A differential evolution algorithm for the capacitated VRP with flexibility of mixing pickup and delivery services and the maximum duration of a route in poultry industry

Dechampai, Darat; Tanwanichkul, Ladda; Sethanan, Kanchana; Pitakaso, Rapeepan

doi:10.1007/s10845-015-1055-3

Cited by 52 publications

(31 citation statements)

References 31 publications

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“…The result of the study was an index of dispatcher settings returning the best value by using crossover parameters to develop different answers from the local optimum. Later, Dechampai et al [39] presented a DE algorithm for a VRP, which had a vehicle capacity limit for the poultry industry, using two heuristics. Both heuristics were used for grouping customers before arranging transportation routes, which provided 7.59-31.28% lower costs than the present method and was better than the first heuristic by 0.84-13.15%.…”

Section: Literature Reviewmentioning

confidence: 99%

An Improved Differential Evolution Algorithm for Crop Planning in the Northeastern Region of Thailand

Ketsripongsa

Pitakaso

Sethanan

et al. 2018

MCA

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Abstract:This research aimed to solve the economic crop planning problem, considering transportation logistics to maximize the profit from cultivated activities. Income is derived from the selling price and production rate of the plants; costs are due to operating and transportation expenses. Two solving methods are presented: (1) developing a mathematical model and solving it using Lingo v.11, and (2) using three improved Differential Evolution (DE) Algorithms-I-DE-SW, I-DE-CY, and I-DE-KV-which are DE with swap, cyclic moves (CY), and K-variables moves (KV) respectively. The algorithms were tested by 16 test instances, including this case study. The computational results showed that Lingo v.11 and all DE algorithms can find the optimal solution eight out of 16 times. Regarding the remaining test instances, Lingo v.11 was unable to find the optimal solution within 400 h. The results for the DE algorithms were compared with the best solution generated within that time. The DE solutions were 1.196-1.488% better than the best solution generated by Lingo v.11 and used 200 times less computational time. Comparing the three DE algorithms, MDE-KV was the DE that was the most flexible, with the biggest neighborhood structure, and outperformed the other DE algorithms.

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Section: Literature Reviewmentioning

confidence: 99%

An Improved Differential Evolution Algorithm for Crop Planning in the Northeastern Region of Thailand

Ketsripongsa

Pitakaso

Sethanan

et al. 2018

MCA

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“…In the work by Dechampai et al (2015), the utilization of two heuristics is considered to resolve the vehicle routing problem, considering a flexible demand in the mix of pickup and delivery service with constraints on maximum time of the route, thus extending the well-known VRP with simultaneous pickups and deliveries.…”

Section: The Problem Of Territory and Vehicle Route Designmentioning

confidence: 99%

“…Specifically, compact territories (compactness) is required, so that the pickup and delivery trips are short, which has the benefit of reducing time and cost related to transport, in such a way that meet very specific criteria of balancing as is the Workload, another point to be considered is that the model contemplates the use of a single central deposit which the vehicle must use as its origin and final destination in contrast to different works in the literature as (Dechampai, Tanwanichkul, Sethanan & Pitakaso, 2015;Ugurlu, Bozcaya & Kervenoael, 2013;Jiaoman, Xiang, Lean, Ralescu & Jiandong, 2017), which handle several deposits to make their pickups in the distribution network, likewise, it is intended to define the number of teams necessary to meet the demand and the allocation of clients and vehicles to the routes for the efficient utilization of these teams, is important to mention than in emerging market context, it is important to consider that the proposed solution seeks to respond to a recurrent problem in an emerging market context where on the one hand there is a limited availability of professional, technological and economic resources and on the other hand, given the scarcity of resources, there is a growing demand for robust solutions that minimize costs and enable companies to improve their connectivity in the market, is use existing infrastructure to be more efficient in their services and at the same time reduce their risks by complying with existing regulations. These are dimensions that are measured in Agility's Emerging Markets Logistics Index 2016.…”

Section: Introductionmentioning

confidence: 99%

Optimization of territories and transport routes for hazardous products in a distribution network

Cantú

Solano²,

Lagarda-Leyva

et al. 2017

JIEM

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Abstract:Purpose: This work presents a general model in mixed integer programming that integrates the design of the territory and distribution route planning, seeking to minimize the total distance covered by the vehicle in each territory.Design/methodology/approach: In this work, a mathematical optimization model has been proposed using an exact algorithm based in mixed integer linear programming, to seek of minimizing the cost of pickups and/or deliveries of products considered to be hazardous in a distribution network, using AMPL software as an interface, with CPLEX as an optimizer to solve a practical real problem. Findings:The model reports an efficient solution, which provide the process administrator with sufficient information to optimize the use of the available (Limited) distribution resources in SMEs of these types of markets that are considered emerging. Originality/value:In contrast to the typical models applied to the VRP with pickups and/or delivery of hazardous materials, this work proposes the use of an exact algorithm that gives a quick and efficient solution for a real optimization problem, considered balance in workload in -604-Journal of Industrial Engineering and Management -https://doi.org/10.3926/jiem.2107 each territory and using of a single central deposit which the vehicle must use as its origin and final destination.

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“…Saenkaeo (2015) developed differential evolution for minimize the total transportation cost in vehicle routing problem (VRP) (12) . Dechampai (2015) propose hybrid differential evolution algorithm for capacitated VRP with flexibility of mixing pickup and delivery services and the maximum duration of a route in poultry industry. The results obtained from this study show that the MESOMDE_G-Q-DVRP-FD algorithm provides lower total cost values than that of the firm's current practice by 7.59-31.28 and 0.84-13.15% etc (13) .…”

Section: Literature Reviewmentioning

confidence: 99%

“…Dechampai (2015) propose hybrid differential evolution algorithm for capacitated VRP with flexibility of mixing pickup and delivery services and the maximum duration of a route in poultry industry. The results obtained from this study show that the MESOMDE_G-Q-DVRP-FD algorithm provides lower total cost values than that of the firm's current practice by 7.59-31.28 and 0.84-13.15% etc (13) . Sethanan and Pitakaso (2016) presents DE algorithm to solve the generalized assignment problem (GAP) with the objective to minimize the assignment cost and applied three local search : shifting, exchange, and k-variable move algorithms for improve DE algorithm and competition performance with Bee algorithm (BEE) and Tabu search algorithm (TABU) (14) From the review of the literature, it was found that the available models could not be applied to the case.…”

Section: Literature Reviewmentioning

confidence: 99%

Differential Evolution Algorithm for House Size Determination In a Poultry Production

Sriputthimeth

Sethanan

Moonsri

2016

The Proceedings of the 4th International Conference on Industrial Application Engineering 2016

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This study focuses on determining size (or capacity) and an appropriate number of hen houses for raising chicks in each week along the planning horizons. The chicks are raised in pullet farms until the age of 17 weeks. These pullets are then moved to hen houses and fed to a prescribed body weight to support egg production, and remain in the hen houses until they reach 75 weeks of age. Normally, the capacities of the pullet and hen houses are heterogeneous depending on the level of investment of contract farms. The complexity of poultry production that there are different sizes of pullet and hen houses then the different ages of hen could be mixed then the production cost get higher because hen will be slaughtered earlier than their ages. The problem is to determine how many hen houses to set up, new hen house to build and determine the capacity of them to cover changing trends in demand and/or cost. The purposed DE algorithm was developed to minimize the total cost including cost of open hen house, cost of ordering hen and cost of mix aged hen and also meet the demands over the planning horizons. Finally the metaheuristics using differential evolution (DE) was developed then the solutions compared to the lower bound. The results of DE algorithms have heuristics performance 87.28% to 92.73% and the average is 89.69%

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A differential evolution algorithm for the capacitated VRP with flexibility of mixing pickup and delivery services and the maximum duration of a route in poultry industry

Cited by 52 publications

References 31 publications

An Improved Differential Evolution Algorithm for Crop Planning in the Northeastern Region of Thailand

An Improved Differential Evolution Algorithm for Crop Planning in the Northeastern Region of Thailand

Optimization of territories and transport routes for hazardous products in a distribution network

Differential Evolution Algorithm for House Size Determination In a Poultry Production

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