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The previous studies on scheduling problem with tool changes take processing time as the only reason for the tool wear, which is not accurate in the real manufacturing system. This paper takes processing speed and processing time into consideration simultaneously and proposes a new unrelated parallel machine scheduling problem (UPMSP) with tool changes caused by the tool wear, in which the energy consumption rate of the parallel machines is influenced by two factors: tool changes and corresponding processing speed. A new effective heuristic evolutionary algorithm (NHEA) is presented to solve the proposed UPMSP with objectives of optimising total energy consumption and makespan. For the NHEA, some effective operators such as target-searching operators are designed to accelerate the search efficiency and further exploit the solution space. A first fit decreasing algorithm is presented and incorporated into the NHEA to reduce the number of tool changes. The Taguchi method of Design of Experiments is used to obtain the best combination of key parameters of the NHEA. Extensive computational experiments are carried out to compare the NHEA with some well-known algorithms. The results validate that the proposed NHEA is able to obtain better Pareto solutions for UPMSP with tool changes. [ABSTRACT FROM AUTHOR]

During the driving process in expressway, the vehicle carrying efficiency is influenced by many factors. However, in the existing researches on microscopic traffic flow, there is a lack of evaluation index system and corresponding calculation, so it is necessary to choose appropriate indicators and evaluation methods to calculate the vehicle carrying efficiency comprehensively. Therefore, in this paper, we establish a cellular automata model of the three-lane expressway and select multiple parameters that influence the vehicle carrying efficiency, which includes three input indicators: vehicle travel time, vehicle fuel consumption, vehicle emission, and two output indicators: vehicle average speed and vehicle ridership. Meanwhile, the data envelopment analysis is used to calculate the vehicle carrying efficiency under different maximum speed limits. From the simulation results, the traffic condition of vehicles is analyzed and corresponding improvement strategies are proposed, which have certain research significance for the traffic management department to formulate reasonable management measures. [ABSTRACT FROM AUTHOR]

The classical distributed production scheduling problem (DPSP) assumes that factories are identical, and each factory is composed of just some machines. Inspired by the fact that manufacturers these days typically work across different factories, and each of these factories normally has some workshops, we study an important extension of the DPSP with different factories and workshops (DPFW), where jobs can be processed and transferred between the factories, workshops and machines. To the best of our knowledge, this is the very first time distributed production scheduling with different factories and workshops is studied. We propose a novel memetic algorithm (MA) to solve this DPFW, aiming to minimize the makespan and total energy consumption. The proposed MA is incorporated with a well-designed chromosome encoding method and a balance-transfer initialization method to generate a good initial population. An effective local search operator is also presented to improve the MA's convergence speed and fully exploit its solution space. A total of 50 DPFW benchmark instances are used to evaluate the performance of our MA. Computational experiments carried out confirm that the MA is able to easily obtain better solutions for the majority of the tested problem instances compared to three other well-known algorithms, demonstrating its superior performance over these algorithms in terms of solution quality. Our proposed method and the results presented here may be helpful for production managers who work with distributed manufacturing systems in scheduling their production activities by considering different factories and workshops. With this DPFW, imbalanced resource loads and unexpected bottlenecks, which regularly arise in traditional DPSP models, can be easily avoided. [ABSTRACT FROM AUTHOR]

The classical economic order quantity (EOQ) model is simple and practicable, but it would be invalid without the rigorous assumption that demand is constant. This work breaks through the limitation and develops an extended EOQ model under uncertain demand, in order to derive different optimal acquisition strategies. Under the carbon cap and trade mechanism, the optimal quantities in environmental and economic optimization are usually different, while we deduce the optimal acquisition strategies (III and IIII) to solve multi-objective optimization problems. We rigorously prove the uniqueness of optimal order quantity in each strategy and provide close-form expressions for possible holding and opportunity costs caused by the uncertain supply-demand relationship. Other than conducting an optimization analysis from the perspective of firms, we also analyze the effects of taxation policy on the optimal strategies from the perspective of the government. An interesting phenomenon is that increasing carbon tax will not only reduce profit margins, but will also change the optimal acquisition decisions. Consequently, remanufacturing enterprises can be guided in a sustainable direction, by adopting more energy-efficient strategies to enter the “profitable region”. [ABSTRACT FROM AUTHOR]

Machine on/off control is an effective way to achieve energy-efficient production scheduling. Turning off machines and restarting them frequently, however, would incur a considerable amount of additional energy and may even cause damage to the machines. In this paper, we propose a mathematical model based on the energy-efficient flexible job shop scheduling problem (EEFJSP), aiming to minimize not just the makespan and total energy consumption but also the total number of machine restarts. Our idea here is that shifting the start time of operations on different machines appropriately can effectively decrease the number of restarts required and the total energy consumption. We present a two-stage memetic algorithm (TMA) to solve the EEFJSP. A variable neighborhood search approach is designed to improve the convergence speed and fully exploit the solution space of the TMA. An operation-block moving operator is developed to further reduce the total energy consumption as well as the total number of machine restarts without affecting the makespan. Extensive computational experiments carried out to compare the TMA with some well-known algorithms confirm that the proposed TMA can easily obtain better Pareto solutions for the EEFJSP. [ABSTRACT FROM AUTHOR]

This paper studies an important extension of energy-efficient production scheduling research, where machine on/off control and machine maintenance are considered simultaneously. The inspiration of this extension is that a machine must be turned off if it needs to be maintained, and an already-turned-off machine can be maintained without needing to be restarted. We therefore formulate an energy-efficient production scheduling problem with machine maintenance through machine on/off control, aiming to optimise three objectives – the makespan, total number of machine restarts, and energy consumption – at the same time. Four rules are designed to set the machine on/off criteria, maintenance periods and predefined maintenance windows, based on solutions of the job shop scheduling problem (JSP) as a test case. Three heuristics are proposed to insert the maintenance activities into the solutions and move their maintenance-operation blocks to optimise the objectives. The effectiveness of the first rule and the moving of maintenance-operation blocks have been proven mathematically. Our proposed heuristics, unlike traditional heuristic algorithms, are expected to be applicable and effective even if we change the objectives and constraints, require minimal computational time (only a few seconds) to optimise a scheduling solution, and can solve different types of scheduling problems without needing any modification. Experiments undertaken indicate promising performance of the proposed heuristics based on 182 JSP benchmark instances. • Energy-efficient scheduling is achieved via machine on/off control during maintenance. • Four rules are devised to set on/off criteria, maintenance periods and time windows. • Three heuristics are designed to set maintenance activities and move operation blocks. • Results on 182 problem instances confirm the efficacy of the proposed heuristics. [ABSTRACT FROM AUTHOR]

• Energy-efficient flexible flow shop scheduling with worker flexibility is studied. • Green production, processing time and human related objectives are considered. • A hybrid evolutionary algorithm (HEA) is proposed to solve the problem. • A total of 54 benchmarks are used to test the performance of the HEA. • Results show that the HEA is superior in terms of solution quality and efficiency. The classical flexible flow shop scheduling problem (FFSP) only considers machine flexibility. Thus far, the relevant literature has not studied FFSPs with worker flexibility, which is widely seen in practical manufacturing systems. Worker flexibility may greatly affect production efficiency and productivity. Furthermore, with the increase of environmental pollution and energy consumption, manufacturers require innovative methods to improve energy efficiency. In this paper, we propose an energy-efficient FFSP with worker flexibility (EFFSPW), in which the flexibility of machines and workers as well as the processing time, energy consumption and worker cost related factors are considered simultaneously. A hybrid evolutionary algorithm (HEA) is then presented to solve the proposed EFFSPW, where some effective operators and a new variable neighborhood search approach are designed. Comprehensive experiments including 54 benchmark instances of the EFFSPW are carried out, and Taguchi analysis is used to determine the best combination of key parameters for the HEA. Experimental results show that the proposed HEA can obtain better solutions for most of these benchmark instances compared to two other well-known algorithms, demonstrating its superior performance in terms of both solution quality and computational efficiency. [ABSTRACT FROM AUTHOR]