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Process planning and scheduling are two of the most important functions of a manufacturing system. Traditionally, these two functions are executed separately. Since they are interrelated, conducting process planning and scheduling simultaneously will provide more advantages. In this paper, according to the characteristics of the integrated process planning and scheduling (IPPS) problem, a hybrid honey bee mating optimization (HBMO) algorithm, which combines the HBMO algorithm and variable neighborhood search (VNS), is proposed to settle the problem with makespan criterion. Different with conventional HBMO, we utilize VNS with two effective and efficient neighborhood structures in the algorithm to simulate the workers' brood caring action to avoid premature convergence and to find more excellent broods. In addition, a novel individual initialization method is developed in the algorithm. The proposed algorithm is tested on typical benchmark instances taken from related literature, and the computational results are compared with those of other algorithms. Experimental results show the effectiveness and efficiency of the hybrid HBMO algorithm. New upper bounds have been captured for 16 instances, and most instances have been improved within reasonable CPU times. [ABSTRACT FROM AUTHOR]

In addition to energy consumption, the use of cutting fluids, deposition of worn tools and certain other manufacturing activities can have environmental impacts. All these activities cause carbon emission directly or indirectly; therefore, carbon emission can be used as an environmental criterion for machining systems. In this article, a direct method is proposed to quantify the carbon emissions in turning operations. To determine the coefficients in the quantitative method, real experimental data were obtained and analysed in MATLAB. Moreover, a multi-objective teaching–learning-based optimization algorithm is proposed, and two objectives to minimize carbon emissions and operation time are considered simultaneously. Cutting parameters were optimized by the proposed algorithm. Finally, the analytic hierarchy process was used to determine the optimal solution, which was found to be more environmentally friendly than the cutting parameters determined by the design of experiments method. [ABSTRACT FROM AUTHOR]

Since multi-objective flow shop scheduling problem (MFSP) plays a key role in practical scheduling, there has been an increasing interest in MFSP according to the literature. However, there still have been wide gaps between theories and practical applications, and the review research of multi-objective optimization algorithms in MFSP (objectives > 2) field is relatively scarce. In view of this, this paper provides a comprehensive review of both former and the state-of-the-art approaches on MFSP. Firstly, we introduce a broad description and the complexity of MFSP. Secondly, a taxonomy of multi-objective optimizations and an analysis of the publications on MFSP are presented. It is noteworthy that heuristic and meta-heuristic methods and hybrid procedures are proven much more useful than other methods in large and complex situations. Finally, future research trends and challenges in this field are proposed and analyzed. Our survey shows that algorithms developed for MFSP continues to attract significant research interest from both theoretical and practical perspectives. [ABSTRACT FROM AUTHOR]

Abstract: Traditionally, process planning and scheduling were performed sequentially, where scheduling was done after process plans had been generated. Considering the fact that these two functions are usually complementary, it is necessary to integrate them more tightly so that the performance of a manufacturing system can be improved greatly. In this paper, an agent-based approach has been developed to facilitate the integration of these two functions. In the approach, the two functions are carried out simultaneously, and an optimization agent based on an evolutionary algorithm is used to manage the interactions and communications between agents to enable proper decisions to be made. To verify the feasibility and performance of the proposed approach, experimental studies have been conducted and comparisons have been made between this approach and some previous works. The experimental results show the proposed approach has achieved significant improvement. [Copyright &y& Elsevier]

The integration of scheduling and process planning can eliminate resource conflicts and hence improve the performance of a manufacturing system. However, the focus of most existing works is mainly on the optimization techniques to improve the makespan criterion instead of more efficient uses of energy. In fact, with a deteriorating global climate caused by massive coal-fired power consumption, carbon emission reduction in the manufacturing sector is becoming increasingly imperative. To ease the environmental burden caused by energy consumption, e.g., coal-fired power consumption in use of machine tools, this research considers both makespan as well as environmental performance criteria, e.g., total power consumption, in integrated process planning and scheduling using a novel multi-objective memetic algorithm to facilitate a potential amount of energy savings; this can be realized through a better use of resources with more efficient scheduling schemes. A mixed-integer linear programming (MILP) model based on the network graph is formulated with both makespan as well as total power consumption criteria. Due to the complexity of the problem, a multi-objective memetic algorithm with variable neighborhood search (VNS) technique is then developed for this problem. The Kim's benchmark instances are employed to test the proposed algorithm. Moreover, the TOPSIS decision method is used to determine the most satisfactory non-dominated solution. Several scenarios are considered to simulate different machine automation levels and different machine workload levels. Computational results show that the proposed algorithm can strike a balance between the makespan criterion and the total power consumption criterion, and the total power consumption can be affected by machine tools with different automation levels and different workloads. More importantly, results also show that energy saving can be realized by completing machining as early as possible on a machine tool and taking advantage of machine flexibility. [ABSTRACT FROM AUTHOR]

Industry is responsible for nearly half of the global energy consumption. Recent studies on sustainable manufacturing focused on energy saving to reduce the unit production cost and environmental impacts. Besides energy consumption, certain manufacturing activities in machine shops, such as the use of cutting fluids, disposal of worn tools, and material consumption, also cause other environmental impacts. Since all these activities lead to carbon footprint directly or indirectly, carbon footprint can be employed as a new and overall environment criterion in manufacturing. In this study, an integrated model for processing parameter optimization and flow-shop scheduling was developed. Objectives to minimize both makespan and carbon footprint were considered simultaneously, which was solved by a multi-objective teaching − learning-based optimization algorithm. Furthermore, three carbon-footprint-reduction strategies were employed to optimize the scheduling results: (i) postponing strategy, (ii) setup strategy, and (iii) processing parameter preliminary optimization strategy. In the theoretical aspect, the strategies greatly improved the performance of the optimization results through reducing machine idle time and cutting down the search space. From the perspective of practical applications, these strategies greatly help elevate production efficiency and reduce environmental impacts. [ABSTRACT FROM AUTHOR]

Recent years have seen a great deal of attention in energy conservation for production and manufacturing activities, particularly for energy-intensive industries. One of the useful strategies in reducing unnecessary energy consumption is to schedule these activities by considering both energy-driven and time-oriented criteria. This scheduling model can make an interaction between the energy consumption and the production cost to realize an efficient and sustainable production process. In this regard, the customers' expectation for due date is another important factor for decision-makers to control the delay in delivery. Making these decisions is extremely difficult due to uncertain circumstances to extract the accurate information of facilities and jobs in advance. Aforementioned issues in the context of urgent need for energy-conservation as well as the advent of globalized and multi-factory manufacture motivate our attempts to address a stochastic multi-objective distributed permutation flow shop scheduling problem by considering total tardiness constraint via minimizing the makespan and the total energy consumption. Due to the uncertainty of the proposed problem, a chance-constrain approach is used to describe decision-makers' awareness for the total tardiness, and accordingly, a chance-constrained programming model is utilized to formulate this problem. As a complicated optimization problem, a new multi-objective brain storm optimization algorithm incorporating stochastic simulation approach is specifically designed to better solve problem. A comparative study based on a set of benchmark test problems as well as two classical and popular algorithms is provided. The experimental results demonstrate that the proposed algorithm shows a very competitive performance in dealing with the investigated problem. [ABSTRACT FROM AUTHOR]

Disassembly sequencing has significant effects on the performance of remanufacturing and recycling of used or discarded products. Studies on disassembly sequence optimization have largely focused on sequential disassembly. However, for large or complex products sequential disassembly takes long time to complete and is rather inefficient since it removes only one part or subassembly at a time with only one operator assigned to disassemble a product. This work studies selective cooperative disassembly sequence planning (SCDSP) problem which is essential to disassemble large or complex products in an efficient way. Similar to sequential disassembly planning, SCDSP aims at finding the optimal disassembly task sequence, but is more complicated. SCDSP is a nonlinear NP-complete combinatorial optimization problem, and evolutionary algorithms can be adopted to solve it. In this paper exclusive and cooperative relationships are introduced as additional constraints besides the common precedence relationship. A novel procedure to generate feasible cooperative disassembly sequences (GFCDS) is proposed. A mathematical programming model of SCDSP is developed based on the parallel disassembly characteristics with two optimization objectives i.e. disassembly time and profit, considered. A multi-objective evolutionary algorithm (MOEA), i.e., multi-objective discrete artificial bee colony optimization (MODABC), is adopted to solve the problem to create the Pareto frontier. This approach is applied to real-world disassembly processes of two products (a small product and a medium/large one) to verify its feasibility and effectiveness. Also, the proposed method is compared with the well-known NSGA-II. For our comparative study, the nondominated solutions of the two MOEAs are compared in both cases, and two quantitative metrics, i.e., inverted generational distance ( IGD ) and spacing ( SP ), are adopted to further measure the algorithm performance. Results indicate that the set of nondominated solutions from MODABC are better for each instance tested, and the Pareto front is overall superior to that from NSGA-II. For both cases, IGD and SP are decreased by up to 81.5% and 62.2%, respectively. [ABSTRACT FROM AUTHOR]