Abdelwaheb Rebai

A combinatorial particle swarm optimisation for solving permutation flowshop problems

The m-machine permutation flowshop problem PFSP with the objectives of minimizing the makespan and the total flowtime is a common scheduling problem, which is known to be NP-complete in the strong sense, when m>=3. This work proposes a new algorithm for solving the permutation FSP, namely combinatorial Particle Swarm Optimization. Furthermore, we incorporate in this heuristic an improvement procedure based on the simulated annealing approach. The proposed algorithm was applied to well-known benchmark problems and compared with several competing metaheuristics.

Canonical fuzzy bags and bag fuzzy measures as a basis for MADM with mixed non cardinal data

Abstract This contribution introduces what will be called ‘SINE analysis’, a new approach to multiple attribute decision making (MADM) with mixed noncardinal data. This approach is based upon the notion of canonical fuzzy bags and their scalar evaluations by bag fuzzy measures.

A fuzzy bag approach to choosing the “best” multiattributed potential actions in a multiple judgement and non cardinal data context

Abstract The present work is concerned with showing how a fuzzy bag construct along with the concept of a positive outstandingness predicate and that of a strict t-norm operator can be used to resolve conflicts and aggregation problems in decisions involving multiple judgements and non-cardinal data. It is also shown how the differential expertness degrees of the judges may be taken into account in the aggregation process. The choice device proposed is decisive, i.e., it excludes incomparability.

Solving multi-criteria scheduling flow shop problem through compromise programming and satisfaction functions

The multi-criteria scheduling problem is one of the main research subjects in the field of multiple objectives programming. Several procedures have been developed to deal with this type of problem where some conflicting criteria have to be optimized simultaneously. The aim of our paper is to propose an aggregation procedure that integrates three different criteria to find the best sequence in a flow shop production environment. The compromise programming model and the concept of satisfaction functions will be utilized to integrate explicitly the managers preferences according to the deviations between the achievement and the aspiration levels of the following criteria: Makespan, total flow time and total tardiness.

A multi-attribute method for choosing among potential alternatives with ordinal evaluation

Abstract Some concepts are introduced with a view to dealing with the problem of choosing the “best” potential alternative(s) in a multi-attribute ordinal data context. We assume that we know the possibilistic weights of attributes and ordinal evaluations of each potential alternative on all attributes. We define what we refer to as positive/negative coalitions of attributes and positiveness/negativeness numerical bags associated with each potential alternative. We also define what will be called positional advantage operators and we describe how all these concepts are used to lay the foundation for the choice method appearing in this paper.

A branch and bound enhanced genetic algorithm for scheduling a flowline manufacturing cell with sequence dependent family setup times

This paper addresses the permutation flowline manufacturing cell with sequence dependent family setup times problem with the objective to minimize the makespan criterion. We develop a cooperative approach including a genetic algorithm and a branch and bound procedure. The latter is probabilistically integrated in the genetic algorithm in order to enhance the current solution. Moreover, the application of the branch and bound algorithm is based upon the decomposition of the problem into subproblems. The performance of the proposed method is tested by numerical experiments on a large number of representative problems.

A GRASP based on DE to solve single machine scheduling problem with SDST

When handling combinatorial optimization problems, we try to get the optimal arrangement of discrete entities so that the requirements and the constraints are satisfied. These problems become more and more important in various industrial and academic fields. So, over the past years, several techniques have been proposed to solve them. In this paper, we are interested in the single machine scheduling problem with Sequence-Dependent Setup Times, which can be solved through different approaches. We present a hybrid algorithm which combines Greedy Randomized Adaptive Search Procedure and Differential Evolution for tackling this problem. Our algorithm is tested on benchmark instances from the literature. The computational experiments prove the efficiency of this algorithm.

A Goal Programming model for Aggregate Production Planning problem

Aggregate Production Planning (APP) involves the simultaneous determination of companys production, inventory and employment levels over a finite time horizon to minimise the total costs while meeting the forecasted demand. Other objectives can be to minimise the fluctuation on the employment levels, the inventory levels or to reach some standard on the performance levels. The objective of this paper is to propose an effective method to elaborate an aggregate plan which takes into account the managers preferences. We propose a Goal Programming (GP) approach, with satisfaction functions. This latter has not been used in the field of APP. Applied to a real case problem, weighted GP has initially been used; the results were not satisfactory for the manager. Our GP with satisfaction function gives, very satisfactory results for the manager.

Job satisfaction measurement: the multi-criteria satisfaction analysis

Job satisfaction is the attitude of employment widely studied, as well as one of the most extensively researched subjects in industrial/organisational psychology. University teachers’ job satisfaction has a constructive role for raising the level of teaching and research, enhancing academic competitiveness, attracting and retaining talented people. In this study, a multi-criteria satisfaction analysis (MUSA) method is employed to measure the university teachers’ job satisfaction in Sfax University. The basic principle of MUSA is the aggregation of individual judgments into a collective value function, assuming that university teachers’ global satisfaction depends on a set of criteria representing job characteristic dimensions.

A new destructive bounding scheme for the bin packing problem

In this paper, we present a new lower bounding scheme for the one-dimensional bin packing problem based on a destructive approach and we prove its effectiveness to solve hard instances. Performance comparison to other available lower bounds shows the effectiveness of our proposed lower bounds.