and cash flows between energy producers and consumers through the adjustment of pricing signals. This study proposes variation indices about the statistics of renewables and a control law for an energy storage system. A deep reinforcement learning based pricing strategy of an aggregator for profit maximization in consideration of the energy balance is developed accordingly. The proposed approach can consider opponents’ behaviors, variability of renewables, and varying bounds of charging and discharging events in a nonstationary environment, which can be hardly addressed by pricing strategies based on conventional learning algorithms such as Q-learning and deep Q-network. Numerical analysis using real-world data shows that the proposed approach can outperform existing pricing strategies in terms of the learning speed and profit of aggregators.

As an efficient commercial information technology, cloud computing has attracted more and more users and enterprises to use it. Faced with such a large number and variety of customers, it is necessary for cloud providers (CPs) with limited budget to provide satisfactory customized pricing services, profitable customer and system investments, and flexible system resource provisioning strategies to improve both customer experience and long-term profit. Existing profit optimization research rarely considers customer diversity and dynamics, which may have a negative impact on long-term profit growth due to poor management of customer relations. In this article, we implement customer relationship management by considering both customer diversity and dynamics, and propose a customer adaptive resource provisioning scheme to maximize long-term profit under constrained budget. We consider four customer types (i.e., loyal, old, new, and lost) that can transition to each other during the customer's lifetime of interaction with the CP. The CP builds multiple cloud service sub-platforms, each of which contains multiple multiserver systems and serves the same type of customers. For the cloud service platform, we first analyze single multiserver system using an analytical method to obtain its optimal profit, invested funding, and system configuration. In particular, for systems serving new and lost customers, we develop a novel customer lifetime value (CLV)-based customer investment scheme that selects valuable customers for investment under limited marketing budget. Based on the above analysis, we then present a customer retention rate (CRR)-driven three-stage heuristic scheme that prioritizes investment in multiserver systems with endangered customers under limited infrastructure budget for reducing customer churn and promoting long-term profit growth. We conduct extensive simulation experiments to validate the effectiveness of our method. Simulation results show that compared with the benchmark algorithms, our method can improve the long-term profit and CRR by up to 3.4x and 7.8x, respectively.

Try-Before-You-Buy (TBYB) is an emerging retail strategy of online retailers that allows consumers to have multiple items delivered to them and then determine which items they wish to keep or return for free. This paper addresses the TBYB retailer’s problem in its operations: the decision regarding how many items to send to a consumer to maximize its expected profit when the products are chosen by a retailer recommendation system. To this end, we develop a stylized model of the retailer’s quantity decision by taking into account the consumer’s purchase decision, which is influenced by the consumer’s budget constraint and the retailer’s recommendation accuracy. We derive the retailer’s optimal quantity decision and discuss how it is determined. We also examine how the retailer’s optimal quantity decision and corresponding profit are influenced by two factors reflecting the TBYB industry: recommendation accuracy and new consumer influx. Regarding recommendation accuracy, our results show that a minimum level of accuracy exists, which acts as an entry barrier to TBYB retail. Once the retailer’s accuracy exceeds the threshold, we show that the relationship between the accuracy and the optimal quantity is characterized as an inverted-U relationship. Regarding the latter, interestingly, we find that an influx of new consumers may hurt the retailer’s profit even when the accuracy applied to them is above the entry barrier.

Ordering and pricing are two crucial decisions for retailers and are widely studied. We focus on a situation where a retailer uses successive discounts, and we study the joint decision of the order quantity and selling prices. Earlier works often simplify this problem by assuming that demands in the discount periods are deterministic and independent, which is unrealistic. This work proves that there exists a unique optimal order quantity when the demands are stochastic and correlated. In determining the optimal order quantity and regular selling price for the multi-discount problem, earlier studies often assume special price structures, for example, equally spaced discount prices. This study optimises the selling prices and proves that the discount schemes used in earlier multi-discount works are suboptimal. In this study, both hierarchical and joint optimisation methods are derived for when the demand distribution information is known. In the static pricing situation, using small discounts in the beginning and marking down with an accelerating increase rate can benefit the retailer instead of discounting with a constantly increasing rate. Numerical examples show that the performance of the hierarchical method, often used in earlier one-discount models, depends on the profit margin and the demand correlation ratio in a multi-discount situation. Compared with the hierarchical method, the joint optimisation method yields more benefits in profit in both static and dynamic pricing situations when the profit margin is low and demand correlation is high. We also provide a robust optimisation method for the partial-demand-information case in this study. Other insights for managers are also provided.

Photovoltaic (PV) systems have been gaining popularity among distribution network engineers. The integration of these small-scale sources has a variety of benefits for the distribution network. One of the most important benefits is the power loss reduction. However, a high penetration level of PV systems may disrupt the protection coordination of conventional overcurrent relays of the distribution network, reducing the profit of the electricity distribution company (EDC) due to healthy feeder de-energization. This paper determines the optimal penetration level of PV systems in the distribution network considering both power loss reduction profit and protection miscoordination cost to maximize the profit of the EDC. The merits of the proposed methodology are verified through several case studies on a simulation model of the IEEE 33-bus test system in the Electrical Transient Analyzer Program (ETAP) environment considering the reliability data of the IEEE Roy Billinton Test System.

In battery swapping environments, electric vehicles (EVs) can play roles as battery providers as well as consumers. In this paper, we propose an optimal battery charging algorithm (OBCA) where a battery swapping station (BSS) charges batteries in its storage with the consideration of the profile of the electricity price and the arrival rates of EVs. To maximize the net profit of BSS while maintaining the battery changing probability above a certain level (i.e., maintaining high quality of service (QoS) of BSS), we formulate a constraint Markov decision process (CMDP) problem and the optimal charging schedule for batteries in BSS is obtained by a linear programming (LP). Evaluation results demonstrate that OBCA with the optimal policy can improve the net profit of BSS up to 418% compared to an electric price-aware scheme while maintaining high QoS of BSS.

We investigate incentive-driven information flow in a setting where a monopolistic supplier sells, through two retailers, to a market with uncertain demand. Market competition is either in quantity or in price. Each retailer has a signal useful for updating demand forecast, but the supplier has no signal access. Horizontal information sharing occurs as the two retailers exchange signals, while vertical information sharing occurs as the supplier acquires the retailers’ signals with differential payments. The retailers can adopt a cooperative (competitive) regime to manage operations and information decisions jointly (independently). We demonstrate that the decision regime adopted by the retailers and market competition mode intricately influence information flow and firms’ profit performance. Adopting the cooperative regime by the retailers insulates the system from the change in the mode of market selling, which would, however, impact the strategic interplay of horizontal and vertical information sharing, producing substantial profit implications, as the retailers adopt the competitive regime. Moreover, cooperation between the retailers, despite precluding system-wide information transparency, can increase system profit gain from information flow.

Credit purchasing has seen dramatic growth recently, requiring retailers to adapt their operations in response to an online platform's strategic credit offering. This article presents a model for exploring the platform's strategy for offering credit and the retailer's response, considering consumer spillover, that is, how much the benefit of credit outweighs the drawbacks. The consumers differ in their disposable personal incomes and valuations for the product, and they may perceive a spillover from the credit. We study an integrated system, where the platform acts as a retailer and determines the credit-offering strategy and the price simultaneously. We find that the platform will offer credit only when the spillover is above a given negative threshold, and will charge an interest rate for credit only when the spillover is positive. In addition, the price exhibits a nonmonotonic change with the credit spillover, but the demand remains unchanged at a high level. We then extend the analysis to an independent system, where the retailer sells through the platform. We find that the revenue-sharing percentage the retailer pays is critical for the platform to optimize its credit strategy. Specifically, the platform will never offer interest-free credit when the percentage is low, but in other cases may do so and the credit offering does not harm the retailer's profit. Interestingly, when the spillover is moderate, the demand may decrease instead of increase, indicating a deviation from the presumed intention of offering credit.

International audience; We consider the problem of an operator controlling a fleet of electric vehicles for use in a ride-hailing service. The operator, seeking to maximize profit, must assign vehicles to requests as they arise as well as recharge and reposition vehicles in anticipation of future requests. To solve this problem, we employ deep reinforcement learning, developing policies whose decision making uses [Formula: see text]-value approximations learned by deep neural networks. We compare these policies against a reoptimization-based policy and against dual bounds on the value of an optimal policy, including the value of an optimal policy with perfect information, which we establish using a Benders-based decomposition. We assess performance on instances derived from real data for the island of Manhattan in New York City. We find that, across instances of varying size, our best policy trained with deep reinforcement learning outperforms the reoptimization approach. We also provide evidence that this policy may be effectively scaled and deployed on larger instances without retraining.

When demand exceeds available inventory, suppliers ration their inventory among customers (e.g., retail channels). While there are quantitative methods to facilitate these choices, in practice, humans play an important role in making final decisions that affect allocation efficiency. How do inventory risk and contractual differences (i.e. payment schemes) behaviorally affect allocation decisions when there is scarcity? We study inventory allocations between two retail channels: a high value channel with risk, and a low value channel without risk. The retail channels may also differ in terms of the timing and type of payments that take place. We develop theoretical predictions from behavioral models based on risk aversion, loss aversion, and mental accounting and test these through incentivized controlled laboratory experiments. When profit differences between channels are medium to large, subjects allocate significantly less inventory than the expected–profit–maximizing quantity to the risky, yet more profitable, channel and subjects with stronger risk appetite allocate larger quantities to the risky channel. More interestingly, risk appetite moderates the effect of the timing and type of payments on allocations in these settings. When profit differences between channels are small, the effect of risk appetite depends on the timing of payments. Overall, the possibility of experiencing negative payments (e.g., through buy–backs) reduces allocated quantities across settings. Our insights can inform planner assignment to tasks but also the design of support systems that provide information to planners who make allocation decisions.

Efficiency has become the basis for boosting the environmental satisfaction and economic performance of gardens. The major goal of this research is to optimize the date production process by minimizing the quantity of inputs in the process using parametric and non-parametric approaches, as well as to investigate the amount of energy consumption and economic indicators in the date palm production in Iran's Khuzestan region. For this purpose, data envelopment analysis (DEA: CRS & VRS and Anderson-Peterson) was used to optimize energy efficiency and present the ideal pattern of input consumption. Also, the transcendental production function was developed to represent the relationship between energy consumption and product performance. The outcomes revealed that the average entire energy expenditure in date palm production was 35.52 GJ ha−1. Among inputs, irrigation, diesel and manure have contributed to the highest energy consumption and their contribution was calculated to be 43.92%, 32.86% and 9.49%, respectively. The average of energy ratio, energy productivity, energy intensity and net energy gain were equal to 3.66, 0.39 kg MJ−1, 2.70 MJ kg−1 and 91290.66 MJ ha−1, respectively. Moreover, direct and indirect energy, as well as renewable and non-renewable energy, were computed at 76.64%, 26.46%, 56.28%, and 43.72%, respectively. DEA approach showed that there is potential to prompt sustainability, and based on the constant return to scale 10.15% of input energy is able to be alleviated. The coefficient determination of the transcendental production function model was obtained as 0.92 and gross return, benefit-cost ratio, economic productivity, payback period, invest return rate, percentage of production at the break-even point and profit per kg were calculated as 3550.63 $ ha−1, 0.44, 1.67, 2.23 year, 0.44, 2.26 and 0.26 $ kg−1, respectively. To save energy, it is suggested that solar energy be used instead of diesel fuel to extract water, and that simultaneous cultivation be implemented to limit the usage of chemical fertilizers.

Prosociality is an important part of the work context. For instance, employees, leaders, and organizations show various forms of prosocial behavior such as supporting colleagues suffering from heavy workload, voluntarily organizing social events fostering a good organizational climate, or providing goods and services that benefit society at large. From the plethora of constructs related to prosociality at work, I herein provide a brief introduction to the currently most prominent ones with regard to organizational members in general (organizational citizenship behavior) and leaders (servant leadership), respectively. Moreover, I briefly sketch how research on prosociality at work would probably profit from a stronger integration of research from related fields, within and beyond the organizational literature. Prosociality is an important part of the work context. Employees, leaders, and organizations show various forms of prosocial behavior such as supporting colleagues suffering from heavy workload, voluntarily organizing social events fostering a good organizational climate, or providing goods and services that benefit society at large. From the plethora of constructs related to prosociality at work, I herein provide a brief introduction to the currently most prominent ones with regard to organizational members in general (organizational citizenship behavior) and leaders (servant leadership), respectively. Moreover, I briefly sketch how research on prosociality at work would likely profit from a stronger integration of research from related fields, within and beyond the organizational literature.

As an efficient commercial computing paradigm, cloud computing provides various computing and storage resources to users in a pay-as-you-go manner. However, existing cloud pricing models and mechanisms are deterministic to some degree, thus, may not work well in a real-world environment where user perceived values with respect to cloud services are dynamically changing and highly personalized. In this paper, we develop a reinforcement learning (RL)-based dynamic cloud pricing scheme to optimize both cloud provider's profit and costs of heterogeneous users with distinct personalities. Specifically, we first propose a novel personality-guided user perceived value prediction scheme to proactively capture the dynamics of the users' perceived values with respect to services. The prediction scheme models the relationship among user personality, service price, quality of service (QoS), user satisfaction and perceived value in the cloud service market. Secondly, on the basis of the prediction model, a RL-based cloud pricing mechanism is developed to learn sequential service pricing decision-making for profit and costs optimization. Particularly, the profit and costs optimization problem is modeled as a discrete-time Markov decision process (MDP) that is solved by using Q-learning. Simulation results show that our pricing mechanism can obtain up to 19.39% more profit than the state-of-the-art scheme.

Rebate policies are widely used by manufacturers to stimulate sales. This paper analyzes a supply chain with revenue sharing and wholesale price contracts under manufacturer rebate policy, where the stochastic multiplicative demand function depends on both retail price and manufacturer rebate. We identify Stackelberg equilibrium for the supply chain under each contract. It is our finding that under the manufacturer’s respective optimal decisions for revenue sharing and wholesale price contracts with rebates, the retailer’s optimal retail prices are the same. We recognize sufficient conditions under which a revenue sharing contract and a wholesale price contract with rebate can achieve Pareto improvement, i.e., both the manufacturer and the retailer obtain a greater expected profit, compared with the cases without rebate. A sufficient condition is found, under which a revenue sharing contract with rebate policy can achieve Kaldor–Hicks improvement, i.e., yielding a greater expected profit for the manufacturer, a lower expected profit for the retailer, and a greater expected total profit, over pure revenue sharing contract without rebate. Further, it is shown that with rebate, a revenue sharing contract can achieve Kaldor–Hicks improvement over a wholesale price contract. Finally, our findings are demonstrated using numerical studies.

With the continuous deepening of power market reforms, studying the interaction of multi-stakeholder in multi-microgrid (MMG) energy trading is of great significance to improve the economic performance of the distribution network. In order to analyze the economic optimization method of multiple stakeholders in the distributed energy system, this paper proposes a multi-stakeholder benefit optimization method based on Stackelberg game theory (SGT), and establishes a local market energy transaction model. The model is composed of external distribution network, leader-intermediary agent (IA) and follower-microgrids (MGs), and uses the master–slave game method to solve the optimization strategy. Under this mode, the profit of each stakeholder in MMG increased by 2.64%, 4.24%, 1.38% respectively. The case results show that based on the method in this paper, multi-stakeholder in MMG can improve economic performance and can increase the level of energy autonomy of MMG.

Motivated by the commonly observed commercial used apparel collection (UAC) programs in fashion retail supply chain operations, this paper analytically explores the associated operational challenges that firms face. First, in the basic model, we consider the case when a fashion retail brand promotes its UAC program and collects the used apparel from consumers. Depending on the conditions of the collected apparel products, the fashion retail brand will classify and either donate them for charity or send them to remanufacturing. For either case, the fashion retail brand gains a benefit. We analytically derive in closed-form the optimal promotion effort and study the mechanism for “profit” coordination. Our results indicate that many traditional supply chain contracts fail to achieve “profit” coordination. Thus, the effort cost sharing (ECS) contract is proposed and proven to be effective for “profit” coordination. Finally, to check the robustness of findings from the basic model, we examine various UAC practice related extended models, such as i) consumer coupon offering, ii) “no remanufacturing” model, iii) consumer heterogeneity in environmental consciousness, iv) own-brand collection vs any-brand collection. Our findings show that the qualitative insights continue to hold in the extended scenarios. Some novel scenario-dependent findings are also obtained. Insights derived in this paper not only contribute to the literature but also provide scientifically sound managerial guidance and insights to practitioners on how UAC programs can be best operated.

This paper considers an optimal pricing problem for the black box producer-consumer Stackelberg game. A producer sets price over a set of goods to maximize profit (the difference in revenue and cost function). The consumer buys a quantity to maximize the difference between the value of the quantity consumed and the cost. The value function of the consumer and the cost function of the producer are ‘black box’ functions (unknown functions with limited or costly evaluations). Using Gaussian processes, Bayesian optimization and Bayesian quadrature we derive an algorithm for learning the optimal price. The method has the following significant advantages: (i) the method is efficient and scales well compared to existing techniques, (ii) the cost function of the producer could be non-convex, (iii) the value function and/or cost function can be time varying. We illustrate, using a real dataset, optimal pricing in electricity markets.

The aim of this study is to determine the cost of goat milk in Saanen goat enterprises in Çanakkale province and to examine the factors affecting profitability in enterprises. The material of the study consists of 92 Saanen goat enterprises. The enterprises have been grouped according to their animal density; as 25-75 heads (small-scale), 76-150 heads (medium-scale) and 151 heads and above (large-scale). The findings of the enterprises for 2017 were determined by descriptive statistics, and multiple regression analysis was used to determine the factors affecting the total profitability of the enterprises. Since feed costs represented 46.22% of total production cost, it was followed by labour costs (27.19%), fuel costs (5.44%) and veterinarian-health service costs (5.19%). The cost of 1 L milk and absolute profit in enterprises was determined as 0.42/0.01 $/kg in small-scale, 0.41/0.02 $/kg in medium-scale and 0.38/0.05 $/kg in large-scale enterprises, respectively. It was observed that the share of inputs in enterprises generally decreases as the scale of the enterprise increases and large-scale enterprises were found to be more successful in solving technical, health and economic problems.

In this study, technical and economic analysis of dairy cattle enterprises in Balıkesir province were carried out. The factors which affect the competitiveness of enterprises and are important in terms of the continuity of production were determined. The stratified sampling method was used to determine the sample, and the study material was two-year (2017–2018) data obtained from 147 enterprises. In addition to the economic analysis of the data obtained, factors affecting unit profit in enterprises were estimated using the multiple linear regression model. The cost elements in total enterprises in Balıkesir province in 2017–2018 include feed expenses (47.90%-47.29%), livestock depreciation (16.64%-16.13%), labor expenses (13.84%-14.30%), veterinary health expenses (4.03%-4.50%), fuel transportation expenses (3.43%-4.15%), building equipment depreciation (3.37%-3.64%), and other expense items. A distinct difference was observed between scales in terms of profit and loss states of the enterprises in the study, with an increase in the profitability level from small toward large scale enterprise. The small-scale enterprises, in particular, are at a loss. The most important determinant of competitiveness in the study is the region’s development level, where the enterprises are established in the borders of Balıkesir province. Additionally, among the technical parameters, the calving interval and the reduction of the disease rate have a positive effect on the competitiveness of the enterprises. Therefore, improving the financial structure of the enterprises and boosting the rate of technology utilization while simultaneously increasing enterprise scales will contribute positively to competitiveness.

Battery energy storage (BES) and demand response (DR) are two important resources to increase the operational flexibility of a virtual power plant (VPP) and thus reduce its economic risks in the market. This paper develops a data-driven approach for VPP resource planning (VRP), in which BES sizing and DR customer selection are optimized synergistically to maximize VPP profit in the electricity market. Heterogeneity in DR potential across individual customers are considered in the planning framework by utilizing the knowledge learnt from smart meter data. The overall VRP problem is formulated by a risk-managed, multistage stochastic programming framework to address the uncertainties from the intermittent renewable energy sources, load demands, market prices and DR resources. Case studies under two market imbalance settlement schemes demonstrate that jointly optimizing BES and DR customer selection leveraging the smart meter data can improve the VPP expected profit under both market settings

Mitigating the adverse effects of uncertainty in appointment systems, arising from heterogeneous patient needs and preferences, is critical to the effective use of scarce medical resources and patient satisfaction. This study addresses an online scheduling problem with multiple servers and consideration of patient preference for physicians and their appointment times. The receptionist immediately determines whether a request should be accommodated and offers an appointment time slot for each accepted patient request. The patient may reject an undesirable appointment time slot with a certain probability, or may accept it, but the no-show probability will be higher. A stochastic overbooking model is formulated to maximize the expected profit, which is defined as the revenue generated from accepted requests minus the cost incurred by patients waiting and physicians’ overtime. A myopic scheduling policy is developed based on certain structural properties of the objective function. This study advances the study of appointment systems by generating a non-unimodal profit evolution. Moreover, both the decision of accommodating more requests for certain slots and scheduling of appointments depend on the patient choice rather than the patient type. Further, computational experiments and analysis offer valuable insights into performance improvement in outpatient clinics.

Coordinated operation of renewable resources and storage devices can reduce the undesirable effects of poor predictability of renewable producers. This article proposes a three-stage hybrid robust/stochastic framework to model the coordinated operation of wind producers and compressed air energy storage in the form of a mixed-integer linear programming problem. The proposed algorithm derives robust offering curves to participate in the day-ahead market. In addition to the day-ahead market, intraday and balancing markets are considered for reducing the negative effects of poor predictability of wind speed. In the proposed hybrid framework, intraday and balancing market prices as well as wind speed are modeled by proper scenarios, while robust optimization is used to model the day-ahead price uncertainty. Besides, the risks of stochastic parameters are modeled by the conditional value at risk (CVaR). Based on the results of robust optimization, the uncertainty of the day-ahead market price reduces the aggregator profit by 21.3% in the worst case, whereas CVaR results show a 13.2% profit reduction due to the worst-case scenario realization in stochastic parameters.