Omega-international Journal of Management Science最新文献

This paper considers a single-machine scheduling problem to minimize the total weighted completion time with a weight modifying activity, after which the job weights are discounted by a given factor. The problem is known to be ordinary NP-hard. We propose two mixed integer linear programs (MILPs) and a dynamic programming algorithm to optimally solve the problem. Optimality properties are established and then formulated as pruning constraints to improve the problem-solving efficiency of the MILPs. Special cases are discussed and shown to be solvable by polynomial time algorithms. Complexity status of the studied problem with several instance characteristics is shown. Computational experiments indicate that the optimality properties can reduce the computing efforts and that one of the proposed MILPs can solve instances of 200 jobs in a few seconds.

In this paper we introduce and study the rescheduling problem to trade off between global disruption of the original jobs with flexibility and the scheduling cost of the new jobs. A set of original jobs has been scheduled on a single machine. But before the processing of original jobs begins, a set of new jobs arrives unexpectedly. The scheduler needs to adjust the existing schedule with a view to finding a cost-efficient schedule for the new jobs without causing too much disruption of the original schedule. We make three assumptions that are different from those in the literature: (i) the original jobs are regarded as a unified whole (a big job) and the global disruption of the original jobs is considered, (ii) the original jobs can be split into small pieces in a schedule, which enables effective control of the global disruption, and (iii) the cost of the original jobs depends on the global disruption, while the cost of the new jobs is expressed as a regular scheduling criterion, such as the maximum lateness, the total weighted completion time, and total weighted number of tardy jobs. We analyze the computational complexity of variants of the rescheduling problem.

The PROMETHEE II method is a classical multiple criteria decision making method. However, it also exists the rank reversal which is a highly important problem for analyzing the reliability of a MCDM method. The main objective of this study is to analyze the sensitivity of the parameters for preference functions and the rank reversal problem in the PROMETHEE II method. By analyzing the parameters for preference functions from the standpoint of theoretics, a method is proposed to calculate the ranges of the parameters for four types of preference functions to remain the ranking of all the alternatives unchanged. Second, the sufficient and necessary condition of the rank reversal is obtained in the PROMETHEE II method when there are only three types of criteria, i.e., usual criteria, $U$-shape criteria and level criteria. Finally, two minor modification methods for the PROMETHEE II method itself are proposed by observing the net outranking flow formula. Numerical simulations show that the occurrence of the rank reversal is clearly reduced and the ranges of fault tolerance of the parameters for preference functions are significantly larger for each new modified PROMETHEE II method. The similarity of rankings is tested by using the similarity rank coefficient $WS$. This indicates the rationality of the two proposed modifications.

We introduce a novel Bayesian hierarchical model for consumer preference analysis, addressing two significant challenges in this domain. First, it accommodates preference heterogeneity at both individual and segment levels. This enables actionable insights for targeting and pricing decisions while quantifying uncertainty. Second, it incorporates probabilistic value-based ranking to handle inconsistent and sparse preference data. This way, it mitigates the impact of cognitive biases and alleviates uncertainty in estimates. The proposed method performs robust inference of consumers’ preferences through hierarchical priors, allowing for flexible parameter learning and borrowing statistical strength from well-informed individuals. We demonstrate its practical usefulness by analyzing the real preferences of almost one hundred consumers considering mobile phone contracts. We also report the results of an extensive experimental study. The proposed method outperforms its counterpart, executing an independent estimation and the state-of-the-art approaches regarding predictive accuracy and preference similarity within identified customer groups. The performance improvements are more pronounced with larger sample sizes, smaller sets of items, and in contexts with reduced heterogeneity and increased consistency among consumers.

Recently Waste Electrical and Electronic equipment (WEEE) has become urgent environmental issues around the world. Generally, the e-waste recycling reverse supply chain (RSC) features higher uncertainties and complicated network structures, making the operation more difficult. Since the study on impact of hybrid uncertainty mitigation strategy in RSC is rare, we study the coupling effect of two strategies — capacity redundancy and process flexibility through the design of two hybrid strategies. A distributionally robust optimization model is proposed to handle both waste returns and resource yields uncertainties, and solved by a proposed Outer Approximation (OA) algorithm after an adaptive robust formulation. With numerical experiments based on real data implemented, our research finds conflict situation of using capacity redundancy and process flexibility strategies together. We also give guidelines for the use of hybrid strategies in RSC. We find that the optimal strategy changes as required service level increases. We also suggest moderate investment in capacity redundancy with long chain flexibility and resource centralization when applying hybrid strategy with optimal flexibility.

This paper addresses the electric vehicle charging scheduling problem in a charging station with a limited overall power capacity and a limited number of chargers. Electric vehicle drivers submit their charging demands. Given the limited resources, these charging demands are either accepted or rejected and accepted demands must be satisfied. The objective of the scheduler is to maximize the number of satisfied demands. The paper provides theoretical results on the scheduling problem and proposes different linear programming models and heuristic methods to provide good-quality solutions in a shorter computational time.

Many agricultural firms procure products from smallholders and sell them on platforms by paying a proportional fee. Generally, smallholders lack capital for production, and e-commerce platforms can provide loans to them. However, smallholders are risk averse, leading them to make conservative production decisions. Additionally, smallholders face bankruptcy risk due to output uncertainty and the interest burden of platforms’ loans. These factors further adversely affect smallholders’ conservative decisions. To alleviate this situation, the firm can provide loan guarantees for smallholders. This study considers a supply chain consisting of risk-averse farmers, an agricultural firm, and an e-commerce platform. The firm first decides the number of farmers to provide guarantees, and then the platform sets loan interest rates for guaranteed and non-guaranteed farmers. Thereafter, the firm decides purchase prices, and each farmer decides his production input. Given the number of guaranteed farmers, we find that the platform will charge each farmer a positive loan interest if the proportional fee is small, but it will offer interest-free (non-negative) loans to guaranteed (non-guaranteed) farmers if the proportional fee is large. Additionally, with the increase in guaranteed farmers, the firm’s profit and the farmers’ utilities are not necessarily monotonic. We further show that guaranteeing a portion of farmers is always detrimental to the firm but may be better for all the farmers. Therefore, it is better for the firm to provide a guarantee to all farmers or just offer no guarantee to any farmer, depending on the magnitudes of proportional fees and the production input efficiency.

This paper explores a novel multi-period portfolio decision model for loss-averse investors with dynamically adapted reference points in a market with serially correlated returns. We demonstrate that the optimal policy is a piecewise linear function of the deviation between current wealth and reference level, and its slopes are a path-dependent function of the historical returns, in sharp contrast to the constant slopes generated by the simplified model that ignores the diminishing sensitivity and assumes independent returns. This distinctive characteristic significantly departs from the conventional V-shaped pattern of the risky position, leading to a more intricate nonlinear functional mapping. Our study underscores the potential pitfalls of relying on the simplified model and offers valuable insights for investors and practitioners seeking to formulate effective portfolio strategies under realistic market conditions. Furthermore, our simulation analysis indicates that the predictability of returns, coupled with a slight degree of diminishing sensitivity, may amplify the disposition effect. Lastly, we establish that the new policy also effectively addresses the multi-period mean-conditional-value-at-risk (CVaR) portfolio optimization problem in the context of correlated returns, thereby expanding the practical applications of our findings.

Dual-sourcing inventory management, which is aimed at replenishing inventory through two supply sources, has been extensively incorporated across various industries as it can mitigate supply chain related operational risks. Given the practical relevance of this framework, many dual-sourcing inventory models based on stochastic and robust optimization approaches have been developed. However, these approaches encounter challenges such as the curse of dimensionality or solution conservativeness. In this study, we developed a data-driven distributionally robust optimization model for dual-sourcing inventory management under uncertain demand conditions, in which partial information regarding the distribution of the uncertain demand is available. A tractable model was constructed to solve the problem, and an optimal solution was derived in a closed-form expression. Numerical experiments were conducted to evaluate the performance of the proposed model in comparison with benchmark models in terms of the order-, stock-, and rolling-horizon-related parameters and demand distributions. The results demonstrated the benefit of adopting the dual-sourcing strategy in inventory management based on the distributionally robust optimization approach. In addition, the proposed model outperformed the benchmark models in terms of mitigating the bullwhip effect.

The escalating frequency and severity of disasters on a global scale have sparked inquiries into the efficacy of current disaster planning strategies in various scenarios. Despite the pivotal role of humanitarian supply chain planning in aiding impacted populations, much of the existing research is grounded in simplistic assumptions that limit their practicality. Addressing this gap, our proposed bi-objective model aligns response time and total cost, while also accommodating the collaboration between non-governmental organizations and governmental organizations to mirror real-world intricacies. This study comprehensively delves into various logistics aspects, encompassing pre- and post-disaster phases, including location, allocation, supplier selection, fleet size, supply contract, inventory, distribution, and transportation. This multifaceted approach enhances the model's suitability for managing genuine real-world emergencies. To mitigate disruption risks and unforeseen events, the model introduces pre-positioning, quantity flexibility contract, backup suppliers, and a multi-sourcing policy, thus enhancing the resilience and reliability of the logistics network. We present solutions for diverse scenarios through a scaled weighted sum method, while tackling uncertainty via a heuristic approach known as the backward scenario reduction method. Furthermore, to manage large-scale problems within an acceptable time frame, we propose an advanced hybrid algorithm. This algorithm synergizes a parallel differential evolution framework with reinforcement learning-enhanced local search mechanisms, aiming to improve both computational efficiency and solution accuracy. Finally, we validate the model's applicability through a real case study focusing on a flood scenario in Iran.