Liquidity is a liveness property of programs managing resources that pinpoints those programs not freezing any resource forever. We consider a simple stateful language whose resources are assets (digital currencies, non fungible tokens, etc.). Then we define a type system that tracks in a symbolic way the input-output behaviour of functions with respect to assets. These types and their composition, which define types of computations, allow us to design two algorithms for liquidity that have different precisions and costs. We also demonstrate the correctness of the algorithms.
The conventional wisdom is that you must reveal something about how you pick stocks in order to prove that you have stock-picking skill. In this paper I show that, prior to executing any trades, it is possible to prove you have stock-picking skill without revealing any additional information about your underlying trading signal. Here is how the protocol works. The evaluator presents you with a sequence of paired return data sets, one real and the other suitably randomized. A profitable trading signal will only be able to predict the cross-section of returns in the real data set. So by repeatedly using your trading signal to identify the real data set, you can prove that you have stock-picking skill without revealing anything else about your underlying signal. This protocol represents a zero-knowledge proof of stock-picking skill—i.e., a proof which reveals nothing except for the validity of your claim. Zero-knowledge proofs allow any skilled stock picker to advertise his ability without fear of his trading signal getting scooped. As a result, they have important implications for how the active-management industry is organized.
Beatriz Abdul‐Jalbar, Roberto Dorta‐Guerra, José M. Gutiérrez, Joaquı́n Sicilia
Trade credit is a crucial source of capital particularly for small businesses with limited financing opportunities. Inventory models considering trade credit financing have been widely studied. However, while there is extensive research on the single-vendor single-buyer inventory model allowing delays in payments, the systems where the vendor supplies to more than one buyer have received less attention. In this paper, we analyze a two-echelon inventory system where a single vendor supplies an item to two buyers who face a constant deterministic demand. The vendor produces the items at a finite rate and offers the buyers a delay payment period. That is, the buyers can delay the payment for the purchased items until the end of the credit period. Therefore, during such a period, the buyers sell the items and use the sales revenue to earn interest. At the end of the credit period, the buyers should pay the purchasing cost to the vendor for which external funding may be necessary. It is widely accepted that, in general, centralized policies reduce the total cost of the supply chain. Therefore, we first deal with an integrated model assuming that the vendor and the buyers make decisions jointly. However, in some cases, the buyers are not willing to collaborate, and the management of the supply chain has to be carried out in a decentralized manner. Hence, we also address the problem under a non-cooperative setting. Numerical examples are presented to illustrate both models. Additionally, we perform a computational experiment to compare both strategies, and a sensitivity analysis of the parameters is also carried out. From the results, we derived that, in general, it was more profitable to follow the integrated policy excepting when the replenishment costs for the buyers were high. Finally, in order to validate the computational results, a statistical analysis is performed.
Yield farming has been an immensely popular activity for cryptocurrency holders since the explosion of Decentralized Finance (DeFi) in the summer of 2020. In this Systematization of Knowledge (SoK), we study a general framework for yield farming strategies with empirical analysis. First, we summarize the fundamentals of yield farming by focusing on the protocols and tokens used by aggregators. We then examine the sources of yield and translate those into three example yield farming strategies, followed by the simulations of yield farming performance, based on these strategies. We further compare four major yield aggregrators -- Idle, Pickle, Harvest and Yearn -- in the ecosystem, along with brief introductions of others. We systematize their strategies and revenue models, and conduct an empirical analysis with on-chain data from example vaults, to find a plausible connection between data anomalies and historical events. Finally, we discuss the benefits and risks of yield aggregators.
Johannes Fottner, Dana Clauer, Fabian Hormes, Michael Freitag · 13 authors
The paper at hand presents a definition of autonomous intralogistics systems and a classification of intralogistics systems with regard to their degree of autonomy. Intralogistics -; a complex interplay of different logistics functions - covers the organization, control, execution and optimization of internal material and information flows. Over the past two decades, numerous authors have observed and proclaimed an increase in complexity in manufacturing and supply chain operations. A key approach to face this challenge is a paradigm shift from centralized, hierarchical organization structures towards, networked and autonomous systems. Autonomous intralogistics systems enable self-contained, decentralized planning, execution, control, and optimization of internal material and information flows through cooperation and interaction with other systems and with humans.Based on the definition of autonomous intralogistics systems, the authors propose a two-dimensional classification framework covering different automation stages for different intralogistics task levels. The developed classification framework is applied to various industry use cases to evaluate and discuss the state of the art regarding the implementation of autonomous intralogistics systems. Finally, the paper provides an outlook on future research and poses key research questions.
Nina-Birte Schirrmacher, Johannes Rude Jensen, Michel Avital
An emerging type of organization challenges the assumptions of what an organization is and how actors work: fluid organizations are characterized by continually changing templates of boundaries, decision-making, and task and role allocation. Increasingly, fluid organizations form around digital tokens, which resemble common shares in a corporation. In this study, we draw on the theoretical lens of practices to explore how the use of tokens shapes work in fluid organizations. We conduct a netnography among actors of two token-issuing fluid organizations in the decentralized finance sector. We identify token-centric practices that (i) leave actors striving toward a goal, giving rise to flexibility, and (ii) are institutionalized, giving rise to a structure. However, these practices also evoke tensions that the actors seek to continuously mitigate through action on a continuum of solutions to emerging problems. The findings contribute to the emerging literature on work in fluid organizations.
This study introduces a dual‐channel supply chain including a supplier and a retailer with capital constraints, in which the retailer can apply for the trade credit financing from the supplier. This work investigates the effects of two typical behaviors, free riding behavior and consumer switching behavior, on inventory, ordering, and sales effort decisions in decentralized and centralized decision situations with stochastic demand. In order to achieve the optimal performance in the centralized system, this research designs a partial buyback contract to coordinate the supply chain. Furthermore, numerical analysis is provided to test the feasibility of the model. The results indicate that in the dual‐channel supply chain with the above two behaviors, (1) the optimal sales effort level, optimal order quantity, the optimal offline, and online profits under the centralized decision‐making are more than those under decentralized scenario, except for the optimal inventory level; (2) the increase of the offline consumer switching rate will lead to the reduction of the offline order quantity and the offline expected profit and raise the online inventory level and the online expected profit; (3) the increase of the online consumer switching rate will raise the offline order quantity and the offline expected profit but has no significant impact on the online inventory level and the online expected profit; (4) the increase of the free riding coefficient of the supplier, no matter whether in decentralized or centralized systems, will reduce the offline sales effort level, the offline expected profit, and the online expected profit and raise the inventory level. Finally, this work provides some managerial implication.
Data protection and transparency are highly recommended modern approaches for any transaction domain. The proposed layer model will provide an improved and secure approach for transporting of goods using crypto currencies and excluding intermediate parties. Paper gives temporarily overview of crypto currency, logistics movement channels, block chain evaluation, challenges, types, applications and layer model that shows movements of good from scratch (source) to delivery (client destination) through secured transactions medium, excluding intermediate parties (banks etc.) storing signatures in distributed ledger.
Roberta Pellegrino, Nicola Costantino, Danilo Tauro
The purpose of this paper is to study how advanced information about customer needs obtained through an Advance Purchase Discount (APD) contract can be exploited to coordinate the capital flow and enhance the efficiency of a two-stage supply chain (SC) under decentralized control in cases of stochastic customer demand. We developed an APD model in the form of an option contract, where the model and evaluation include the flexibility for the upstream firm to decide whether to provide a discount for an advance purchase at its own discretion. Applying the model to a Fortune 100 company, a leader in the Fast Mover Consumer Goods (FMCG) industry, showed that under certain conditions, and with suitably chosen contract parameters, management of decentralized control via APD contracts can lead to system-wide efficiency, and the individual decision makers pursue their own best interests, ensuring a win-win condition.
<p style='text-indent:20px;'>Manufacturers often face capital constraints when opening up online channel, at this time external financing and internal financing are usually considered. Previous literature has shown that internal financing, turns out to be a better option. To figure out how trade credit financing discount contract affects operations and performances of supply chain, this paper studies the pricing decision of a retailer-dominant dual-channel supply chain with manufacturer's capital constraints. The Stackelberg game models under centralized decision and decentralized decision are constructed. Moreover, this paper conducts research about the effects of revenue-sharing (RS) contract, direct channel price discount (DP) contract and retail channel price discount (RP) contract on the performance of supply chain. Numerical examples are provided to explore the comparison of the optimal pricing strategies and total profits under different contracts. The results show that the retailer prefers RS and DP contracts to RP contract. Among them, RS contract has a broader scope of coordination, while DP contract can achieve a higher profit. The results can serve as insights for decision-makers to choose the most appropriate financial discount contract.
Conventional railway operations employ specialized software and hardware to ensure safe and secure train operations. Track occupation and signaling are governed by central control offices, while trains (and their drivers) receive instructions. To make this setup more dynamic, the train operations can be decentralized by enabling the trains to find routes and make decisions which are safeguarded and protocolled in an auditable manner. In this paper, we present the case study findings of a first-of-its-kind blockchain-based prototype implementation for railway control, based on decentralization but also ensuring that the overall system state remains conflict-free and safe. We also show how a blockchain-based approach simplifies usage billing and enables a train-to-train/machine-to-machine economy. Finally, first ideas addressing the use of blockchain technology as a life-cycle approach for condition-based monitoring and predictive maintenance in train operations are outlined.
Conventional railway operations employ specialized software and hardware to\nensure safe and secure train operations. Track occupation and signaling are\ngoverned by central control offices, while trains (and their drivers) receive\ninstructions. To make this setup more dynamic, the train operations can be\ndecentralized by enabling the trains to find routes and make decisions which\nare safeguarded and protocolled in an auditable manner. In this paper, we\npresent the findings of a first-of-its-kind blockchain-based prototype\nimplementation for railway control, based on decentralization but also ensuring\nthat the overall system state remains conflict-free and safe. We also show how\na blockchain-based approach simplifies usage billing and enables a\ntrain-to-train/machine-to-machine economy. Finally, first ideas addressing the\nuse of blockchain as a life-cycle approach for condition based monitoring and\npredictive maintenance in train operations are outlined.\n
The persistent development towards decreasing batch sizes due to an ongoing product individualization, as well as increasingly dynamic market and competitive conditions lead to new changeability requirements in production environments. Since each of the individualized products might require different base materials or components and manufacturing resources, the paths of the products going through the factory as well as the required internal transport and material supply processes are going to differ for every product. Conventional planning and control systems, which rely on predefined processes and central decision-making, are not capable to deal with the arising system’s complexity along the dimensions of changing goods, layouts and throughput requirements. The concepts of “self-organization” in combination with “autonomous control” provide promising solutions to solve these new requirements by using among other things the potential of autonomous, decentralized and target-optimized decision-making. A major enabler for the development towards autonomous changeable intralogistics systems are intelligent logistical objects (e.g. smart products, bins and conveyor systems) which are able to communicate and interact with each other as well as with human workers. To investigate the potential of automation and human-robot collaboration for intralogistics, a research project for the development of a collaborative tugger train has been started at the ESB Logistics Learning Factory in line with various student projects in neighboring research areas. This collaborative tugger train system in combination with other manual (e.g. handcarts) and (semi-)automated conveyor systems (e.g. automated guided forklift) will be integrated into a dynamic, self-organized scenario with varying production batch sizes to develop a method for target-oriented self-organization and autonomous control of intralogistics systems. For a structured investigation of self-organized scenarios a generic intralogistics model as well as a criteria catalogue has been developed. The ESB Logistics Learning will serve as a practice-oriented research, validation and demonstration environment for these purposes.
José Barbosa, Paulo Leitão, Adriano Ferreira, Jonas Queiroz · 6 authors
This paper describes the development of a multiagent system (MAS) to support the implementation of zero-defect manufacturing strategies in multi-stage production systems. The MAS infrastructure, combined with on-line inspection tools, data analytics and knowledge generation, constitutes a suitable approach to integrate process and quality control in multi-stage environments. This will allow the early detection of product defects, the adaptation to operating condition changes and the optimisation of manufacturing processes. This type of integrated management structure is aligned with a zero-defect manufacturing production model which is of paramount importance in the actual state-of-the-art manufacturing paradigms. As a proof of concept, the devised manufacturing supervision model was deployed into an experimental multi-stage system that run a set of several tests on electrical motors. The agent-based solution was implemented using the JADE framework and the exchange of information structured by proper data models and industrial based Internet-of-Things and Machine-to-Machine technologies, such as OPC-UA, REST and JSON. The obtained results demonstrate the suitability of the devised integrated management model as a vehicle to achieve dynamic and continuous system improvement in multi-stage manufacturing environments.
Christoph Berger, Urs Hoffmann, Stefan Braunreuther, Gunther Reinhart
Regarding the changing market environment in terms of logic requirements, production planning and control recently contribute significantly to fulfilling these demands. Logistic command variables, particularly adherence to schedule, are becoming the crucial parameters to satisfy the customer's needs. Cyber-Physical production Systems (CPPS) with their characteristics decentralized organization, autonomous control, real-time capability and smart data processing offer new possibilities of production monitoring and control. For this purpose, this paper proposes a new event-based approach in order to improve adherence to schedule in production by using the potential of CPPS. Control loops close to production shop floor provide a fast identification of events. Based on an activity list, the production control is able to react adequately to the different events e.g. machine disturbance or urgent orders. The activities initiated by the Manufacturing Execution System (MES) affect the whole production system while the production. In a final step, the developed concept of an event-driven production control was implement in a simulation.
Blockchain is a form of distributed ledger technology (DLT) that has grown in prominence, although its full potential and possible downsides are not yet fully understood, especially with respect to Operations Management (OM). This manuscript contributes to filling in this gap. We identify three research themes in applying Blockchain technology to OM, illustrated through several applications to OM problems. Elsewhere, in a companion article, (Babich and Hilary (2018)), we provide a conceptual framework for the role of Blockchain and other DLT in OM, along with specific examples of research questions, and we demonstrate how research in economics can inform research in OM on Blockchain applications. Finally, we discuss possible future uses for the technology.
For any industry or company, to be a competent in market needs increase in performance in all possible ways. Quick response to market and sufficient production as per requirement is the only way to increase the returns, which require critical planning and production systems. For this co-ordination between business units or workstations is essential. Many of the executive managers in industries has to instruct only the task flows to its subordinates, that is a single straightforward production planning process is followed as executed from top level of organization; their capacity as per their education level is not utilized more than 10%. The use of multi-agent system allows physical distribution of the decisional system and procures a hierarchical organization structure with decentralized control that guarantees the autonomy of each entity and flexibility of network. Our study focuses on managers/partners that adapt together their local planning process to face different requirements of supply chain environment using different planning strategies, when decisions are supported by distributed planning systems. The agent based system has the advantage of making collaborative management of disturbances in supply chain as the agents has the advantage of making autonomous decisions in a distributed network. Because each partner can choose different behavior and all behavior has an impact on the overall performance, it is difficult to know which is preferable for each partner to increase their performance. Thus, in this paper study of Multi-behavior planning agent model is done using different planning strategies when decisions are supported by distributed planning system.
The innovation of supply chain financial services can alleviate the plight of SMEs financing difficulties. In the aspect of supply chain finance model, there is a credit guarantee financing model, which is different from the simple external financing and internal financing mode of supply chain. Based on this, this paper studies the decision-making of supply chain finance under the partial credit guarantee of core enterprises. First of all, the paper constructs a simple supply chain financing model, consisting of a bank, a core enterprise and a retailer. And then, considering the credit guarantee financing model, calculate the expected profit function. Stackelberg game model is used to give the optimal decision of each subject in decentralized system and the optimal decision in centralized system. Finally, in order to make a more specific and detailed study on the profit and decision-making based on the credit guarantee financing model, the important parameters of the model are analyzed. Through the calculation, it is proved that under the credit guarantee of the core enterprise, the retailer has the optimal ordering strategy, and the core enterprise has the best wholesale price. The influences of the partial credit guarantee coefficient and the retailer’s loan coefficient on the supply chain finance decision-making are also studied.
Future factory production and assembly environments require smart automation and are controlled by a massively increasing number of computers with sensorial feedback from machines, parts, products, and humans, consisting of a wide variety of different networked devices and software programs, which can be considered as one big use case of pervasive and cloud computing with vanishing boundaries between the computing and the environment, and with a strong focus on decentralized distributed computing and information storage. These new complex information processing architectures are composed of hierarchical network graphs, and require some kind of self-organization and adaptability to overcome single-point of failure and robustness constraints. The data acquired from machines and sensitive products or parts are growing at a fast rate, leading to a large data volume that must be handled distributed with pre-processing, map- and reduce, and filtering algorithms. Mobile Agents can be deployed in such large-scale and hierarchical network environments crossing barriers transparently, for example, industrial manufacturing and assembly environments, the Internet, Sensor Networks, and Cyber-Physical Systems. The networks can consist of high- and low-resource nodes ranging from generic computers to microchips, and the supported network classes range from body area networks to the Internet including any kind of sensor and ambient network. Mobile Agents can perform distributed computation in an autonomous manner.In this work Agents are represented by mobile program code that can be modified at run-time by the Agents themselves. The presented approach enables the development of sensor clouds and smart systems of the future integrated in daily use computing environments and the Internet. Agents can migrate between different hardware and software platforms by migrating the program code of the agent, which embeds the control state and the private data of an agent, finally encapsulated in self-initializing and self-containing code frames.This cross-platform interoperability is ensured by a modular and scalable Agent Processing Platform. The entire information exchange and co-ordination of Agents with other Agents and the environment is performed by using a Tuple Space database, unifying the platform and architecture specific data representation. The Tuple Space is used for any kind of information exchange including program code and directory and file system services mapped on the Tuple Space. Beside architecture specific hardware and software implementations of the agent processing platform, there is a JavaScript (JS) implementation layered on the top of a distributed co-ordination and management layer including distributed file and name services. The JS platform enables the integration of Multi-Agent Systems (MAS) in Internet server and application environments (e.g., WEB browser). Agents can migrate transparently between different classes of computing devices and environments, ranging from hardware-level sensor networks (embedded in technical structures) to WEB browser applications or network servers without any required transformation.
Muhammad Hafidz Fazli Md Fauadi, Fairul Azni Jafar, Adi Saptari, Wan Ling Li
The increasingly challenging and dynamic nature of manufacturing industry has driven organizations to enhance their system efficiency. One of the most important aspects in manufacturing plant is the Material Transportation System (MTS). In order to address dynamic factors, MTS need to be equipped with rescheduling capability. This paper focuses on reactive transportation task assignment to a fleet of autonomous Automated Guided Vehicles (AGVs). This paper proposes a Mixed Integer Programming method to reschedule transportation tasks for a non-disruptive job shop manufacturing system based on Multi Agent System (MAS) architecture. The result shows that proposed rescheduling method is able to outperform conventional method significantly.
Up-to-date market dynamics and intense competition have forced a production system to be more widely distributed and decentralized than ever, and the production system itself can be regarded as a collaborative network of autonomous production resources in which the responsibility of decision making is also decentralized into individual autonomous entities. The conventional resource management models, however, are not suitable for the distributed and decentralized environment because of their centralized nature. In this paper, an agent-based resource management model is proposed. The proposed model applies employment relation-driven fractal organization (FrOrg) into organizational model for distributed production resources and presents a resource management framework based on employment contracts. The fractal organization is a structured association in which a self-similar pattern recursively appears, and employment relations between production resources are recursively constructed throughout the entire production system.
Daniel Roy, Didier Anciaux, Thibaud Monteiro, Latifa Ouzizi
The purpose of this paper is to propose a new approach for the supply chain management. This approach is based on the virtual enterprise paradigm and the used of multi-agent concept. Each entity (like enterprise) is autonomous and must perform local and global goals in relation with its environment. The base component of our approach is a Virtual Enterprise Node (VEN). The supply chain is viewed as a set of tiers (corresponding to the levels of production), in which each partner of the supply chain (VEN) is in relation with several customers and suppliers. Each VEN belongs to one tier. The main customer gives global objectives (quantity, cost and delay) to the supply chain. The Mediator Agent (MA) is in charge to manage the supply chain in order to respect those objectives as global level. Those objectives are taking over to Negotiator Agent at the tier level (NAT). These two agents are only active if a perturbation occurs; otherwise information flows are only exchange between VENs. This architecture allows supply chains management which is completely transparent seen from simple enterprise of the supply chain. The used of Multi-Agent System (MAS) allows physical distribution of the decisional system. Moreover, the hierarchical organizational structure with a decentralized control guaranties, in the same time, the autonomy of each entity and the whole flexibility.