Yanzhi Li

Bio: Yanzhi Li is an academic researcher from City University of Hong Kong. The author has contributed to research in topics: Heuristic & Genetic algorithm. The author has an hindex of 17, co-authored 55 publications receiving 1787 citations. Previous affiliations of Yanzhi Li include Hong Kong University of Science and Technology.

Carbon Footprint and the Management of Supply Chains: Insights From Simple Models

Abstract: Using relatively simple and widely used models, we illustrate how carbon emission concerns could be integrated into operational decision-making with regard to procurement, production, and inventory management. We show how, by associating carbon emission parameters with various decision variables, traditional models can be modified to support decision-making that accounts for both cost and carbon footprint. We examine how the values of these parameters as well as the parameters of regulatory emission control policies affect cost and emissions. We use the models to study the extent to which carbon reduction requirements can be addressed by operational adjustments, as an alternative (or a supplement) to costly investments in carbon-reducing technologies. We also use the models to investigate the impact of collaboration among firms within the same supply chain on their costs and carbon emissions and study the incentives firms might have in seeking such cooperation. We provide a series of insights that highlight the impact of operational decisions on carbon emissions and the importance of operational models in evaluating the impact of different regulatory policies and in assessing the benefits of investments in more carbon efficient technologies. Note to Practitioners-Firms worldwide, responding to the threat of government legislation or to concerns raised by their own consumers or shareholders, are undertaking initiatives to reduce their carbon footprint. It is the conventional thinking that such initiatives will require either capital investments or a switch to more expensive sources of energy or input material. In this paper, we show that firms could effectively reduce their carbon emissions without significantly increasing their costs by making only operational adjustments and by collaborating with other members of their supply chain. We describe optimization models that can be used by firms to support operational decision making and supply chain collaboration, while taking into account carbon emissions. We analyze the effect of different emission regulations, including strict emission caps, taxes on emissions, cap-and-offset, and cap-and-trade, on supply chain management decisions. In particular, we show that the presence of emission regulation can significantly increase the value of supply chain collaboration.

Crossdocking—JIT scheduling with time windows

Abstract: In this paper, we study a problem central to crossdocking that aims to eliminate or minimize storage and order picking activity using JIT scheduling. The problem is modelled naturally as a machine scheduling problem. As the problem is NP-hard, and for real-time applications, we designed and implemented two heuristics. The first uses Squeaky Wheel Optimization embedded in a Genetic Algorithm and the second uses Linear Programming within a Genetic Algorithm. Both heuristics offer good solutions in experiments where comparisons are made with the CPLEX solver.

Air cargo operations: Literature review and comparison with practices

Abstract: This study reviews the literature on air cargo operations and compares theoretical studies with the practical problems of airlines, freight forwarders, and terminal service providers In particular, we review studies in which mathematical models were used to identify the essential characteristics of air cargo operations, such as the intrinsic differences from passenger operations, and to explore the service processes in air cargo operations The typical models used in previous studies are summarized We then highlight the insightful findings from an industrial interview and present the gaps between previous research and practical realities We finally discuss the new research opportunities of air cargo operations according to the gaps

Note---Pricing and Inventory Control for a Perishable Product

Abstract: In this note, we study the concurrent determination of pricing and inventory replenishment decisions for a perishable product in an infinite horizon. Demands in consecutive periods are independent and influenced by prices charged in each period. In particular, we treat price as a decision variable to maximize the total discounted profit. We analyze the optimal solution-structure of a two-period lifetime problem and from insights gained in numerical experiments, develop a base-stock/list-price heuristic policy for products with arbitrary fixed lifetimes. Experiments show this policy to be effective.

Operations research at container terminals: a literature update

Abstract: The current decade sees a considerable growth in worldwide container transportation and with it an indispensable need for optimization. Also the interest in and availability of academic literatures as well as case reports are almost exploding. With this paper an earlier survey which proved to be of utmost importance for the community is updated and extended to provide the current state of the art in container terminal operations and operations research.

Application of decision-making techniques in supplier selection: A systematic review of literature

Abstract: Despite the importance of decision-making (DM) techniques for construction of effective decision models for supplier selection, there is a lack of a systematic literature review for it. This paper provides a systematic literature review on articles published from 2008 to 2012 on the application of DM techniques for supplier selection. By using a methodological decision analysis in four aspects including decision problems, decision makers, decision environments, and decision approaches, we finally selected and reviewed 123 journal articles. To examine the research trend on uncertain supplier selection, these articles are roughly classified into seven categories according to different uncertainties. Under such classification framework, 26 DM techniques are identified from three perspectives: (1) Multicriteria decision making (MCDM) techniques, (2) Mathematical programming (MP) techniques, and (3) Artificial intelligence (AI) techniques. We reviewed each of the 26 techniques and analyzed the means of integrating these techniques for supplier selection. Our survey provides the recommendation for future research and facilitates knowledge accumulation and creation concerning the application of DM techniques in supplier selection.