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With rapid e-commerce growth, on-demand urban delivery is having a high timeespecially for food, grocery, and retail, often requiring delivery in a veryshort amount of time after an order is placed. This imposes significantfinancial and operational challenges for traditional vehicle-based deliverymethods. Crowdshipping, which employs ordinary people with a low pay rate andlimited time availability, has emerged as an attractive alternative. This paperproposes a multi-tier adaptive memory programming (M-TAMP) to tackle on-demandassignment of requests to crowdsourcees with spatially distributed requestorigins and destination and crowdsourcee starting points. M-TAMP starts withmultiple initial solutions constructed based on different plausiblecontemplations in assigning requests to crowdsourcees, and organizes solutionsearch through waves, phases, and steps, imitating both ocean waves and humanmemory functioning while seeking the best solution. The assignment is furtherenforced by proactively relocating idle crowdsourcees, for which acomputationally efficient cluster- and job-based strategy is devised. Numericalexperiments demonstrate the superiority of MTAMP over a number of existingmethods, and that relocation can greatly improve the efficiency ofcrowdsourcee-request assignment.

Multi-Tier Adaptive Memory Programming and Cluster- and Job-based Relocation for Distributed On-demand Crowdshipping