Omega-international Journal of Management Science最新文献

Fruit and vegetable supply chains require tight integration due to freshness-keeping requirements during the post-harvest, production, and distribution stages. This paper addresses the scheduling problem of a two-stage flow shop in the fruit and vegetable supply chain involving procurement, production, distribution, and order assignment. The supply chain consists of contract growers, processors, and demand points. Growers and processors maintain limited capacity. Growers have processing lines to sort and grade fruits and vegetables, which are then shipped to processors for further processing. The first stage of the problem involves determining the production and distribution schedules of growers to processors. The second stage concerns assigning demand points to processors, scheduling production at processors, and distributing from processors to demand points. This paper considers its two sub-problems and proposes a mixed-integer linear programming problem: growers’ and processors’ production and distribution scheduling with order assignment. Also, we present two respective strongly polynomial algorithms for solving the two sub-problems. Besides, we develop a two-phase heuristic algorithm for the integrated problem by solving sub-problems together. We conduct computational experiments to justify the proposed algorithms and integration value. Computational results reveal that the proposed algorithms outperform the CPLEX on large-scale problem instances. Finally, we analyze the impacts of waste reduction, unit processing time and cost, and inbound and outbound shipping rates on supply chain performance, along with managerial insights.

With the practical implementation of carbon taxes and the adoption of trade-in programs, the decision environment for manufacturing and remanufacturing has become increasingly complicated. Taking the carbon tax and the trade-in program into consideration, this study develops manufacturing-remanufacturing models for two market scenarios (i.e., emerging market and mature market) when cores for remanufacturing are limited by used product collection, and finds that the determining factors of optimal decisions are different across the two market scenarios. In addition to the carbon tax and carbon emissions of remanufactured product, the optimal decisions are also affected by the segment size of the replacement consumer in the mature market. Furthermore, we find that carbon tax policy does not always encourage the manufacturer to work on remanufacturing, and it may prevent remanufacturing under certain circumstances. Moreover, the impacts of supply chain parameters on optimal decisions, total carbon emissions, and profits are explored. The carbon tax has similar impacts across the two market scenarios; however, the carbon emissions of remanufactured product and the remaining value of the used product show different impacts. From the social welfare perspective, the optimal carbon tax depends on the regulator’s environmental concern.

Many supply chains have taken action to consider the principles of sustainability in their business over the past two decades. However, addressing sustainability is still a challenge due to imposing some restrictions. This could be more challenging in a food supply chain because of consuming a large amount of natural resources and its connection to public health. Regarding this significance, this research develops a mechanism to integrate all the primary functions of a meat supply chain given three pillars of sustainability: (i) cost, (ii) social aspect, and (iii) environmental aspect. To this end, we develop a three-objective mathematical formulation to integrate supplier selection, production planning, and product distribution in a meat supply chain. This study also identifies criteria representing suppliers’ impact on the environment and utilizes a Stochastic Analytic Hierarchy Process (SAHP) and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) methods to gauge that impact and evaluate suppliers’ scores. Furthermore, a solution method is designed based on the Normalized Normal Constraint method to generate Pareto-optimal solutions. Lastly, we apply the developed mathematical formulation and the solution methodology using a numerical study inspired by a real case in Ontario, Canada. The results demonstrate that the developed solution methodology can generate diverse and practical Pareto-optimal solutions in which social and environmental aspects could be improved significantly by incurring a minor extra cost.

In group decision making, the consensus model with minimum cost has been researched with the aim of improving group efficiency and saving resources. However, one limitation of the minimum cost consensus model is that the reach of consensus is usually at the expense of some group members. We consider two issues that we see as keys in group consensus: efficiency and fairness. We propose the price of fairness in the opinion revision process and give two kinds of fairness schemes. According to the individual's perception of inequity, we introduce inequity aversion parameters and classify experts into two types: experts with non-cooperative behaviors and with altruistic behaviors. Experts with altruistic behaviors will be allowed to contribute more than the recommended number of modifications. Then, we discuss how to achieve the tradeoff between efficiency and fairness. Furthermore, with the rapid development of social media, cloud, and e-government platforms, collective intelligence (CI), i.e., groups of individuals doing things collectively that seem intelligent, has been a hot topic. We expand our work to a crowd context with many individuals. We investigate how the opinion revision process and fairness schemes can influence the emergence of CI. Results suggest that the proportional fairness and max-min fairness have similar performance in stimulating CI. Moreover, the improvement of group accuracy is mainly related to two factors: the group consensus level of initial opinions and the relative distance between group aggregated opinion and the ground truth.

Social media retailing (SMR) platform becomes popular nowadays. It is characterized by creative combination of content creation and product selling, which differs from traditional e-tailing (TE) with product selling alone. Motivated by the real-world practices like social media platforms “TikTok” and douyin.com, we endeavor to study if the SMR model performs better than the TE model in a monopoly setting. By building a stylized economic model, we find that the SMR model does not always outperform the TE model. Specifically, when the SMR platform collects less commission from the seller than the TE platform, the seller, consumers, and the social welfare all benefit more from the SMR model, while the platform benefits more from the SMR model if and only if the creator's social influence is high enough or the cost of content creation is small enough. For the incentive structure of the content rewards in the SMR model, we find that a strong incentive mechanism (e.g., the quadratic form) is more powerful than a weak one (e.g., the linear form). The previous one will encourage the creator to choose a much higher quality level of content creation and meanwhile allow the platform, consumers, and social welfare to become better off. Counterintuitively, providing more generous content rewards is not always helpful for the creator (seller) and it may reduce her profit. Our findings will guide the platform to effectively design incentive mechanisms to boost the content creation and retailing in the SMR model and help the influencers efficiently create contents, engage their followers (fans), and price their products sold on the SMR platform.

During the COVID-19 pandemic, many countries faced challenges in developing and maintaining a reliable national pandemic vaccination calendar due to vaccine supply uncertainty. Deviating from the initial calendar due to vaccine delivery delays eroded public trust in health authorities and the government, hindering vaccination efforts. Motivated by these challenges, we address the problem of developing a long-term national pandemic vaccination calendar under supply uncertainty. We propose a novel two-stage mixed integer programming model that considers critical factors such as multiple doses, varying dosing schemes, and uncertainties in vaccine delivery timing and quantity. We present an efficient aggregation-based algorithm to solve this complex problem. Additionally, we extend our model to allow for dynamic adjustments to the vaccine schedule in response to mandatory policy changes, such as modifications to dose intervals, during ongoing vaccinations. We validate our model based on a case study developed by using real COVID-19 vaccination data from Norway. Our results demonstrate the advantages of the proposed stochastic optimization approach and heuristic algorithm. We also present valuable managerial insights through extensive numerical analysis, which can aid public health authorities in preparing for future pandemics.

As part of a widespread frustration with partisan gerrymandering, many states have considered or implemented redistricting reforms – and others will eventually have to – that include a higher degree of citizen participation in proposing and evaluating redistricting plans. In some states without redistricting reform, public interest groups have created shadow commissions that encourage citizens to submit their own maps. For example, the new map for Pennsylvania Congressional districts, chosen by the state Supreme Court, was proposed by a citizens group.

As citizen participation grows, analytical methods for rating plans that recognize the different mapping criteria are needed to sort through multiple maps, both for highlighting good maps and for providing measures that allow courts to rule that a map is gerrymandered. Using a modified version of a model called data envelopment analysis (DEA), we present a nonpartisan approach that can score maps while not imposing any prior weights on the criteria. Our modification measures how close a plan is to the convex hull of the Pareto frontier when bigger is better for some criteria and smaller is better for others. Thus, we provide a novel and scalable way to filter out poor plans from large corpora of redistricting plans.

Through digitization as well as globalization, communication in the workplace has changed massively, and email communication is nowadays one important—if not the most important—communication tool. Many people at work, especially managers, feel overwhelmed by the sheer volume and content of the emails that they have to handle. Moreover, many emails lead to a sense of being strained due to regular task-switching and has negative consequences for fully utilizing an individual's cognitive capacity. These aspects of emails can result in cognitive overload, described as email overload, accompanied by lower work performance. Therefore, email management strategies are required that avoid email overload with its negative consequences and that increase email management performance.

Previous studies have investigated single factors influencing the perceived email overload as well as specific single-item email management strategies that individuals apply in order to handle emails. Since email management strategies are best described by constructs comprising several items, this paper identifies for the first time a full set of well-validated email management strategies and their effects on email management performance. To obtain information about the individual email behavior of managers at work, a survey was conducted and factor as well as regression analyses were performed. The findings provide scientifically substantiated advice on which strategies can be used to improve the aforementioned performance: most particularly, by using the email client as a to-do list and by keeping the email inbox at zero. Explanatory approaches for these results are described against the background of cognitive theories.

Vertical integration has been growing steadily in healthcare and several studies have explored the impact of this phenomenon on prices, consumers, etc. In this work, motivated by changes in healthcare systems in China, we provide an operational perspective by investigating how vertical integration influences the referral behavior in primary care and the corresponding service provision at hospitals or specialists. We present an optimization model to explore the effect of different levels of vertical integration on generalists and specialists. We find that the healthcare system efficiency can indeed be improved with a moderate level of integration. However, when the level of integration increases beyond a certain threshold, healthcare costs may actually increase with more integration especially when generalists or primary care providers are inadequately trained and specialists are highly utilized. We find that full integration is not optimal. Moreover, aiming for a moderate level of integration is especially appropriate if the generalist’s ability to treat complex patients is limited and/or timely access to treatment is important. Our work also shows that physicians get higher payment without necessarily improving service quality when the integration level is higher. We discuss the implications of our results in the context of the Chinese healthcare system, although some insights may apply more broadly.

Since African swine fever (ASF) was first detected in China in August 2018, it has killed more than one million pigs and caused pork prices to skyrocket. To address this, the Chinese government offers two farm subsidies: (i) the compulsory culling subsidy (CCS), which cushions losses from new outbreaks by compensating for pigs culled due to ASF, and (ii) the large-scale breeding subsidy (LBS), which maintains or increases farms’ breeding scales (BSs) by requiring a minimum BS. We develop a game-theoretic model to capture the underlying dynamics between the government and farms. In particular, farms have different production capacities and must decide their BSs under yield uncertainty due to possible new outbreaks. We analyze the optimal design of subsidy programs with an earmarked budget to maximize social welfare, and we examine the impacts on different stakeholders. Our analysis reveals several insights. First, the government should offer the CCS only if the budget is very constrained; otherwise, it should simultaneously offer the two subsidies and prioritize compensating farm losses by providing a good CCS. Second, the optimal subsidy programs can increase consumer surplus regardless of the budget, and programs with a small or large budget can make all farms better off. However, small- and moderate-scale farms (that do not enroll in the LBS) will be worse off under these programs with a moderate budget. Third, the optimal subsidy programs can create positive net social value that is nondecreasing in the budget; hence, a win-win-win situation can be achieved by establishing a sufficiently large budget for these programs. Finally, we calibrate our model using Chinese pig industry data and provide further insights into ASF subsidy programs.