Reza Tavakkoli-Moghaddam

A hybrid multi-objective immune algorithm for a flow shop scheduling problem with bi-objectives: Weighted mean completion time and weighted mean tardiness

This paper investigates a novel multi-objective model for a no-wait flow shop scheduling problem that minimizes both the weighted mean completion time (C¯w) and weighted mean tardiness (T¯w). Obtaining an optimal solution for this type of complex, large-sized problem in reasonable computational time by using traditional approaches and optimization tools is extremely difficult. This paper presents a new hybrid multi-objective algorithm based on the features of a biological immune system (IS) and bacterial optimization (BO) to find Pareto optimal solutions for the given problem. To validate the performance of the proposed hybrid multi-objective immune algorithm (HMOIA) in terms of solution quality and diversity level, various test problems are examined. Further, the efficiency of the proposed algorithm, based on various metrics, is compared against five prominent multi-objective evolutionary algorithms: PS-NC GA, NSGA-II, SPEA-II, MOIA, and MISA. Our computational results suggest that our proposed HMOIA outperforms the five foregoing algorithms, especially for large-sized problems.

Solving a dynamic cell formation problem using metaheuristics

In this paper, solving a cell formation (CF) problem in dynamic condition is going to be discussed by using some traditional metaheuristic methods such as genetic algorithm (GA), simulated annealing (SA) and tabu search (TS). Most of previous researches were done under the static condition. Due to the fact that CF is a NP-hard problem, then solving the model using classical optimization methods needs a long computational time. In this research, a nonlinear integer model of CF is first given and then solved by GA, SA and TS. Then, the results are compared with the optimal solution and the efficiency of the proposed algorithms is discussed.

Design of a facility layout problem in cellular manufacturing systems with stochastic demands

This paper presents a new mathematical model to solve a facility layout problem in cellular manufacturing systems (CMSs) with stochastic demands. The objective is to minimize the total costs of inter and intra-cell movements in both machine and cell layout problems simultaneously. This model depends on the attitude of the decision maker towards uncertainty in such a way that the optimal layout in CMSs can be changed significantly. A number of instances are optimally solved by the Lingo software to validate and verify the proposed model. Finally, computational results are reported and analyzed.

An efficient algorithm for solving a new mathematical model for a quay crane scheduling problem in container ports

This paper presents a novel, mixed-integer programming (MIP) model for the quay crane (QC) scheduling and assignment problem, namely QCSAP, in a container port (terminal). Obtaining an optimal solution for this type of complex, large-sized problem in reasonable computational time by using traditional approaches and optimization tools is extremely difficult. This paper, thus, proposes a genetic algorithm (GA) to solve the above-mentioned QCSAP for the real-world situations. Further, the efficiency of the proposed GA is compared against the LINGO software package in terms of computational times for small-sized problems. Our computational results suggest that the proposed GA is able to solve the QCSAP, especially for large sizes.

Solving a group layout design model of a dynamic cellular manufacturing system with alternative process routings, lot splitting and flexible reconfiguration by simulated annealing

This paper presents a novel mixed-integer non-linear programming model for the layout design of a dynamic cellular manufacturing system (DCMS). In a dynamic environment, the product mix and part demands are varying during a multi-period planning horizon. As a result, the best cell configuration for one period may not be efficient for successive periods, and thus it necessitates reconfigurations. Three major and interrelated decisions are involved in the design of a CMS; namely cell formation (CF), group layout (GL) and group scheduling (GS). A novel aspect of this model is concurrently making the CF and GL decisions in a dynamic environment. The proposed model integrating the CF and GL decisions can be used by researchers and practitioners to design GL in practical and dynamic cell formation problems. Another compromising aspect of this model is the utilization of multi-rows layout to locate machines in the cells configured with flexible shapes. Such a DCMS model with an extensive coverage of important manufacturing features has not been proposed before and incorporates several design features including alternate process routings, operation sequence, processing time, production volume of parts, purchasing machine, duplicate machines, machine capacity, lot splitting, intra-cell layout, inter-cell layout, multi-rows layout of equal area facilities and flexible reconfiguration. The objective of the integrated model is to minimize the total costs of intra and inter-cell material handling, machine relocation, purchasing new machines, machine overhead and machine processing. Linearization procedures are used to transform the presented non-linear programming model into a linearized formulation. Two numerical examples taken from the literature are solved by the Lingo software using a branch-and-bound method to illustrate the performance of this model. An efficient simulated annealing (SA) algorithm with elaborately designed solution representation and neighborhood generation is extended to solve the proposed model because of its NP-hardness. It is then tested using several problems with different sizes and settings to verify the computational efficiency of the developed algorithm in comparison with the Lingo software. The obtained results show that the proposed SA is able to find the near-optimal solutions in computational time, approximately 100 times less than Lingo. Also, the computational results show that the proposed model to some extent overcomes common disadvantages in the existing dynamic cell formation models that have not yet considered layout problems.

A hybrid simulated annealing for capacitated vehicle routing problems with the independent route length

This paper presents a linear integer model of capacitated vehicle routing problems (VRP) with the independent route length to minimize the heterogeneous fleet cost and maximize the capacity utilization. In the proposed model, the fleet cost is independent on the route length and there is a hard time window over depot. In some real-world situations, the cost of routes is independent on their length, but it is dependent to type and capacity of vehicles allocated to routes where the fleet is mainly heterogeneous. In this case, the route length or travel time is expressed as restriction, that is implicated a hard time window in depot. The proposed model is solved by a hybrid simulated annealing (SA) based on the nearest neighborhood. It is shown that the proposed model enables to establish routes to serve all given customers by the minimum number of vehicles and the maximum capacity used. Also, the proposed heuristic can find good solutions in reasonable time. A number of small and large-scale problems in little and large scale are solved and the associated results are reported.

A multi-objective scatter search for a mixed-model assembly line sequencing problem

A mixed-model assembly line (MMAL) is a type of production line where a variety of product models similar to product characteristics are assembled. There is a set of criteria on which to judge sequences of product models in terms of the effective utilization of this line. In this paper, we consider three objectives, simultaneously: minimizing total utility work, total production rate variation, and total setup cost. A multi-objective sequencing problem and its mathematical formulation are described. Since this type of problem is NP-hard, a new multi-objective scatter search (MOSS) is designed for searching locally Pareto-optimal frontier for the problem. To validate the performance of the proposed algorithm, in terms of solution quality and diversity level, various test problems are made and the reliability of the proposed algorithm, based on some comparison metrics, is compared with three prominent multi-objective genetic algorithms, i.e. PS-NC GA, NSGA-II, and SPEA-II. The computational results show that the proposed MOSS outperforms the existing genetic algorithms, especially for the large-sized problems.

A hybrid method for solving stochastic job shop scheduling problems

This paper presents a nonlinear mathematical programming model for a stochastic job shop scheduling problem. Due to the complexity of the proposed model, traditional algorithms have low capability in producing a feasible solution. Therefore, a hybrid method is proposed to obtain a near-optimal solution within a reasonable amount of time. This method uses a neural network approach to generate initial feasible solutions and then a simulated annealing algorithm to improve the quality and performance of the initial solutions in order to produce the optimal/near-optimal solution. A number of test problems are randomly generated to verify and validate the proposed hybrid method. The computational results obtained by this method are compared with lower bound solutions reported by the Lingo 6 optimization software. The compared results of these two methods show that the proposed hybrid method is more effective when the problem size increases.

A memetic algorithm for the flexible flow line scheduling problem with processor blocking

This paper introduces an efficient memetic algorithm (MA) combined with a novel local search engine, namely, nested variable neighbourhood search (NVNS), to solve the flexible flow line scheduling problem with processor blocking (FFLB) and without intermediate buffers. A flexible flow line consists of several processing stages in series, with or without intermediate buffers, with each stage having one or more identical parallel processors. The line produces a number of different products, and each product must be processed by at most one processor in each stage. To obtain an optimal solution for this type of complex, large-sized problem in reasonable computational time using traditional approaches and optimization tools is extremely difficult. Our proposed MA employs a new representation, operators, and local search method to solve the above-mentioned problem. The computational results obtained in experiments demonstrate the efficiency of the proposed MA, which is significantly superior to the classical genetic algorithm (CGA) under the same conditions when the population size is increased in the CGA.

Design of a genetic algorithm for bi-objective unrelated parallel machines scheduling with sequence-dependent setup times and precedence constraints

This paper presents a novel, two-level mixed-integer programming model of scheduling N jobs on M parallel machines that minimizes bi-objectives, namely the number of tardy jobs and the total completion time of all the jobs. The proposed model considers unrelated parallel machines. The jobs have non-identical due dates and ready times, and there are some precedence relations between them. Furthermore, sequence-dependent setup times, which are included in the proposed model, may be different for each machine depending on their characteristics. Obtaining an optimal solution for this type of complex, large-sized problem in reasonable computational time using traditional approaches or optimization tools is extremely difficult. This paper proposes an efficient genetic algorithm (GA) to solve the bi-objective parallel machine scheduling problem. The performance of the presented model and the proposed GA is verified by a number of numerical experiments. The related results show the effectiveness of the proposed model and GA for small and large-sized problems.