(1962). Management Models and Industrial Applications of Linear Programming. Journal of the Operational Research Society: Vol. 13, No. 3, pp. 274-275.

https://link.springer.com/content/pdf/10.1057%2Fjors.1962.42.pdf

Management Models and Industrial Applications of Linear Programming

Multiple criteria group decision making based on group satisfaction

Over the past decades, container transportation has achieved considerable growth, and maritime trade now constitutes 80% of the global trade. The vessel sizes increased in parallel, up to 21,400 TEU (Twenty-foot-equivalent unit container). Accordingly, global containerized trade reached up to 150 million TEU in 2017 (UNCTAD 2018). This growth brings the need to use scientific methods to manage and operate container terminals more economically throughout the globe. In order to manage container transshipment and to use large vessels efficiently, the docking time at the container port for each vessel should be minimized. The decrease in the docking time enables the vessel to move to its next destination faster, decreasing turnover time and facilitating more containers to be transported. Container terminals can be divided into five main areas as the berth, the quay, the storage yard, the transport area, and the gate. The vessels must be berthed in suitable positions, after which many containers have to be unloaded or loaded via quay cranes, transshipped by vehicles inside the terminal, and stacked by yard cranes to suitable positions, all by using expensive equipment. With the invention of new technologies, the bottleneck at the berth side is almost overcome; however, the yard and the quayside operations have to be further perfected to obtain efficient plans. In this comprehensive literature review study, we aim to combine the literature on both yard and quayside operations, carefully examining independently studied problems as well as integrated ones. General information about port operations and relevant literature is provided. For the quayside, the literature on quay crane assignment and scheduling problems is investigated, whereas, for the yard side, yard crane scheduling, transport vehicle dispatching and scheduling, vehicle routing and traffic control, and storage location and space planning problems are reviewed in depth. In addition to these individual problems, their integrations are also analyzed, relevant publications and their respective contributions are explained in detail. Besides the milestone papers that lead the literature on container terminals, recent publications and advances are also reviewed, and managerial insights and future research directions are identified.

A comprehensive review of quay crane scheduling, yard operations and integrations thereof in container terminals

An exact algorithm for the unrestricted block relocation problem

In the era of the Internet and big data, data permeate the entire process of multiple criteria decision making (MCDM). Therefore, generation of rational solutions from current observations and historical data has become an important and interesting issue. To address this issue, this paper proposes a data-driven MCDM framework in the context of the evidential reasoning approach. Three challenges in the framework are met, including the transformation of observations into assessments, the learning of parameters and their constraints from historical data, and the generation of a data-driven solution. The proposed framework is then used to model the diagnosis of thyroid cancer and generate data-driven diagnostic results. The three challenges in the application are met to aid radiologists in improving the diagnostic accuracy of thyroid cancers. To examine whether the application of the proposed data-driven MCDM framework to the diagnosis of thyroid cancer can help improve diagnostic accuracy, we conduct a case study by using the examination reports of three radiologists from July 2015 to October 2017 in the ultrasonic department of a tertiary hospital located in Hefei, Anhui Province, China.

Data-driven multiple criteria decision making for diagnosis of thyroid cancer

The trade determination problem is an important decision problem for multi-attribute exchanges in E-brokerages. As of now, some studies have focused on this issue. However, theories and guidelines for the trade determination problem under fuzzy environments are still sparse. In this paper, taking into account the fuzzy information involved in one-shot multi-attribute exchanges, a new fuzzy matching model is proposed for the trade determination problem. In the model, we present a novel calculation method of the matching degree based on the improved fuzzy information axiom as a baseline study. Also, the credibility measure and Hurwicz criterion are introduced to convert the model into a crisp one. Since the crisp model is a 0---1 integer programming problem, the commonly used branch and bound algorithm and related optimization techniques become applicable. Finally, an example is employed to illustrate the application and sensitivity analysis of the proposed model.

A fuzzy matching model with Hurwicz criteria for one-shot multi-attribute exchanges in E-brokerage

Problem definition : We have witnessed a rapid rise of on-demand platforms, such as Uber, in the past few years. Although these platforms allow workers to choose their own working hours, they have ...

Making the Most of Your Regret: Workers’ Relocation Decisions in On-Demand Platforms

The stack pre-marshalling (SPM) problem is a complex combinatorial optimization problem which arises mainly within the rapidly growing field of container logistics. This paper proposes a heuristic-guided branch-and- bound (HGB&B) algorithm which can effectively be used to solve the SPM problem. The HGB&B algorithm has a guiding heuristic feature, which is used to search only “valuable” branches. Valueless branches cannot give the optimum solution and hence, are removed prior to the calculation of bounds, which greatly improves the efficiency of algorithm. Additionally, two heuristics have been developed to generate initial upper bounds of the number of moves. Experiments indicate that the time taken to solve the algorithm is acceptable if the product of the number and maximum height of stacks is within about 35. The HGB&B algorithm is faster than existing optimization algorithms, and more efficient than a number of other heuristics. Therefore, it is broadly applicable and valuable in most real-life scenarios, including container logistics, reducing the time taken to find optimal solutions to complex stacking problems.

http://faculty.neu.edu.cn/ise/zhangruiyou/2015-IJIE-Zhang-Jiang-Yun.pdf

Stack pre-marshalling problem: a heuristic-guided branch-and-bound algorithm

Diversity climate—shared employee perceptions of the extent to which an organization is inclusive and fair—is of increasing interest to public administration scholars. While research has linked diversity climate to a range of employee and organizational outcomes, less is known about how common managerial practices affect diversity climate. This article addresses this gap by examining three such practices: workplace voice, centralized decision-making, and teamwork. Each is theoretically expected to act upon both the inclusion and fairness dimensions of diversity climate. We test these expectations using regression analysis of departmental-level data collected through surveys of four North Carolina public organizations. The results suggest that workplace voice and teamwork enhance diversity climate, while centralized decision-making diminishes it in workplaces with mostly white employees. Practically speaking, the results imply that common management techniques that benefit public organizations also foster positive diversity climates. Evidence for Practice Employee perceptions of the extent to which an organization is fair and inclusive, known as diversity climate, have been linked to a range of important organizational outcomes. Common managerial practices that produce organizational benefits also appear to strengthen diversity climate, specifically workplace voice, decentralized decision-making, and teamwork. Public managers wishing to improve diversity climate should consider giving employees meaningful voice in workplace decisions, pushing decisions downward, and fostering teamwork. 

Managerial Practices and Diversity Climate: The Roles of Workplace Voice, Centralization and Teamwork

Problem Definition: We have witnessed a rapid rise of on-demand platforms, such as Uber, in the past few years. While these platforms allow workers to choose their own working hours, they have limited leverage in maintaining the availability of workers within a region. As such, platforms often implement various policies, including offering financial incentives and/or communicating customer demand to workers in order to direct more workers to regions with shortage in supply. This research examines how behavioral biases such as regret aversion may influence workers' relocation decisions and ultimately the system performance. 

Academic/Practical Relevance: Studies on on-demand platforms often assume that workers are rational agents who make optimal decisions. Our research investigates workers' relocation decisions from a behavioral perspective. A deeper understanding of workers' behavioral biases and their causes will help on-demand platforms design appropriate policies to increase their own profit, worker surplus, and the overall efficiency of matching supply with demand. \newline

Methodology: We use a combination of behavioral modeling and controlled lab experiments. We develop analytical models that incorporate regret aversion to produce theoretical predictions, which are then tested and verified via a series of controlled lab experiments. 

Results: We find that regret aversion plays an important role in workers' relocation decisions. Regret averse workers are more willing to relocate to the supply-shortage zone than rational workers. This increased relocation behavior, however, is not sufficient to translate to a better system performance. Platform interventions, such as demand information sharing and dynamic wage bonus, can help further improve the system. We find that workers' regret-aversion behavior may lead to an increased profit for the platform, a higher surplus for the workers, and an improved demand-supply matching efficiency, thus benefiting the entire on-demand system. 

Managerial Implications: Our research emphasizes the importance and necessity of incorporating workers' behavioral biases such as regret aversion into the policy design of on-demand platforms. Policies without considering the behavioral aspect of workers' decisions may lead to lost profit for the platform and reduced welfare for workers and customers, which may ultimately hurt the on-demand business.

Zhong-Zhong Jiang

Papers

A fuzzy matching model with Hurwicz criteria for one-shot multi-attribute exchanges in E-brokerage

Making the Most of Your Regret: Workers’ Relocation Decisions in On-Demand Platforms

Stack pre-marshalling problem: a heuristic-guided branch-and-bound algorithm

Managerial Practices and Diversity Climate: The Roles of Workplace Voice, Centralization and Teamwork

Making the Most of Your Regret: A Behavioral Investigation of Workers’ Relocation in On-Demand Platforms