Encroachment and product diversification are two effective strategies that manufacturers can leverage to gain a competitive edge and increase revenue. This study focuses on a manufacturer producing two quality-differentiated products, exploring various strategies for market encroachment. In addition to conventional retail channel, the manufacturer can pursue encroachment by directly selling one or both products through its own channel or the agency channel provided by the platform retailer. We show that in the case of direct channel encroachment, only when the selling cost is very low, the manufacturer will choose to encroach through both products. As the selling cost rises, encroaching with one single product becomes the preferred strategy, and at a high selling cost, the manufacturer will abandon encroachment. There is a similar conclusion when the manufacturer encroaches with agency channel, and the choice of the manufacturer depends on the commission rate. Moreover, only the single product through the agency channel encroachment strategy can lead to a win-win outcome for the manufacturer and the retailer under certain conditions. Furthermore, we identify a critical selling cost threshold that dictates the manufacturer’s approach to encroachment. These findings provide insights into how manufacturers can navigate encroachment dynamics in markets with differentiated products.

The decarbonization of power systems, while widely studied, seldom compares the cost implications of the underlying planning assumptions. For example, the time horizon that is considered, and the scope of regional cooperation, can have a significant impact on costs. To highlight the benefits of long-term, multi-stage, cooperative plans, we compare them with myopic and less-cooperative cases. The model is tuned to Northeastern North America, stylized as five regions in Canada and the United States. Generation, storage and transmission decisions are included, as well as investment cost and load growth uncertainties. Assuming optimization over three stages and regional cooperation in transmission, simply decarbonizing the system for 2050 under a stable load leads to a mild 70% increase over 2020 yearly costs. Tripling the load brings a 15-fold increase in costs. However, if optimization is performed myopically, one stage at a time, or if deployment of new transmission lines is prevented, tripling the load brings an approximate 17-fold increase over 2020 costs. If both myopic optimization is used and no new transmission lines are allowed, costs increase by a factor of 29.

Greenhouse gas (GHG) is a main cause for climate anomalies, and thus, declining GHG emissions becomes an essential task for enterprises, especially for international liner shipping companies, to achieve the goal of corporate social responsibility (CSR). To liner shipping companies, main emitting gases are CO2, SOx, and NOx that cause marine pollution and global warming. To decline environmental warming and marine pollution, efficiency measure about GHG emissions for liner shipping companies is important. Due to data specifications of GHG, slack-based measure (SBM) data envelopment analysis (DEA) and super SBM DEA models focus on undesirable outputs are proposed and integrated into new DEA models. Through these models above, GHG emissions of liner shipping companies for varied years viewed as peer decision-making units (DMUs) are measured. Then, relative efficient DMUs are useful references for GHG emissions of liner shipping companies in emitting policies and technologies to reduce marine pollution.

The demand for transportation services often depends on what services are offered. Recognizing this dependency can enable transportation service providers to plan operations that are more profitable and sustainable. Yet little research on optimizing the planning of transportation systems explicitly recognizes that demand can be endogenous. We identify three challenges encountered when developing such methods. The first challenge is to develop models for demand prediction that are accurate, including for supply scenarios not observed in historical data. The second involves establishing how the accuracy of these demand models should be assessed in order to align with the downstream decision-making problem. The third involves formulating optimization models, and solution approaches for those models, that capture supply-demand interactions through an embedded demand model. For each challenge we present pointers to relevant research in different domains (econometrics, machine learning, operations research, and reinforcement learning) and identify future research directions. We ground the discussion of these challenges in a well-studied problem solved to plan freight transportation operations, the Scheduled Service Network Design Problem.

This study evaluates the circular economy performance of global chemical companies from 2013 to 2022, focusing on production efficiency and circular efficiency. Dynamic network Data Envelopment Analysis model is employed to assess the overall, production and circular performance of firms. Additionally, management decision matrix is presented, offering firms a clearer understanding of their efficiency levels and a detailed projection analysis on the optimization adjustments needed for both resource inputs and outputs. The findings reveal varying levels of overall efficiency among the observed firms. While some companies have managed to maintain or improve their efficiency over the study period, others exhibit significant inefficiencies. Opportunities for operational streamlining and resource optimization are identified, particularly in reducing resource consumption without compromising production capacity. The analysis of circular efficiency further highlights substantial potential for improving sustainability practices, particularly through enhanced waste reuse and a reduction in environmental impact. This study provides practical implication of circular economy performance in the chemical industry, offering a new way to measure and improve resource efficiency and sustainability practices.

This paper empirically evaluates group efficiency in the banking sector by applying an extended Common Set of Weights (CSWs) approach within a two-stage Data Envelopment Analysis (DEA) framework. Our study assesses efficiency across multiple production stages and adapts CSWs to capture the intermediate processes inherent in banking operations. We provide a comprehensive ranking of bank branches within seven prominent banking groups in Guilan province, Iran. By applying our modified methodology to real-world data, we demonstrate its practical utility in delivering precise efficiency assessments and actionable insights for enhancing operational performance. Our findings highlight the superiority of the extended CSWs methodology over traditional DEA models, offering a more accurate and fair evaluation of group efficiency in the complex and interdependent context of banking processes.

Decision-making problems can be represented as mathematical optimization models, finding wide applications in fields, such as economics, engineering, transportation, and health care. One of the primary barriers to deploying these models in practice is the challenge of helping practitioners understand and interpret such models, particularly when they are infeasible, meaning no decision satisfies all the constraints. Existing methods for diagnosing infeasible optimization models often rely on expert systems, necessitating significant background knowledge in optimization. In this paper, we introduce OptiChat, a first-of-its-kind natural language-based system equipped with a chatbot GUI for engaging in interactive conversations about infeasible optimization models. OptiChat can provide natural language descriptions of the optimization model itself, identify potential sources of infeasibility, and offer suggestions to make the model feasible. The implementation of OptiChat is built on GPT-4, which interfaces with an optimization solver to identify the minimal subset of constraints that render the entire optimization problem infeasible, known as the Irreducible Infeasible Subset (IIS). We utilize few-shot learning, expert chain-of-thought, key-retrieve, and sentiment prompts to enhance OptiChat’s reliability. Our experiments demonstrate that OptiChat assists both expert and non-expert users in improving their understanding of the optimization models, enabling them to quickly identify the sources of infeasibility.

Currently, many e-commerce websites issue online/electronic coupons as an effective tool for promoting sales of various products and services. We focus on the problem of optimally allocating coupons to customers subject to a budget constraint on an e-commerce website. We apply a robust portfolio optimization model based on customer segmentation to the coupon allocation problem. We also validate the efficacy of our method through numerical experiments using actual data from randomly distributed coupons. Main contributions of our research are twofold. First, we handle six type of coupons, thereby making it extremely difficult to accurately estimate the difference in the effects of various coupons. Second, we demonstrate from detailed numerical results that the robust optimization model achieved larger uplifts of sales than did the commonly-used multiple-choice knapsack model and the conventional mean–variance optimization model. Our results open up great potential for robust portfolio optimization as an effective tool for practical coupon allocation.

Index tracking aims at replicating the benchmark index performance by constructing a tracking portfolio consisting of partial component stocks of the index. This is an NP-hard question because of the cardinality constraint which limits the number of component stocks in the tracking portfolios. This paper proposes a new method to solve this problem in an end-to-end way by using a continuous subset sampling relaxation which can provide differentiable gradients calculation in neural networks. The proposed approach is tested with the FTSE-100 index data and the S&P100 index data, and compared with widely used index tracking methods, such as the forward and backward selection and optimization solvers. According to the empirical results, our proposed approach achieves the best tracking performance for different evaluation criteria.

The rising of e-commerce demands an increase in the performance of warehousing systems, which are being redesigned to deal with a mass volume of demands to be fulfilled as quickly as possible. The manual and robotic mobile fulfilment systems (RMFS) are the most commonly used for these activities. The former is a human-centred system that handles complex operations that current robots cannot perform. However, newer generations of autonomous robots are leading to a gradual replacement by the latter to increase productivity. Regardless of the system used, several interdependent problems must be solved for efficient storage and picking processes. Storage problems concern decisions on where to store products within the warehouse. Batching and picking problems include batching orders to be fulfilled, and the routes the pickers and robots should follow to retrieve the products demanded. This paper identifies the most relevant problems and the state-of-the-art research on these topics. We study the interactions among many of these interrelated problems and provide a critical analysis of the literature. Finally, eight research directions are identified and discussed.