A novel hybrid tabu search approach to container loading

Liu, Jiamin; Yue, Yong; Dong, Zong-ran; Maple, Carsten; Keech, Malcolm

doi:10.1016/j.cor.2010.09.002

Cited by 54 publications

(23 citation statements)

References 22 publications

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“…The second subproblem (SP2) is also a relaxation of the NLMIP model, but differs from SP1 in that SP2 includes the weight distribution considerations, and requires as inputs some of the information contained in the solution from SP1. This approach of separating the problem is based on the strategy of improving weight distribution after a valid loading arrangement has been obtained for CLP problems (see Eley, ; García‐Cáceres et al., ; Liu et al., ; Baldi et al., ; Lim et al., ). The applied solution methodology is described below: Step 1 : Generate SP1 by removing constraints CG2 (which are responsible for the nonlinearity of the problem) and CG3, variables

c o g y_{v s}

and

d e v c o g_{v s}

.…”

Section: Solution Approach Methodologymentioning

confidence: 99%

A nonlinear optimization model for the balanced vehicle routing problem with loading constraints

Vega-Mejía

Montoya‐Torres

Islam

2018

Int Trans Operational Res

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The vehicle routing problem with loading constraints (VRPLC) is related to real‐life transportation problems and integrates two of the most important activities in distribution logistics: packing of items inside vehicles and planning of delivery routes. In spite of its relevance, literature on VRPLCs is still limited. The majority of the solution approaches have concentrated on heuristic solution methods, and few have presented mathematical optimization models to help characterize the problem. Furthermore, few studies have considered several practical loading and routing constraints that could be used to approximate the problem toward more realistic situations. To help fill this gap in the literature, this article extends an existing VRPLC optimization model to a nonlinear optimization model that considers weight‐bearing strength of three‐dimensional items, vehicle weight capacity, weight distribution inside vehicles, delivery time windows, and a balanced fleet of vehicles. The model is solved by applying a simple procedure that isolates the nonlinearity of the model. Computational experiments show that the new proposed model gives a more streamlined formulation than the model it extended on, and that the addition of practical loading constraints can improve the solutions of the original model by reducing the measure of tardiness due to late deliveries and by producing cargo patterns with better weight distribution.

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c o g y_{v s}

and

d e v c o g_{v s}

.…”

Section: Solution Approach Methodologymentioning

confidence: 99%

A nonlinear optimization model for the balanced vehicle routing problem with loading constraints

Vega-Mejía

Montoya‐Torres

Islam

2018

Int Trans Operational Res

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“…The heuristics can be developed taking into acount different specific distributions: wall-building [3], [6], cuboid arrangement [7], [8], stack-building [9], [10], or guillotine cuts [11]. Although there are many heuristic algorithms [2], [6], [3], [12], [13], [14], [15], in recent years the attention to the metaheuristics such as genetic algorithms [9], [16], [17], simulated annealing [18], [19], [20], tabu searches [7], [21], and hybrid algorithms [22], [23], [24], [25], has been increased. Sometimes, these processes may include parallelizations, this is the case of some parallel genetic algorithms [26], parallel tabu searches [27], or parallel hybrid local searches [28].…”

Section: Introductionmentioning

confidence: 99%

Parallelization of the multi-objective container loading problem

Armas

González

Miranda

et al. 2012

2012 IEEE Congress on Evolutionary Computation

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This work presents a multi-objective approach to solve the Container Loading Problem. The single-objective formulation of the problem has been widely studied in the related literature, trying to optimise the total volume of the packed pieces into the container. However, a rather common aspect in the scope of this problem is the weight limit of the containers, since they normally cannot exceed a certain weight for their transportation, and they should make the most without exceeding that limit. For this reason, we have focused on a multi-objective formulation of the problem which seeks to maximize the volume at the same time that the weight, without exceeding the weight limit. To solve this multi-objective problem we have applied multi-objective optimisation evolutionary algorithms given their great effectiveness with other types of real-world multi-objective problems. One of the goals of this work is to improve the results of the only known work in the literature that addresses the same problem with multiple objectives. Once we have achieved it, we have parallelized the problem applying different islandbased models to enhance the effectiveness and efficiency of our approach.

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“…S Equation (7) states that the ratio of the number of type g boxes to the total number of boxes on a pallet should equal to g R , the desired box proportion of type g . Equation (8) indicates that the total cumulative box volumes should equal to the pallet's volume. By solving Equations (7) and (8), the number of boxes for type g can be obtained as:…”

Section: Determination Of Proportion Of Assigned Number Of Boxes In Amentioning

confidence: 99%

“…In general, optimal solutions are computationally impractical to achieve [8]. For this reason, most of the studies have focused on the practical aspects of loading a container and developing heuristic solutions based on the concept of filling out the container with boxes organized in layers, walls, and columns.…”

mentioning

confidence: 99%