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Consolidation lies at the heart of the last-mile logistics problem. Urban consolidation centers (UCCs) have been set up to facilitate such consolidation all over the world. To the best of our knowledge, most—if not all—of the UCCs operate on volume-based fixed-rate charges. To achieve environmental sustainability while ensuring economic sustainability in urban logistics, we propose, in this paper, a bicriteria auction mechanism for the automated assignment of last-mile delivery orders to transport resources. We formulate and solve the winner determination problem of the auction as a biobjective programming model. We then present a systematic way to generate the Pareto frontier to characterize the tradeoff between achieving economic and environmental sustainabilities in urban logistics. Finally, we demonstrate that our proposed bicriteria auction produces the solutions that significantly dominate those obtained from the fixed-rate mechanisms. Our sensitivity analysis on the willingness of carriers to participate in the UCC operation reveals that higher willingness is favorable toward achieving greater good for all, if UCC is designed to be nonprofit and self-sustaining.

The annual trading agent competition offers agent designers a forum for evaluating programmed trading techniques in a challenging market scenario. TAC aims to spur research by enabling researchers to compare techniques on a common problem and build on each other's ideas. A fixed set of assumptions and environment settings facilitates communication of methods and results. As a multiyear event, TAC lets researchers observe trading agents' progress over time, in effect accelerating the evolution of an adapted population of traders. Given all the participant effort invested, it is incumbent on us to learn as much from the experience as possible. After three years of TAC, we're ready to examine there we stand. To do this, we used data from actual TAC tournaments and some post-competition experimentation. We based our analysis almost entirely on outcomes (profits and allocations), with very little direct accounting for specific agent techniques.

In modern production systems, it is critical to perform maintenance, calibration, installation, and upgrade tasks during planned downtime. Otherwise, the systems become unreliable and new product introductions are delayed. For reasons of safety, testing, and access, task performance often requires the vicinity of impacted equipment to be left in a specific “end state” when production halts. Therefore, planning the shutdown of a production system to balance production goals against enabling non-production tasks yields a challenging optimization problem. In this paper, we propose a mathematical formulation of this problem and a dynamic programming approach that e ciently finds optimal shutdown policies for deterministic serial production lines. An event-triggered re-optimization procedure that is based on the proposed deterministic dynamic programming approach is also introduced for handling uncertainties in the production line for the stochastic case. We demonstrate numerically that in these cases with random breakdowns and repairs, the re-optimization procedure is e cient and even obtains results that are optimal or nearly optimal.