Incorporating supplier selection and order allocation into the vehicle routing and multi-cross-dock scheduling problem

Nasiri, Mohammad Mahdi; Rahbari, Ali; Werner, Frank; Karimi, Roya

doi:10.1080/00207543.2018.1471241

Cited by 38 publications

(18 citation statements)

References 36 publications

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“…Constraint (6) complements constraint (5) and establishes that when there is no transfer (0 products are transferred), variable v ijs does not take the value of 1 because we assume there are 0 products transferred. Constraint (7) establishes that the total number of products transferred from all inbound trucks to outbound truck j through temporary storage location s does not exceed the capacity of temporary storage location s that is in front of the dock where truck j is. Similarly, constraint (8) establishes that the total number of products transferred from inbound truck i to all outbound trucks, through temporary storage location s, does not exceed the capacity of temporary storage location s that is in front of the dock where truck i is.…”

Section: Constraintsmentioning

confidence: 99%

“…Cross-docking consists of transferring the flow of products directly to outbound vehicles without storing the products, which in practice helps to consolidate shipments, reduce delivery times, and reduce storage costs [4,5]; Mavi et al [6]. Nasiri et al [7] present a MILP model incorporating supplier selection and order allocation into the VRPCD in a multi-cross-dock system minimizing the total costs, including purchasing, transportation, cross-docking, inventory, and early/tardy delivery penalty costs.…”

Section: Introductionmentioning

confidence: 99%

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Mathematical Optimization Model for Truck Scheduling in a Distribution Center with a Mixed Service-Mode Dock Area

Issi

Linfati

Escobar

2020

Journal of Advanced Transportation

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Cross-docking is a logistics strategy in which products arriving at a distribution center are unloaded from inbound trucks and sorted for transfer directly to outbound trucks, reducing costs and storage and product handling times. This paper addresses a cross-docking problem by designing a mixed-integer linear programming (MILP) model to determine a schedule for inbound and outbound trucks in a mixed service-mode dock area that minimizes the time from when the first inbound truck arrives until the last outbound truck departs (makespan). The model is developed using AMPL software with the CPLEX and Gurobi solvers, which provide results for different instances, most of these with actual shift data from an integrated distribution center of a multinational food company located in Concepción, Chile. The results obtained from the case study are notable and show the effectiveness of the proposed mathematical model.

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Section: Constraintsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mathematical Optimization Model for Truck Scheduling in a Distribution Center with a Mixed Service-Mode Dock Area

Issi

Linfati

Escobar

2020

Journal of Advanced Transportation

View full text Add to dashboard Cite

show abstract

“…In other words, there are four stages of receiving, storage, order selection, and sending in traditional warehouses [8]. Still, in the cross-dock, the storage stage, one of the main obstacles of inventory systems, is removed [9,10]. Therefore, it reduces inventory and increases customer satisfaction [11].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Multi Cross-Docking Systems with a Multi-Objective Green Location Routing Problem Considering Carbon Emission and Energy Consumption

Meidutė‐Kavaliauskienė

Sütütemiz

Yıldırım

et al. 2022

Energies

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Cross-docking is an excellent way to reduce the space required to store goods, inventory management costs, and customer order delivery time. This paper focuses on cost optimization, scheduling incoming and outgoing trucks, and green supply chains with multiple cross-docking. The three objectives are minimizing total operating costs, truck transportation sequences, and carbon emissions within the supply chain. Since the linear programming model is an integer of zero and one and belongs to NP-hard problems, its solution time increases sharply with increasing dimensions. Therefore, the non-dominated sorting genetic algorithm-II (NSGA-II) and the multi-objective particle swarm optimization (MOPSO) were used to find near-optimal solutions to the problem. Then, these algorithms were compared with criteria such as execution time and distance from the ideal point, and the superior algorithm in each criterion was identified.

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“…A clear trend found in the vehicle routing studies of recent decades' hovers around the transportation by trucks. In this regard, we mention some of them in this paper [2,3,4]. Coelho et al investigated the routing problem of a heterogeneous fleet that allowed to travel multiple times [5].…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

A Vehicle Routing Problem with Two Types of Ships in Container Terminals

2020

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The Vehicle Routing Problem (VRP) is one of well-established and important topic in supply chain and logistics networks. The location-routing decision-making has a significant role to optimize the traveling cost per distance, total traveling time and the number of vehicles in the supply chain networks. One of the recent advances in these systems refers to container terminals in the global trade centers. The new contributions of this model are to consider the routes of two types of ships (Tramp and Liner) with different properties and the time windows in order to minimize costs, simultaneously. The proposed model is solved by an exact solver by using LINGO software and because of inherent complexity of problem proposed in the real-world cases, the Genetic Algorithm (GA) is used to find an optimal/global solution in a reasonable time. Finally, an in-depth analysis and discussion is provided to conclude the main findings and practical implications of the results.

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Incorporating supplier selection and order allocation into the vehicle routing and multi-cross-dock scheduling problem

Cited by 38 publications

References 36 publications

Mathematical Optimization Model for Truck Scheduling in a Distribution Center with a Mixed Service-Mode Dock Area

Mathematical Optimization Model for Truck Scheduling in a Distribution Center with a Mixed Service-Mode Dock Area

Optimizing Multi Cross-Docking Systems with a Multi-Objective Green Location Routing Problem Considering Carbon Emission and Energy Consumption

A Vehicle Routing Problem with Two Types of Ships in Container Terminals

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