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Abstract

Maintaining an appropriate environmental condition plays a vital role in preventing food poisoning and food spoilage. Thus, how to allocate a large number of perishable food products with different intrinsic characteristics to a cold store with different cabins is quite important for decision makers. In this paper, we define a mathematical model of the fuzzy cold storage problem (FCSP), which can be regarded as a complex variant of the knapsack problem. Profits of perishable food products are fuzzified and represented by triangular fuzzy numbers (TFNs). To solve the fuzzy system, we use the k-preference integration as the defuzzification method. Given that the FCSP is highly combinatorial, we design a new discrete optimization algorithm which combines the firefly algorithm (FA) and the greedy algorithm to get near-optimal solutions. In this algorithm, we use the Hamming distance to denote the distance between two fireflies and propose a four-phase repair operator to correct and optimize the solutions. Furthermore, processes of initialization and movement of the brightest firefly are also improved to strengthen the algorithm. Finally, computational simulations with randomly generated data are analyzed and reported to evaluate the performance of the proposed algorithm.

Keywords

Cold storage problem firefly algorithm k-preference integration triangular fuzzy number perishable food

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References

James

S.J.

and James

, The food cold-chain and climate change, Food Research International43(7) (2010), 1944–1956.

Thompson

A.K.

, Fruit and vegetables: Harvesting, handling and storage. Oxford, Oxon: Blackwell publishing, 2009.

Kramer

, Bufler

, Ulrich

, Leitenberger

, Conrad

, Carle

and Kammerer

D.R.

, Effect of ethylene and 1-methylcyclopropene on bitter compounds in carrots (Daucus carota L.), Postharvest Biology and Technology73 (2012), 28–36.

Suslow

T.V.

and Cantwell

, Onions, Green Bunch: Recommendations for Maintaining Postharvest Quality, http://postharvest.ucdavis.edu/pfvegetable/OnionsGreen/.

Ehsan

, Udoncy

O.E.

, Hanim

A.R.S.

and Nima

, An economic order quantity model considering different holding costs for imperfect quality items subject to fuzziness and learning, Journal of Intelligent & Fuzzy Systems30(5) (2016), 2985–2997.

Chen

, Hao

J.K.

and Glover

, An evolutionary path relinking approach for the quadratic multiple knapsack problem, Knowledge-Based Systems92 (2010), 23–34.

Sabet

, Shokouhifar

and Farokhi

, A discrete artificial bee colony for multiple Knapsack problem, International Journal of Reasoning-based Intelligent Systems5(2) (2013), 88–95.

Zhou

, Chen

and Zhou

, An improved monkey algorithm for a 0-1 knapsack problem, Applied Soft Computing38 (2016), 817–830.

Baykasoğlu

and Ozsoydan

F.B.

, An improved firefly algorithm for solving dynamic two-dimensional knapsack problems, Expert Systems with Applications41(8) (2014), 3712–3725.

10.

Zhang

, Pan

Q.K.

, Zhang

X.L.

and Duan

P.Y.

, An effective hybrid harmony search-based algorithm for solving multidimensional knapsack problems, Applied Soft Computing29 (2015), 288–297.

11.

Changdar

, Mahapatra

G.S.

and Pal

R.K.

, An improved genetic algorithm based approach to solve constrained knapsack problem in fuzzy environment, Expert Systems with Applications42(4) (2015), 2276–2286.

12.

Changdar

, Mahapatra

G.S.

and Pal

R.K.

, An Ant colony optimization approach for binary knapsack problem under fuzziness, Applied Mathematics and Computation223 (2013), 243–253.

13.

Fister

Jr , Fister

, Yang

X.S.

and Brest

, A comprehensive review of firefly algorithm, Swarm and Evolutionary Computation13 (2013), 34–46.

14.

Yang

X.S.

, Firefly algorithms for multimodal optimization, in Stochastic Algorithms: Foundations and Applications, Eds. Berlin: Springer, 2009, pp. 169–78.

15.

Jati

G.K.

, and Suyanto, Evolutionary Discrete Firefly Algorithm for Travelling Salesman Problem, in Adaptive and Intelligent Systems, Eds. Berlin: Springer, 2011, pp. 393–403.

16.

S.C.

and Wang

X.F.

, A firefly algorithm based approach for the two-dimensional min-cost covering problem, in Proceedings of 2015 IEEE 6th International Conference on Software Engineering and Service Sciences, 2015, pp. 1003–1006.

17.

Fukunaga

A.S.

and Korf

R.E.

, Bin completion algorithms for multicontainer packing, knapsack, and covering problems, Journal of Artificial Intelligence Research28 (2007), 393–429.

18.

Zimmermann

H.J.

, Fuzzy Set Theory And Its Applications. MA, Norwell: Kluwer Academic Publishers, 1996.

19.

Chen

S.H.

and Hsieh

C.H.

, A new method of representing generalized fuzzy number, Tamsui Oxford Journal of Management Sciences13-14 (1998), 133–143.

20.

Yang

X.S.

, Nature-inspired Metaheuristic Algorithm, Somerset, Luniver Press, 2008.

21.

Gandomi

A.H.

, Yang

X.S.

and Alavi

A.H.

, Mixed variable structural optimization using Firefly Algorithm, Computers and Structures89(23–24) (2011), 2325–2366.

22.

Wang

, Wang

S.Y.

and Xu

, An effective hybrid EDA-based algorithm for solving multidimensional knapsack problem, Expert Systems with Applications39(5) (2012), 5593–5599.

23.

Food and Agriculture Organization of the United Nations, Manual for the preparation and sale of fruits and vegetables, http://www.fao.org/docrep/008/y4893e/y4893e06.htm.

Modeling the fuzzy cold storage problem and its solution by a discrete firefly algorithm

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