Jul 2026· International Conference on Control, Decision and Information Technologies· pp. 2674-2679· 0 citations· 13 references
Abstract
Order picking is one of the most costly activities in manual picker-to-parts warehouses, and routing quality has a direct impact on travel effort and operational efficiency. However, commercial warehouse management systems (WMSs) still often rely on simple rule-based policies because exact optimization is difficult to reconcile with real-time execution requirements. This paper presents a WMS-compatible graph-based routing method for manual warehouses based on mission-dependent graph reduction, shortest-path computation, and sequencing optimization over mission-relevant locations. Starting from the physical warehouse graph, the proposed method builds a compact reduced representation that preserves shortest-path distances while significantly decreasing the online computational burden, enabling seamless integration into existing WMSs and real-time operation. The method is validated on a real household-goods distribution center and compared with both practical rule-based routing policies typically adopted in commercial WMSs and exact optimization benchmarks. Results on both synthetic missions and real warehouse orders show substantial travel-distance reductions with runtimes fully compatible with online warehouse operation.
Storage Location Assignment Problem (SLAP) is a study on how products should be stored in a warehouse space. Although studies have shown that arranging products based on their duration of stay can decrease the stacker crane travel time, this policy requires exact knowledge of the product's duration of stay. To the best of the author's knowledge, most studies on duration-of-stay storage policy (DOS) are on single-deep automated storage/retrieval systems (AS/RS), and hence their viability in double-deep AS/RS has not yet been extensively studied. To close this literature gap, this paper aims to study the performance of DOS on double-deep AS/RS in third-party logistics (3PL) warehouses. The study starts by building a simulation model of a double-deep automated warehouse. Based on the warehouse model, simulations using three different policies: random storage policy (RAN), closest-open-location storage policy (COL) and DOS are performed. To evaluate the effectiveness of the storage policies, the average dual-command-cycle (DCC) travel time for each operation is compared under varying warehouse conditions. The results indicate that the duration-of-stay (DOS) storage policy consistently outperforms both random (RAN) and closest-open-location (COL) policies in reducing travel time and mitigating blockage. This demonstrates the importance of incorporating expected retrieval behaviour into storage location assignment decisions.
Song-Yi Ng, Lee-Yeng Ong, M. Leow· International Conference on...· 0 citations
This paper studies the Vehicle Routing Problem with Simultaneous Pickup and Delivery and Time Windows (VRPSPDTW). The problem arises from reverse logistics, last-mile distribution, and circular logistics, where vehicles must serve delivery and pickup demands while respecting vehicle capacity and customer time-window constraints. This work develops DBLS-SP. The method combines an improved insertion-based initialization procedure, a repair-oriented dynamic scoring strategy, ejection-based reinsertion, multi-armed-bandit control of the route-inheritance ratio, vehicle-layer archive-guided route descent, and quality-first nearest-neighbor pool replacement. Computational experiments on the Wang–Chen (WC) and JD Logistics (JD) benchmark instances show that DBLS-SP obtains the best algorithmic solution on 67 of 68 WC instances and the best or tied-best objective value on 16 of 20 JD instances. The ablation study confirms the contribution of the main components, and the proposed reduction strategy removes more infeasible paths than the original reduction rule while preserving feasibility.
Real-time order arrivals and stringent timeliness demands in e-commerce pose significant challenges to production-distribution coordinated scheduling in distributed manufacturing systems. This paper tackles the integrated production and distribution scheduling problem within a dynamic distributed hybrid flow shop. We formulate a mixed-integer linear programming model aimed at minimizing average order tardiness and total operational cost. To address dynamic uncertainties, we propose an improved decomposition-based multi-objective evolutionary algorithm (I-MOEA/D) operating within a rolling horizon framework. The algorithm integrates three critical components: an urgency-based emergency window management strategy that caps computational complexity, a 2-opt local search operator that boosts vehicle routing efficiency, and acceleration techniques—including order dictionary indexing, lightweight object replication, and distance caching—that guarantee real-time responsiveness. The window size is determined as Wmax = 50 through sensitivity analysis. Extensive experiments conducted on 100, 200, and 300-order scenarios with 30 independent random seeds demonstrate that I-MOEA/D markedly outperforms NSGA-II, MOALNS, and a reinforcement learning-driven hyper-heuristic (RL-HH). For 200 orders, I-MOEA/D reduces average tardiness by 18.5%, 41.0%, and 58.7% compared to NSGA-II, MOALNS, and RL-HH, respectively, while maintaining competitive cost performance (a cost gap of 16.99% relative to the static lower bound). The IGD metric achieves 0.0031, confirming good convergence and diversity. Acceleration techniques cut computation time by 56% without sacrificing solution quality. The algorithm scales efficiently with problem size. These results validate that I-MOEA/D delivers effective real-time decision support for dynamic distributed production-distribution systems.
The Multi-Depot Capacitated Vehicle Routing Problem (MDCVRP) asks for minimum-cost delivery tours from several capacitated depots to a set of customers. Like most vehicle-routing variants it is NP-hard, so practical solvers must trade solution quality against speed. We revisit this trade-off through the lens of graph matching. Adapting a matching-based construction first developed for the Traveling Tournament Problem, we present two algorithms, Cluster-First and Match-First, that reduce routing to a sequence of minimum-weight matchings. This is more than a heuristic. We prove that for tours of up to two targets the matching formulation solves the MDCVRP exactly in polynomial time for any number of depots, and that both algorithms are constant-factor approximations, with a tight factor of two, in the structured regimes. This matching optimum coincides with the exact combinatorial-auction optimum, so the auction serves as a strong quality baseline. On instances of 1000 customers and 20 depots our methods match or slightly beat that baseline in tour length while running two to three orders of magnitude faster, in tens of milliseconds against tens of seconds, a scale at which exact and auction-based solvers become impractical. Because Cluster-First routes each depot independently, the approach also re-routes cheaply when new customers arrive.
Jayant Chandwani, Pranav M R, Anand Jat et al.· arXiv.org· 0 citations
ABSTRACT Defining delivery routes is an effective way to reduce transportation costs. In particular, a cross-dock distribution center in a retail network requires coordinated decisions on delivery routes and internal operations, leading to the cross-dock scheduling with routing decisions problem. To the best of our knowledge, no empirical study has shown the advantage of integrating these decisions. Previous works mainly focus on fresh food distribution centers with specific operational features. Based on a retail network, we address this problem by developing two mixed-integer models: one basic and another with additional routing variables that improve the problem representation. Furthermore, we assess the importance of integrating routing and cross-dock scheduling by comparing integrated and hierarchical strategies. Overall, our results indicate that integration can improve the distribution system’s scheduling efficiency under different demand patterns and dependency levels between customers and suppliers.
E. D. Bernardes, F. Toledo· Pesquisa Operacional· 0 citations
Autonomous mobile robots are increasingly considered for campus delivery and service logistics, where route efficiency can reduce unnecessary travel under spatial constraints. This study develops an obstacle-aware routing framework that combines a 1 m occupancy grid, A* shortest-path computation, and an Improved Mayfly Optimization Algorithm (IMOA). The A* stage constructs a pairwise distance matrix using orthogonal costs of 1, diagonal costs of 2, an octile heuristic, and a no-corner-cutting rule; IMOA then optimizes the closed visiting order through random-key decoding, elite 2-opt, and stagnation handling. Validation comprises ten independent benchmark instances, the fixed G40 scenario, and a campus-derived G-real application. Under a common budget of 50,000 full-tour evaluations and 30 independent runs, a Friedman test detected overall differences across the ten instances (χ2(7) = 66.2488, p = 8.434 × 10−12). After Holm correction, IMOA significantly outperformed GA, PSO, GWO, ACO, and MOA, showed no significant difference from MS2OPT, and had a worse average rank than the deterministic LKH reference, which achieved the best overall rank. On G-real, IMOA obtained a median distance of 8178.37 m, compared with 8223.99 m for MS2OPT; this difference was not significant, while LKH achieved the lowest deterministic cost of 8076.46 m. A three-instance exploratory ablation ranked IMOA first and consistently identified elite 2-opt as the principal observed improvement source; component-level inference remains exploratory because only three instances were available. These findings establish routing-efficiency gains under the evaluated protocol. Such gains may support more resource-efficient campus logistics, but energy consumption and carbon emissions were not evaluated.
Ze Yang, Xin-Ying Cheng, Hao-Min Wang· Sustainability· 0 citations
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