Annals of Operations Research最新文献

The manufacturer-rebate where the manufacturer directly provides consumer rebates and the channel-rebate where the manufacturer stimulates downstream retailers to sell more products with channel rebates are two typical rebate strategies for manufacturers to increase sales. Considering the prevalence of rebate promotions and the downstream retailer’s optimism on the effect of rebating consumers, we incorporate rebate promotions, retailers’ optimism, and the corresponding information asymmetry issue into the manufacturer’s wholesale-ordering contract design problem. We find that with rebate promotions, the wholesale price and the order quantity should be higher than without rebate promotion. Specially, under the manufacturer-rebate strategy, the wholesale price and the order quantity should increase with the manufacturer’s rebate and the retailer’s optimism. However, under the channel-rebate strategy, the order quantity (the wholesale price) is no longer affected by the manufacturer’s rebate (the retailer’s optimism). Moreover, when the retailer’s optimism is private information, the retailer’s information distortion behaviors under the two rebate strategies are similar, but the manufacturer should accept different kinds of wholesale price-order quantity contract menus to reveal the retailer’s information and maximize profits. Specifically, under the manufacturer-rebate strategy (the channel-rebate strategy), differentiated contracts (a pooling contract) to different types of retailers are optimal. Besides, under the manufacturer-rebate strategy, the value of the rebate can play a moderating role in contract design. Hence, when the rebate is exogenously decided, the manufacturer can punish the retailer who is likely to distort information by decreasing the rebate.

Digital game platforms have provided “pay-to-play” (P2P) games for players to purchase and play on platforms for many years. Recently, platforms have adopted the hybrid bundling (HB) strategy that provides a bundled service of digital games in addition to P2P games. This strategy has the potential to facilitate the growth of digital games and address efficient decarbonization of physical copies of games. This study develops a theoretic model to explore whether the platform should adopt HB and, if so, what are the optimal value and price of the bundled service. We find that HB benefits profits when the usage cost of the bundled service is lower than that of P2P games, the unit price of P2P games is relatively high, and the spillover effect is low. Under such circumstances, the platform decides to provide a low-value and high-price bundled service to supplement P2P games. Moreover, when the usage cost of the bundled service is higher, HB benefits market size which also promotes digital games and reduces carbon emissions. Under such circumstances, when the unit price of P2P games is high, the platform provides a high-value and low-price service; otherwise, a low-value and low-price service. Interestingly, HB cannot simultaneously benefit both profits and market size. We find that a win–win–win situation exists for the platform, players, and social welfare when HB is profitable for the platform. When HB expands market size, it is always beneficial to players but may hurt social welfare. Finally, the bundled service may lead to social inefficiency.

The research field of Attended Home Delivery (AHD) and Attended Home Service (AHS) problems has experienced fast growing interest in the last two decades, with the rapid growth of online platforms and e-commerce transactions. The radical changes in consumer lifestyles and habits as well as the COVID-19 pandemic contingency have reinforced that interest, raising further challenges and opportunities that need to be addressed by innovative methodologies and policies. The aim of this work is to provide an extensive literature review on the state of the art for AHD and AHS problems, with a particular focus on real-world applications. A discussion of promising future research directions is also provided.

Noisy intermediate-scale quantum computers are now readily available, motivating many researchers to experiment with Variational Quantum Algorithms. Among them, the Quantum Approximate Optimization Algorithm is one of the most popular one studied by the combinatorial optimization community. In this tutorial, we provide a mathematical description of the class of Variational Quantum Algorithms, assuming no previous knowledge of quantum physics from the readers. We introduce precisely the key aspects of these hybrid algorithms on the quantum side (parametrized quantum circuit) and the classical side (guiding function, optimizer). We devote a particular attention to QAOA, detailing the quantum circuits involved in that algorithm, as well as the properties satisfied by its possible guiding functions. Finally, we discuss the recent literature on QAOA, highlighting several research trends.

This article is written in memory of Harry Markowitz, the founder of modern portfolio theory. We report a few human perspectives of his character, we review a large number of his contributions, published both in operations research and finance oriented journals, and we focus on one of the most critical, and still open, portfolio theory issues, the forecast of covariance matrices. Our contribution in this paper is placed exactly towards this direction. More specifically, we compare the performances of several approaches to predict the variance-covariance matrices of vectors of asset returns, through simulated and real data experiments: some dynamic models such as Dynamic Conditional Correlation (DCC) and C-vine GARCH on one side, and several agnostic methods (Average Oracle, usual “Sample” matrix) on the other side. The most robust methods seem to be DCC and the Average Oracle approaches.

By the Almost First-degree Stochastic Dominance (AFSD) rule, corresponding only to economically relevant preferences, for an infinite horizon the (theoretical) claim of both Markowitz and Samuelson is not intact. However, for the practically more relevant case of the long but finite horizon, with stocks-bonds portfolios, Markowitz (empirically) is right as we find that the MGM portfolio coincides with the optimal myopic portfolio for all risk aversion parameters (alpha < 1.7). For (alpha ge 1.7) the MGM portfolio dominates by AFSD rule all optimal myopic portfolios, as long as the investment horizon is 12–15 years or longer.

In this paper, we introduce a new topology, connected-hubs, to the multimodal transportation literature. As strategic planning, the multimodal transportation network design decides the locations of consolidation centers and the routes between the origins and the destinations with the minimum of total cost. Most existing papers formulate the problem based on the hub-and-spoke topology, while our work is the first attempt to apply the connected-hub topology, which considers local consolidation by vehicle routing and extends some widely-used assumptions in the hub-and-spoke topology. A mathematical model is developed to formulate and solve the problem to optimality. Further, considering the problem complexity, a particle swarm optimization (PSO) algorithm is also proposed to generate efficient solutions for large-scale cases. The results of a case study in Normandy, France illustrate the efficiency and practicability of the proposed PSO in terms of economic and environmental sustainability objectives.

How to best deliver goods to consumers has been a logistics question since time immemorial. However, almost all traditional delivery models involved a form of company employees, whether employees of the company manufacturing the goods or whether employees of the company transporting the goods. With the growth of the gig economy, however, a new model not involving company employees has emerged: relying on crowdsourced delivery. Crowdsourced delivery involves enlisting individuals to deliver goods and interacting with these individuals using the internet. In crowdsourced delivery, the interaction with the individuals typically occurs through a platform. Importantly, the crowdsourced couriers are not employed by the platform and this has fundamentally changed the planning and execution of the delivery of goods: the delivery capacity is no longer under (full) control of the company managing the delivery. We present the challenges this introduces, review how the research community has proposed to handle some of these challenges, and elaborate on the challenges that have not yet been addressed. In this update, we expand the literature review and discuss new challenges that have emerged in the past years. (This is an updated version of the paper “Challenges and Opportunities in Crowdsourced Delivery Planning and Operations” that appeared in 4OR, 20(1), 1-21 (2022)).

In light of the growing consumer emphasis on delivery speed and corporate environmental responsibility, it becomes paramount to simultaneously address the alignment of customer expectations with corporate objectives. We undertake a comprehensive examination of delivery delays and carbon emissions stemming from e-commerce logistics, leading us to formulate a delivery delay penalty function informed by customer behavior traits such as loss aversion. Concurrently, we analyze various factors influencing customer satisfaction and integrate them into our model. Similarly, we incorporate multiple determinants impacting vehicle emissions, devising a logistics cost-minimization model encompassing carbon emissions and cooling expenses. By amalgamating considerations of customer satisfaction, logistics expenses, and environmental concerns, we devise a dual-objective optimization model. To tackle this complex challenge, we introduce a multi-objective Artificial Bee Colony algorithm based on MOEA/D principles, substantiating its efficacy through extensive numerical experiments. Our findings demonstrate the algorithm's ability to intelligently optimize logistics routes, thus reducing vehicle utilization. Finally, we present a Pareto front, illustrating how mitigating customer satisfaction can alleviate logistics and carbon emission costs.