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The circular economy is gaining in importance globally and locally. The COVID-19 crisis, as an exceptional event, showed the limits and the fragility of supply chains, with circular economy practices as a potential solution during and post-COVID. Reverse logistics (RL) is an important dimension of the circular economy which allows management of economic, social, and environmental challenges. Transportation is needed for RL to effectively operate, but research study on this topic has been relatively limited. New digitalization opportunities can enhance transportation and RL, and therefore further enhance the circular economy. This paper proposes to review practical research and concerns at the nexus of transportation, RL, and blockchain as a digitalizing technology. The potential benefits of blockchain technology through example use cases on various aspects of RL and transportation activities are presented. This integration and applications are evaluated using various capability facets of blockchain technology, particularly as an immutable and reliable ledger, a tracking service, a smart contract utility, as marketplace support, and as tokenization and incentivization. We also briefly introduce the physical internet concept within this context. The physical internet paradigm proposed last decade, promises to also disrupt the blockchain, transportation, and RL nexus. We include potential research directions and managerial implications across the blockchain, transportation, and RL nexus.
Abdelghani Bekrar; Abdessamad Ait El Cadi; Raca Todosijevic; Joseph Sarkis. Digitalizing the Closing-of-the-Loop for Supply Chains: A Transportation and Blockchain Perspective. Sustainability 2021, 13, 2895 .
AMA StyleAbdelghani Bekrar, Abdessamad Ait El Cadi, Raca Todosijevic, Joseph Sarkis. Digitalizing the Closing-of-the-Loop for Supply Chains: A Transportation and Blockchain Perspective. Sustainability. 2021; 13 (5):2895.
Chicago/Turabian StyleAbdelghani Bekrar; Abdessamad Ait El Cadi; Raca Todosijevic; Joseph Sarkis. 2021. "Digitalizing the Closing-of-the-Loop for Supply Chains: A Transportation and Blockchain Perspective." Sustainability 13, no. 5: 2895.
Cross-docking is a logistics process in which products are unloaded through receiving docks and then transferred to shipping docks with almost no storage in between. In this paper, a mixed integer linear programming model (MILP) is proposed to optimise the scheduling, storage, assignment and sequencing of trucks at receiving and shipping docks for a problem inspired from a multiple-door cross-dock facility of an industrial partner with multiple temporary storage zones. The multiple storage zones are separated and located in the centre of the cross-dock handling different types of products. The objective is to minimise the total tardiness of inbound and outbound trucks. A heuristic (H) is proposed to find an initial solution. Then, three meta-heuristics are developed, namely Random Search (RS), Tabu Search (TS) and Simulated Annealing (SA) to improve the scheduling of trucks in order to minimise the tardiness of inbound and outbound trucks. Experimental results indicate that the three meta-heuristics (RS, TS and SA) are able to find good quality results within reasonable computational times. Finally, since SA showed the best performance compared to RS and TS, it was chosen to be compared to the current manual method using discrete event simulation.
Tarik Chargui; Abdelghani Bekrar; Mohamed Reghioui; Damien Trentesaux. Scheduling trucks and storage operations in a multiple-door cross-docking terminal considering multiple storage zones. International Journal of Production Research 2020, 1 -25.
AMA StyleTarik Chargui, Abdelghani Bekrar, Mohamed Reghioui, Damien Trentesaux. Scheduling trucks and storage operations in a multiple-door cross-docking terminal considering multiple storage zones. International Journal of Production Research. 2020; ():1-25.
Chicago/Turabian StyleTarik Chargui; Abdelghani Bekrar; Mohamed Reghioui; Damien Trentesaux. 2020. "Scheduling trucks and storage operations in a multiple-door cross-docking terminal considering multiple storage zones." International Journal of Production Research , no. : 1-25.
In job-shop manufacturing systems, an efficient production schedule acts to reduce unnecessary costs and better manage resources. For the same purposes, modern manufacturing cells, in compliance with industry 4.0 concepts, use material handling systems in order to allow more control on the transport tasks. In this paper, a job-shop scheduling problem in vehicle based manufacturing facility that is mainly related to job assignment to resources is addressed. The considered job-shop production cell has two types of resources: processing resources that accomplish fabrication tasks for specific products, and transporting resources that assure parts’ transport to the processing area. A Variable Neighborhood Search algorithm is used to schedule product manufacturing and handling tasks in the aim to minimize the maximum completion time of a job set and an improved lower bound with new calculation method is presented. Experimental tests are conducted to evaluate the efficiency of the proposed approach.
Moussa Abderrahim; Abdelghani Bekrar; Damien Trentesaux; Nassima Aissani; Karim Bouamrane. Bi-local search based variable neighborhood search for job-shop scheduling problem with transport constraints. Optimization Letters 2020, 1 -26.
AMA StyleMoussa Abderrahim, Abdelghani Bekrar, Damien Trentesaux, Nassima Aissani, Karim Bouamrane. Bi-local search based variable neighborhood search for job-shop scheduling problem with transport constraints. Optimization Letters. 2020; ():1-26.
Chicago/Turabian StyleMoussa Abderrahim; Abdelghani Bekrar; Damien Trentesaux; Nassima Aissani; Karim Bouamrane. 2020. "Bi-local search based variable neighborhood search for job-shop scheduling problem with transport constraints." Optimization Letters , no. : 1-26.
Anirut Kantasa-Ard; Maroua Nouiri; Abdelghani Bekrar; Abdessamad Ait El Cadi; Yves Sallez. Machine learning for demand forecasting in the physical internet: a case study of agricultural products in Thailand. International Journal of Production Research 2020, 1 -25.
AMA StyleAnirut Kantasa-Ard, Maroua Nouiri, Abdelghani Bekrar, Abdessamad Ait El Cadi, Yves Sallez. Machine learning for demand forecasting in the physical internet: a case study of agricultural products in Thailand. International Journal of Production Research. 2020; ():1-25.
Chicago/Turabian StyleAnirut Kantasa-Ard; Maroua Nouiri; Abdelghani Bekrar; Abdessamad Ait El Cadi; Yves Sallez. 2020. "Machine learning for demand forecasting in the physical internet: a case study of agricultural products in Thailand." International Journal of Production Research , no. : 1-25.
The industry 4.0 concepts are moving towards flexible and energy efficient factories. Major flexible production lines use battery-based automated guided vehicles (AGVs) to optimize their handling processes. However, optimal AGV battery management can significantly shorten lead times. In this paper, we address the scheduling problem in an AGV-based job-shop manufacturing facility. The considered schedule concerns three strands: jobs affecting machines, product transport tasks’ allocations and AGV fleet battery management. The proposed model supports outcomes expected from Industry 4.0 by increasing productivity through completion time minimization and optimizing energy by managing battery replenishment. Experimental tests were conducted on extended benchmark literature instances to evaluate the efficiency of the proposed approach.
Moussa Abderrahim; Abdelghani Bekrar; Damien Trentesaux; Nassima Aissani; Karim Bouamrane. Manufacturing 4.0 Operations Scheduling with AGV Battery Management Constraints. Energies 2020, 13, 4948 .
AMA StyleMoussa Abderrahim, Abdelghani Bekrar, Damien Trentesaux, Nassima Aissani, Karim Bouamrane. Manufacturing 4.0 Operations Scheduling with AGV Battery Management Constraints. Energies. 2020; 13 (18):4948.
Chicago/Turabian StyleMoussa Abderrahim; Abdelghani Bekrar; Damien Trentesaux; Nassima Aissani; Karim Bouamrane. 2020. "Manufacturing 4.0 Operations Scheduling with AGV Battery Management Constraints." Energies 13, no. 18: 4948.
This paper proposes an appropriate model to forecast the trend of white sugar consumption rate in Thailand due to the fluctuation of consumption rate nowadays. This paper will focus on two main forecasting model types, which are the regression models and neural network models. Moreover, the performance is evaluated by using Root Mean Square Error (RMSE) and Theil’U statistic value. After processing the experiments, the results demonstrate that Long Short-Term Memory (LSTM) recurrent neural network provides the best performance for the forecasting, with the condition of combination between the existing consumption rate and other relevant factors like production supply, import rate, export rate, and inventory stock. Also tuning the model’s parameters is an important issue.
Anirut Kantasa-Ard; Abdelghani Bekrar; Abdessamad Ait El Cadi; Yves Sallez. Artificial intelligence for forecasting in supply chain management: a case study of White Sugar consumption rate in Thailand. IFAC-PapersOnLine 2019, 52, 725 -730.
AMA StyleAnirut Kantasa-Ard, Abdelghani Bekrar, Abdessamad Ait El Cadi, Yves Sallez. Artificial intelligence for forecasting in supply chain management: a case study of White Sugar consumption rate in Thailand. IFAC-PapersOnLine. 2019; 52 (13):725-730.
Chicago/Turabian StyleAnirut Kantasa-Ard; Abdelghani Bekrar; Abdessamad Ait El Cadi; Yves Sallez. 2019. "Artificial intelligence for forecasting in supply chain management: a case study of White Sugar consumption rate in Thailand." IFAC-PapersOnLine 52, no. 13: 725-730.
Physical Internet (PI) was introduced to transform the current logistics systems into a global sustainable logistics network. This paper focuses on two-way scheduling/grouping in both sections of a Road-Rail PI-hub (Road→Rail and Rail→ Road). It consists in scheduling both inbound and outbound trucks as well as grouping of PI-containers in the train’s wagons and outbound trucks. The problem is formulated as a mixed integer programming model (MILP). Then, a simulation-optimization (SO) approach is proposed to provide robust solutions which remain feasible in spite of perturbations. The proposed approach uses a Modified Threshold Accepting meta-heuristic (MTA) as an optimizer combined with an embedded perturbation simulator to ensure the robustness of the solutions. Results show that the proposed SO approach provides robust solutions able to handle perturbations.
Tarik Chargui; Abdelghani Bekrar; Mohamed Reghioui; Damien Trentesaux. A Simulation-Optimization Approach for Two-Way Scheduling/Grouping in a Road-Rail Physical Internet Hub. IFAC-PapersOnLine 2019, 52, 1644 -1649.
AMA StyleTarik Chargui, Abdelghani Bekrar, Mohamed Reghioui, Damien Trentesaux. A Simulation-Optimization Approach for Two-Way Scheduling/Grouping in a Road-Rail Physical Internet Hub. IFAC-PapersOnLine. 2019; 52 (13):1644-1649.
Chicago/Turabian StyleTarik Chargui; Abdelghani Bekrar; Mohamed Reghioui; Damien Trentesaux. 2019. "A Simulation-Optimization Approach for Two-Way Scheduling/Grouping in a Road-Rail Physical Internet Hub." IFAC-PapersOnLine 52, no. 13: 1644-1649.
Currently, enhancing sustainability, and in particular reducing energy consumption, is a huge challenge for manufacturing enterprises. The vision of the fourth industrial revolution (so-called “industry 4.0”) is not only to optimize production and minimize costs, but also to reduce energy consumption and enhance product life-cycle management. To address this challenge, a multi-agent architecture aimed at elaborating predictive and reactive energy-efficient scheduling through collaboration between cyber physical production and energy systems is proposed in this paper. Smart, sustainable decision tools for cyber physical production systems (CPPS) and cyber physical energy systems (CPES) are proposed. The decision tools are data-driven, agent-based models with dynamic interaction. The main aim of agent behaviours in the cyber part of CPPS is to find a predictive and reactive energy-efficient schedule. The role of agents in CPES is to control the energy consumption of connected factories and switch between the different renewable energy sources. Dynamic mechanisms in CPPS and CPES are proposed to adjust the energy consumption of production systems based on the availability of the renewable energy. The proposed approach was validated on a physically distributed architecture using networked embedded systems and real-time data sharing from connected sensors in each cyber physical systems. A series of instances inspired from the literature were tested to assess the performance of the proposed method. The results prove the efficiency of the proposed approach in adapting the energy consumption of connected factories based on a real-time energy threshold.
Maroua Nouiri; Damien Trentesaux; Abdelghani Bekrar. Towards Energy Efficient Scheduling of Manufacturing Systems through Collaboration between Cyber Physical Production and Energy Systems. Energies 2019, 12, 4448 .
AMA StyleMaroua Nouiri, Damien Trentesaux, Abdelghani Bekrar. Towards Energy Efficient Scheduling of Manufacturing Systems through Collaboration between Cyber Physical Production and Energy Systems. Energies. 2019; 12 (23):4448.
Chicago/Turabian StyleMaroua Nouiri; Damien Trentesaux; Abdelghani Bekrar. 2019. "Towards Energy Efficient Scheduling of Manufacturing Systems through Collaboration between Cyber Physical Production and Energy Systems." Energies 12, no. 23: 4448.
Scheduling can be defined as the allocation of available resources over time while optimising a set of criteria like early completion time of task, holding inventory, etc. The complexity of the scheduling problem, already known to be high, increases if dynamic events and disruptions are considered. In addition, in production and logistics, designers of scheduling systems must consider sustainability-related expectations. This paper presents an energy-efficient scheduling and rescheduling method (named Green Rescheduling Method, GRM). GRM aims at the solving of the dynamic scheduling problem under the condition of a certain level of routing flexibility enabling the reassignment of tasks to new resources. The key performance indicators integrated into the proposed GRM are effectiveness and efficiency-oriented. Applications concern the domains of production and logistics. In order to assess the proposed approach, experimentations have been made and results illustrate the applicability of GRM to build efficient and effective scheduling and rescheduling both for flexible manufacturing systems and inventory distribution systems in a physical internet network. A mathematical formulation for flexible job shop problem with energy consumption is also proposed using mixed Integer programming to evaluate the performance of the predictive part of GRM.
Maroua Nouiri; Abdelghani Bekrar; Damien Trentesaux. An energy-efficient scheduling and rescheduling method for production and logistics systems†. International Journal of Production Research 2019, 58, 3263 -3283.
AMA StyleMaroua Nouiri, Abdelghani Bekrar, Damien Trentesaux. An energy-efficient scheduling and rescheduling method for production and logistics systems†. International Journal of Production Research. 2019; 58 (11):3263-3283.
Chicago/Turabian StyleMaroua Nouiri; Abdelghani Bekrar; Damien Trentesaux. 2019. "An energy-efficient scheduling and rescheduling method for production and logistics systems†." International Journal of Production Research 58, no. 11: 3263-3283.
Physical Internet (PI) was introduced as a global standardised and interconnected logistics system based on PI-nodes, PI-movers and PI-containers as a mean toward global logistics sustainability. One important issue regarding PI-nodes concerns the planning and scheduling of operations and the management of PI-containers, both in a deterministic and a perturbed environment. This research considers the Road-Rail PI-hub sustainable truck scheduling and PI-containers grouping problem. In our research we consider the weighted sum of the number of used wagons, the internal distance travelled by PI-containers from PI-docks to wagons as well as the trucks’ tardiness, which translate the search for sustainable logistics. In this paper, an effective and reactive multi-agent system based model (MAS) is developed for the resolution of the trucks scheduling and PI-containers grouping. To ensure the efficiency of the MAS and improve the quality of each of its solutions, three concurrent hybrid meta-heuristics are embedded within three parallel scheduling agents. Then, a mixed integer linear programming model (MILP) is proposed to evaluate the performance of the MAS. Finally, the MAS is also evaluated under internal perturbations. The obtained results show the ability of the MAS to provide alternative sustainable solutions by rescheduling trucks in case of disruptions.
Tarik Chargui; Abdelghani Bekrar; Mohamed Reghioui; Damien Trentesaux. Proposal of a multi-agent model for the sustainable truck scheduling and containers grouping problem in a Road-Rail physical internet hub. International Journal of Production Research 2019, 58, 5477 -5501.
AMA StyleTarik Chargui, Abdelghani Bekrar, Mohamed Reghioui, Damien Trentesaux. Proposal of a multi-agent model for the sustainable truck scheduling and containers grouping problem in a Road-Rail physical internet hub. International Journal of Production Research. 2019; 58 (18):5477-5501.
Chicago/Turabian StyleTarik Chargui; Abdelghani Bekrar; Mohamed Reghioui; Damien Trentesaux. 2019. "Proposal of a multi-agent model for the sustainable truck scheduling and containers grouping problem in a Road-Rail physical internet hub." International Journal of Production Research 58, no. 18: 5477-5501.
In the context of supply chain sustainability, Physical Internet (PI or π ) was presented as an innovative concept to create a global sustainable logistics system. One of the main components of the Physical Internet paradigm consists in encapsulating products in modular and standardized PI-containers able to move via PI-nodes (such as PI-hubs) using collaborative routing protocols. This study focuses on optimizing operations occurring in a Rail–Road PI-Hub cross-docking terminal. The problem consists of scheduling outbound trucks at the docks and the routing of PI-containers in the PI-sorter zone of the Rail–Road PI-Hub cross-docking terminal. The first objective is to minimize the energy consumption of the PI-conveyors used to transfer PI-containers from the train to the outbound trucks. The second objective is to minimize the cost of using outbound trucks for different destinations. The problem is formulated as a Multi-Objective Mixed-Integer Programming model (MO-MIP) and solved with CPLEX solver using Lexicographic Goal Programming. Then, two multi-objective hybrid meta-heuristics are proposed to enhance the computational time as CPLEX was time consuming, especially for large size instances: Multi-Objective Variable Neighborhood Search hybridized with Simulated Annealing (MO-VNSSA) and with a Tabu Search (MO-VNSTS). The two meta-heuristics are tested on 32 instances (27 small instances and 5 large instances). CPLEX found the optimal solutions for only 23 instances. Results show that the proposed MO-VNSSA and MO-VNSTS are able to find optimal and near optimal solutions within a reasonable computational time. The two meta-heuristics found optimal solutions for the first objective in all the instances. For the second objective, MO-VNSSA and MO-VNSTS found optimal solutions for 7 instances. In order to evaluate the results for the second objective, a one way analysis of variance ANOVA was performed.
Tarik Chargui; Abdelghani Bekrar; Mohamed Reghioui; Damien Trentesaux. Multi-Objective Sustainable Truck Scheduling in a Rail–Road Physical Internet Cross-Docking Hub Considering Energy Consumption. Sustainability 2019, 11, 3127 .
AMA StyleTarik Chargui, Abdelghani Bekrar, Mohamed Reghioui, Damien Trentesaux. Multi-Objective Sustainable Truck Scheduling in a Rail–Road Physical Internet Cross-Docking Hub Considering Energy Consumption. Sustainability. 2019; 11 (11):3127.
Chicago/Turabian StyleTarik Chargui; Abdelghani Bekrar; Mohamed Reghioui; Damien Trentesaux. 2019. "Multi-Objective Sustainable Truck Scheduling in a Rail–Road Physical Internet Cross-Docking Hub Considering Energy Consumption." Sustainability 11, no. 11: 3127.
Maroua Nouiri; Damien Trentesaux; Abdelghani Bekrar. EasySched : une architecture multi-agent pour l’ordonnancement prédictif et réactif de systèmes de production de biens en fonction de l’énergie renouvelable disponible dans un contexte industrie 4.0. Génie industriel et productique 2019, 2, 1 .
AMA StyleMaroua Nouiri, Damien Trentesaux, Abdelghani Bekrar. EasySched : une architecture multi-agent pour l’ordonnancement prédictif et réactif de systèmes de production de biens en fonction de l’énergie renouvelable disponible dans un contexte industrie 4.0. Génie industriel et productique. 2019; 2 (1):1.
Chicago/Turabian StyleMaroua Nouiri; Damien Trentesaux; Abdelghani Bekrar. 2019. "EasySched : une architecture multi-agent pour l’ordonnancement prédictif et réactif de systèmes de production de biens en fonction de l’énergie renouvelable disponible dans un contexte industrie 4.0." Génie industriel et productique 2, no. 1: 1.
Within the emerging industrial sustainability domain, production efficiency interventions are gaining practical interest since manufacturing plants are facing increasing pressure to reduce their carbon footprint, driven by concerns related to energy costs and climate changes. This work focuses on the challenging issue of energy aware production scheduling and rescheduling systems (EAPSRS). The proposed multi-agent architecture (MA-EAPSRS) is hybrid, combining the predictive and the reactive phase while taking into account sustainability in both parts. It is composed of two cooperating multi-agent systems: the first one represents the smart manufacturing plant and the second one is the smart energy supply plant. It is based on interactions and negotiations between factory schedulers and energy providers. Uncertainties in term of machine’s disruptions and variation of processing time and in term of energy availability are also considered. In order to assess the proposed approach, an illustrative case study addressing the problem is presented and discussed.
Maroua Nouiri; Damien Trentesaux; Abdelghani Bekrar; Adriana Giret; Miguel A. Salido. Cooperation Between Smart Manufacturing Scheduling Systems and Energy Providers: A Multi-agent Perspective. Artificial Intelligence: Foundations, Theory, and Algorithms 2018, 197 -210.
AMA StyleMaroua Nouiri, Damien Trentesaux, Abdelghani Bekrar, Adriana Giret, Miguel A. Salido. Cooperation Between Smart Manufacturing Scheduling Systems and Energy Providers: A Multi-agent Perspective. Artificial Intelligence: Foundations, Theory, and Algorithms. 2018; ():197-210.
Chicago/Turabian StyleMaroua Nouiri; Damien Trentesaux; Abdelghani Bekrar; Adriana Giret; Miguel A. Salido. 2018. "Cooperation Between Smart Manufacturing Scheduling Systems and Energy Providers: A Multi-agent Perspective." Artificial Intelligence: Foundations, Theory, and Algorithms , no. : 197-210.
Cross-docking, in its classical form, is a warehousing strategy which consists on unloading products from suppliers’ trucks and loading them into customers’ trucks directly or after being stored temporarily in the storage zone. In this paper, a tabu search meta-heuristic is proposed to solve the truck scheduling problem for both the classical cross-dock and the PI-hub. The objective is to minimize the total tardiness of both inbound and outbound trucks. Since in real world supply chain resources failures are possible, a robustness testing mechanism is presented to evaluate the robustness of the tabu search solutions by generating internal transportation resources breakdowns with different probabilities for both the classical cross-dock and the PI-hub. The impact of the internal transportation resources failure on the performance of the cross-dock and the PI-hub is compared. The proposed solving methods are tested on a case study instance inspired from a cross-dock of an industrial partner.
Tarik Chargui; Abdelghani Bekrar; Mohamed Reghioui; Damien Trentesaux. Tabu Search Robustness for Cross-Dock and PI-Hub Scheduling Under Possible Internal Transportation Breakdowns. Artificial Intelligence: Foundations, Theory, and Algorithms 2018, 295 -307.
AMA StyleTarik Chargui, Abdelghani Bekrar, Mohamed Reghioui, Damien Trentesaux. Tabu Search Robustness for Cross-Dock and PI-Hub Scheduling Under Possible Internal Transportation Breakdowns. Artificial Intelligence: Foundations, Theory, and Algorithms. 2018; ():295-307.
Chicago/Turabian StyleTarik Chargui; Abdelghani Bekrar; Mohamed Reghioui; Damien Trentesaux. 2018. "Tabu Search Robustness for Cross-Dock and PI-Hub Scheduling Under Possible Internal Transportation Breakdowns." Artificial Intelligence: Foundations, Theory, and Algorithms , no. : 295-307.
This paper presents an efficient approach for solving the optimal reactive power dispatch problem. It is a non-linear constrained optimization problem where two distinct objective functions are considered. The proposed approach is based on the hybridization of the particle swarm optimization method and the tabu-search technique. This hybrid approach is used to find control variable settings (i.e., generation bus voltages, transformer taps and shunt capacitor sizes) which minimize transmission active power losses and load bus voltage deviations. To validate the proposed hybrid method, the IEEE 30-bus system is considered for 12 and 19 control variables. The obtained results are compared with those obtained by particle swarm optimization and a tabu-search without hybridization and with other evolutionary algorithms reported in the literature.
Zahir Sahli; Abdellatif Hamouda; Abdelghani Bekrar; Damien Trentesaux. Reactive Power Dispatch Optimization with Voltage Profile Improvement Using an Efficient Hybrid Algorithm. Energies 2018, 11, 2134 .
AMA StyleZahir Sahli, Abdellatif Hamouda, Abdelghani Bekrar, Damien Trentesaux. Reactive Power Dispatch Optimization with Voltage Profile Improvement Using an Efficient Hybrid Algorithm. Energies. 2018; 11 (8):2134.
Chicago/Turabian StyleZahir Sahli; Abdellatif Hamouda; Abdelghani Bekrar; Damien Trentesaux. 2018. "Reactive Power Dispatch Optimization with Voltage Profile Improvement Using an Efficient Hybrid Algorithm." Energies 11, no. 8: 2134.
In order to deploy AGVs in industry, it is mandatory to consider the tradeoff between smartness and embeddability. This paper aims at making the manufacturing research community more sensitive about this tradeoff and its consequences. Nowadays, AGVs are widely chosen by manufacturers to implement flexible material-handling systems which are necessary to cover the industrial requirements. However, many issues, presented in this paper, must be tackled to deploy these AGVs. A tradeoff-oriented procedure is proposed by considering these issues in flexible manufacturing system applications. Then, an approach is proposed to illustrate this procedure by providing simulation and experimental results. This approach is also used to roughly describe the smartness/embeddability tradeoff.
Guillaume Demesure; Damien Trentesaux; Michael Defoort; Abdelghani Bekrar; Hind Bril; Mohamed Djemai; André Thomas. Smartness Versus Embeddability: A Tradeoff for the Deployment of Smart AGVs in Industry. Econometrics for Financial Applications 2018, 395 -406.
AMA StyleGuillaume Demesure, Damien Trentesaux, Michael Defoort, Abdelghani Bekrar, Hind Bril, Mohamed Djemai, André Thomas. Smartness Versus Embeddability: A Tradeoff for the Deployment of Smart AGVs in Industry. Econometrics for Financial Applications. 2018; ():395-406.
Chicago/Turabian StyleGuillaume Demesure; Damien Trentesaux; Michael Defoort; Abdelghani Bekrar; Hind Bril; Mohamed Djemai; André Thomas. 2018. "Smartness Versus Embeddability: A Tradeoff for the Deployment of Smart AGVs in Industry." Econometrics for Financial Applications , no. : 395-406.
In real-world manufacturing systems, schedules are often confronted with uncertainty factors such as random machine breakdown, random process time, random job arrivals or job cancellations. A large number of impromptu disruptions frequently affect the scheduled operations and invalidate the original schedule. There is still the need for rescheduling methods that can work effectively in disruption management. In this work, an algorithm for rescheduling the affected operations in a flexible job shop is presented and its performance, with respect to measures of efficiency and stability, is compared with the Right Shift Rescheduling technique. The proposed method is tested on different benchmark scheduling problems with various disruption scenarios. Experimental results show that the proposed rescheduling method improves the efficiency and stability when compared to Right Shift Rescheduling method.
Maroua Nouiri; Abdelghani Bekrar; Abderrazak Jemai; Ahmed Chiheb Ammari; Smail Niar. A New Rescheduling Heuristic for Flexible Job Shop Problem with Machine Disruption. Econometrics for Financial Applications 2018, 461 -476.
AMA StyleMaroua Nouiri, Abdelghani Bekrar, Abderrazak Jemai, Ahmed Chiheb Ammari, Smail Niar. A New Rescheduling Heuristic for Flexible Job Shop Problem with Machine Disruption. Econometrics for Financial Applications. 2018; ():461-476.
Chicago/Turabian StyleMaroua Nouiri; Abdelghani Bekrar; Abderrazak Jemai; Ahmed Chiheb Ammari; Smail Niar. 2018. "A New Rescheduling Heuristic for Flexible Job Shop Problem with Machine Disruption." Econometrics for Financial Applications , no. : 461-476.
Cross-docking is a logistics process which consists in receiving goods through unloading docks and then transferring them to the outgoing docks with almost no storage in between. A new concept named Physical Internet is applied to logistics based on the metaphor of the digital internet to improve the flexibility and synchronization of logistics systems. This paper presents a set of simulation scenarios of a real cross-dock facility of an industrial partner to compare the performances of the cross-dock with those of the PI-hub and to evaluate the contribution of the implementation of Physical Internet in cross-docking facilities. In this paper, two simulation models are proposed to compare performances of a classical cross dock and a PI-hub under the same flow of products and inter-arrival time of inbound and outbound trucks. Several scenarios are presented to test the robustness by changing the synchronization and the inter-arrival time distribution. To compare the performance of the cross-dock facility and the PI-hub, several key performance indicators (KPIs) are considered, such as waiting time, number of trucks waiting, resources usage and cycle time of the products.
Tarik Chargui; Abdelghani Bekrar; Mohamed Reghioui; Damien Trentesaux. Simulation for PI-Hub Cross-Docking Robustness. Artificial Intelligence: Foundations, Theory, and Algorithms 2018, 317 -328.
AMA StyleTarik Chargui, Abdelghani Bekrar, Mohamed Reghioui, Damien Trentesaux. Simulation for PI-Hub Cross-Docking Robustness. Artificial Intelligence: Foundations, Theory, and Algorithms. 2018; ():317-328.
Chicago/Turabian StyleTarik Chargui; Abdelghani Bekrar; Mohamed Reghioui; Damien Trentesaux. 2018. "Simulation for PI-Hub Cross-Docking Robustness." Artificial Intelligence: Foundations, Theory, and Algorithms , no. : 317-328.
Zhu Wang; Binghai Zhou; Damien Trentesaux; Abdelghani Bekrar. Approximate optimal method for cyclic solutions in multi-robotic cell with processing time window. Robotics and Autonomous Systems 2017, 98, 307 -316.
AMA StyleZhu Wang, Binghai Zhou, Damien Trentesaux, Abdelghani Bekrar. Approximate optimal method for cyclic solutions in multi-robotic cell with processing time window. Robotics and Autonomous Systems. 2017; 98 ():307-316.
Chicago/Turabian StyleZhu Wang; Binghai Zhou; Damien Trentesaux; Abdelghani Bekrar. 2017. "Approximate optimal method for cyclic solutions in multi-robotic cell with processing time window." Robotics and Autonomous Systems 98, no. : 307-316.
Maroua Nouiri; Abdelghani Bekrar; Abderrazak Jemai; Damien Trentesaux; Ahmed Chiheb Ammari; Smail Niar. Two stage particle swarm optimization to solve the flexible job shop predictive scheduling problem considering possible machine breakdowns. Computers & Industrial Engineering 2017, 112, 595 -606.
AMA StyleMaroua Nouiri, Abdelghani Bekrar, Abderrazak Jemai, Damien Trentesaux, Ahmed Chiheb Ammari, Smail Niar. Two stage particle swarm optimization to solve the flexible job shop predictive scheduling problem considering possible machine breakdowns. Computers & Industrial Engineering. 2017; 112 ():595-606.
Chicago/Turabian StyleMaroua Nouiri; Abdelghani Bekrar; Abderrazak Jemai; Damien Trentesaux; Ahmed Chiheb Ammari; Smail Niar. 2017. "Two stage particle swarm optimization to solve the flexible job shop predictive scheduling problem considering possible machine breakdowns." Computers & Industrial Engineering 112, no. : 595-606.