This paper investigates an unrelated parallel machine scheduling problem to minimize the total weighted completion time. We develop a discrete adaptive particle swarm optimization algorithm (called DAPSODP), incorporating a dynamic programming-based local search. To conduct the computational experiment, a total of 650 existing benchmark instances are used, consisting of 210 instances from Wang and Alidaee (2019) and 440 instances from B & uuml;lb & uuml;l and den (2017), who solve the same problem by designing a Tabu Search (TS)-based metaheuristic algorithm known as the best-to-date metaheuristic for this problem, and a Benders decomposition (BDS)-based exact algorithm, respectively. A convex quadratic programming relaxation (CQPR) is employed to obtain lower bounds for all instances. The experimental results demonstrate that, for the benchmark instances of Wang and Alidaee (2019), DAPSODP attains solutions with the maximum duality gap below 1.45%, significantly outperforming TS. Remarkably, it produces strictly superior solutions than TS in 141 out of 210 instances and, for instances with 3000 jobs and 100 machines, achieves solutions comparable to TS while requiring on average less than two-thousandth of the computation time. Concerning the benchmark instances of B & uuml;lb & uuml;l and den (2017), DAPSODP's solutions deviate by at most 4.06% from the best lower bounds provided by BDS or CQPR, with deviations under 0.1% in most cases, clearly illustrating the high effectiveness of the algorithm. Additional comparison with a discrete particle swarm optimization (DPSO) adapted from the literature further verifies the superiority of DAPSODP.

This paper considers unrelated parallel machine scheduling involving machine usage costs, in addition to classic job completion time-related costs. The usage cost of each machine is made up of a fixed usage cost and a variable usage cost proportional to the total processing time of the jobs assigned to it. These features model many practical situations where machine usage costs include, for example, rental fees when the machines are not owned but rented. To tackle this problem, four mathematical models based on the Shortest Weighted Processing Time (SWPT) rule are introduced. Additionally, the problem is formulated into a set-partitioning model, for which a branch-and-price algorithm is proposed with an appropriate branching strategy. This facilitates the development of an efficient pseudo-polynomial dynamic programming algorithm and a polynomial-time heuristic to solve the pricing problem. Extensive numerical experiments demonstrate the superior performance of the proposed branch-and-price algorithm over the four SWPT-based mathematical formulations and an existing branch-and-price algorithm designed for a special case. Notably, it can optimally solve instances involving up to 225 jobs and 15 machines within one hour. Moreover, statistical analyses reveal that the proposed polynomial-time heuristic significantly reduces the computation time, and the mathematical model based on the contribution of every job to the total weighted completion time exhibits the best overall performance.

Lot scheduling is a promising manufacturing mode in green logistics that can efficiently save energy and reduce production costs. It has been widely applied to integrate circuit tests in semiconductor factories, textile processing in garment workshops, etc. Each processing lot is of a fixed capacity and identical processing time, and completes more than one job simultaneously. Jobs with sizes and due dates are allowed to be arbitrarily split and processed in consecutive lots. They are delivered immediately upon completion. To the best of our knowledge, in the domain of lot scheduling, there exist no mathematical programming models that describe the above features simultaneously. In this work, we focus on the single machine environment and mainly consider two lot scheduling problems with the objectives of minimizing the maximum lateness and the total tardiness, respectively. For the problems, we first propose new mixed integer linear programming models (solved by commercial solvers), which enable a systematic understanding of the studied problems and serve as a mathematical programming basis for more complicated problems. We then prove that the Earliest Due-Date (EDD) first rule and the Shortest Processing Time (SPT) first rule can optimally solve the two problems, respectively, provided that the due dates and job sizes are agreeable, i.e., a later due date indicates a larger size of job. Experimental results show the efficiency of our methods and managerial insights are drawn.

Food-safety inspection is regularly executed by the government for quality assessment. Evidence from recent research demonstrates that inspection accuracy and consistency are affected by inspection biases that result from an operational decision: inspector scheduling. More precisely, an inspector's stringency in an inspection is affected by the inspection results at the previous-inspected establishment (outcome effects) and when this inspection occurs within a workday (daily-schedule effects). To our best knowledge, the impact of these effects on scheduling decisions has not been studied in the scheduling literature. In this paper, we study a novel food inspector scheduling problem with these effects, where the inspector should scrutinise establishments with different locations. The problem is viewed as a single-machine scheduling problem with a complex objective function including (i) inspection accuracy, (ii) inspection consistency and (iii) workload of the inspector. To facilitate quantitative analyses of these effects, we model them by sequence-dependent functions and formulate a mixed integer linear programming model. To overcome the computational difficulty in large-scale problems, an efficient Tabu Search algorithm is developed. Experiment results on 135 randomly generated instances with up to 50 establishments and 10 workdays validate the efficiency of the solution method. Besides, managerial insights are drawn.

Robotic cell scheduling has received considerable attention from researchers. Several existing review papers have presented then-existing studies on robotic cell scheduling and related fields from different perspectives. Since the latest review, numerous new results have emerged, contributing significant advancements in recent years. Furthermore, certain features of these problems have not been appropriately addressed. This paper aims to provide an up-to-date review and present our particular perspectives to address recent publications on scheduling robotic cells with fixed processing times or time windows. First, we identify various characteristics of robotic cell scheduling and explain their impact on the structure and complexity. Based on these characteristics, the related robotic cell scheduling studies are classified and compared in various dimensions. Second, we present complexity results, along with both exact and approximation solution techniques, for the robotic cell scheduling problem with time windows or fixed processing times. These results are presented in tabular forms to visually reveal the relationships between different problem configurations and their respective solution approaches. Finally, we discern emerging trends in the realm of robotic cell scheduling research, subsequently indicating prospective directions for future investigations.

The development of data science and sensing technologies provides opportunities to mitigate uncertainty in hybrid manufacturing-remanufacturing systems, and drives manufacturers to invest in intelligent manufacturing (IM). This paper considers the adoption of IM to mitigate classification errors in the end-of-use (EoU) products’ acquisition while improving the operational efficiency of manufacturing-remanufacturing systems. By constructing game models, we jointly investigate the investment, pricing, production and order quantities, and collection decisions in a closed-loop supply chain. Interestingly, we find that IM does not always increase manufacturers’ enthusiasm to engage in remanufacturing, firms’ profits or consumer surplus. It does not always reduce environmental impact either. Analytical results show that IM reduces manufacturers’ enthusiasm to engage in remanufacturing if the initial core misclassification rate is low. Furthermore, we identify key factors that impact the firms’ profits, consumer surplus, and carbon emissions. For a manufacturer producing high-cost new products, investing in IM hurts its profits when the remanufacturing cost savings due to intelligent manufacturing is small and the manufacturing benefit due to IM is not significant. Moreover, investing in IM reduces consumer surplus if the cost of new products is high, and the IM does not lead to significant remanufacturing cost saving and manufacturing benefit increase. The results reveal that investing in IM is preferred from the environmental point of view if the IM-induced manufacturing cost saving is small. Our research provides management insights into the intelligent transformation of manufacturers with hybrid manufacturing-remanufacturing systems, as well as policymakers concerned with value creation for customers and environmental impacts.

This paper presents a bi-objective solution approach to address the production scheduling challenge encountered by manufacturers in a shared manufacturing environment. In such scenarios, manufacturers are required to manage orders received through a cloud platform (referred to as cloud orders) while simultaneously fulfilling orders from their long-term and regular clients (local orders). The problem is to efficiently coordinate the production of both types of orders within shared manufacturing facilities. We formulate the problem into a bi-objective mixed integer programming model aimed at simultaneously minimizing the delivery time of cloud orders and mitigating the disruptions to local order production caused by cloud orders. This solution approach comprises three key components: computation of cloud orders’ starting times, construction of available time intervals of manufacturing facilities, and a bi-objective heuristic. This heuristic combines an enhanced hybrid discrete differential evolution with a modified forward–backward earliest starting time algorithm. We introduce an advanced population initialization technique, a novel individual update strategy, and an adaptive local search mechanism based on Pareto-dominance principles to improve the search capabilities of the algorithm towards discovering Pareto non-dominated solutions. Computational results show that the proposed approach outperforms the existing algorithm in most test instances in terms of five common metrics. Insights are discussed, highlighting the practical implications and potential benefits of the proposed approach for shared manufacturing scheduling.

This paper considers a promising alternative for freight transport using urban passenger rail networks. It addresses a freight rail transport scheduling problem (FRTSP), which is one of the key operational issues to integrate freight into passenger transport in urban areas. In this paper, we develop a robust ant colony optimization (ACO) metaheuristic to tackle large-size instances. Taguchi method is used to set control parameters to make the algorithm robust. Computational results clearly highlight the proposed ACO algorithm’s capability of yielding near-optimal solutions in short computation times, even in large-size instances. They also show that the proposed algorithm dominates classic ACO variants, even when the parameters of these variants are also set using Taguchi method.

As an important part of urban demand-response transportation,cus-tomized bus network planning directly affects passenger satisfaction and system op-erating costs.In order to solve the morning and evening peak commuting customized bus network design problem of passenger separation and heterogeneous fleets,a mixed integer nonlinear programming model with the goal of minimizing the total operating cost and the passenger travel cost is established to simultaneously optimize vehicle routes,vehicle type selection,travel time and passenger allocation in this paper.To effectively solve the model,an improved adaptive large neighbourhood search algo-rithm(ALNS)based on problem properties is designed respectively.Especially,a new separation destroy operator is proposed to improve the search ability of the algorithm.Finally,the model and algorithm are tested on a large number of randomly generated instances to demonstrate their effectiveness and efficiency.The results show that,when solving small-scale instances,the average difference between the solutions ob-tained by ALNS and the best solutions is only 0.24％and the solving time is all less than 12 s;when solving large-scale instances,compared with the solutions obtained by the large neighbourhood search algorithm and Genetic algorithm in the literature,the average cost saving are respectively 1.20％and 2.27％;and compared with the case without considering passenger separation and heterogeneous fleets,considering passenger separation and heterogeneous fleets reduces 11.46％cost,and improves the resource utilization.

Carbon emissions drive climate change. Especially with the rapid development of economy, carbon emissions are increasing in recent years, and the carbon emission data sets are more comprehensive. How to analyze the data is important. Furthermore, to find the main characteristics of carbon emission, we propose a new method of segmentation in the time series that adopts communities finding in complex network, graph convolution networks (GCN) and visibility graph (VG). Experiments on carbon emission datasets show that the detector has better detection performance than existing graph connectivity-based detectors. In addition, we find that combining the results of GCN segmentation can highlight economic geographic attributes such as resource endowment, industrial structure, and market demand in carbon emission regions, thus complementing the existing applications of complex network methods in the energy field and providing insights for decision support of carbon emissions.

In this paper, we propose an efficient segmentation approach in order to divide a multivariate time series through integrating principal component analysis (PCA), visibility graph theory, and community detection algorithm. Based on structural characteristics, we can automatically divide the high-dimensional time series into several stages. First, we adopt the PCA to reduce the dimensions; thus, a low dimensional time series can be obtained. Hence, we can overcome the curse of dimensionality conduct, which is incurred by multidimensional time sequences. Later, the visibility graph theory is applied to handle these multivariate time series, and corresponding networks can be derived accordingly. Then, we propose a community detection algorithm (the obtained communities correspond to the desired segmentation), while modularity Q is adopted as an objective function to find the optimal. As indicated, the segmentation determined by our method is of high accuracy. Compared with the state-of-art models, we find that our proposed model is of a lower time complexity (O(n3)), while the performance of segmentation is much better. At last, we not only applied this model to generated data with known multiple phases but also applied it to a real dataset of oil futures. In both cases, we obtained excellent segmentation results.

The global trend of increasing resource utilization in industry has drawn great attention from researchers and practitioners. Industrial managers are increasingly optimizing their production strategies based on time-of-use (TOU) electricity tariffs to reduce energy consumption costs. This study investigates a new energy-efficient single-machine scheduling problem with release dates under TOU electricity tariffs. It consists in sequencing a set of jobs with release dates to a machine under TOU pricing to minimize the total energy cost given a bounded maximum completion time. We first formulate the problem using the widely used time-indexed mixed-integer linear programming (T-MILP). Due to its time-consuming disadvantage, a new period-based MILP (P-MILP) model is developed based on the characteristics of the problem. Given the NP-hardness of the problem, a two-stage heuristic (TSH) algorithm is proposed to solve practical-sized problems. Specifically, in the first stage, a constructive heuristic is designed to obtain an initial solution, and in the second one, a tailored tabu search is devised to yield a better satisfactory solution. Numerical experiments are conducted on a real-life case and 1150 randomly generated instances with up to 1000 jobs. Computational results show that (i) the proposed models can save the total electricity cost by about 30% compared with an existing empirical scheduling method when solving small-sized instances; (ii) the efficiency of P-MILP is about 140.94% higher than that of T-MILP; and (iii) the proposed TSH algorithm can efficiently obtain high-quality solutions for practical-sized instances with gaps of less than 7% compared to the lower bounds, which can effectively support production managers to save the energy costs in practical production.

Due to the impact of the global COVID-19, numerous industries have suffered from the disruption propagating along the supply chain, i.e. the ripple effect. To reduce adverse impact of the ripple effect, supply chain (SC) risk management under it is becoming an increasingly hot topic in both practice and research. In our former research, a robust dynamic bayesian network (DBN) approach has been developed for disruption risk assessment, whereas the solution methods adopted before (commercial solvers and simulated annealling algorithm) are not efficient enough, especially for large-size instances. For this reason, a new reinforcement learning variable neighborhood search (QVNS) is developed for solving the robust DBN optimization model, where the Q-learning algorithm is implemented to select the most efficient neighborhood structure in different stages of the search process. We conduct computational experiments on randomly generated instances, which indicates that Q-learning algorithm can improve significantly the performance of the VNS on large-size instances of the robust DBN optimization problem.

Most existing lane reservation studies usually consider a static transportation network with assuming constant link travel times. However, in reality the link travel times are highly uncertain due to various factors such as weather, accidents, road maintenance, intersections, etc. Moreover, the precise link travel time probability distribution is usually difficult to be obtained. This paper studies a new stochastic bus lane reservation problem with partial link travel time information, i.e., only the mean and covariance matrix are known. The objective is to maximize the bus service level measured by the probability of the event that all lines are jointly scheduled on time. For the problem, we formulate a service-oriented distributionally robust optimization model. Its complexity is shown to be NP-hard. To solve the problem, a sample average approximation (SAA)-based method is first adapted. Since the SAA-based approach is computational expensive, a new approximated mixed integer second-order cone programming (MI-SOCP)-based approach is developed. Computational results on a real-life case show that the proposed MI-SOCP-based approach can efficiently obtain satisfactory solutions of high quality. Besides, our results indicate that the proposed model and algorithm can provide better solutions with higher service level, as compared with general stochastic models with known distributions and without considering service levels.

This paper considers freight transport in urban areas using passenger rail networks as an eco-friendly alternative to current predominantly fossil-fueled trucks. It addresses one of the essential problems, called freight rail transport scheduling problem (FRTSP), where the objective is to minimize the total waiting time of the parcels at their departure stations which is equivalent to minimizing the space occupancy at the stations and maximizing the turnover of the freight transport. In this paper, we focus on designing an effective and robust method to solve the FRTSP. For this purpose, a genetic algorithm is proposed whose performances, as it is widely known, highly depend on the length of chromosomes and control parameters. In this genetic algorithm, the length of chromosomes is reduced owing to a mathematical formulation with fewer integer decision variables representing a parcel-to-train assignment. We prove that as long as a parcel-to-train assignment is known, it takes a polynomial time to check whether this assignment can lead to a feasible solution and, if yes, solve the remaining problem by transforming it into computing the longest distances in a digraph. This result makes it possible to only consider parcel-to-train assignment variables in chromosomes, while efficiently determining the values of the other variables in fitness evaluation. In order to achieve robustness regarding all instances, the control parameters of the genetic algorithm are set with Taguchi method. Computational results show that the method developed in this way is very effective. For small-size instances, it yields optimal solutions within considerably shorter computation times than a commonplace optimization solver does. For large-size instances, it outperforms such a solver, not only providing much higher-quality solutions but also consuming much shorter computation times. This method thus meets very well operational requirements where high-quality solutions are expected while computation time is very limited.

The outbreak of extraordinary disruptive events, e.g., the COVID-19 pandemic, has greatly impacted the orderly operation in global supply chains (SCs), and may lead to the SC breakdown. Regulatory actions, such as government interventions during the pandemic, can greatly mitigate the disruption propagation (i.e., the ripple effect) and improve SC viability. However, existing works that focus on the disruption propagation management have not considered the possibility of such interventions. Motivated by the fact, in this study, we investigate a new disruption propagation management problem in a multi-echelon SC with limited intervention budget. The aim is to minimize disruption risk measured by the disrupted probability of target participants in the SC. For the problem, a novel approach, combining the Causal Bayesian Network (CBN), the do-calculus and the mathematical programming, is developed. Specially, two mixedinteger non-linear programming models are constructed to determine appropriate interventions. To enhance the proposed mathematical models, two valid inequalities are proposed. Then, a problem-specific genetic algorithm (GA) is developed for handling large-scale problem instances. Numerical experiments on a case study and randomly generated instances are conducted to evaluate the efficiency of the proposed models, the valid inequalities and the GA. Based on experiment analysis, managerial insights are drawn. (C) 2022 Elsevier Ltd. All rights reserved.

The impact of the COVID-19 pandemic in the supply chain (SC) evokes the need for valid measures to cope with the SC disruption risk. Supplier selection and disruption risk assessment, as valid measures, have received increasing attentions from academia. However, most of existing works focus on supplier selection and disruption risk assessment separately. This work investigates an integrated supplier selection and disruption risk assessment problem under ripple effect. The objective is to minimize the weighted sum of the disrupted probability and the total cost for the manufacturer. For the problem, a new stochastic programming model combined with Bayesian network (BN) is formulated. Then, an illustrative example is conducted to demonstrate the proposed method.

In ride-sharing, a sustainable transportation mode, drivers share idle seats with riders. This paper ad-dresses two key subproblems to find a ride-sharing solution: matching drivers and riders to achieve the system-wide optimality and allocating the resulting travel-cost saving among them by considering their self interests or the stability of the solution. Solutions to either subproblem significantly affect the effi-ciency of overall dynamic ride-sharing systems measured in total travel-cost saving and individual ben-efits of people involved. We evaluate the worst-case performance of the best stable solutions associated with four ridematching-defined pricing methods existing in the literature and widely used in practice. We then analyze a Vickrey-Clarke-Groves (VCG)-based pricing mechanism that lets the system achieve optimal system efficiency, prove its stability, and identify its drawbacks such as budget unbalance. We formulate the matching subproblem to maximize the total travel-cost saving and propose a simple and consistent pricing method to guarantee that the resulting ride-sharing solution not only is stable and system-wide optimal but also makes the system deficit-free and thus financially sustainable. Furthermore, such a ride-sharing solution can be obtained in polynomial time. In the numerical experiments, we eval-uate our method against existing ones through a real-life instance and randomly generated instances in a dynamic environment. (c) 2020 Elsevier Ltd. All rights reserved.

Existing works usually focus on the single-product disassembly line balancing problem (DLBP). In practice, end-of-life (EOL) products to be disassembled may be heterogeneous, and the actual processing time of each task may vary with its assigned worker. This work studies a stochastic multi-product DLBP with workforce assignment, to minimise the system cost. Due to historical data scarcity, we assume that only partial distributional information of uncertain task processing times is known. Exceeding the preset cycle time may lead to a disassembly performance reduction, thus we control the cycle time violation via conditional Value-at-Risk (CVaR) constraints, i.e. in a risk-averse fashion. For the problem, we first propose a novel formulation with distributionally robust CVaR constraints. Then some valid inequalities are proposed, leading to an improved model. Two solution approaches, i.e. an exact cutting-plane method and an approximation method, are further proposed and compared, via numerical experiments. Some managerial insights are also drawn.

The past years have witnessed increasingly widespread terrorism, violently destroying world peace and regional prosperity. Therefore, uncovering terrorist plots has become the most crucial step for eliminating terrorist attacks. However, with the terrorist scheme being disguised under the huge amount of data flow on the internet, identifying terrorist organizations still remains challenging. Since many terrorist organizations are prone to launch terrorist attacks together, here, we model their relationships as a Terrorist Organization Alliance (TOA) network and propose a novel method to identify the key terrorist organizations in the TOA network. The TOA network utilizes existing key nodes in order to extract useful information, and, with the help of the entropy weight method, the new solution to the TOA network is effective and precise. The experiments are performed on the dataset from the Global Terrorism Database, and the results are statistically validated through t-tests and convergence analysis. Compared with the traditional methods, our method is proven to be superior in terms of measure the harm of terrorist attack organizations and find the key terrorist organizations.