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The purpose of this paper is to propose a yard allocation model via objective programming. This is initially postulated based on a rolling‐horizon strategy, which aims at allotting outbound containers into yard.

To resolve the NP‐hard problem regarding the yard allocation model, a hybrid algorithm, which applies heuristic rules and distributed genetic algorithm (DGA), is then employed.

It could be observed from the case study that this proposed approach is proven effective for resolving the container yard allocation problem. The total loading time onto vessels, the total horizontal transportation distance and the imbalance among blocks are improved.

This paper does not deal with equipment scheduling.

This approach helps to minimize turnaround time; handling cost of vessels; the workloads among blocks are balanced for each vessel; and the total distance of container transportation.

This paper designs a hybrid algorithm, which integrates heuristic rules and DGA. In details, the heuristic rules are developed for generating feasible solutions, while the DGA was applied for optimizing these solutions.

With student, scholarly, and especially corporate interest in the study of logistics dramatically increasing, it is important for institutions of higher learning to act as quickly as possible to help meet this demand. In doing so, these institutions run into many roadblocks and challenges. This paper explores these and offers suggestions to minimize their effects.

This study proposes targeted modernization of the Department of Defense (DoD's) Joint Forces Ammunition Logistics information system by implementing the optimized innovative information technology open architecture design and integrating Radio Frequency Identification Device data technologies and real-time optimization and control mechanisms as the critical technology components of the solution. The innovative information technology, which pursues the focused logistics, will be deployed in 36 months at the estimated cost of $568 million in constant dollars. We estimate that the Systems, Applications, Products (SAP)-based enterprise integration solution that the Army currently pursues will cost another $1.5 billion through the year 2014; however, it is unlikely to deliver the intended technical capabilities.

An integrated paradigm of using multiple software programs in logistics education is proposed as an effective problem‐solving tool in addition to computer programming and spreadsheet modelling. Presents the essentials of logistics systems management and how each software program can be used individually and jointly to address logistics issues in detail. In the integrated paradigm, a Lotus 123 spreadsheet program is used as an integrator which works together with other software to facilitate student learning and to aid better decision making process. In‐class surveys show that the proposed approach is both effective and efficient in terms of getting the right results in less time. The approach is also realistic because the mixed mainframe and microcomputer operating environment and multiple software programs are being used by the business community.

In this paper, a systematic design procedure for an international mutual parts supply system for global car manufacturing is proposed and investigated. This procedure offers a rational way to determine the production countries of individual parts/components as well as the import countries for their procurement operations. The proposed procedure consists of three major steps, mathematical programming model development for minimum cost production‐logistic network design, database construction for model analysis and execution of mathematical programming model to obtain a recommendable global production‐logistics system. Actual data from the ASEAN‐China region are collected and processed by the proposed procedure for validity analysis. The results obtained through the investigation have confirmed the effectiveness of the procedure as a decision support model/database for designing such very large systems.

The purpose of this paper is to steer the logistics efforts during different phases of cash and voucher and lead aid assistance operation to right destination by analyzing logistics data and converting it to feasibility information.

This research paper depends basically on a quantitative approach by depending on previous references relevant to this topic. Furthermore, interviews with experts in the field consider the dependable references for this research paper.

There are many findings in this research paper such as a simply logistics algorithm that could help logistics experts to determine the key components in market supply chains and draw different scenarios to clarify the full picture.

This research paper can be considered as reference for advance research papers in future. Furthermore, it stimulates academic researchers to open a serious discussion about logistics data analysis in all phases because it considers as a good source for decision-making information.

Rapid sensitivity analysis and near-optimal decision-making in contested environments are valuable requirements when providing military logistics support. Port of debarkation denial motivates maneuver from strategic operational locations, further complicating logistics support. Simulations enable rapid concept design, experiment and testing that meet these complicated logistic support demands. However, simulation model analyses are time consuming as output data complexity grows with simulation input. This paper proposes a methodology that leverages the benefits of simulation-based insight and the computational speed of approximate dynamic programming (ADP).

This paper describes a simulated contested logistics environment and demonstrates how output data informs the parameters required for the ADP dialect of reinforcement learning (aka Q-learning). Q-learning output includes a near-optimal policy that prescribes decisions for each state modeled in the simulation. This paper's methods conform to DoD simulation modeling practices complemented with AI-enabled decision-making.

This study demonstrates simulation output data as a means of state–space reduction to mitigate the curse of dimensionality. Furthermore, massive amounts of simulation output data become unwieldy. This work demonstrates how Q-learning parameters reflect simulation inputs so that simulation model behavior can compare to near-optimal policies.

Fast computation is attractive for sensitivity analysis while divorcing evaluation from scenario-based limitations. The United States military is eager to embrace emerging AI analytic techniques to inform decision-making but is hesitant to abandon simulation modeling. This paper proposes Q-learning as an aid to overcome cognitive limitations in a way that satisfies the desire to wield AI-enabled decision-making combined with modeling and simulation.

In the last four years, since Volume I of this Bibliography first appeared, there has been an explosion of literature in all the main functional areas of business. This wealth of material poses problems for the researcher in management studies — and, of course, for the librarian: uncovering what has been written in any one area is not an easy task. This volume aims to help the librarian and the researcher overcome some of the immediate problems of identification of material. It is an annotated bibliography of management, drawing on the wide variety of literature produced by MCB University Press. Over the last four years, MCB University Press has produced an extensive range of books and serial publications covering most of the established and many of the developing areas of management. This volume, in conjunction with Volume I, provides a guide to all the material published so far.

In a logistics system the number of possible applications of qualitative and quantitative modelling efforts is practically infinite. The situations or problems which can be analysed range from micro operations (e.g., analysing alternative order picking systems) to macro planning issues (e.g., determining the number and location of warehouses to be employed in a future distribution system). This range of potential applications not only reflects the scope of the models (micro to macro) but it also incorporates another relevant dimension: planning. The planning axis is anchored at one end by operations models which are constructed to analyse daily operating practices. The other end of the continuum is represented by long range planning models. These models are used to aid logistics managers in their attempts to design systems to cope with the future. In many situations exact classification of individual models along these two dimensions is difficult. However, this categorisation is a useful framework within which to view the scope of the modelling discussed in this paper. This presentation will focus on the macro planning models that are being employed both in military and industrial logistics to aid the systems planner in his attempts to structure logistics systems for the future.

From a supply chain perspective, logistics firms collaborate with other supply chain members to extend their business scope. Investment in circular economy projects in the supply chain can not only broaden the scope of business but also increase the value of the entire supply chain. Third-party logistics companies are gradually participating in the construction and operation of many circular economy projects. How to coordinate multiple circular economy supply chain projects is at the core of its operation.

This paper first analyzes some typical supply chain projects in China and summarizes the main features of these projects. Secondly, considering the benefits of the project and the stakes of each project, a multi-stage stochastic programming model is established. Finally, Cplex, nested decomposition, LocalSolver and other methods are adopted to simulate and analyze the model.

The final experimental results find that the importance of coordinating multiple circular economy supply chain projects to increase the value of the entire supply chain. The multi-stage stochastic programming model presented in this research can provide a useful tool for logistics enterprises and third-party logistics companies to optimize their investment decisions and maximize their profits in the context of a circular economy.

There are still some limitations to this study; for example, it is limited to the analysis of circular economy supply chain projects in China. The study focused on third-party logistics companies, and other enterprises in the circular economy supply chain were not considered. The research also assumed that the benefits of each circular economy project and the stakes of each project were known, which may not always be the case in real-world scenarios.

This manuscript found that investing in other circular economy projects in the supply chain can broaden the scope of business and increase the value of the entire supply chain. Third-party logistics companies are gradually participating in the construction and operation of many circular economy projects, such as recycling and repurposing initiatives. It highlights the importance of coordinating multiple circular economy supply chain projects to increase the value of the entire supply chain. The multi-stage stochastic programming model presented in this research can provide a useful tool for logistics enterprises and third-party logistics companies to optimize their investment decisions and maximize their profits in the context of a circular economy.