Under the current environmental pressure, many manufacturing enterprises are urged or forced to adopt effective energy-saving measures. However, environmental metrics, such as energy consumption and CO₂ emission, are seldom considered in the traditional production scheduling problems. Recently, the energy-related scheduling problem has been paid increasingly more attention by researchers. In this paper, an energy-efficient job shop scheduling problem (EJSP) is investigated with the objective of minimizing the sum of the energy consumption cost and the completion-time cost. As the classical JSP is well known as a non-deterministic polynomial-time hard (NP-hard) problem, an improved whale optimization algorithm (IWOA) is presented to solve the energy-efficient scheduling problem. The improvement is performed using dispatching rules (DR), a nonlinear convergence factor (NCF), and a mutation operation (MO). The DR is used to enhance the initial solution quality and overcome the drawbacks of the random population. The NCF is adopted to balance the abilities of exploration and exploitation of the algorithm. The MO is employed to reduce the possibility of falling into local optimum to avoid the premature convergence. To validate the effectiveness of the proposed algorithm, extensive simulations have been performed in the experiment section. The computational data demonstrate the promising advantages of the proposed IWOA for the energy-efficient job shop scheduling problem.

The conventional production scheduling problem has mainly emphasized the time-related metrics, such as makespan, machine workload and tardiness/earliness, and so on. With the advent of the sustainable manufacturing, the green scheduling problem has been received more and more attention from scholars and researchers. In this paper, we investigate a green flexible job shop scheduling problem (GFJSP) with the consideration of environmental factors. To formulate the GFJSP problem, a mathematical model is first established to minimize the amount of total energy-consumption. To solve the model, a kind of improved African buffalo optimization (IABO) algorithm is proposed based on the characteristics of the problem. In the proposed IABO, a two-vector solution representation method is first designed, and a population initialization method is adopted to generate the initial solutions with certain quality and diversity. Based on the original ABO, several improvement strategies are introduced to enhance the performance of the algorithm, i.e., the modified individual learning mechanism and the aging-based re-initializaiton mechanism. In addition, in order to adapt our algorithm to the scheduling problem, a discrete individual updating method is developed to ensure the algorithm search directly in a discrete domain. Finally, a number of experiments have been conducted to test the performance of the proposed IABO algorithm. The simulation data demonstrate the effectiveness of the proposed IABO for the considered GFJSP.

Energy-saving scheduling has been pointed out as an interesting research issue in the manufacturing field, by which energy consumption can be effectively reduced through production scheduling from the operational management perspective. In recent years, energy-saving scheduling problems in flexible job shops (ESFJSPs) have attracted considerable attention from scholars. However, the majority of existing work on ESFJSPs assumed that the processing of any two consecutive operations in a job cannot be overlapped. In order to be close to real production, the processing overlapping of consecutive operations is allowed in this paper, while the job transportation tasks are also involved between different machines. To formulate the problem, a mathematical model is set up to minimize total energy consumption. Due to the NP-hard nature, a new interior search algorithm (NISA) is elaborately proposed following the feature of the problem. A number of experiments are conducted to verify the effectiveness of the NISA algorithm. The experimental results demonstrate that the NISA provides promising results for the considered problem. In addition, the computational results indicate that the increasing transportation time and sub-lot number will increase the transportation energy consumption, which is largely responsible for the increase in total energy consumption.

The energy-saving scheduling problem (ESSP) has gained increasing attention of researchers in the manufacturing field. However, there is a lack of studies on ESSPs in the assembly job shop environment. In contrast with traditional scheduling problems, the assembly job shop scheduling problem (AJSP) adds the additional consideration of hierarchical precedence constraints between different jobs of each final product. This paper focuses on developing a methodology for an energy-saving assembly job shop scheduling problem with job transportation times. Firstly, a mathematical model is constructed with the objective of minimizing total energy consumption. Secondly, an improved elephant herding optimization (IEHO) is proposed by considering the problem’s characteristics. Finally, thirty-two different instances are designed to verify the performance of the proposed algorithm. Computational results and statistical data demonstrate that the IEHO has advantages over other algorithms in terms of the solving accuracy for the considered problem.

This study analyses consumer last-mile delivery service choice behaviour by developing a cross-nested logit model (CNL); we then compare the analytical results with three nested logit models (NL). The model parameters are estimated using the data from a questionnaire collected from consumers residing in Beijing, Shanghai, Tianjin, Guangdong, Zhejiang, Jiangsu, and Shandong. The direct elasticities and cross-elasticities are then calculated to assess the change in probability of each alternative caused by utility variables. Parameter estimation results demonstrate that the CNL model outperforms the three NL models. Consumers are usually reluctant to change the way they are served when utility variables are altered. Moreover, elasticity analysis results suggest that service factors have the strongest effect on choice probability, followed by socioeconomic factors and delivery activity factors. Thus, enterprises should first strive to promote the service experience of consumers in corresponding delivery services, then account for the effect of socioeconomic factors, and finally consider changing delivery service fees if they want to induce consumers to select a specified delivery service.

The growing interest in the material flow (MF) theory has invoked much interesting research in recent years. Although the MF theory is relatively new, a review of the related literature from a historical perspective shows that MF theory represents a new stage of the evolutionary development of interrelated subjects such as Physical Distribution (PD), Logistics, and Supply Chain Management (SCM). The purpose of this paper is to provide a summative review of the evolution of the subjects of PD, Logistics, and SCM, and their new development, MF theory. The paper aims at tracing how concepts and findings in PD, Logistics, SCM, and MF have been developed and have evolved. The study shows that PD evolved to Logistics in middle of the 1980s; starting from the late 1990s, Logistics has evolved to SCM; and today PD, Logistics, and SCM can be considered to be under the umbrella provided by a new theory called MF theory. This paper points out that MF theory is a necessity to deal with the overwhelming complexity of material flow systems in the global economy of the twenty-first century.