In this paper, we address a rich Traveling Salesman Problem with Prof-
its encountered in several... more In this paper, we address a rich Traveling Salesman Problem with Prof- its encountered in several real-life cases. We propose a unied solution approach based on variable neighborhood search. Our approach combines several removal and insertion routing neighborhoods and eefficient constraint checking procedures. The loading problem related to the use of a multi-compartment vehicle is addressed carefully. Two loading neighborhoods based on the solution of mathematical programs are proposed to intensify the search. They interact with the routing neighborhoods as it is commonly done in matheuristics. The performance of the proposed matheuristic is assessed on various instances proposed for the Orienteering Problem and the Orienteering Problem with Time Window including up to 288 customers. The computational results show that the proposed matheuristic is very competitive compared with the state-of-the-art methods. To better evaluate its performance, we generate a new testbed including instances with various attributes. Extensive computational experiments on the new testbed conrm the efficiency of the matheuristic. A sensitivity analysis highlights which components of the matheuristic contribute most to the solution quality.
Warehouse order picking activities are among the ones that impact the most the bottom lines of wa... more Warehouse order picking activities are among the ones that impact the most the bottom lines of warehouses. They are known to often account for more than half of the total warehousing costs. New practices and innovations generate new challenges for managers and open new research avenues. Many practical constraints arising in real-life have often been neglected in the scientific literature. We introduce, model, and solve a rich order picking problem under weight, fragility, and category constraints, motivated by our observation of a real-life application arising in the grocery retail industry. This difficult warehousing problem combines complex picking and routing decisions under the objective of minimizing the distance traveled. We first provide a full description of the warehouse design which enables us to algebraically compute the distances between all pairs of products. We then propose two distinct mathematical models to formulate the problem. We develop five heuristic methods, including extensions of the classical largest gap, mid point, S-shape, and combined heuristics. The fifth one is an implementation of the powerful adaptive large neighborhood search algorithm specifically designed for the problem at hand. We then implement a branch-and-cut algorithm and cutting planes to solve the two formulations. The performance of the proposed solution methods is assessed on a newly generated and realistic test bed containing up to 100 pickups and seven aisles. We compare the bounds provided by the two formulations. Our in-depth analysis shows which formulation tends to perform better. Extensive computational experiments confirm the efficiency of the ALNS matheuristic and derive some important insights for managing order picking in this kind of warehouses.
ABSTRACT This is a summary of the author’s Ph.D. thesis jointly supervised by Frédéric Semet and ... more ABSTRACT This is a summary of the author’s Ph.D. thesis jointly supervised by Frédéric Semet and Mahdi Khemakhem and defended on 13 June 2014 at Ecole Centrale de Lille. The thesis, written in English, is a joint work between Ecole Centrale de Lille and Faculté des Sciences Economiques et de Gestion de Sfax, Tunisia. The document is available from the author upon request at rahma.lahyani@gmail.com. The purpose of this thesis is to develop a solution framework for multi-constrained vehicle routing problems (VRPs).In recent years, methodological progress and the development of computer technologies has led to an increasing academic attention to new variants of VRPs. This research avenue is stimulated by the complex features of real-life VRPs and represents the class of rich vehicle routing problems (RVRPs). This thesis provides a comprehensive survey of the RVRPs literature as well as a generic taxonomy with respect to relevant real-life issues. Selected papers addressing various variants of VRPs ...
ABSTRACT Over the last years, several variants of multi-constrained Vehicle Routing Problems (VRP... more ABSTRACT Over the last years, several variants of multi-constrained Vehicle Routing Problems (VRPs) have been studied, forming a class of problems known as Rich Vehicle Routing Problems (RVRPs). The purpose of the paper is twofold: (i) to provide a comprehensive and relevant taxonomy for the RVRP literature and (ii) to propose an elaborate definition of RVRPs. To this end, selected papers addressing various cases are classified using the proposed taxonomy. Once the articles have been classified, a cluster analysis based on two discriminating criteria is performed and leads to the definition of RVRPs.
2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO), 2013
ABSTRACT We present a Rich variant of the Profitable Tour Problem (RPTP) arising when customer re... more ABSTRACT We present a Rich variant of the Profitable Tour Problem (RPTP) arising when customer requests involve several products and multi-compartment vehicles are used. The RPTP addressed may be considered as a variant of the capacitated profitable tour problem with time windows and incompatibility constraints. We propose a Variable Neighborhood Search Algorithm embedded with an Adaptive Large Neighborhood Search for the RPTP. This method includes a perturbation phase based on the Ruin and Recreate paradigm. The efficiency of the proposed algorithm is assessed by solving the instances of the Orienteering Problem with Time Windows.
2011 4th International Conference on Logistics, 2011
Over the last years various extensions of the Vehicle Routing Problem (VRP), considering complica... more Over the last years various extensions of the Vehicle Routing Problem (VRP), considering complicated constraints encountered in the real-life, has been studied. These extensions are often coined as rich VRP. In this work, we tackle a rich VRP namely the Multi Compartment Multi Commodity Heterogeneous Fixed Fleet Vehicle Routing Problem with hard Time Windows (MCMCHFFVRPTW). The objective of the problem
Jarboui/Metaheuristics for Production Scheduling, 2013
ABSTRACT This chapter surveys the literature on vehicle routing problems (VRPs) with scheduling c... more ABSTRACT This chapter surveys the literature on vehicle routing problems (VRPs) with scheduling constraints. This class of problems is split into two subclasses: time-constrained VRPs and VRPs with resource-availability constraints. In the first subclass, vehicle routing problems with time windows (VRPTWs), with multiple periods and with cross-docking are discussed, while in the second class, the vehicle routing problems with multiple routes and with crew scheduling are explained. For each problem, after a brief description, the chapter presents the pertinent, and for the most part recent, solution methods.
In this paper, we address a rich Traveling Salesman Problem with Prof-
its encountered in several... more In this paper, we address a rich Traveling Salesman Problem with Prof- its encountered in several real-life cases. We propose a unied solution approach based on variable neighborhood search. Our approach combines several removal and insertion routing neighborhoods and eefficient constraint checking procedures. The loading problem related to the use of a multi-compartment vehicle is addressed carefully. Two loading neighborhoods based on the solution of mathematical programs are proposed to intensify the search. They interact with the routing neighborhoods as it is commonly done in matheuristics. The performance of the proposed matheuristic is assessed on various instances proposed for the Orienteering Problem and the Orienteering Problem with Time Window including up to 288 customers. The computational results show that the proposed matheuristic is very competitive compared with the state-of-the-art methods. To better evaluate its performance, we generate a new testbed including instances with various attributes. Extensive computational experiments on the new testbed conrm the efficiency of the matheuristic. A sensitivity analysis highlights which components of the matheuristic contribute most to the solution quality.
Warehouse order picking activities are among the ones that impact the most the bottom lines of wa... more Warehouse order picking activities are among the ones that impact the most the bottom lines of warehouses. They are known to often account for more than half of the total warehousing costs. New practices and innovations generate new challenges for managers and open new research avenues. Many practical constraints arising in real-life have often been neglected in the scientific literature. We introduce, model, and solve a rich order picking problem under weight, fragility, and category constraints, motivated by our observation of a real-life application arising in the grocery retail industry. This difficult warehousing problem combines complex picking and routing decisions under the objective of minimizing the distance traveled. We first provide a full description of the warehouse design which enables us to algebraically compute the distances between all pairs of products. We then propose two distinct mathematical models to formulate the problem. We develop five heuristic methods, including extensions of the classical largest gap, mid point, S-shape, and combined heuristics. The fifth one is an implementation of the powerful adaptive large neighborhood search algorithm specifically designed for the problem at hand. We then implement a branch-and-cut algorithm and cutting planes to solve the two formulations. The performance of the proposed solution methods is assessed on a newly generated and realistic test bed containing up to 100 pickups and seven aisles. We compare the bounds provided by the two formulations. Our in-depth analysis shows which formulation tends to perform better. Extensive computational experiments confirm the efficiency of the ALNS matheuristic and derive some important insights for managing order picking in this kind of warehouses.
ABSTRACT This is a summary of the author’s Ph.D. thesis jointly supervised by Frédéric Semet and ... more ABSTRACT This is a summary of the author’s Ph.D. thesis jointly supervised by Frédéric Semet and Mahdi Khemakhem and defended on 13 June 2014 at Ecole Centrale de Lille. The thesis, written in English, is a joint work between Ecole Centrale de Lille and Faculté des Sciences Economiques et de Gestion de Sfax, Tunisia. The document is available from the author upon request at rahma.lahyani@gmail.com. The purpose of this thesis is to develop a solution framework for multi-constrained vehicle routing problems (VRPs).In recent years, methodological progress and the development of computer technologies has led to an increasing academic attention to new variants of VRPs. This research avenue is stimulated by the complex features of real-life VRPs and represents the class of rich vehicle routing problems (RVRPs). This thesis provides a comprehensive survey of the RVRPs literature as well as a generic taxonomy with respect to relevant real-life issues. Selected papers addressing various variants of VRPs ...
ABSTRACT Over the last years, several variants of multi-constrained Vehicle Routing Problems (VRP... more ABSTRACT Over the last years, several variants of multi-constrained Vehicle Routing Problems (VRPs) have been studied, forming a class of problems known as Rich Vehicle Routing Problems (RVRPs). The purpose of the paper is twofold: (i) to provide a comprehensive and relevant taxonomy for the RVRP literature and (ii) to propose an elaborate definition of RVRPs. To this end, selected papers addressing various cases are classified using the proposed taxonomy. Once the articles have been classified, a cluster analysis based on two discriminating criteria is performed and leads to the definition of RVRPs.
2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO), 2013
ABSTRACT We present a Rich variant of the Profitable Tour Problem (RPTP) arising when customer re... more ABSTRACT We present a Rich variant of the Profitable Tour Problem (RPTP) arising when customer requests involve several products and multi-compartment vehicles are used. The RPTP addressed may be considered as a variant of the capacitated profitable tour problem with time windows and incompatibility constraints. We propose a Variable Neighborhood Search Algorithm embedded with an Adaptive Large Neighborhood Search for the RPTP. This method includes a perturbation phase based on the Ruin and Recreate paradigm. The efficiency of the proposed algorithm is assessed by solving the instances of the Orienteering Problem with Time Windows.
2011 4th International Conference on Logistics, 2011
Over the last years various extensions of the Vehicle Routing Problem (VRP), considering complica... more Over the last years various extensions of the Vehicle Routing Problem (VRP), considering complicated constraints encountered in the real-life, has been studied. These extensions are often coined as rich VRP. In this work, we tackle a rich VRP namely the Multi Compartment Multi Commodity Heterogeneous Fixed Fleet Vehicle Routing Problem with hard Time Windows (MCMCHFFVRPTW). The objective of the problem
Jarboui/Metaheuristics for Production Scheduling, 2013
ABSTRACT This chapter surveys the literature on vehicle routing problems (VRPs) with scheduling c... more ABSTRACT This chapter surveys the literature on vehicle routing problems (VRPs) with scheduling constraints. This class of problems is split into two subclasses: time-constrained VRPs and VRPs with resource-availability constraints. In the first subclass, vehicle routing problems with time windows (VRPTWs), with multiple periods and with cross-docking are discussed, while in the second class, the vehicle routing problems with multiple routes and with crew scheduling are explained. For each problem, after a brief description, the chapter presents the pertinent, and for the most part recent, solution methods.
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Papers by Rahma Lahyani
its encountered in several real-life cases. We propose a unied solution approach based on variable neighborhood search. Our approach combines several removal and insertion routing neighborhoods and eefficient constraint checking procedures. The loading problem related to the use of a multi-compartment vehicle is addressed carefully. Two loading neighborhoods based on the solution of mathematical programs are proposed to intensify the search. They interact with the routing
neighborhoods as it is commonly done in matheuristics. The performance of the proposed matheuristic is assessed on various instances proposed for the Orienteering Problem and the Orienteering Problem with Time Window including up to 288 customers. The computational results show that the proposed matheuristic is very competitive compared with the state-of-the-art methods. To better evaluate its performance, we generate a new testbed including instances with various attributes. Extensive computational experiments on the new testbed conrm the efficiency of the matheuristic. A sensitivity analysis highlights which components of the matheuristic contribute most to the solution quality.
and innovations generate new challenges for managers and open new research avenues. Many practical constraints arising in real-life have often been neglected in the scientific literature. We introduce, model,
and solve a rich order picking problem under weight, fragility, and category constraints, motivated by our observation of a real-life application arising in the grocery retail industry. This difficult warehousing problem combines complex picking and routing decisions under the objective of minimizing the distance traveled. We first provide a full description of the warehouse design which enables us to algebraically compute the distances between all pairs of products. We then propose two distinct mathematical models
to formulate the problem. We develop five heuristic methods, including extensions of the classical largest gap, mid point, S-shape, and combined heuristics. The fifth one is an implementation of the powerful adaptive large neighborhood search algorithm specifically designed for the problem at hand. We then implement a branch-and-cut algorithm and cutting planes to solve the two formulations. The performance of the proposed solution methods is assessed on a newly generated and realistic test bed containing up to 100 pickups and seven aisles. We compare the bounds provided by the two formulations. Our in-depth analysis shows which formulation tends to perform better. Extensive computational experiments confirm the efficiency of the ALNS matheuristic and derive some important insights for managing order picking in this kind of warehouses.
its encountered in several real-life cases. We propose a unied solution approach based on variable neighborhood search. Our approach combines several removal and insertion routing neighborhoods and eefficient constraint checking procedures. The loading problem related to the use of a multi-compartment vehicle is addressed carefully. Two loading neighborhoods based on the solution of mathematical programs are proposed to intensify the search. They interact with the routing
neighborhoods as it is commonly done in matheuristics. The performance of the proposed matheuristic is assessed on various instances proposed for the Orienteering Problem and the Orienteering Problem with Time Window including up to 288 customers. The computational results show that the proposed matheuristic is very competitive compared with the state-of-the-art methods. To better evaluate its performance, we generate a new testbed including instances with various attributes. Extensive computational experiments on the new testbed conrm the efficiency of the matheuristic. A sensitivity analysis highlights which components of the matheuristic contribute most to the solution quality.
and innovations generate new challenges for managers and open new research avenues. Many practical constraints arising in real-life have often been neglected in the scientific literature. We introduce, model,
and solve a rich order picking problem under weight, fragility, and category constraints, motivated by our observation of a real-life application arising in the grocery retail industry. This difficult warehousing problem combines complex picking and routing decisions under the objective of minimizing the distance traveled. We first provide a full description of the warehouse design which enables us to algebraically compute the distances between all pairs of products. We then propose two distinct mathematical models
to formulate the problem. We develop five heuristic methods, including extensions of the classical largest gap, mid point, S-shape, and combined heuristics. The fifth one is an implementation of the powerful adaptive large neighborhood search algorithm specifically designed for the problem at hand. We then implement a branch-and-cut algorithm and cutting planes to solve the two formulations. The performance of the proposed solution methods is assessed on a newly generated and realistic test bed containing up to 100 pickups and seven aisles. We compare the bounds provided by the two formulations. Our in-depth analysis shows which formulation tends to perform better. Extensive computational experiments confirm the efficiency of the ALNS matheuristic and derive some important insights for managing order picking in this kind of warehouses.