Reordering Constraints Research Articles

A systematic evaluation of freight carrier response to receiver reordering behaviour

Abstract The fields of behavioural transport modelling has been gaining momentum and many researchers have focused on incorporating some elements of stakeholder behaviour and decision making into their freight planning tools. However, investigation of the literature reveals that few papers are devoted to understanding and integrating logistics behaviour of freight receivers into urban transport simulations, and the impact of receiver reordering constraints on other freight agents in the urban transportation network has not been thoroughly investigated. This paper is therefore concerned with evaluating the impacts that constraints set by freight receivers during reordering have on carriers’ behaviour and cost in an urban freight transport simulation. To achieve this, three key receiver reordering constraints scenarios are simulated: delivery time window durations; delivery frequencies and its associated quantities; and delivery unloading or service time at receiver facilities. These scenarios are then implemented in an agent-based transport simulation and the carrier’s behaviour and delivery cost are evaluated. Results indicate that narrowing time windows could result in delivery and penalty cost increases of up to 93%. Extending unloading times can see costs and penalties increase by up to 111%. Delivery frequency (and therefore order quantity) also has a major impact of the carrier’s cost, with cost increases of up to 142% when requesting more frequent deliveries of smaller quantities. These results confirm that carrier decisions are influenced significantly by changes in receiver reordering behaviour and unnecessary constraints imposed by receivers during reordering could have significant negative implications on the delivery cost of the carrier. This emphasises the importance of finding a balance between restrictions set by supply chain customers during reordering and the cost associated with those restrictions and highlights the importance of finding ways to urge freight agents, especially receivers, to change their current behaviour to lower the total delivery cost of the supply chain.

Dynamically Shaping the Reordering Search Space of Phrase-Based Statistical Machine Translation

Defining the reordering search space is a crucial issue in phrase-based SMT between distant languages. In fact, the optimal trade-off between accuracy and complexity of decoding is nowadays reached by harshly limiting the input permutation space. We propose a method to dynamically shape such space and, thus, capture long-range word movements without hurting translation quality nor decoding time. The space defined by loose reordering constraints is dynamically pruned through a binary classifier that predicts whether a given input word should be translated right after another. The integration of this model into a phrase-based decoder improves a strong Arabic-English baseline already including state-of-the-art early distortion cost (Moore and Quirk, 2007) and hierarchical phrase orientation models (Galley and Manning, 2008). Significant improvements in the reordering of verbs are achieved by a system that is notably faster than the baseline, while bleu and meteor remain stable, or even increase, at a very high distortion limit.

A word-to-phrase statistical translation model

This article addresses the development of statistical models for phrase-based machine translation (MT) which extend a popular word-alignment model proposed by IBM in the early 90s. A novel decoding algorithm is directly derived from the optimization criterion which defines the statistical MT approach. Efficiency in decoding is achieved by applying dynamic programming, pruning strategies, and word reordering constraints. It is known that translation performance can be boosted by exploiting phrase (or multiword) translation pairs automatically extracted from a parallel corpus. New phrase-based models are obtained by introducing extra multiwords in the target language vocabulary and by estimating the corresponding parameters from either: (i) a word-based model, (ii) phrase-based statistics computed on the parallel corpus, or (iii) the interpolation of the two previous estimates. Word-based and phrase-based MT models are evaluated on a traveling domain task in two translation directions: Chinese-English (12k-word vocabulary) and Italian-English (16k-word vocabulary). Phrase-based models show Bleu score improvements over the word-based model by 19% and 13% relative, respectively.

Co-synthesis of pipelined structures and instruction reordering constraints for instruction set processors

This paper presents a hardware/software co-synthesis approach to pipelined ISP (instruction set processor) design. The approach synthesizes the pipeline structure from a given instruction set architecture (behavioral) specification. In addition, it generates a set of reordering constraints that guides the compiler back-end (reorderer) to properly schedule instructions so that possible pipeline hazards are avoided and throughput is improved. Co-synthesis takes place while resolving pipeline hazards, which can be attributed to interin-struction dependencies (IIDs). An extended taxonomy of IIDs have been proposed for the systematic analysis of pipeline hazards. Hardware/software methods are developed to resolve IIDs. Algorithms based on taxonomy and resolutions are constructed and integrated into the pipeline synthesis process to explore hardware and software design space. Application benchmarks are used to evaluate possible designs and guide the design decision. The power of the co-synthesis tool PIPER is demonstrated through pipeline synthesis of one illustrative example and two ISPs, including an industrial one (TDY-43). In comparison with other related approaches, our approach achieves higher throughput and provides a systematic way to explore the hardware/software trade-off.