Flexible Services and Manufacturing Journal最新文献

This paper investigates a dynamic and stochastic shipment matching problem faced by network operators in hinterland synchromodal transportation. We consider a platform that receives contractual and spot shipment requests from shippers, and receives multimodal services from carriers. The platform aims to provide optimal matches between shipment requests and multimodal services within a finite horizon under spot request uncertainty. Due to the capacity limitation of multimodal services, the matching decisions made for current requests will affect the ability to make good matches for future requests. To solve the problem, this paper proposes an anticipatory approach which consists of a rolling horizon framework that handles dynamic events, a sample average approximation method that addresses uncertainties, and a progressive hedging algorithm that generates solutions at each decision epoch. Compared with the greedy approach which is commonly used in practice, the anticipatory approach has total cost savings up to 8.18% under realistic instances. The experimental results highlight the benefits of incorporating stochastic information in dynamic decision making processes of the synchromodal matching system.

Optimizing Home Care Services is receiving a great attention in Operations Research. We address arrival time consistency, person-oriented consistency and demand uncertainty in Home Care, while jointly optimizing assignment, scheduling and routing decisions over a multiple-day time horizon. Consistent time schedules are very much appreciated by patients who, in this setting, are very sensitive to changes in their daily routines. Also person-oriented consistency positively impacts on service quality, guaranteeing that almost the same set of caregivers take care of a patient in the planning horizon. Demand uncertainty plays a pivotal role, too, since both the set of patients under treatment and their care plan can change over time. To the best of our knowledge, this is the first paper dealing with all these aspects in Home Care via a robust approach. We present a mathematical model to the problem, and a pattern-based algorithmic framework to solve it. The framework is derived from the model via decomposition, i.e. suitably fixing the scheduling decisions through the concept of pattern. We propose alternative policies to generate patterns, taking into account consistency and demand uncertainty; when embedding them in the general framework, alternative pattern based algorithms originate. The results of a rich computational experience show that introducing consistency and demand uncertainty in pattern generation policies is crucial to efficiently compute very good quality solutions, in terms of robustness and balancing of the caregiver workload. In addition, a comparison with a simpler model, where no kind of consistency is imposed, shows the importance of considering consistency in pursuing a valuable patient-centered perspective, with a positive effect also on the efficiency of the solution approach.

The retail industry is becoming increasingly competitive; as a result, companies are seeking to reduce inefficiencies in their supply chains. One way of increasing the efficiency of operations inside a warehouse is by better allocating products in the available spaces. In this paper, we propose a new heuristic approach to solving the storage location assignment problem (SLAP) considering precedence constraints, in multi-aisle, multi-product picking warehouses. A two-phase heuristic procedure is developed: the products are clustered and assigned to the available spaces. We tested the procedure in the non-perishables warehouse of a real-world Portuguese retail chain, which supplies 191 stores per day. The results show that the new assignment of products allows for an improvement of up to 15% on the distance travelled by the pickers, which implies savings of approximately 477 km per month. This problem is a special case of SLAP since we are dealing with large percentages of non-uniform products. This procedure incorporates four relevant criteria for the allocation decision: the products' similarity, demand and weight, and the distance travelled by the picker. By using a two-phase heuristic method, this study offers companies and academics an alternative and more effective solution for SLAP than the usual methods based on the creation of density zones.