Truck scheduling in multi-door cross docking terminal by modified particle swarm optimization

Wisittipanich, Warisa; Hengmeechai, Piya

doi:10.1016/j.cie.2017.01.004

Cited by 49 publications

(32 citation statements)

References 22 publications

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“…small, medium and large size. The solution quality and convergence rate of evolutionary algorithms are mainly influenced by the parameter tuning of the algorithm (Wisittipanich & Hengmeechai 2017). Therefore, suitable parameters of the algorithms are essential to avoid the bad simulation results.…”

Section: Problem Instancesmentioning

confidence: 99%

An MINLP model to support the movement and storage decisions of the Indian food grain supply chain

Mogale

Kumar

Tiwari

2018

Control Engineering Practice

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2018. An MINLP model to support the movement and storage decisions of the Indian food grain supply chain. Control Engineering Practice 70 , pp. Abstract:This paper addresses the novel three stage food grain distribution problem of Public Distribution System (PDS) in India which comprises of farmers, procurement centers, base silos and field silos. The Indian food grain supply chain consists of various activities such as procurement, storage, transportation and distribution of food grain. In order to curb transportation and storage losses of food grain, the Food Corporation of India (FCI) is moving towards the modernized bulk food grain supply chain system. This paper develops a Mixed Integer Non-Linear Programming (MINLP) model for planning the movement and storage of food grain from surplus states to deficit states considering the seasonal procurement, silo capacity, demand satisfaction and vehicle capacity constraints. The objective function of the model seeks to minimize the bulk food grain transportation, inventory holding, and operational cost. Therein, shipment cost contains the fixed and variable cost, inventory holding and operational cost considered at the procurement centers and base silos. The developed mathematical model is computationally complex in nature due to nonlinearity, the presence of numerous binary and integer variables along with a huge number of constraints, thus, it is very difficult to solve it using exact methods. Therefore, recently developed, Hybrid Particle-Chemical Reaction Optimization (HP-CRO) algorithm has been employed to solve the MINLP model. Different problem instances with growing complexities are solved using HP-CRO and the results are compared with basic Chemical Reaction Optimization (CRO) and Particle Swarm Optimization (PSO) algorithms. The results of computational experiments illustrate that the HP-CRO algorithm is competent enough to obtain the better quality solutions within reasonable computational time.

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Section: Problem Instancesmentioning

confidence: 99%

An MINLP model to support the movement and storage decisions of the Indian food grain supply chain

Mogale

Kumar

Tiwari

2018

Control Engineering Practice

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“…Several scholars only answered the first question which has been called as truck-to-door assignment (Nassief et al, 2016;Yu & Egbelu, 2008), while others answered the second question, which has been considered as truck sequencing (Dondo & Cerdá, 2013;Fazel Zarandi et al, 2016;Larbi et al, 2009;Yazdani et al, 2015). Most studies tried to solve the cross-docking problems with one inbound and outbound door (Boloori Arabani et al, 2011;Cóccola et al, 2015;Liao et al, 2012;Maknoon & Baptiste, 2009;Yu & Egbelu, 2008), which is impractical in the real world (Wisittipanich & Hengmeechai, 2017). In general, the literature on truckto-door sequencing focuses on the order or sequence of trucks and the doors which trucks should be assigned, In order to simulate real cross-dock operations, scholars should consider planning and scheduling problems with a realistic number of trucks and doors (Dondo & Cerdá, 2014;Shahin Moghadam et al, 2014;Wisittipanich & Hengmeechai, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Most studies tried to solve the cross-docking problems with one inbound and outbound door (Boloori Arabani et al, 2011;Cóccola et al, 2015;Liao et al, 2012;Maknoon & Baptiste, 2009;Yu & Egbelu, 2008), which is impractical in the real world (Wisittipanich & Hengmeechai, 2017). In general, the literature on truckto-door sequencing focuses on the order or sequence of trucks and the doors which trucks should be assigned, In order to simulate real cross-dock operations, scholars should consider planning and scheduling problems with a realistic number of trucks and doors (Dondo & Cerdá, 2014;Shahin Moghadam et al, 2014;Wisittipanich & Hengmeechai, 2017). Li et al (2004) presented research as a two-phase parallel machine scheduling problem with earliness and tardiness in an integer programming model.…”

Section: Introductionmentioning

confidence: 99%

“…The metaheuristic algorithm, such as simulated annealing and firefly algorithms was used to solve the problem. In continue, Wisittipanich and Hengmeechai (2017) proposed a truck scheduling problem in the multi-door crossdocking terminal. They presented a Mixed Integer Programming (MIP) model for minimizing the makespan and then solved the model with a modified PSO Meta-heuristic algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Truck-to-door sequencing in multi-door cross-docking system with dock repeat truck holding patter

Ardakani¹,

Fei²,

Beldar³

2020

10.5267/j.ijiec

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Cross-docking is a logistics strategy that consolidates the products of different inbound trucks according to their destinations in order to reduce the inventory, order picking, and transportation costs. It requires a high level of collaboration between inbound trucks, internal operations, and outbound trucks. This article addresses the truck-to-door sequencing problem. Truck-to-door sequencing has been studied by some researchers in different titles such as scheduling and sequencing of inbound and outbound trucks of the cross-dock center. However, previous studies have not considered repeat truck holding pattern. Therefore, it is important to determine the doors and the sequence of the inbound and outbound trucks that should be assigned in a cross-dock center. This paper focuses on optimizing truck-to-door sequencing with consideration of repeat truck holding pattern in inbound trucks in order to minimize makespan. Two methods are considered to solve this problem, including mathematical modeling and a heuristic algorithm. In the first method, a mixed integer-programming model is developed to minimize the makespan. Then, GAMS software is used to solve small-scale problems. In the second approach, a heuristic algorithm is developed to find near-optimal solutions within the shortest time possible and the algorithm is used to solve large-scale problems. The results of the mathematical model and the heuristic algorithm are slightly different and show the good quality of the presented heuristic algorithm.

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“…To provide insights on promising solution methodologies and behavior of the system, some researchers have focused on cases with one inbound and one outbound dock (Yu and Egbelu, 2008;Chen and Song, 2009;Vahdani and Zandieh, 2010;Boloori Arabani et al, 2010, 2011bLiao et al, 2012;Amini and Tavakkoli-Moghaddam, 2016;Keshtzari et al, 2016), but cases with multiple inbound and outbound docks have been becoming more frequent (Bellanger et al, 2013;Van Belle et al, 2013;Ladier and Alpan, 2018;Assadi and Bagheri, 2016;Cota et al, 2016;Wisittipanich and Hengmeechai, 2017). The scheduling problem is usually modeled with the goal of minimizing total operation time (Yu and Egbelu, 2008;Chen and Song, 2009;Vahdani and Zandieh, 2010;Liao et al, 2012;Keshtzari et al, 2016;Bellanger et al, 2013;Cota et al, 2016;Wisittipanich and Hengmeechai, 2017;Boloori Arabani et al, 2011a;Shakeri et al, 2012;Gelareh et al, 2016;Bazgosha et al, 2017). It is also possible to find examples of other metrics: Storage (volume or cost) or material handling (Ladier and Alpan, 2018;Rahmanzadeh Tootkaleh et al, 2016;Maknoon et al, 2017), travel distance (Chmielewski et al, 2009) and earliness and/or tardiness alone (Boloori Arabani et al, 2010;Assadi and Bagheri, 2016) or combined with others such as travel time (Van Belle et al, 2013) and probability of breakdowns (Amini and Tavakkoli-Moghaddam, 2016).…”

Section: Introductionmentioning

confidence: 99%

Output maximization container loading problem with time availability constraints

Castellucci

Toledo

Costa

2019

Operations Research Perspectives

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Research on container loading problems has been proved effective in increasing the filling rate of containers in different practical situations. However, the broader logistic context might pressure the loading process, leading to sub-optimal solutions. Some facilities like cross-docks have reduced storage space which might force early loading activities. We propose a container loading problem which accounts for this limited storage by explicitly considering the schedule of arrival for the boxes and the departure time of the trucks. Also, we design a framework which handles the geometric and temporal characteristics of the problem separately, enabling the use of methods found in the literature for solving the extended problem. Our framework can handle uncertainty in the schedule and be used to quantify the impact of delays on capacity utilization and departure time of trucks.

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Truck scheduling in multi-door cross docking terminal by modified particle swarm optimization

Cited by 49 publications

References 22 publications

An MINLP model to support the movement and storage decisions of the Indian food grain supply chain

An MINLP model to support the movement and storage decisions of the Indian food grain supply chain

Truck-to-door sequencing in multi-door cross-docking system with dock repeat truck holding patter

Output maximization container loading problem with time availability constraints

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