Scheduling trucks in cross-docking systems: Robust meta-heuristics

Vahdani, Behnam; Zandieh, M.

doi:10.1016/j.cie.2009.06.006

Cited by 152 publications

(81 citation statements)

References 16 publications

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“…Some authors have studied cross-docking as a network and considered cross-docks in a supply chain system that cross-docks location and customer allocations are decision variables (Jayaraman and Ross 2003;Mousavi et al 2014;Ross and Jayaraman 2008;Seyedhoseini et al 2015). In addition, most of the papers related to the operational decisions focused on the trucks scheduling in cross-docking system (Alpan et al 2011;Arabani et al 2011;Donaldson et al 1998b;Gholami et al 2015;Konur and Golias 2013a, b;Kuo 2013;Liao et al 2014Liao et al , 2013Mohtashami 2015;Soltani and Sadjadi 2010;Vahdani and Zandieh 2010;Yu and Egbelu 2008). In addition, in the cross-docking literature, a few number of researches considered both vehicle routing and cross-docking together.…”

Section: Literature Reviewmentioning

confidence: 99%

“…It is proved that the cross-docking in compare with traditional distribution centers dealing with cost and storage reduction, has shorter delivery lead time, better customer servicing, fast inventory turn and less loss and damage risks (Apte and Viswanathan 2000). Implementation of the cross-docking can decrease warehousing costs up to 70% (Vahdani and Zandieh 2010) and decreases transportation costs by using full trucks and consolidating shipments (Apte and Viswanathan 2000). The most important constraint in the cross-docking is the limited time for products storage inside the cross-dock (e.g., 24 h) (Schaffer 1998).…”

Section: Introductionmentioning

confidence: 99%

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Two phase genetic algorithm for vehicle routing and scheduling problem with cross-docking and time windows considering customer satisfaction

2017

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Cross-docking is a new warehousing policy in logistics which is widely used all over the world and attracts many researchers attention to study about in last decade. In the literature, economic aspects has been often studied, while one of the most significant factors for being successful in the competitive global market is improving quality of customer servicing and focusing on customer satisfaction. In this paper, we introduce a vehicle routing and scheduling problem with cross-docking and time windows in a three-echelon supply chain that considers customer satisfaction. A set of homogeneous vehicles collect products from suppliers and after consolidation process in the cross-dock, immediately deliver them to customers. A mixed integer linear programming model is presented for this problem to minimize transportation cost and early/tardy deliveries with scheduling of inbound and outbound vehicles to increase customer satisfaction. A two phase genetic algorithm (GA) is developed for the problem. For investigating the performance of the algorithm, it was compared with exact and lower bound solutions in small and large-size instances, respectively. Results show that there are at least 86.6% customer satisfaction by the proposed method, whereas customer satisfaction in the classical model is at most 33.3%. Numerical examples results show that the proposed two phase algorithm could achieve optimal solutions in small-size instances. Also in large-size instances, the proposed two phase algorithm could achieve better solutions with less gap from the lower bound in less computational time in comparison with the classic GA.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Two phase genetic algorithm for vehicle routing and scheduling problem with cross-docking and time windows considering customer satisfaction

2017

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“…VNS has manifested its successful application to other problems including [53][54][55] . The rationale behind the employment of VNS is that meta-heuristic algorithms get trapped in local optima.…”

Section: Continuous General Variable Neighborhood Search (Cgvns) Metamentioning

confidence: 99%

A new enhanced support vector model based on general variable neighborhood search algorithm for supplier performance evaluation: A case study

Vahdani¹,

Mousavi²,

Tavakkoli–Moghaddam³

et al. 2017

IJCIS

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In sustainable supply chain networks, companies are obligated to have a systematic decision support system in place to help it adopt right decisions at right times. Among strategic decisions, supplier selection and evaluation outranks other decisions in terms of importance due to its long-term impacts. Besides, the adoption of such strategic decision entails exploring several factors that contribute to the complexity of decision making in the supply chain. For the purpose of solving non-linear regression problems, a novel neural network technique known as least squaresupport vector machine (LS-SVM) with maximum generalization ability has successfully been implemented. However, the performance quality of the LS-SVM is recognized to notoriously vary depending on the rigorous selection of its parameters. Therefore, in this paper, a continuous general variable neighborhood search (CGVNS) which is an effective meta-heuristic algorithm to solve the real world engineering continuous optimization problems is proposed to be integrated with LS-SVM. The CGVNS is hybridized in our novel integrated LS-SVM and CGVNS model, to tune the parameters of the LS-SVM to better estimate performance rating of supplier selection and evaluation problem. To demonstrate the improved performance of our proposed integrated model, a real data set from a case study of a supplier selection and evaluation problem is presented in a cosmetics industry. Additionally, comparative evaluations between our proposed model and the conventional techniques, namely nonlinear regression, multi-layer perceptron (MLP) neural network and LS-SVM is provided. The experimental results simply manifest the outperformance of our proposed model in terms of estimation accuracy and effective prediction.

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“…The product assignment to trucks and the docking sequences of the inbound and outbound trucks were all calculated, accordingly. Vahdani and Zandieh (2010) investigated the implementation of five meta-heuristic algorithms including Tabu search (TS), genetic algorithm (GA), simulated annealing (SA), electromagnetismlike algorithm (EMA) and variable neighborhood search (VNS) to schedule the trucks in cross-dock systems for minimization of total operation time by considering a temporary storage buffer to hold temporarily items at the shipping dock and implemented response surface methodology (RSM) to tune their problem parameters. They also considered two types of objective functions to develop multiple objective decision making model.…”

Section: Introductionmentioning

confidence: 99%

Scheduling trucks in cross docking systems with temporary storage and dock repeat truck holding pattern using genetic algorithm

Ghobadian¹,

Tavakkoli–Moghaddam²,

Naderi-Beni³

et al. 2013

msl

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Cross docking is one of the most important issues in management of supply chains. In cross docking, different items delivered to a warehouse by inbound trucks are directly arranged and reorganized based on customer demands, routed and loaded into outbound trucks for delivery purposes to customers without virtually keeping them at the warehouse. If any item is kept in storage, it is normally for a short amount of time, say less than 24 hours. In this paper, we consider a special case of cross docking where there is temporary storage and implements genetic algorithm to solve the resulted problem for some realistic test problems. In our method, we first use some heuristics as initial solutions and then improve the final solution using genetic algorithm. The performance of the proposed model is compared with alternative solution strategy, the GRASP method.

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Scheduling trucks in cross-docking systems: Robust meta-heuristics

Cited by 152 publications

References 16 publications

Two phase genetic algorithm for vehicle routing and scheduling problem with cross-docking and time windows considering customer satisfaction

Two phase genetic algorithm for vehicle routing and scheduling problem with cross-docking and time windows considering customer satisfaction

A new enhanced support vector model based on general variable neighborhood search algorithm for supplier performance evaluation: A case study

Scheduling trucks in cross docking systems with temporary storage and dock repeat truck holding pattern using genetic algorithm

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