Integrated Scheduling of Production and Distribution Operations with Site Selection

Alarcon-Gerbier, Eduardo; Buscher, Udo

doi:10.1007/978-3-030-29821-0_17

Cited by 3 publications

(5 citation statements)

References 16 publications

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“…Constraint set (5) implies that each trip starts from the depot. Constraint sets (6) and (7) indicate that a vehicle cannot travel to the dummy depot from a supplier node or a manufacturer node, respectively. Constraint set (8) implies that the capacity of a given vehicle is not exceeded at any time during a trip.…”

Section: B Model Formulationmentioning

confidence: 99%

“…Some manufacturers employ sub-contractors that offer specific services at various locations in order to improve the efficiency of the supply chain network by reducing delivery times [6]. To ensure timely delivery of products, some manufacturers adhere to the distributed manufacturing process, which seeks to move their production sites closer to the location of demand [7]. An emerging concept, which is adopted by certain production companies to address the change and uncertainty associated with customer demand and other challenges related to supply chain management, is a "factory-in-a-box.…”

Section: Introductionmentioning

confidence: 99%

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An Optimization Model and Solution Algorithms for the Vehicle Routing Problem With a “Factory-in-a-Box”

Pasha

Dulebenets

Kavoosi³

et al. 2020

IEEE Access

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The "factory-in-a-box" concept involves assembling production modules (i.e., factories) in containers and transporting the containers to different customer locations. Such a concept could be highly effective during emergencies, when there is an urgent demand for products (e.g., the COVID-19 pandemic). The "factory-in-a-box" planning problem can be divided into two sub-problems. The first sub-problem deals with the assignment of raw materials to suppliers, sub-assembly decomposition, assignment of subassembly modules to manufacturers, and assignment of tasks to manufacturers. The second sub-problem focuses on the transport of sub-assembly modules between suppliers and manufacturers by assigning vehicles to locations, deciding the order of visits for suppliers, manufacturers, and customers, and selecting the appropriate routes within the transportation network. This study addresses the second sub-problem, which resembles the vehicle routing problem, by developing an optimization model and solution algorithms in order to optimize the "factory-in-a-box" supply chain. A mixed-integer linear programming model, which aims to minimize the total cost of the "factory-in-a-box" supply chain, is presented in this study. CPLEX is used to solve the model to the global optimality, while four metaheuristic algorithms, including the Evolutionary Algorithm, Variable Neighborhood Search, Tabu Search, and Simulated Annealing, are employed to solve the model for large-scale problem instances. A set of numerical experiments, conducted for a case study of "factory-in-a-box", demonstrate that the Evolutionary Algorithm outperforms the other metaheuristic algorithms developed for the model. Some managerial insights are outlined in the numerical experiments as well. INDEX TERMS Factory-in-a-box, metaheuristics, supply chains, urgent demand, vehicle routing problem.

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Section: B Model Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Optimization Model and Solution Algorithms for the Vehicle Routing Problem With a “Factory-in-a-Box”

Pasha

Dulebenets

Kavoosi³

et al. 2020

IEEE Access

View full text Add to dashboard Cite

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“…Check whether we have a minimum of N such executed history track segments or not; Initially, N could be 10, but it will be a parameter, which can be adjusted time, after obtaining enough data; (3) For those N historical data trips, compare the segments and count the paths that are similar to an acceptable percentage (e.g., more than 90%), as the common road traveled from X to X + 1; (4)…”

Section: Proposed Historical Routing Algorithmmentioning

confidence: 99%

“…Thus, the number of customers ordering online has increased and they are putting pressure on shipping companies because they want fast and cheap delivery. In order not to delay the delivery of goods, manufacturers adhere to the process of distributed manufacturing by moving production sites as close as possible to the location of demand [3]. Companies that 2 of 27 transport small packages minimize the cost by keeping up the routes of the drivers and want to build customer relationships by using the same agents to visit the same customers at about the same time every day [4], which is why it should come as no surprise that the vehicle routing problem (VRP) is a popular optimization problem when it comes to finding the best possible routes (several types of routing problems have been extended from the VRP).…”

Section: Introductionmentioning

confidence: 99%

iRoute—An Adaptive Route Planning Solution for Commercial Vehicle Fleets

Anghelache,

Marian,

Mitrea

et al. 2023

Applied Sciences

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This article presents the results and conclusions of a research and development project for creating a commercial solution for vehicle fleets that will help businesses to have more adaptive routes optimized to the way they are running their businesses, local conditions, and drivers’ knowledge, avoiding road hazards known by drivers that frequently use the road path. Our solution consists of a data ingestion service from GPS devices, an integration layer with the end-customer applications, a route optimization engine, and two end-user applications: web and mobile. The solution presented in this article differs from other approaches as it uses historical route data to better adjust and optimize the final routes so that the results are more likely to be executed by drivers according to the initial criteria and plan in terms of distance and time. By using an innovative way of adjusting the optimized routes considering historical tracks, we obtained good results with up to a 20% improvement in terms of real executed distance and time versus standard optimizing algorithms. That means the business owners can better rely on the optimization process results, having access to a more realistic optimization plan that their drivers can easily follow.

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“…Nevertheless, the simultaneous planning of production, distribution, and choosing facility location is a complex problem that can be solved with the help of exact solution methods with short computational times, but only for small-sized problems. This is due to the combination of three complex problems in the same case [61]. In order to solve bigger instances, new algorithms should be developed that help solve this novel type of simultaneous planning.…”

mentioning

confidence: 99%

Software-Defined Mobile Supply Chains: Rebalancing Resilience and Efficiency in Production Systems

et al. 2022

Self Cite

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The miniaturization and modularization of production capacity brings with it not only greater agility and efficiency, but also increased flexibility in the form of mobility. This flexibility allows production capacity to be moved when and where it is most needed, generating new business opportunities, e.g., allowing modular units to be rented, leased, or shared. This flexibility, however, requires information and control systems that ensure a correct and secure flow of information between different stakeholders of the supply chain. Based on this, the present article characterizes the concept of software-defined mobile supply chains, presenting its main requirements, opportunities, and limitations. In addition, we present two case studies in which the proposed concept is evaluated in order to demonstrate its applicability. Here, due to the new optimization problems that arise when considering mobile facilities, special interest is given to the planning and control of the required operations, as well as the difficulties associated with the exchange of physical and intellectual assets between different stakeholders.

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Integrated Scheduling of Production and Distribution Operations with Site Selection

Cited by 3 publications

References 16 publications

An Optimization Model and Solution Algorithms for the Vehicle Routing Problem With a “Factory-in-a-Box”

An Optimization Model and Solution Algorithms for the Vehicle Routing Problem With a “Factory-in-a-Box”

iRoute—An Adaptive Route Planning Solution for Commercial Vehicle Fleets

Software-Defined Mobile Supply Chains: Rebalancing Resilience and Efficiency in Production Systems

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