Scheduling cross-docking operations under uncertainty: A stochastic genetic algorithm based on scenarios tree

A cross-docking terminal enables consolidating and sorting fast-moving products along supply chain networks and reduces warehousing costs and transportation efforts. The target efficiency of such logistic systems results from synchronizing the physical and information flows while scheduling receiving, shipping and handling operations. Within the tight time-windows imposed by fast-moving products (e.g., perishables), a deterministic schedule hardly adheres to real-world environments because of the uncertainty in trucks arrivals. In this paper, a stochastic MILP model formulates the minimization of penalty costs from exceeding the time-windows under uncertain truck arrivals. Penalty costs are affected by products' perishability or the expected customer’ service level. A validating numerical example shows how to solve (1) dock-assignment, (2) while prioritizing the unloading tasks, and (3) loaded trucks departures with a small instance. A tailored stochastic genetic algorithm able to explore the uncertain scenarios tree and optimize cross-docking operations is then introduced to solve scaled up instaces. The proposed genetic algorithm is tested on a real-world problem provided by a national delivery service network managing the truck-to-door assignment, the loading, unloading, and door-to-door handling operations of a fleet of 271 trucks within two working shifts. The obtained solution improves the deterministic schedule reducing the penalty costs of 60%. Such results underline the impact of unpredicted trucks’ delay and enable assessing the savings from increasing the number of doors at the cross-dock

A comprehensive framework for sustainable closed-loop supply chain network design

Many companies face challenges in reducing their supply chain costs while increasing sustainability and customer service levels. A comprehensive framework for a sustainable closed-loop supply chain (CLSC) network is a practical solution to these challenges. Hence, for the first time, this study considers an integrated multio-bjective mixed-integer linear programming (MOMILP) model to design sustainable CLSC networks with cross-docking, location-inventory-routing, time window, supplier selection, order allocation, transportation modes with simultaneous pickup, and delivery under uncertainty. An intelligent simulation algorithm is proposed to produce CLSC network data with probabilistic distribution functions and feasible solution space. In addition, a fuzzy goal programming approach is proposed to solve the MOMILP model under uncertainty. Eight small and medium-size test problems are used to evaluate the performance of the proposed model with the simulated data in GAMS software. The results obtained from test problems and sensitivity analysis show the efficacy of the proposed model

Warehousing and Inventory Management in Dual Channel and Global Supply Chains

More firms are adopting the dual-channel supply chain business model where firms offer their products to customers using dual-channel sales (to offer the item to customers online and offline). The development periods of innovative products have been shortened, especially for high-tech companies, which leads to products with short life cycles. This means that companies need to put their new products on the market as soon as possible. The dual-channel supply chain is a perfect tool to increase the customer’s awareness of new products and to keep customers’ loyalty; firms can offer new products online to the customer faster compared to the traditional retail sales channel. The emergence of dual-channel firms was mainly driven by the expansion in internet use and the advances in information and manufacturing technologies. No existing research has examined inventory strategies, warehouse structure, operations, and capacity in a dual-channel context. Additionally, firms are in need to integrate their global suppliers base; where the lower parts costs compensate for the much higher procurement and cross-border costs; in their supply chain operations. The most common method used to integrate the global supplier base is the use of cross-dock, also known as Third Party Logistic (3PL). This study is motivated by real-world problem, no existing research has considered the optimization of cross-dock operations in terms of dock assignment, storage locations, inventory strategies, and lead time uncertainty in the context of a cross-docking system. In this dissertation, we first study the dual-channel warehouse in the dual-channel supply chain. One of the challenges in running the dual-channel warehouse is how to organize the warehouse and manage inventory to fulfill both online and offline (retailer) orders, where the orders from different channels have different features. A model for a dual-channel warehouse in a dual-channel supply chain is proposed, and a solution approach is developed in the case of deterministic and stochastic lead times. Ending up with numerical examples to highlight the model’s validity and its usefulness as a decision support tool. Second, we extend the first problem to include the global supplier and the cross-border time. The impact of global suppliers and the effect of the cross-border time on the dual-channel warehouse are studied. A cross-border dual-channel warehouse model in a dual-channel supply chain context is proposed. In addition to demand and lead time uncertainty, the cross-border time is included as stochastic parameter. Numerical results and managerial insights are also presented for this problem. Third, motivated by a real-world cross-dock problem, we perform a study at one of the big 3 automotive companies in the USA. The company faces the challenges of optimizing their operations and managing the items in the 3PL when introducing new products. Thus, we investigate a dock assignment problem that considers the dock capacity and storage space and a cross-dock layout. We propose an integrated model to combine the cross-dock assignment problem with cross-dock layout problem so that cross-dock operations can be coordinated effectively. In addition to lead time uncertainty, the cross-border time is included as stochastic parameter. Real case study and numerical results and managerial insights are also presented for this problem highlighting the cross-border effect. Solution methodologies, managerial insights, numerical analysis as well as conclusions and potential future study topics are also provided in this dissertation