A procedure-based heuristic for 0-1 Multiple Knapsack Problems

Lalami, Mohamed Esseghir; Elkihel, Moussa; Baz, Didier El; Boyer, Vincent

doi:10.1504/ijmor.2012.046684

Cited by 19 publications

(8 citation statements)

References 14 publications

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“…On the other hand, any dynamic programming approach would result in strictly exponential time bounds [19]. Up to now, there exist two main approaches to deal with large MKPs: i) speed up the solution process using various branchand-bound (B&B) techniques (e.g., [19], [26] - [28]); ii) find good but not necessarily optimal solutions using heuristic algorithms (e.g., [29] - [32]). In this paper, both of the above approaches are used to deal with the problem (9).…”

Section: Rb Allocation Proceduresmentioning

confidence: 99%

Effective resource block allocation procedure for quality of service provisioning in a single-operator heterogeneous LTE-A network

Asheralieva

Miyanaga

2016

Computer Networks

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We consider the problem of resource block (RB) allocation in the integrated pico/macrocell Long Term Evolution-Advanced (LTE-A) network. It is assumed that the network is controlled by a single service provider (SP) and all the operation of the picocells is coordinated with a macro-network. To improve the quality of service (QoS) for end-to-end applications, we take into account the individual traffic demands of the users and allocate the RBs to minimize the sum of user utilities which are expressed in terms of the size of their queues. The formulated RB allocation problem belongs to the family of the multiple knapsack problems (MKPs) and, therefore, it is non-deterministic polynomial time (NP) hard in the strong sense. To reduce the complexity of this problem, we propose a simple heuristic technique to find the suitable (but not necessarily optimal) solution. The proposed RB allocation procedure requires only two additional signalling steps (necessary to maintain the coordination among different cells) and, therefore, its impact of the control signalling overhead is neglectable. It was shown (using OPNET-based simulations) that the proposed technique has low complexity, fast solution time, and shows improved performance when compared to other relevant schemes.

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Section: Rb Allocation Proceduresmentioning

confidence: 99%

Effective resource block allocation procedure for quality of service provisioning in a single-operator heterogeneous LTE-A network

Asheralieva

Miyanaga

2016

Computer Networks

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“…The heuristic (MTHM) of Martello and Toth [12] is a very efficient heuristic to solve the problem MKP It takes place in stages present in the following Figure. [13] is a heuristic with a polynomial time complexity for solving the MKP. Unfortunately, this heuristic resolve any problems that could be solved using optimality Mulknap i.e.…”

Section: Heuristicsmentioning

confidence: 99%

“…The Multiple Knapsack Problem (MKP) is a variant of the knapsack problem (KP) whose resolution is much more difficult, the fact that we have this problem in areas as different application than the economy, industry, transport, cargo loading and distributed computing, gives it a great practical interest [1].…”

Section: Introductionmentioning

confidence: 99%

Local Search Heuristic for Multiple Knapsack Problem

Balbal¹

2015

IJIIS

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“…Two objectives are considered in the second model as maximizing the total profit and minimizes the time and cost by adding the three parameters ( Deb & Sinha, 2009 ). Moreover, many other variants are added in traveling thief problem to solve this by molded the problem in other dimensions that is, Multiple Knapsack Problem ( Lalami et al, 2012 ), multi-objective knapsack ( Bazgan, Hugot & Vanderpooten, 2009 ), fractional knapsack ( Ishii, Ibaraki & Mine, 1977 ), bi-level knapsack ( Chen & Zhang, 2013 ), etc. After this, the benchmark is designed with different algorithms to solve TTP but these are the simple techniques to verify this problem ( Polyakovskiy et al, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

A novel approach for solving travelling thief problem using enhanced simulated annealing

Ali

Rafique

Sarfraz

et al. 2021

PeerJ Computer Science

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Real-world optimization problems are getting more and more complex due to the involvement of inter dependencies. These complex problems need more advanced optimizing techniques. The Traveling Thief Problem (TTP) is an optimization problem that combines two well-known NP-Hard problems including the 0/1 knapsack problem and traveling salesman problem. TTP contains a person known as a thief who plans a tour to collect multiple items to fill his knapsack to gain maximum profit while incurring minimum cost in a standard time interval of 600 s. This paper proposed an efficient technique to solve the TTP problem by rearranging the steps of the knapsack. Initially, the picking strategy starts randomly and then a traversal plan is generated through the Lin-Kernighan heuristic. This traversal is then improved by eliminating the insignificant cities which contribute towards profit adversely by applying the modified simulated annealing technique. The proposed technique on different instances shows promising results as compared to other state-of-the-art algorithms. This technique has outperformed on a small and medium-size instance and competitive results have been obtained in the context of relatively larger instances.

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A procedure-based heuristic for 0-1 Multiple Knapsack Problems

Cited by 19 publications

References 14 publications

Effective resource block allocation procedure for quality of service provisioning in a single-operator heterogeneous LTE-A network

Effective resource block allocation procedure for quality of service provisioning in a single-operator heterogeneous LTE-A network

Local Search Heuristic for Multiple Knapsack Problem

A novel approach for solving travelling thief problem using enhanced simulated annealing

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