Annals of Operations Research最新文献

We study the Joint Replenishment Problem (JRP), which arises from the need for coordinating the replenishment of multiple items that share a common fixed cost. Even in the basic setting, determining the optimal replenishment plan is an NP-Hard problem. We analyze both the JRP under indirect grouping policy and its variant with restrictions like transportation capacity, budget capacity, and item transportation compatibility. Additionally, we consider uncertainty characteristics such as imperfect item quality, as highlighted in related literature studies. We propose a novel matheuristic method that determines the best basic cycle time while addressing the problem with a fixed cycle time using a linear integer model. The proposed method is quite versatile to handle additional real-life constraints effectively. Based on an extensive computational study, we conclude that for the basic setting under indirect grouping policy, the proposed algorithm outperforms the benchmark algorithms in the literature by 0.3% on average. For more complicated settings with additional restrictions, our proposed algorithm outperforms the benchmark algorithm by around 5% on average.

In today’s fast-paced world, delays or prolonged customer waiting times pose a threat to the firm’s profitability. This study utilizes the mean-CVaR metric to incorporate the risk associated with prolonged customer waiting times into the optimal trade-off decisions. For this purpose, we consider a single inventory system that faces Poisson demand and utilizes a base-stock policy to replenish its inventory, which takes a fixed amount of time. The firm implements a preorder strategy, encouraging customers to place their orders a fixed amount of time in advance of their actual needs, a period referred to as the commitment lead time. The firm rewards customers with a bonus termed the commitment cost, which increases with the length of the commitment lead time. We aim to determine the optimal control policy, including the optimal base-stock level and optimal commitment lead time, that minimizes the long-run average cost. The cost includes inventory holding, commitment, and customer waiting costs, with the latter adjusted for the firm’s degree of risk aversion. The optimal policy depends on the interdependence of the decisions, with the optimal commitment lead time following a “bang-bang” pattern, and the corresponding optimal base-stock level taking an “all-or-nothing” form. For linear commitment costs with a cost factor per time unit, we identify a threshold that increases with the firm’s risk aversion degree. Firms with greater risk aversion typically favor the buy-to-order strategy, while those with lower risk aversion may opt for either buy-to-stock or buy-to-order depending on their assessment of waiting costs.

Today, as market competition intensifies and competition costs rise, manufacturers are increasingly incentivized to engage in anti-competitive contracts or collusion, disrupting the market and harming non-colluding members; on top of all, the growing dominance of e-tailers further complicates supply chain relationships. While addressing the research gap in the specific dynamics between e-tailers and multiple manufacturers in the context of collusion and competition, the principal objective of this article is to get the best sales strategies and decision-making methods according to the standpoint of manufacturers, the e-tailer, and the entire supply chain. This study investigates the relationships and decisions of supply chain members, including an online retailer (e-tailer) and two manufacturers. Our findings indicate that fines for manufacturer collusion and higher e-tailer referral fees can reduce collusion incentives in reselling formats. When both manufacturers sell their products in a reselling sale format, the overall supply chain profit is appreciably higher in all cases, including centralized decision-making, Stackelberg-Bertrand competition, collusion, and deviation. Additionally, different sales formats increase collusion likelihood compared to identical formats, though reselling formats make collusion more sustainable. E-tailers should invest in high-intensity services to boost profitability, while manufacturers prioritize the agency sale format for optimal profits. The findings of this paper are significant as they provide practical guidance for supply chain management and help in monitoring commercial behavior to prevent collusion.

Buy and hold and periodical revisions are two competing investment strategies. Revising to the optimal one-period investment weights seemingly dominates the buy-and-hold strategy with random and uncontrolled investment weights determined by asset price changes. This intuition is misleading as both investment strategies are theoretically included in the risk aversion efficient set. Considering only economically relevant preferences, with stocks-bonds portfolios, both strategies are empirically included in the risk aversion efficient set as long as the investment horizon is shorter than 20 years. However, for an investment horizon longer than twenty years, the buy and hold strategy empirically dominates the revision strategy by Almost First-degree Stochastic Dominance ((AFSD)) rule, namely by all economically relevant utility functions. When the horizon is indefinitely long, holding only stocks dominates the stock–bond portfolios of both the B&H(S) and the RV(S). However, this theoretical result may be practically irrelevant for most investors with a horizon shorter than 20 years.

Sustainable agriculture has emerged as a critical topic in the context of sustainable development goals set by the United Nations. A key aspect impacting the sustainability of the agriculture supply chain is the usage of agrochemicals. Transitioning to sustainable alternatives from agrochemicals poses challenges, as it affects farms’ productivity, income, and food supply to the market. The delicate balance between farm income and greenhouse gas emissions related to chemical fertilizer usage has not been addressed adequately using a dynamic system behavior perspective. This study employs a System Dynamics model to simulate the impact of adopting biofertilizers on the triple-bottom-line performance of the agrochemical supply chain from a policy perspective. The model aims to understand stakeholder behavior within the fertilizer supply chain and enhance its sustainability. Additionally, the study models the effects of various input subsidies using the design of experiments in an Indian agrochemical supply chain, examining trade-offs involved in the triple-bottom-line (social, environmental, and economic) parameters for each subsidy. The simulation model offers policymakers insights into determining appropriate subsidy levels to facilitate a sustainable transition of agricultural supply chains. In this context, various possible scenarios were obtained by simulating the policy parameters (agriproduct price, chemical fertilizer prices, biofertilizer fixed costs, and biofertilizer subsidies) resulting in optimal levels of environmental impact, producer profit, and social benefit. It also provides a comprehensive evaluation of the triple-bottom-line effects of policy strategies, thereby facilitating the comprehension of trade-offs in the supply chains of lower/middle-income countries. The study contributes valuable guidance for policymakers to make informed decisions for promoting sustainable agriculture and achieving the triple-bottom-line objectives in the agrochemical industry.

Harry Markowitz’s pioneering work on portfolio choice was developed in the supportive environment of the Cowles Commission (later Foundation), a remarkable organization devoted to advanced formal mathematical and statistical methods in economics. Markowitz made use of Tjalling Koopmans’s activity analysis and influenced the monetary and financial economics of James Tobin and his Cowles associates, who studied the implications for economic equilibrium of investors following Markowitz’s optimal portfolio diversification. Milton Friedman’s reluctance to accept Markowitz’s dissertation was part of growing methodological frictions between Friedman and the Cowles Commission that led the commission to leave the University of Chicago to become the Cowles Foundation at Yale.

This study proposes an enhanced multiobjective assembly inventory routing model to assess the impact of sustainability practices on supply network performance goals. The model considers supplier incentives, supply risk, carbon emission penalty, heterogeneous fleet configuration, and demand uncertainty. Specifically, the model has economic (e.g., minimizing network cost), environmental (e.g., minimizing emission penalty), and social (e.g., maximizing supplier incentives) goals. The model incorporates a supply risk reduction policy. The novelty of this study lies in its simultaneous consideration of diverse factors in an inventory-routing context. Data sets from the existing literature are used to validate the model across various problem sizes. A modified hybrid non-dominated sorting genetic algorithm-II (HNSGA-II) is proposed to determine Pareto solutions and compare them with those derived from a speed-constrained multiobjective particle swarm optimization algorithm (SMPSO). HNSGA-II outperforms SMPSO in several critical performance metrics. The study further explores the impact of incentive schemes, low-risk supplier prioritization, and fleet configurations on sustainability performance. This study demonstrates a factorial experimentation-based sensitivity analysis on four objectives. The findings reveal that a heterogeneous fleet configuration can reduce emission penalties. However, this can result in increased network costs. A combination of low- and medium-duty vehicles is also recommended to attain economic and environmental efficiency. Service-level-based supplier incentives are found to enhance supply reliability and reduce shortages. However, this can elevate network costs and emissions. In scenarios of high demand variability, supplier incentives can ensure reliability. Conversely, cost and emission reduction can be prioritized over maximizing supplier incentives in high-supply risk scenarios.

Mobility as a Service (MaaS) transforms urban transportation from car ownership to subscription-based models. A key factor for the success of MaaS is accurately predicting users’ Willingness to Pay (WTP) for various subscription packages, enhancing their adoption and satisfaction. This paper employs a “smart predict-then-optimize” framework, where the weekly, annual, and monthly MaaS subscription models are formulated as online, offline, and hybrid online-offline mobility resource allocation problems, respectively. We develop a group method of data handling (GMDH)-driven self-adaptive harmony search (SAHS) algorithm to solve the proposed mobility resource allocation problems effectively. Initially, GMDH-type neural networks predict users’ WTP using their historical travel data, such as travel distance and service time, and socio-demographic characteristics, including inconvenience tolerance and travel delay budget; then these predicted WTP values are fed into the weekly, annual, and monthly mobility resource allocation problems, respectively. Comprehensive numerical experiments based on a simulated dataset demonstrate the robust prediction performance of the GMDH neural network across weekly, monthly, and annual subscription models, as well as the effectiveness of the GMDH-driven SAHS algorithm in managing resource allocation for these models. Our numerical findings highlight that the monthly subscription model strikes an optimal balance, combining the flexibility of the weekly model with the strategic depth of the annual model. This study proposes three distinct MaaS subscription models and a data-driven metaheuristic algorithm to customize MaaS offerings to user needs.

Peasants occupy many rural areas with low income, excessive self-consumption, and deficient access to resources, which translate into poverty and lack of food security. In developing countries, support programs push incentives for better crop planning decisions, exchanging crops between farmers, and improving market access. Decision-making in this context is complex, given the many available options and criteria regarding nutrition, income, and work capacity that must be satisfied, as well as several uncertainties. Considering the primary agricultural operations, we develop a mixed integer optimization model that maximizes farmer's profit and food security. We test our model for cooperatives from the Department of Caazapá, Paraguay. First, we solve the deterministic model for 60-month horizon planning and generate alternative scenarios. Then, we compare the plan for the actual production with the plan from the model solution. To study the effects of uncertainty, we also develop a two-stage stochastic model in which results for four cooperatives and 24-month horizon planning are compared considering sources of uncertainty on the supply side (harvest yield) and on the demand side (sales price). The proposed plans have economic, environmental, and social advantages: a mix of crop production, crop rotation performance, and the partial fulfillment of nutritional requirements. In the short term, the plans could guide production decision-making. In the long term, the results could support the generation of concrete line actions for programs or projects according to the Sustainable Development Goals (SDG), such as food security and small-scale production planning.