International Transactions in Operational Research最新文献

“L'eau c'est la vie” is a well-known French expression for “water is life,” which reflects the fact that water is undoubtedly the most vital resource in the world. The main mission for water utility companies is to convey and distribute water that is of acceptable quality to satisfy the demand of the population at any time of the day. In the recent years, achieving this mission has become very challenging for these companies. Indeed, on the one hand, the rapid growth of population and the expansion of urbanization have significantly increased the demand for water, while on the other hand, natural phenomenon, such as drought, as well as the impact of climate changes are making it almost impossible for water distribution companies to convey the right amount of water where and when it is needed. The presence of water storage tanks in a water distribution network is aimed at alleviating this pressure by storing water and distributing it later in response of the variability of the demand across the network. The management of the tanks of the network is done based on the assignment of three-level set points, which allows to meet the outflow demand of water from each tank, while maintaining the adequate flow rate through the network. The set points define the level at which the valves that enable inflow and outflow of water to the tank have to be switched on or off. However, operating the valves during different periods of the day to meet the water demand may yield extremely high operational cost because opening some of the valves will induce the running of pumps to maintain an adequate flow rate of water in the network. We present a network optimization model for managing the network of storage tanks in a drinking water distribution system while minimizing the total cost of electricity involved. Computational experiments have been conducted on up to three sets of distribution networks. The results show that our proposed optimization model can be used to reduce the operational cost of managing the network of storage tanks for a water distribution system by up to 38%, while still being able to maintain the right amount of water in the tanks.

We investigate the incentives of an electric vehicle manufacturer (EVM) to improve the driving range of electric vehicles (EVs) following the entry of a third-party business-to-consumer EV-sharing platform (sharing platform). Particularly, we examine the two parties’ price leadership strategy and evaluate two scenarios: the EVM is unable (i.e., the short-term scenario) and able (i.e., the long-term scenario) to improve the driving range. In the short-term scenario, the sharing platform (or EVM) should act as price leader if the hassle cost faced by the sharing platform is low (or high). Moreover, the EVM's profits are found to be lower in the short-term scenario (with sharing services), compared to the case without such services. In the long-term scenario, the EVM must always act as a price leader. Additionally, when faced with high hassle cost, a higher driving range should be established. Improving the driving range will increase the long-term profitability of the EVM.

Integrating location and routing into a unified decision-making framework offers a more comprehensive reflection of real-world scenarios where different facility layouts directly influence routing plans. Consequently, addressing the issue holistically becomes imperative, especially when considering service level metrics like customer waiting times. In this paper, we address the latency location routing problem with opening costs and split deliveries (LLRP-OCSD). This complex problem involves optimizing the design of a supply chain network by determining depot locations, assigning vehicles and demand nodes to these depots, and designing routes. This must be done while accommodating split deliveries and prioritizing minimizing waiting times for demand nodes. We formulate a mathematical model and propose an iterated local search (ILS) algorithm to solve the problem. To validate the performance of our approach, we adapted a set of benchmark instances previously used for LLRP-OC without considering split deliveries. Computational results demonstrate that the proposed model is able to optimally solve only 2 out of the 38 tested instances within a 2-hour timeframe. On the other hand, the proposed ILS algorithm is able to find good-quality solutions in significantly less computational time for the LLRP-OCSD. Extensive experimentation on larger instances (up to 200 nodes) yields consistent results within reasonable computational limits. In summary, our findings highlight how split deliveries enhance vehicle utilization, enabling the use of smaller vehicles and creating balanced routes with minimal increases in latency costs.

Entrepreneurship plays a crucial role in economic development, and wage subsidies for new firms serve as a key instrument to promote it. This study integrates system dynamics with geometric compromise programming to assess the impact of entrepreneurship wage subsidies on four macroeconomic objectives: maximizing the number of new firms, employment, and tax revenue, while minimizing subsidy costs. It is the first study to combine these methodologies, enabling an analysis of the complex interactions between the labor market, the real sector, and government interventions. Key findings reveal that decision variables, such as subsidy parameters, impact utility objectives with a one-year lag. Increasing the number of benefit variables extends the optimal subsidy period while reducing the subsidy limit. Most study cases show similar effects for workers and tax revenue. The model developed in this study provides valuable decision support for policymakers, offering insights into the macro-level dynamics of a national entrepreneurial ecosystem.

Third-party logistics (3PL) offers credit guarantee financing and direct financing to capital-constrained small and medium-sized enterprises (SMEs), leveraging their informational advantages. However, SMEs’ default risk due to demand fluctuations poses a challenge for 3PL in providing the appropriate financing mode. This research investigates the financing strategies offered by 3PL with resilience considerations. We scrutinize the financing preferences of 3PL, the manufacturer, and the capital-constrained retailer by comparing credit guarantee financing, 3PL direct financing, and trade credit. Furthermore, we analyze conflicts and coordination regarding equilibrium financing modes. Our findings reveal that 3PL's financing provision strategy depends on the manufacturer's cost and the retailer's initial capital. Besides, the financing equilibrium may be detrimental to supply chain members due to their inconsistencies in financing strategies. We propose a coordination mechanism to improve the overall profitability of supply network stakeholders. This study offers managerial insights for 3PL to provide financing modes while considering resilience rationally.

This work discusses a supply chain composed of one third-party logistics (3PL) firm and one capital-constrained seller. To cover the seller's ordering and logistics costs, two distinct mixed financing schemes are considered to address the funding needs: (i) equity financing and bank credit financing (EBF) and (ii) equity financing and 3PL firm financing (ELF). We examine how the equity financing ratio influences equilibrium outcomes and derive the financing preference for each member. The results reveal that a large equity financing ratio prompts the seller to buy more. Interestingly, the seller's profit under EBF either monotonically decreases or exhibits a concave relationship with respect to the equity financing ratio. By contrast, the seller's profit under ELF is consistently concave with respect to the equity financing ratio. Additionally, a Pareto zone of the 3PL firm's interest rate exists in which a win–win situation can be achieved. The seller tends to opt for ELF when the sum of the unit transportation fee and ordering cost is high but the equity financing ratio is moderate. Comparatively, the 3PL firm is reluctant to offer financing when both the sum of the unit transportation fee and ordering cost, and the sum of the unit transportation cost and ordering cost are high, while the equity financing ratio is low. Finally, we investigate an endogenous equity financing ratio and further discuss the effect of the seller's initial working capital.

The significance of last-mile logistics in the healthcare supply chain is growing steadily, especially in pharmacies where the growing prevalence of medication delivery to patients' homes is remarkable. This paper proposes a novel mathematical model for the last-mile logistics of the pharmaceutical supply chain and optimizes a pharmacy's logistical financial outcome while considering medication synchronization, different delivery modes, and temperature requirements of medicines. We propose a mathematical formulation of the problem using mixed-integer linear programming evolved from the actual problem of an outpatient pharmacy of a Dutch hospital. We create a case study by gathering, preparing, processing, and analyzing the associated data. We find the optimal solution, using Python MIP package and the Gurobi solver, which indicates the number of order batches, the composition of these batches, and the number of staff related to the preparation of the order batches. Our results show that our optimal solution increases the pharmacy's logistical financial outcome by 34%. Moreover, we propose other model variations and perform extensive scenario analysis to provide managerial insights applicable to other pharmacies and distributors in the last step of cold supply chains. Based on our scenario analysis, we conclude that improving medication synchronization can significantly enhance the pharmacy's logistical financial outcome.

The tree $�t$-spanner problem ($��T�r�\_�t�$-SP), for a given edge-weighted, undirected, and connected graph, is an $�\mathrm{NP}$-hard problem with practical applications that aims to find a spanning tree with minimum $�t$, called the stretch factor. Due to its computational intractability, metaheuristic techniques have attracted much attention in the literature for finding good solutions within an acceptable computational time. In this article, we make the first attempt to propose a single solution-based iterated local search (ILS) for the $��T�r�\_�t�$-SP. To ensure high search efficiency and efficacy, the proposed ILS framework relies on the perturbation strategy, a problem-specific local search, and the use of two strategies in the acceptance criterion of ILS. On a set of available 48 benchmark instances, experimental results indicate that the proposed ILS consistently outperforms the existing best-so-far approach, particularly with the increase in instance sizes. The results yield new improved values on 32 instances out of 48 instances. We investigate the effectiveness of using two strategies in the acceptance criterion of ILS. Moreover, a statistical analysis is conducted to demonstrate its significance.