heuristic algorithm

Following the need for joint optimization of decisions in supply chains, this paper newly provides an integrated framework to efficiently fulfill a production-planning-routing problem (PPRP). In such integrated scheme, a set of perishable family-products are manufactured on a single batch-processing machine. These products are dispatched to the customers by a third-party logistics service provider with only two types of licensed eco-friendly transportation facilities. In order to efficiently deliver the manufactured products before they become unusable, we propose a combined shipment structure. To accomplish this, we formulate the problem in the context of a MILP model. In particular, we aim to establish two manufacturing policies based on both increasing and decreasing rates of production and also two delivery policies expressing distinct preferences in fulfilling the customers’ demands. In this regards, we investigate the cost structures obtained from the established integrated planning and the resulting distribution configurations as well. Further, four heuristic algorithms are developed for solving the problem with respect to each hybrid production/distribution scheme derived from the former policies. Finally, to compare the mentioned procedures, it is conducted a numerical study illustrating the preferable efficiency of the plan gained according to the hybrid of the increasing-production-rate and decreasing-delivery-distance policies.

The increasing value of facilities, on the one hand, and the complexity of the equipment used in them, on the other, have increased the importance of planning for the maintenance of facilities, especially for companies which their facilities are located in different locations. In this paper, a new hybrid model has been presented to optimize facility maintenance scheduling by a combination of Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and the Monte Carlo Simulation for organizing facilities which are in different locations as well as determining the optimum number of crews with three different skills of mechanical, electrical and simple workers. The main contributions of this paper include: (a) optimizing the number of crew by different skills in the first stage. (b) evaluation of fitness value for each solution through the Monte Carlo Simulation Model. (c) scheduling by consideration different failure rates for different facilities in different locations. In order to evaluate the performance of the proposed model, the model has been compared with Golpira’s model, the results of which have shown that it is possible to reduce the cost by just over 39% and reduce MTBF by over half.

During the years of imposed sanctions against Iran, Iran Khodro Company (IKCO) got into a hazardous situation due to CKD parts’ purchasing cost increment and emersion of new product variants in the competitive market. To examine such situation, this study examines a multi-period semi-centralized dual-channel supply chain where a common retailer (free market) and two manufacturers’ (IKCO and Saipa as a major competitor) direct channels are confronted with reference price dependent and stochastic demand. The problem is analyzed under Stackelberg and cooperative games scenarios using heuristic algorithm and a League Championship algorithm respectively, as solution methods. Results obtained from solving the problem with IKCO data proves higher profitability of the cooperative game and its remarkable resilience for all products’ memory types i.e. short/long term memory against production cost disruption which is imposed to IKCO in some periods. Besides calculating Saipa’s optimal wholesale price in the disruption periods, our approach with support of experimental analyses is able to assign a supply chain’s degree of resilience against disruptions to its product’s memory type and also power structure.

Nowadays, botnets are considered as essential tools for planning serious cyber attacks. Botnets are used to perform various malicious activities such as DDoS attacks and sending spam emails. Different approaches are presented to detect botnets; however most of them may be ineffective when there are only a few infected hosts in monitored network, as they rely on similarity in bots activities to detect the botnet. In this paper, we present a host-based method that can detect individual bot-infected hosts. This approach is based on botnet life-cycle, which includes common symptoms of almost all types of botnet despite their differences. We analyze network activities of each process running on the host and propose some heuristics to distinguish behavioral patterns of bot process from legitimate ones based on statistical features of packet sequences and evaluating an overall security risk for it. To show the effectiveness of the approach, a tool named BotRevealer has been implemented and evaluated using real botnets and several popular applications. The results show that in spite of diversity of botnets, BotRevealer can effectively detect the bot process among other active processes.

In this paper, a new meta-heuristic algorithm based on free vibration of single degree of freedom systems with viscous damping is introduced and it is called vibrating particles system (VPS). The solution candidates are considered as particles that gradually approach to their equilibrium positions. Equilibrium positions are achieved from current population and historically best position in order to have a proper balance between diversification and intensification. To evaluate the performance of the proposed method, it is applied for sizing optimization of four skeletal structures including trusses and frames. Results show that the proposed algorithm is a robust and reliable method.

Distribution of deteriorating items is different from other items. This issue leads distributors to transport with lower volumes. On the other hand, one of the mechanisms that attract buyers to purchase items is discount; but a larger amount of order has a lower price for one item but has a higher risk of deterioration. Despite the importance of issue, previous researches on deteriorating items did not consider discount conditions in designing supply chain network. Hence, in this paper, balancing between the cost of ordering and the cost of deterioration with consideration of discount through a new model is studied. The problem is solved for numerical examples with an improved meta-heuristic composed of simulated annealing (SA) and genetic algorithm (GA) and results are reported. Furthermore, a heuristic method for small scale problems is represented and compared with the introduced algorithm to analyze the performance of method. Finally, results show a significant difference between the costs of the models (with discount and without it).

One of the significant strategies of the power systems is Economic Dispatch (ED) problem, which is defined as the optimal generation of power units to produce energy at the lowest cost by fulfilling the demand within several limits. The undeniable impacts of ramp rate limits, valve loading, prohibited operating zone, spinning reserve and multi-fuel option on the economic dispatch of practical power systems are scrutinized in this paper. Thus, the proposed nonlinear non-convex formulation is solved by a new modified version of bio-inspired bat algorithm. Due to the complexities associated with the large-scale optimization problem of economic dispatch, adaptive modifications are added to the original bat algorithm. The modification methods are applied at two separate stages and pledge augmentation in convergence rate of the algorithm as well as extricating the algorithm from local optima. Veracity of the proposed methodology are corroborated by performing simulations on three IEEE test systems.

In this paper, the recently developed meta-heuristic algorithm, named Ray Optimization (RO), was applied to optimize the thickness of granular layers in railwaytracks. RO is a multi-agent algorithm, each agent of which is modeled as a ray of light that moves in the search space in order to nd global or near-global optimum solutions. To utilize RO, considering structural and serviceability constraints, rst, hypothetical values of three thicknesses were assigned to the layers. Then, stresses under the sleeper and on the subgrade layer were compared with the allowable values. The total minimum thickness of the layers was also compared with possible minimum thickness (serviceability constraint) and then minimum thickness values were selected. Optimization results showed that the moment of inertia of the rail had no signi cant impact on the minimum total thickness of the ballast and sub-ballast layers. On the other hand, train speed and axle load were the parameters that had a considerable e ect on the minimum total thickness of granular layers in the railway track.

This paper considers two-machine Flow shop scheduling problem while there is ineligible transportation lags in production procedure. There is one transporter to convey semi-finished jobs between machines, and another transporter to deliver finished jobs to the warehouse (customers). The problem is formulated as a mixed integer linear programming (MILP) model to minimize the makespan as an objective function. To solve the problem in an efficient way, two heuristic algorithms are also developed. Furthermore, five lower bounds are proposed and computational experiments are carried out to verify the effectiveness of the proposed lower bounds and heuristic algorithms. The results show the performance of the heuristics to deal with medium and large size problems.