The vendor management inventory is one of those practices, in which the supplier is responsible for managing the inventory of the customers in accordance with pre-determined management policies. The problem that considers transportation decisions in addition to inventory management is referred to as the Inventory Routing Problem IRP.
The classic IRP considers a system with one supplier that manages the inventory level of a set of customers aiming at defining when and how much products to supply and how to combine customers in routes while minimizing storage and transportation costs. We present an extension of this problem that considers a two-echelon system with indirect deliveries and route decisions at both levels.
In this variant, the customers demands have to be met by deliveries through distribution centers with minimum total cost. We propose two mathematical formulations for the two-echelon IRP under different inventory policies.
Logistics Division – elogistica
Computational experiments on a set of randomly generated instances evaluate the proposed formulations. The obtained results show that both formulations are able to solve to the proven optimality small-scale instances and one of the models found feasible solutions for almost all instances under all considered inventory policies.
The thermal inertia of a cold room acts as an energy storage and can therefore be used for economic optimization in the presence of a dynamic electricity price, under a bounding constraint on the internal temperature of the cold room. However, a high number of frost production startups may induce premature wear of the cold store's compressors.
Since the thermal losses are a function of the internal temperature of the cold room, conventional inventory management solving techniques are not suited for this problem. In this paper, we use an artificial neural network as temperature forecast. A dynamic programming algorithm is used to solve the model that includes the non-linear artificial neural network temperature forecast and a fixed cost at each compressor startup.
This allows us to solve industrial instances of the problem optimally and within reasonable time. We show the interest of solving the problem optimally as opposed to using a conventional hysteresis-based control method, and discuss the opportunity of using an dynamic hourly price based on the electricity market instead of a traditional contracted price. Currently, low-income families are the ones in charge of collecting recyclable materials directly from mixed-waste bins and taking them to public collection centers, where they are manually classified.
Collection centers become an interesting scenario to introduce collaborative robots in the monotonous and unhealthy task of classifying plastic bottles. However, safety and reliance are critical points that need to be ensured. This paper presents the design of a safety protocol for the waste separation task.
The protocol assessment is conducted on a testbed created to emulate a pre-classification workstation of plastic bottles.
Process Simulation and Optimization in Sustainable Logistics and Manufacturing (EcoProduction)
The objects have to be packed into a cuboid of minimum height under continuous rotations, translations and minimum allowable distances between objects. The problem has various applications and arises, e. Containment, distance and non-overlapping constraints are described using the phi-function technique. The irregular packing problem is formulated in the form of nonlinear programming problem.
A solution algorithm is proposed based on a fast starting point algorithm and efficient local optimization procedure. These intelligent components of the factory robots can execute complex tasks and can make local decisions. Task allocation in this context should satisfy several requirements in terms of time, cost and utilisation rate of production tools. Some expensive tools like grippers or robotic arms are used sporadically during the execution of tasks.
In order to increase the utilisation rate of these kind of devices, several methods can be deployed. Effective and safe resources' sharing between different production tools and process operations is one of the most important challenges for Industry 4. This paper presents an overview of Multi-Robot Task Allocation MRTA based on shared resources constraints for flexible and reconfigurable manufacturing systems.
We found that there is few work on the scheduling and MRTA with shared resources. An illustrative industrial case based on gripper sharing is presented to illustrate the complexity of this problem. Several research directions are pointed out and discussed in the conclusion. In the initial design phase of body shop, throughput is the major concern and simulation is widely used for analysis.
However, simulation requires so much time and effort when it is applied to the optimization problem of manufacturing system design such as optimal buffer allocation. In this study, an approach for developing quadratic meta-model in an automotive body shop with reconfigurable under body line when two types of cars are produced. The accuracy of meta-model is reasonable and it can be used for optimization problem. Agents with different skill types, skill levels and work contract types have to be assigned to tasks required by clients.
Tasks usually require a big number of agents but this number is stable over time. Choosing the assignment, the company has two important objectives to attain including minimizing the global travel costs and maximizing the satisfaction level of its agents related to their average work trip duration. Based on the MILP model proposed, we perform some experiments on a real database provided by a Brazilian partner. These experiments allow us to study the interactions between the objectives, and the impact of some important characteristics on the solutions of the problem.
To speed-up the algorithm, the support vector machine SVM is adopted to help selecting the branching variables at each node of the branch-and-bound tree. Using SVM to select the branching variables can effectively reduce the number of generated branch nodes and hence speed up the algorithm. While strategic decisions focus on long-term aspects of the chain, operational decisions are about the daily processes.
In many cases, these decisions are interconnected and overlooking their interactions and their feedback to each other — which is a common practice in the existing literature - may lead to either infeasible or sub-optimal solutions.
Moreover, the majority of supply chains are embedded in a larger social and economic context where continuous changes in the market competition and regulation or technological developments would constantly influence the performance of companies as well as the whole supply chain. To support the decision making in a global supply chain, a flexible simulation environment is needed that captures the complexity of the system and explicitly address different sources of dynamics in a chain and its surrounding.
This paper aims to present a simulation platform to support the multi-level decision process in a supply chain. This provides the flexibility to model different types of dynamics across different timescales. The simulation platform is illustrated using a case study of the SC network design of a European producer of a fast moving product. The SC network comprises one plant, one central distribution center, and three regional distribution centers. The network delivers the product to five European countries as the main market areas.
The logistics processes are modeled using ABM while the dynamics in the market demand is modeled using an SD model. As a result, all necessary dynamics must be modeled to provide the needed support to make well-informed decisions in a global supply chain. Provider, demander, operators are three main participants in a SOM system. These parts are modeled as members of an integrated hyper-network providing manufacturing services. In these hyper-network the relation of service providers service-network and service demanders demand-network are managed through some specific hyper-edge.
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In this concept, demand may be contain more than one task; in the other words, it could be primitive or compound task each compound task contains more than one primitive task. However, each primitive task should to be matched with a specified service to be fulfilled. In the matching process, it determines whether services are suitable for tasks in terms of functional and non-functional requirements or not.
The issue of supply—demand matching of manufacturing services is one of the most important topics in service-oriented manufacturing. Therefore, it is necessary to consider some requirements from customer aspects which are named quality of service QoS parameters along with the functional once. As well as these parameters, some other factors, such as availability, maintainability, flexibility, and the way of usage, may be effective in whole performance of cloud manufacturing networks.
These issue have been investigated by some researchers in the literature with the focus on the methods based on template, workflow, artificial intelligence, agent, service composition, graph theory and so on; but the paper introduces the new way of modeling service-demand matching process by considering QoS parameters as labels for elements of the hyper-network. This hot metal contains some amount of Silicon HMSi , which describes the thermal state of the blast furnace and thus its running state. The HMSi is also an indicator of the quality of the hot metal produced, and strongly influences the primary and secondary steelmaking processes.
Due to the complexity of the physico-chemical phenomena involved, and the large number of factors governing the process, a tight control of HMSi is yet to be achieved. Nevertheless, the large amount of data recorded on the industrial scale at our disposal, can be leveraged for knowledge extraction using data mining and analytics.
The received data was pre-processed using techniques developed in-house. We devised an algorithm to automatically synchronize and identify lags pertaining to transportation delay, storage and process residence time. This lag identification facilitated knowledge creation, correct mapping of the output to input parameters, and thus model improvement. Subsequently, the key parameters influencing HMSi were found using the ensemble feature selection approach and then utilized to build models for the prediction and forecasting of the Silicon Content.
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- What are Engineering Simulation (PES) Tools? | ARC Advisory Group.
The perception of the customers about the products manufactured by the organization with and without ISO have been explored. A comprehensive structured questionnaire was used for the survey with 24 individual customers and 11 small and medium sized enterprises SMEs. Remanufacturability of end-of-life product is proportional to its remanufacturability level, which is set at the beginning of life cycle of the product by the original equipment manufacturer OEM.https://kandglasnistsa.tk
ISBN 13: 9783319073460
Another issue is not knowing which returned items are suitable for remanufacturing operations, i. When the original product is designed and produced for remanufacturing, other firms also engage in remanufacturing. These are called independent remanufacturers IR.