The maximum profit twoor three-dimensional knapsack packing problem asks to pack a maximum profit subset of some given rectangles or boxes into a larger rectangle or box of fixed dimensions. Items must be orthogonally packed, but no other restrictions are imposed to the problem. The problem could also be considered as a knapsack problem generalized to two or three dimensions. In this paper we p...

We consider a stochastic version of the 0–1 Knapsack Problem in which, addition to profit and weight, each item is associated with probability exploding destroying all contents knapsack. The objective maximise expected selected items. resulting problem, denoted as Time-Bomb (01-TB-KP), has applications logistics cloud computing scheduling. introduce nonlinear mathematical formulation study its ...

A knapsack problem is to select a set of items that maximizes the total profit selected while keeping weight no less than capacity knapsack. As generalized form with multiple knapsacks, multi-knapsack (MKP) disjointed for each To solve MKP, we propose deep reinforcement learning (DRL) based approach, which takes as input available capacities profits and weights items, normalized unselected dete...

It is well-known that the multiple knapsack problem is NP-hard, and does not admit an FPTAS even for the case of two identical knapsacks. Whereas the 0-1 knapsack problem with only one knapsack has been intensively studied, and some effective exact or approximation algorithms exist. A natural approach for the multiple knapsack problem is to pack the knapsacks successively by using an effective ...

this paper presents a new mathematical model for a bi-objective job shop scheduling problem with sequence-dependent setup times that minimizes the weighted mean completion time and the weighted mean tardiness time. for solving this multi-objective model, we develop a fuzzy multi-objective linear programming (fmolp) model. in this problem, a proposed fmolp method is applied with respect to the o...

Abstract This paper proposes a method for improving the diversity of Pareto front and uniformity non-dominated solution distributions in fast elitist sorting genetic algorithm (NSGA-II), which is an evolutionary multi-objective optimization algorithm. Conventional NSGA-II has excellent convergence to front, but it been reported that some test cases, does not produce more diverse distribution th...

The multidimensional multi-choice knapsack problem (MMKP) is one of the most complex members of the Knapsack Problem (KP) family. It has been used to model large problems such as telecommunications, quality of service (QoS), management problem in computer networks and admission control problem in the adaptive multimedia systems. In this paper, we propose a new approach based on strategic oscill...

Greedy algorithm is a group of algorithms that have one common characteristic, making the best choice locally at each step without considering future plans. Thus, the essence of greedy algorithm is a choice function: given a set of options, choose the current best option. Because of the myopic nature of greedy algorithm, it is (as expected) not correct for many problems. However, there are cert...

Multiobjective Evolutionary Algorithms (MOEAs) are increasingly being used for effectively solving many real-world problems, and many empirical results are available. However, theoretical analysis is limited to a few simple toy functions. In this work, we select the well-known knapsack problem for the analysis. The multiobjective knapsack problem in its general form is NP-complete. Moreover, th...

In modeling Multi-Criteria Decision Making (MCDM) problem, we usually assume that the decision maker is able to elicitate his preferences with precision and without difficulty. However, in many situations, the expert is unable to provide his assessment with certainty or he is unwilling to quantify his preferences. To deal with such situations, a new MCDM model under uncertainty is introduced. I...