This paper presents a joint economic lot size model for a single manufacturer-a single buyer. The purposed model involves the greenhouse gas emission from industrial and transport sectors. We divide the emission into two types, namely the direct and indirect emissions. In this paper, we consider the Government’s penalty and incentive policies to reduce the emission. We assume that the demand of the buyer is normally distributed and partially backordered. The objective is to minimize joint total cost incurred by a single manufacturer-a single buyer and involves the transportation costs of the freight forwarder. Transportation costs are the function of shipping weight, distance, fuel price and consumption with two transportation modes: truckload and less-than-truckload shipments. Finally, an algorithm procedure is proposed to determine the optimal order quantity, safety factor, actual shipping weight, total emission and frequency of deliveries. Numerical examples and analyses are given to illustrate the results.

Manufacturing systems need to be able to work under the dynamic and uncertain production environment. Machine and routing flexibility combined with preventive maintenance actions can improve the performance of the manufacturing systems under dynamic conditions. This paper evaluates different levels of machine and routing flexibility combined with different degrees of preventive maintenance policy. The performance measures considered are throughput, work in process and throughput. The performance measures are compared with a system without any flexibility and no preventive maintenance actions. Different levels of flexibility and preventive maintenance actions are examined under a simulation environment. The simulation results highlight more important factors for the performance measures and the best combination of the factors to improve the performance.

The aim of this study is to evaluate the impact of factors such as cutting speed, feed rate, and depth of cut on surface roughness and Material Removed Rate (MRR) when machining in dry face milling AISI 1040 steel with coated carbide inserts GC1030 using the response surface methodology (RSM). For this purpose, a number of machining experiments based on statistical three-factor and three-level factorial experiment designs, completed (L27) with a statistical analysis of variance (ANOVA), were performed in order to develop mathematical models and to identify the significant factors of these technological parameters. Multi-objective optimization procedure for minimizing Ra, Ry and Rz and maximizing MRR using desirability approach has been also implementented. The current study was also carried out to investigate the tool life of the inserts. The models found the relationship between the cutting parameters (Vc, fz and ap) and the studied technological parameters. It has been found that the cutting speed was the most affecting surface roughness which is due to the geometry of the insert which has a scraping edge and enables to obtain low roughness even at important feed rate, followed by the feed rate and the depth of cut at the end. The optimal combination of cutting parameters were cutting speed of 314 m/min, feed rate of 0.16 mm/tooth and depth of cut of 0.6 mm with a composite desirability of 0.924.

A vehicle routing problem with time windows (VRPTW) is an important problem with many real applications in a transportation problem. The optimum set of routes with the minimum distance and vehicles used is determined to deliver goods from a central depot, using a vehicle with capacity constraint. In the real cases, there are other objective functions that should be considered. This paper considers not only the minimum distance and the number of vehicles used as the objective function, the customer’s satisfaction with the priority of customers is also considered. Additionally, it presents a new model for a bi-objective VRPTW solved by a revised multi-choice goal programming approach, in which the decision maker determines optimistic aspiration levels for each objective function. Two meta-heuristic methods, namely simulated annealing (SA) and genetic algorithm (GA), are proposed to solve large-sized problems. Moreover, the experimental design is used to tune the parameters of the proposed algorithms. The presented model is verified by a real-world case study and a number of test problems. The computational results verify the efficiency of the proposed SA and GA.

The paper deals with the performance analysis and optimization for Carbonated Soft Drink Glass Bottle (CSDGB) filling system of a beverage plant using Particle Swarm Optimization (PSO) approach. The CSDGB system consists of seven main subsystems arranged in series namely Uncaser, Bottle Washer, Electronic Inspection Station, Filling Machine, Crowner, Coding Machine and Case Packer. Considering exponential distribution for probable failures and repairs, mathematical modeling is performed using Markov Approach (MA). The differential equations have been derived on the basis of probabilistic approach using transition diagram. These equations are solved using normalizing condition and recursive method to drive out the steady state availability expression of the system i.e. system’s performance criterion. The performance optimization of system has been carried out by varying the number of particles and number of generations. It has been observed that the maximum availability of 90.27% is achieved at flock size of 55 and 90.84% at 300th generation. Thus, findings of the paper will be useful to the plant management for execution of proper maintenance decisions.

This study presents a new mathematical model for the design of reliable cellular manufacturing systems, which leads to reduced manufacturing costs, improved product quality and improved total reliability of the manufacturing system. This model is expected to provide a more noticeable improvement in time and solution quality in comparison with other existing models. Each part to be manufactured may select each of the predefined manufacturing routes, such that the total reliability of the system is increased. On the other hand, the model adopts to categorize the machines to determine the manufacturing cells (cell formation) and reduce the transportation costs. Thereby, both criteria of system reliability and manufacturing costs will be simultaneously improved. Due to the complexity of cell formation problems, a two-layer genetic algorithm is applied on the problem in order to achieve near optimal solutions. Furthermore, the performance of the proposed algorithm is shown for solving some computational experiments. Finally, the results of a practical study for designing a cellular manufacturing system as a case study in Iranian Diesel Engine Manufacturing Co., Tabriz, Iran are present.

An appropriate sequence of assembly operations increases the productivity and enhances product quality there by decrease the overall cost and manufacturing lead time. Achieving such assembly sequence is a complex combinatorial optimization problem with huge search space and multiple assembly qualifying criteria. The purpose of the current research work is to develop an intelligent strategy to obtain an optimal assembly sequence subjected to the assembly predicates. This paper presents a novel hybrid artificial intelligent technique, which executes Artificial Immune System (AIS) in combination with the Genetic Algorithm (GA) to find out an optimal feasible assembly sequence from the possible assembly sequence. Two immune models are introduced in the current research work: (1) Bone marrow model for generating possible assembly sequence and reduce the system redundancy and (2) Negative selection model for obtaining feasible assembly sequence. Later, these two models are integrated with GA in order to obtain an optimal assembly sequence. The proposed AIS-GA algorithm aims at enhancing the performance of AIS by incorporating GA as a local search strategy to achieve global optimum solution for assemblies with large number of parts. The proposed algorithm is implemented on a mechanical assembly composed of eleven parts joined by several connectors. The method is found to be successful in achieving global optimum solution with less computational time compared to traditional artificial intelligent techniques.

Inconel 718 is among difficult to machine materials because of its abrasiveness and high strength even at high temperature. This alloy is mainly used in aircraft and aerospace industries. Therefore, it is very important to reveal and evaluate cutting tools behavior during machining of this kind of alloy. The experimental study presented in this research work has been carried out in order to elucidate surface roughness and productivity mathematical models during turning of Inconel 718 superalloy (35 HRC) with SiC Whisker ceramic tool at various cutting parameters (depth of cut, feed rate, cutting speed and radius nose). A small central composite design (SCCD) including 16 basics runs replicated three times (48 runs), was adopted and graphically evaluated using Fraction of design space (FDS) graph, completed by a statistical analysis of variance (ANOVA). Mathematical models for surface roughness and productivity were developed and normality was improved using the Box-Cox transformation. Results show that surface roughness criterion Ra was mainly influenced by cutting speed, radius nose and feed rate, and that the depth of cut had major effect on productivity. Finally, ranges of optimized cutting conditions were proposed for serial industrial production. Industrial benefit was illustrated in terms of high surface quality accompanied with high productivity. Indeed, results show that the use of optimal cutting condition had an industrial benefit to 46.9 % as an improvement in surface quality Ra and 160.54 % in productivity MRR.

This paper proposes a modified discrete firefly algorithm (DFA) applied to the machine loading problem of the flexible manufacturing systems (FMSs) starting from the mathematical formulation adopted by Swarnkar & Tiwari (2004). The aim of the problem is to identify the optimal jobs sequence that simultaneously maximizes the throughput and minimizes the system unbalance according to given technological constraints (e.g. available tool slots and machining time). The results of the algorithm proposed have been compared with the existing and most recent swarm-based approaches available in the open literature using as benchmark the set of ten problems proposed by Mukhopadhyay et al. (1992). The algorithm shows results that are comparable and sometimes even better than most of the other approaches considering both the quality of the results provided and the computational times obtained.

The research discusses in this paper concerns the improvement allocation policies to reduce the process time in job-shop manufacturing systems. The policies proposed are based on the evaluation of the workload control of the entire manufacturing system. Three policies are proposed: centralized, distributed and proportional. A simulation model is used to test the proposed policies under different conditions as: static and dynamic demand; introduction of machine breakdowns; different level of average manufacturing system utilization. The performance measures are compared to a manufacturing system without policies. The simulation results show that the improvement allocation allows to improve the performance with limited investment (average reduction of process time needed) and how the machine breakdowns and demand changes lead to different better policy. The decision maker can use these results to decide the better policy to use.