Nima Safaei

A hybrid simulated annealing for solving an extended model of dynamic cellular manufacturing system

Abstract This paper develops a mixed-integer programming model to design the cellular manufacturing systems (CMSs) under dynamic environment. In dynamic environment, the product mix and part demand change under a multi-period planning horizon. Thus, the best designed cells for one period may not be efficient for subsequent periods and reconfiguration of cells is required. Reconfiguration may involve adding, removing or relocating machines; it may also involve a change in processing rout of part types from a period to another. The advantages of the proposed model are as follows: considering the batch inter/intra-cell material handling by assuming the sequence of operations, considering alternative process plans for part types, and considering machine replication. The main constraints are maximal cell size and machine time-capacity. The objective is to minimize the sum of the machine constant and variable costs, inter- and intra-cell material handling, and reconfiguration costs. An efficient hybrid meta-heuristic based on mean field annealing (MFA) and simulated annealing (SA) so-called MFA–SA is used to solve the proposed model. In this case, MFA technique is applied to generate a good initial solution for SA. The obtained results show that the quality of the solutions obtained by MFA–SA is better than classical SA, 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.

A genetic algorithm to solve the storage space allocation problem in a container terminal

In this paper, an efficient genetic algorithm (GA) is presented to solve an extended storage space allocation problem (SSAP) in a container terminal. The SSAP is defined as the temporary allocation of the inbound/outbound containers to the storage blocks at each time period with aim of balancing the workload between blocks in order to minimize the storage/retrieval times of containers. An extended version of a SSAP proposed in the literature is considered in this paper in which the type of container affects on making the decision on the allocation of containers to the blocks. In real-world cases, there are different types (as well as different sizes) of containers consisting of several different goods such as regular, empty and refrigerated containers. The extended SSAP is solved by an efficient GA for real-sized instances. Because of existing the several equality constraints in the extended model, the implementation of the GA in order to quick and facilitate achieve to the feasible solutions is one of the outstanding advantages of this paper. The performance of the extended model and proposed GA is verified by a number of numerical examples.

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.

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 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.

A fuzzy programming approach for a cell formation problem with dynamic and uncertain conditions

This paper presents an integration of explicit uncertainty for a cell formation problem (CFP) with a dynamic condition in cellular manufacturing systems (CMS). The dynamic condition indicates a multi-period planning horizon, in which product mix and demand in each period are different. As a result, the best cells designed for one period may not be the most efficient for subsequent periods and thus require reconfigurations. Moreover, in real manufacturing systems, some input parameters are fuzzy in nature. In such cases, the fluctuation in part demand and the availability of manufacturing facilities in each period can also be regarded as fuzzy. In this paper, a fuzzy programming-based approach is developed to solve an extended mixed-integer programming model of the dynamic CFP, in which there are piecewise fuzzy numbers as coefficients in the objective function and the technological matrix. The main purpose of this paper is to determine the optimal cell configuration in each period with the maximum degree of satisfying the fuzzy objective under the given constraints. To illustrate the behavior of the proposed model and verify the performance of the developed fuzzy programming-based approach, we introduce a number of numerical examples to illustrate the use of the foregoing approach. Finally, the related computational results are reported and discussed.

A new capacitated vehicle routing problem with split service for minimizing fleet cost by simulated annealing

We address a capacitated vehicle routing problem (CVRP) in which the demand of a node can be split on several vehicles celled split services by assuming heterogeneous fixed fleet. The objective is to minimize the fleet cost and total distance traveled. The fleet cost is dependent on the number of vehicles used and the total unused capacity. In most practical cases, especially in urban transportation, several vehicles transiting on a demand point occurs. Thus, the split services can aid to minimize the number of used vehicles by maximizing the capacity utilization. This paper presents a mix-integer linear model of a CVRP with split services and heterogeneous fleet. This model is then solved by using a simulated annealing (SA) method. Our analysis suggests that the proposed model enables users to establish routes to serve all given customers using the minimum number of vehicles and maximum capacity. Our proposed method can also find very good solutions in a reasonable amount of time. To illustrate these solutions further, a number of test problems in small and large sizes are solved and computational results are reported in the paper.

Workforce-constrained maintenance scheduling for military aircraft fleet: a case study

The problem is related to a fleet of military aircraft with a certain flying program in which the availability of the aircraft sufficient to meet the flying program is a challenging issue. During the pre- or after-flight inspections, some component failures of the aircraft may be found. In such cases, the aircraft are sent to the repair shop to be scheduled for maintenance jobs, consisting of failure repairs or preventive maintenance tasks. The objective is to schedule the jobs in such a way that sufficient number of aircrafts is available for the next flight programs. The main resource, as well as the main constraint, in the shop is skilled-workforce. The problem is formulated as a mixed-integer mathematical programming model in which the network flow structure is used to simulate the flow of aircraft between missions, hanger and repair shop. The proposed model is solved using the classical Branch-and-Bound method and its performance is verified and analyzed in terms of a number of test problems adopted from the real data. The results empirically supported practical utility of the proposed model.

Solving a dynamic cell formation problem with machine cost and alternative process plan by memetic algorithms

In this paper, we present a new model of a cell formation problem (CFP) for a multi-period planning horizon where the product mix and demand are different in each period, but they are deterministic. As a consequence, the formed cells in the current period may be not optimal for the next period. This evolution results from reformulation of part families, manufacturing cells, and reconfiguration of the CFP as required. Reconfiguration consists of reforming part families, machine groups, and machine relocations. The objective of the model is to determine the optimal number of cells while minimizing the machine amortization/relocation costs as well as the inter-cell movements in each period. In the proposed model, parts have alternative process plans, operation sequence, and produce as batch. The machine capacity is also limited and machine duplication is allowed. The proposed model for real-world instances cannot be solved optimally within a reasonable amount of computational time. Thus, we propose an efficient memetic algorithm (MA) with a simulated annealing-based local search engine for solving the proposed model. This model is solved optimally by the Lingo software then the optimal solution is compared with the MA implementation.