The Bi-objective Periodic Closed Loop Network Design Problem

© 2019 Elsevier Ltd. This manuscript is made available under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International licence (CC BY-NC-ND 4.0). For further details please see: https://creativecommons.org/licenses/by-nc-nd/4.0/Reverse supply chains are becoming a crucial part of retail supply chains given the recent reforms in the consumers’ rights and the regulations by governments. This has motivated companies around the world to adopt zero-landfill goals and move towards circular economy to retain the product’s value during its whole life cycle. However, designing an efficient closed loop supply chain is a challenging undertaking as it presents a set of unique challenges, mainly owing to the need to handle pickups and deliveries at the same time and the necessity to meet the customer requirements within a certain time limit. In this paper, we model this problem as a bi-objective periodic location routing problem with simultaneous pickup and delivery as well as time windows and examine the performance of two procedures, namely NSGA-II and NRGA, to solve it. The goal is to find the best locations for a set of depots, allocation of customers to these depots, allocation of customers to service days and the optimal routes to be taken by a set of homogeneous vehicles to minimise the total cost and to minimise the overall violation from the customers’ defined time limits. Our results show that while there is not a significant difference between the two algorithms in terms of diversity and number of solutions generated, NSGA-II outperforms NRGA when it comes to spacing and runtime.Peer reviewedFinal Accepted Versio

Etudes sur le Transport Collaboratif : Modèles et Métaheuristiques

In collaborative logistics, multiple carriers form an alliance to improve their transportation operations and profitability by exchanging their transportation requests. In this thesis, we focus on the carrier collaboration in Less-than-truckload (LTL) transportation. More precisely three sub-problems of collaborative planning are considered. Centralized multi-carrier problem with pickup and delivery, time windows, exchangeable requests and reserved requests, multi-period Bid Generation Problem with pickup and delivery problem, time windows, profits, reserved requests and multi period Bid Generation Problem with consideration of both cost and delivery lead time. These sub-problems play a vital role in collaborative transportation planning among carriers, but in the literature, there is no in-depth study on them. We have presented new mathematical programming models for these problems and developed efficient heuristics to obtain solutions close to their optimums in a reasonable computation time. The suggested heuristics are more efficient than commercial solver, CPLEX, not only in terms of solution quality but also in terms of computation time.Dans le domaine de la logistique collaborative, plusieurs transporteurs forment une alliance pour améliorer leurs opérations de transport et leur rentabilité en échangeant leurs demandes de transport. Dans cette thèse, nous concentrons sur la collaboration des transporteurs dans le transport de chargement partial (LTL). Plus précisément, trois sous-problèmes de la planification collaborative entre transporteurs sont pris en compte: un problème de planification centralisée de multi-transporteurs avec ramassage et livraison, fenêtres de temps, demandes échangeables et demandes réservées, un problème de génération d’enchères à plusieurs périodes, un problème de ramassage et de livraison, fenêtres de temps, profits, demandes réservées et problème de génération d’enchères à plusieurs périodes avec prise en compte à la fois du coût et du délais de livraison. Ces sous-problèmes jouent un rôle essentiel dans la planification collaborative de transport entre transporteurs, mais dans la littérature, aucune étude profonde n'a été effectuée sur eux. Nous avons présenté de nouveaux modèles de programmation mathématique pour ces problèmes et développé des heuristiques efficaces pour obtenir des solutions proches de leurs optimums dans un temps de calcul raisonnable. Ces heuristiques proposées sont plus efficaces que le solveur commercial, CPLEX, non seulement en termes de qualité de solution mais aussi en termes de temps de calcul

Etudes sur le Transport Collaboratif : Modèles et Métaheuristiques

Dans le domaine de la logistique collaborative, plusieurs transporteurs forment une alliance pour améliorer leurs opérations de transport et leur rentabilité en échangeant leurs demandes de transport. Dans cette thèse, nous concentrons sur la collaboration des transporteurs dans le transport de chargement partial (LTL). Plus précisément, trois sous-problèmes de la planification collaborative entre transporteurs sont pris en compte: un problème de planification centralisée de multi-transporteurs avec ramassage et livraison, fenêtres de temps, demandes échangeables et demandes réservées, un problème de génération d’enchères à plusieurs périodes, un problème de ramassage et de livraison, fenêtres de temps, profits, demandes réservées et problème de génération d’enchères à plusieurs périodes avec prise en compte à la fois du coût et du délais de livraison. Ces sous-problèmes jouent un rôle essentiel dans la planification collaborative de transport entre transporteurs, mais dans la littérature, aucune étude profonde n'a été effectuée sur eux. Nous avons présenté de nouveaux modèles de programmation mathématique pour ces problèmes et développé des heuristiques efficaces pour obtenir des solutions proches de leurs optimums dans un temps de calcul raisonnable. Ces heuristiques proposées sont plus efficaces que le solveur commercial, CPLEX, non seulement en termes de qualité de solution mais aussi en termes de temps de calcul.In collaborative logistics, multiple carriers form an alliance to improve their transportation operations and profitability by exchanging their transportation requests. In this thesis, we focus on the carrier collaboration in Less-than-truckload (LTL) transportation. More precisely three sub-problems of collaborative planning are considered. Centralized multi-carrier problem with pickup and delivery, time windows, exchangeable requests and reserved requests, multi-period Bid Generation Problem with pickup and delivery problem, time windows, profits, reserved requests and multi period Bid Generation Problem with consideration of both cost and delivery lead time. These sub-problems play a vital role in collaborative transportation planning among carriers, but in the literature, there is no in-depth study on them. We have presented new mathematical programming models for these problems and developed efficient heuristics to obtain solutions close to their optimums in a reasonable computation time. The suggested heuristics are more efficient than commercial solver, CPLEX, not only in terms of solution quality but also in terms of computation time

End-of-Life Product Recovery Optimization of Disassembled Parts based on Collaborative Decision-making

Part 5: Maintenance and Life-Cycle ManagementInternational audienceGreenhouse gas emissions are a major problem for the environment. One of the vital activities to reduce the emissions is including the circular economy (CE) approaches like reuse and remanufacture in disassembled products to recovering End-of-life products. In this paper, we consider CE in the disassembly of products not only to reduce CO2 emissions but also to reducing cost and improving fairness among operators. To obtain this goal, collaborative decision-making with three decision-makers (DMs) is considered to set sustainability via choosing the best EOL recovery options in the disassembly of products. Industrial managers, human resource managers, and environmental managers are three decision-makers who will collaborate to improve three indicators, which are cost, setting fairness among operators, and reducing CO2 emissions. To implement this collaboration, a mixed-integer multi-objective mathematical model is proposed and solved by Ɛ-constraint. Accordingto the results, DMs can select the best recovery options of parts to have a trade-off among indicators

Resilience, agility and risk management in production ramp-up

International audienceProduction ramp-up is challenged by several hurdles such as unpredicted events to achieve targets like providing on-time service in critical situations and under budget constraints. The interrelated concepts of resilience, agility and risk management increase the ability of the system to handle the changes effectively. Therefore, in order to successfully manage production ramp-up particularly in high variety environments, resilience, risk, and agility should be considered in a holistic way. This will help address complexity and uncertainty underlying manufacturing and service operations. This paper aims to fill this gap by reviewing the literature and reporting on the basic concepts and interrelationships of resilience, agility, and risk management in the ramp-up phase. Ultimately, the paper aims to lay some foundations for further research in the crossing of these areas

A Hybrid Algorithm for Collaborative Transportation Planning among Carriers

International audienceIn this paper, there is concentration on collaborative transportation planning (CTP) among multiple carriers with pickup and delivery requests and time windows. This problem is a vehicle routing problem with constraints from standard vehicle routing problems and new constraints from a real-world application. In the problem, each carrier has a finite number of vehicles, and each request is a pickup and delivery request with time window. Moreover, each carrier has reserved requests, which must be served by itself, whereas its exchangeable requests can be outsourced to and served by other carriers. This collaboration among carriers can help them to reduce total transportation costs. A mixed integer programming model is proposed to the problem. To solve the model, a hybrid algorithm that combines Genetic Algorithm and Simulated Annealing (GASA) is proposed. This algorithm takes advantages of GASA at the same time. After tuning the parameters of the algorithm with the Taguchi method, the experiments are conducted and experimental results are provided for the hybrid algorithm. The results are compared with those obtained by a commercial solver. The comparison indicates that the GASA significantly outperforms the commercial solver. Keywords-Centralized collaborative transportation, collaborative transportation with pickup and delivery, collaborative transportation with time windows, hybrid algorithm of GA and SA

A Hybrid Algorithm for Collaborative Transportation Planning among Carriers

International audienceIn this paper, there is concentration on collaborative transportation planning (CTP) among multiple carriers with pickup and delivery requests and time windows. This problem is a vehicle routing problem with constraints from standard vehicle routing problems and new constraints from a real-world application. In the problem, each carrier has a finite number of vehicles, and each request is a pickup and delivery request with time window. Moreover, each carrier has reserved requests, which must be served by itself, whereas its exchangeable requests can be outsourced to and served by other carriers. This collaboration among carriers can help them to reduce total transportation costs. A mixed integer programming model is proposed to the problem. To solve the model, a hybrid algorithm that combines Genetic Algorithm and Simulated Annealing (GASA) is proposed. This algorithm takes advantages of GASA at the same time. After tuning the parameters of the algorithm with the Taguchi method, the experiments are conducted and experimental results are provided for the hybrid algorithm. The results are compared with those obtained by a commercial solver. The comparison indicates that the GASA significantly outperforms the commercial solver. Keywords-Centralized collaborative transportation, collaborative transportation with pickup and delivery, collaborative transportation with time windows, hybrid algorithm of GA and SA

An Improved Tabu Search Algorithm for a Multi-Period Bid Generation Problem with the Consideration of Delivery Lead Time

International audienceIn addition to standard delivery costs, shippers are concerned with delivery lead times. Shorter delivery lead times provide better service to customers. This paper investigates the bid generation problem of a carrier in collaborative transportation realized via a combinatorial auction. In this paper, we propose a multi-period bid generation problem with two types of pickup and delivery requests, namely reserved and selective requests. This problem is an extension of pickup and delivery problem with time windows. This problem arises when a shipper applies an auction for the procurement of transportation services from carriers. In each period, the carrier may have reserved requests that must be served by itself. This carrier wants to determine within a time horizon of multi periods which requests to bid and serve among a set of selective requests open for bid and its multi-period routing plan to maximize its profit and minimize delivery lead times. This problem is NP-hard. We propose an Improved Tabu Search (ITS) algorithm to solve it. The algorithm is evaluated on instances with 20 to 100 requests. The computational results show that the proposed algorithm significantly outperforms CPLEX with much shorter computation times