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The purpose of this article is to investigate a practical scheduling problem in which a group of elective surgical cases are scheduled over time, while considering their unpredictable durations and potential delays in the sterilisation of surgical instruments. The primary objectives were to schedule the maximum number of surgeries and decrease overtime for the surgical staff, as well as limit the number of instruments requiring emergency sterilisation. The study was conducted in collaboration with the University Hospital of Angers in France, which also contributed historical data for the experiments. We propose two robust mixed integer linear programming models, which are then solved iteratively through a rolling horizon approach, in which the objective functions are taken into account in lexicographic order. Experiments on randomly generated instances indicated which of the two approaches had better performance. Comparison of the results for a real-world scenario involving actual planning at the hospital indicated a greater than 69% decrease in overtime, and a minimum of 92% fewer stressful situations in the sterilising unit. [ABSTRACT FROM AUTHOR]

This work presents four mixed integer linear programming (MILP) models for the assembly flow shop problem in order to minimize the makespan. This production environment has two stages: production and assembly. The first stage consists of different machines designed to manufacture parts of a product. The second stage is intended for a final assembly. The performance measure considered is highly essential for industries from different segments, as it focuses on the best use of the time available for production. Statistical analysis with different tools was used to assess the performance and efficiency of mathematical models, emphasizing the analysis of performance profiles. Results showed that mathematical models are efficient, and the position-based model presented the best results for small and large instances during computational experimentation. All mathematical models can be used as direct tools in decision-making for the production sequencing problem in the approached environment. [ABSTRACT FROM AUTHOR]

Purpose: The rise of remote work increasingly requires organizations to coordinate a single large, consolidated talent pool into ad-hoc, short-term project teams on demand. This problem involves many simultaneous considerations including project revenues and rejection costs, conflicting projects and roles, worker assignment costs, worker utilization preferences and limits, worker reassignment costs, and arbitrary role start and end times. Moreover, plans must be continuously updated in response to changing circumstances. This paper addresses the problem of dynamic virtual team planning and coordination. Design/methodology/approach: We show this problem is NP-hard and provide a dynamic mixed integer linear programming (MILP) formulation for both optimal initial plan generation as well as continuous plan adjustment and re-optimization. We utilized a factorial experiment design to generate benchmark problems spanning a wide range of characteristics and conducted extensive computational experimentation using a common MILP solver. Findings: Exactly optimal solutions to large, realistically sized problems were consistently obtained in short amounts of time. All observed solution times were sufficient to support the operational decision-making requirements of real-world virtual team coordination, demonstrating the viability of this approach. Practical implications: The approach developed in this research can enable organizations to optimally coordinate virtual teams on a large scale and continually adjust plans in response to changing circumstances, all in an automated manner. Originality/value: This paper addresses a new and complex problem of increasing importance to organizations due to the rise in remote work. We provide a problem formulation and exact approach for optimally solving both the planning and re-planning aspects of this problem. [ABSTRACT FROM AUTHOR]

Robust charging schedules for a growing market of battery-electric bus (BEB) fleets are critical to successful adoption. In this paper, we present a BEB charging scheduling framework that considers spatiotemporal schedule constraints, route schedules, fast and slow charging options, and battery dynamics, modeled as a mixed-integer linear program (MILP). The MILP is based on the berth allocation problem (BAP), a method that optimally assigns vessels for service, and is adapted in a modified form known as the position allocation problem (PAP), which assigns electric vehicles (EVs) for charging. Linear battery dynamics are included to model the charging of buses while at the station. To account for the BEB discharges over their respective routes, we assume that each BEB experiences an average kWh charge loss while in transit. The optimization coordinates BEB charging to ensure that each vehicle maintains a state-of-charge (SOC) above a specified level. The model also minimizes the total number of chargers utilized and prioritizes slow charging for battery health. The validity of the model is demonstrated using a set of routes sampled from the Utah Transit Authority (UTA) for 35 buses and 338 visits to the charging station. The model is also compared to a heuristic algorithm based on charge thresholds, referred to as the Qin-modified method. The results show that the MILP framework encourages battery health by assigning slow chargers to BEBs more readily than the Qin-modified method. The MILP utilized one fast charger and six slow chargers, whereas the Qin-modified method utilized four fast chargers and six slow chargers. Moreover, the MILP maintained a specified minimum SOC of 25% throughout the day and achieved the required minimum SOC of 70% at the end of the working day, whereas the Qin-modified method failed to maintain the SOC above 0% without any constraints applied. Furthermore, it is shown that the spatiotemporal constraints are met while considering the battery dynamics and minimizing both the charger count and consumption cost. [ABSTRACT FROM AUTHOR]

In this article, maintenance operations were incorporated to the sequence-dependent setup blocking flow shop to minimise total completion time subject to total maintenance costs. A mixed integer linear programming and procedures to incorporate maintenance to job sequence were developed. A constructive heuristic and three metaheuristics, greedy randomised adaptative search procedure (GRASP), discrete artificial bee colony (DABC), variable block insertion heuristic (VBIH) and iterated greedy algorithm (IG), designed to the blocking flow shop with total completion time minimisation were adapted to minimise total maintenance costs and the hierarchical function, respectively.All the methods were applied to solve small and medium and large size instance sets, with respective 1920 and 2200 problems. Experimental results shows that for small size instances set, DABC with α = 20 (DABC(20)) obtained the best trade-off between effectiveness and efficiency. For medium and large size instances set, DABC(20) VBIH with α = 20 (VBIH(20)) generated the best trade-off between quality of solution and computational time. Considering both instances set together, both DABC(20) and VBIH(20) obtained the best performance between quality of solution and computational time. [ABSTRACT FROM AUTHOR]

Traffic congestion is a major concern in urban transportation in supply chain management. Road-based logistic companies can mitigate their Carbon dioxide (CO2) emissions effectively by optimising their operation. In this study, we observed a low-carbon, fixed-tour scheduling problem with time windows (LC-FTSP-TW) that is designed to consider the factors that can minimise the greenhouse-gas emissions of logistics systems. Through better planning of the delivery times, we delineated a system to control the schedules of two vehicle types: fossil-fuel-powered and electric-powered vehicles. We formulated the LC-FTSP-TW as a mixed-integer linear programming model that can take into consideration time-varying traffic conditions, customer time windows, and vehicle energy-consumption functions. The proposed model was observed to be convenient for practical use, as it could be solved directly using commercial optimisation toolboxes, such as CPLEX and Gurobi, with continuous optimal results. In addition, we developed an efficient dynamic programming algorithm for solving large-sized problems with discrete optimal results. Computational experiments were conducted on a group of test instances to verify the proposed model and algorithm, which demonstrated considerable reductions in CO2 emissions compared to non-optimised solutions for both the tested fossil-fuel-powered and electric-powered vehicles. [ABSTRACT FROM AUTHOR]

Welding is the most critical operation in the shipbuilding process and has a significant influence on the production cost and quality of ships. Therefore, the welding operation must be optimised. This paper presents a real-world welding gantry robot scheduling problem at shipyards, in which three gantry robots function in parallel. Welding gantry robots cannot cross each other and should operate over a certain distance to avoid collisions. To minimise the makespan, the welding tasks given by line segments should be evenly distributed among the three gantry robots. The welding tasks assigned to each robot should be optimally sequenced to minimise the completion time, including the waiting time required to prevent collisions with neighbouring robots. In addition, long welding edges are split, and the split small length edges are assigned to the gantry robots. This paper proposes a mixed-integer linear programming model, three-stage solution approach, and variable neighbourhood search algorithm to solve this problem. Experimental tests conducted on 20 real problem instances revealed that the proposed approach can reduce the makespan by 14% on average when compared with the conventional method. [ABSTRACT FROM AUTHOR]

We address the discrete time break scheduling problem with no preemption when workers' fatigue impacts their productivity. We propose a Mixed Integer Linear Programming model to solve the one break problem to optimality, using a lexicographic approach where the production amount is maximised first, and then the break length over a discrete time horizon. We develop a Variable Neighbourhood Search algorithm to solve the multiple break problem. In addition to proposing efficient solution methods to the problem, our incentive is to assess the impact on the production amount and on workers' welfare of rest break regulations laid down in legislation or collective agreements. We conducted an extensive simulation study to represent a wide range of workers' profiles defined in terms of fatigability and recovery speed. Simulation results show that regulations slightly affect the production amount whereas they allow for large improvements of workers' welfare as long as breaks are optimised as a second objective. The production amount is also shown to be quite sensitive to the break timing. Finally, multiple breaks can improve the production amount and workers' welfare in many situations, which questions the widespread belief that endowing workers with a single short break would optimise the production amount. [ABSTRACT FROM AUTHOR]

This paper proposes a new mixed‐integer linear programming (MILP) model to consider a multiproduct, multiperiod capacitated disassembly scheduling model with parts commonality and partial disassembly. The major contribution of this research is to probe the possibility of partial disassembly and separation of parts until the desired part is obtained. When disassembly is partial, parts that are not in demand will not disassemble, saving time and costs. In addition, another contribution introduced in this paper is the consideration of relationships between parts. By incorporating these relationships into the disassembly scheduling problem, our proposed model offers a more comprehensive and efficient solution. The objective is to provide the best plan for selecting partial disassembly modes and scheduling disassembly due to capacity constraints to meet the demand for leaf parts over the planning time horizon and minimize setup, holding, operating, and supply costs. The proposed model is solved using the CPLEX solver, and numerical experiments using selected literature data for problem sizes and sensitivity analyses of key model parameters are carried out to demonstrate the proposed model's consistency and robustness. In addition, significant managerial insights have been proposed based on sensitivity analysis to determine the applicability of the proposed model. Key insights reveal that increasing period capacity reduces total costs but requires careful balancing with capacity‐building expenses. Additionally, aligning part demand with the return product structure can significantly cut costs, and managing inventory through the procurement or disposal of less demanded parts can optimize operations. [ABSTRACT FROM AUTHOR]

This paper considers jointly scheduling the production and resource-constrained maintenance activities in a manufacturing setting with unrelated parallel machines. In particular, a single maintenance activity needs to be scheduled on each machine in one of its available time windows, and the maintenance activities require a scarce resource, thereby limiting the number of maintenance activities that can be scheduled simultaneously on different machines. In addition, machine- and sequence-dependent setup times, machine eligibility constraints and job-specific release and due dates are considered. A mixed-integer linear program is formulated with objectives including the makespan and, motivated from practice, a weighted sum of total production completion times at machines and total job tardiness. Additionally, a hybrid genetic algorithm with a novel solution representation is proposed for solving industry-scale large instances. A case study is performed with real-world data from a semiconductor manufacturer, where production and maintenance are scheduled separately. The benefit of simultaneously scheduling production and maintenance is investigated. Tests with real-world data show that the proposed model results in schedules that substantially improve the current factory practice. [ABSTRACT FROM AUTHOR]

To mitigate outpatient care delivery inefficiencies induced by resource shortages and demand heterogeneity, this paper focuses on the problem of allocating and sequencing multiple medical resources so that patients scheduled for clinical care can experience efficient and coordinated care with minimum total waiting time. We leverage highly granular location data on people and medical resources collected via Real-Time Location System technologies to identify dominant patient care pathways. A novel two-stage Stochastic Mixed Integer Linear Programming model is proposed to determine the optimal patient sequence based on the available resources according to the care pathways that minimize patients' expected total waiting time. The model incorporates the uncertainty in care activity duration via sample average approximation.We employ a Monte Carlo Optimization procedure to determine the appropriate sample size to obtain solutions that provide a good trade-off between approximation accuracy and computational time. Compared to the conventional deterministic model, our proposed model would significantly reduce waiting time for patients in the clinic by 60%, on average, with acceptable computational resource requirements and time complexity. In summary, this paper proposes a computationally efficient formulation for the multi-resource allocation and care sequence assignment optimization problem under uncertainty. It uses continuous assignment decision variables without timestamp and position indices, enabling the data-driven solution of problems with real-time allocation adjustment in a dynamic outpatient environment with complex clinical coordination constraints. [ABSTRACT FROM AUTHOR]

In this paper, we deal with a special type of scheduling problem. There are two classes of jobs to be processed on a single machine. Jobs of class A are directly delivered to the customers and we want to minimise their total flow time. Jobs of class B do not contribute to the objective function but must respect a no-idle constraint (i.e. they are required to keep an external downstream machine busy). This problem arises in some real-world production environments where the downstream process must not be interrupted because of technological constraints, economic viability or because the firm is bound to keep the external process continuously active (e.g. a contract with a downstream firm imposing penalties if the supply is interrupted). We prove that the general problem is NP-Hard. We introduce two mathematical programming-based approaches and some constructive heuristics. The various approaches are compared on the basis of a large computational campaign. [ABSTRACT FROM AUTHOR]

Rotary transfer machines are widely used in different industrial sectors. A rich literature concerning their design and optimisation is available, but mainly dedicated to integrated machining systems. This machine architecture is also implemented in the aluminium gravity die casting technology where the specificity of the casting process needs an appropriate design. In particular, the constraints related to processing times and the rigidity of the production sequence impose a specific approach to schedule the production for achieving an optimal cycle time. We approach this problem with an optimisation perspective: first we propose a mixed-integer linear programming formulation for defining the sequencing and scheduling of the machine in order to obtain a specified production with minimum makespan, and discuss strategies for enumerating the variables of the formulation. Second, we describe a heuristic algorithm as an alternative to the solution of the formulation through a general-purpose solver. Eventually, we present extensive computational experiments on a set of instances generated from real data, comparing these alternative approaches. [ABSTRACT FROM AUTHOR]

The scheduling problem in additive manufacturing is receiving increasing attention; however, few have considered the effect of scheduling decisions on machine energy consumption. This research focuses on the nesting and scheduling problem of a single selective laser melting (SLM) machine to reduce total energy consumption. Based on an energy consumption model, a nesting and scheduling problem is formulated, and a mixed integer linear programming model is proposed. This model simultaneously determines part-to-batch assignments, part placement in the batch, and the choice of build orientation to reduce the total energy consumption of the SLM machine. The energy-saving potential of the model is validated through numerical experiments. Additionally, the effect of the number of alternative build orientations on energy consumption is explored. [ABSTRACT FROM AUTHOR]

In this article, a production–assembly scheduling problem is presented in three steps. In the first step, the production operation is performed by dedicated parallel machines; in the second step, the assembly operation is performed by the same parallel machines, and in the third step, the post-assembly operation is performed by one machine, and these three steps exist in parallel factories. This problem is NP hard and cannot be solved in a reasonable time by mathematical models. Therefore, a mixed integer linear programming algorithm is presented for small-sized problems, and a new metaheuristic algorithm is presented for large-sized problems, by combining quantum-behaved particle swarm optimization (QPSO), shortest processing time (SPT) and dominance rules, which is called the hybrid QPSOSPT dominance rules (HQSD) algorithm. Accurate parameter adjustment was achieved by analysis of variance. The HQSD algorithm was shown to obtain better results than the other algorithms. [ABSTRACT FROM AUTHOR]

A novel model called Multi-Skilled, Multi-Mode Resource-Constrained Project Scheduling Problem with Discrete Time-Cost-Quality-Risk Trade-Off (MS-MRCPSP-DTCQRTP) was formulated, focusing primarily on minimizing project risks. This integer programming-based model generates various schedules that aim to optimize time, cost, quality, and risk, either individually or by achieving a balanced combination of these factors. By allocating accessible resources to the tasks of the project and adhering to precedence and resource constraints dictated by availability and skills, a collection of viable schedules was generated. Using a uniform distribution for time, cost, quality, and risk, three project case studies were derived from existing literature and expanded upon. These case studies were then employed to showcase the model. To generate optimal solutions, an open-source Python-MIP package employing the branch-and-cut methodology was employed. It was found that time is not a factor in minimizing the risk of a project, except for large-scale projects. Meanwhile, cost is a factor in all sizes of the project. Quality has no significant impact on risk. Project managers must strike a balance between resources that contain skills set with short to medium duration, moderate to high cost, and moderate to high-quality level to minimize project risk. [ABSTRACT FROM AUTHOR]

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Resource-constrained project scheduling problem (RCPSP) is a broadly researched issue in the literature. The purpose of the classic form of the problem is scheduling a set of activities considering resource and precedence constraints for minimizing the project completion time. Companies mostly deal with the issue of properly assigning multi-skilled workforces and maintaining the needed skill levels while implementing projects. In this study, a novel MILP model with three objectives is presented to tackle multi-skill RCPSP (MS-RCPSP). This study concentrates on minimizing project makespan, minimizing resource costs as well as tardiness costs, and maximizing quality under uncertainty. However, the standard MS-RCPSP is not able to consider several practical engineering requirements owing to its narrow assumptions. Therefore, key assumptions including overlap between activities, tardiness penalties of activities and the rework duration concept for activities in this model are considered. Due to the complexity of the real world, interval valued fuzzy numbers are taken into account for some of the problem's parameters. The efficiency of the proposed mathematical framework is represented using both a real case study to construct a railway bridge with 34 activities and large-size problem instances from MMLIB (MM50 and MM100). Since this model is multi-objective, a new extended IVF-ABS approach is presented in this study. Finally, the proposed approach is compared with two methods, namely SO and ABS, from the literature. [ABSTRACT FROM AUTHOR]

This study considers the scheduling problem of multiple independent and non-resumable jobs on unrelated parallel machines subject to machine availability and eligibility constraints. For each machine, there is a maximum continuous working time due to an unavailable period required for maintenance or tool changeover so that multiple unavailable periods on each machine may occur. The start time of an unavailable period on each machine is flexible and depends on the sum of the processing times of all jobs completed before this unavailability period. The objective is to minimize the makespan, which is the time to complete the processing of all non-resumable jobs. We develop a mixed integer linear programming (MILP) model to solve the problem optimally and a heuristic algorithm to solve the problem instances for which the MILP model cannot achieve an optimal solution in a reasonable allowed solution time. Computational experiments are done to evaluate our solution approaches’ performance in terms of quality and time. The results show that using a mixed integer linear programming model is not a practical alternative, especially for large-sized problem instances. However, the proposed heuristic algorithm finds near-optimal solutions in a very short time.

The previous studies on service resource scheduling for maintenance, repair and operation (MRO) ignore the diverse service processes from different complex equipment and the coordination of task scheduling and resource allocation, which is less realistic when multiple tasks exist. In this work, we propose an integrated scheduling model of distributed service resources for on-site MRO, which simultaneously considers different service processes, service teams and material resources. The MRO service process is decomposed into multiple subtasks with hierarchical relationships (dependency and independency). Within the duration of the subtask, all multi-skill technicians in service team stay together and material resources are delivered from service providers. Resource allocation and task scheduling are integrated to minimise the makespan, the excessive human resources and the cost performance index of material resources. Further, we introduce six classic multi-objective evolutionary algorithms (MOEAs) to solve the scheduling problem, in which a novel and practical encoding method mixed with integers and decimals is developed. The design-of-experiment (DOE) method is used to determine the optimal combination of crucial parameters. Finally, 20 benchmark instances of multiple MRO tasks are tested. Experiment results show MOEA/D-DU could obtain higher quality scheduling solutions with relatively low computational costs in the integrated scheduling model. [ABSTRACT FROM AUTHOR]

The production process of precast components is labor-intensive, involving various manual tasks. The physically demanding tasks usually result in fatigue and overexertion of workers, leading to increased occupational health risks and reduced productivity. An appropriate work–rest strategy is recognized to effectively promote both workers' health and productivity, while it has rarely been studied in the field of the construction industry. To narrow this gap, this study developed a mixed-integer linear programming approach to optimize the work–rest schedule by integrating workers' overexertion. The objective is to maximize the productive time affected by the workers' accumulative fatigue and recovery. Also, the optimized work–rest strategy can be highly customized by considering personalized factors and task characteristics. Experimenting with a case study compared the default rest schedule provided by the superintendent onsite with the optimal solution solved from the developed model. Results suggested that up to 20% improvement in productive time can be achieved, especially for the task with a relatively higher workload. Computational experiments were conducted to evaluate the sensitivity of total productive time to various personalized and task-specific factors. The proposed method provides superintendents with an applicable strategy to improve workers' productivity and reduce their occupational risks resulting from overexertion. This study can promote the implementation of personalized occupational health management and support the improvement of regulations on the required rest with quantified evidence, thereby contributing to more reliable scheduling and sustainable workforce development for the construction industry. The research scope was limited to the precast production process, and further investigation on broader applications will be conducted. [ABSTRACT FROM AUTHOR]

Providing appropriate home health care is one of the increasing concerns in the health care organizations. Home health care provides various services for disabled or elderly individuals at their homes. Also, dealing with the current critical situation of the coronavirus disease (COVID-19) due to the limited capacity of hospitals and the feeling of insecurity in crowded places, home health care is more recommended. This paper addresses a Home Health Care Routing and Scheduling Problem (HHCRSP) with two modes of transportations including public and private modes. Also, multi-depot version of the problem is studied to enhance the service delivery in scattered points. In this study, a mathematical model is presented based on a Mixed Integer Linear Programming (MILP) whose objective function is minimization of the sum of the travel distance and overtime costs. Furthermore, three meta-heuristic algorithms including Invasive Weed Optimization (IWO), Grasshopper Optimization Algorithm (GOA), and Simulated Annealing (SA) are presented for solving large-sized problems. Since the performance of meta-heuristic algorithms depends on setting the parameters, the Taguchi method is used to statistically set parameters of the developed algorithms. The computational results have shown that the proposed IWO has worked better than the other two proposed algorithms statistically. [ABSTRACT FROM AUTHOR]

Generally, when optimizing a rolling stock schedule, the locations of the depots, or places in the network where the composition changes and maintenance occurs, are assumed known. The locations where maintenance is performed naturally influence the quality of any resulting rolling stock schedules. In this paper, the problem of selecting new depot locations and their corresponding capacities is considered. A two-stage mixed integer programming approach for rolling stock scheduling with maintenance requirements is extended to account for depot selection. First, a conventional flow-based model is solved, ignoring maintenance requirements, to obtain a variety of rolling stock schedules with multiple depot locations and capacity options. Then, a maintenance feasible rolling stock schedule can be obtained by solving a series of assignment problems by using the schedules found in the first stage. The proposed methodology is tested on real-life instances, and the numerical experiments of different operational scenarios are discussed. [ABSTRACT FROM AUTHOR]

This study considers the scheduling problem of multiple independent and non-resumable jobs on unrelated parallel machines subject to machine availability and eligibility constraints. For each machine, there is a maximum continuous working time due to an unavailable period required for maintenance or tool changeover so that multiple unavailable periods on each machine may occur. The start time of an unavailable period on each machine is flexible and depends on the sum of the processing times of all jobs completed before this unavailability period. The objective is to minimize the makespan, which is the time to complete the processing of all non-resumable jobs. We develop a mixed integer linear programming (MILP) model to solve the problem optimally and a heuristic algorithm to solve the problem instances for which the MILP model cannot achieve an optimal solution in a reasonable allowed solution time. Computational experiments are done to evaluate our solution approaches' performance in terms of quality and time. The results show that using a mixed integer linear programming model is not a practical alternative, especially for large-sized problem instances. However, the proposed heuristic algorithm finds near-optimal solutions in a very short time. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of scheduling a set of unit‐time jobs on a set of uniform machines. The jobs are subject to conflict constraints modeled by a graph G called the conflict graph, in which adjacent jobs cannot be processed on a same machine. The objective considered herein is the minimization of maximum job completion time in the schedule, which is famous to be NP‐hard in the strong sense. The first part of this paper is an extensive study of the computational complexity of the problem restricted to several graph classes, namely: split graphs, interval graphs, forests, trees, paths and cycles. Afterward, we focus on the resolution of the problem with arbitrary conflict graphs. For this latter, a combination of a mixed integer linear programming (MILP) formulation, lower and upper bounds is proposed. A wild range of computational experiments proved the efficiency of this technique to tremendously reduce runtime and produce more optimal solutions (around 80% in average). Furthermore, a deep analysis of the resolution process based on both the density of the conflict graph as well as machine speeds (including identical machines) is thoroughly reported. [ABSTRACT FROM AUTHOR]

Many operators of water distribution networks (WDNs) are unable to meet the increasing demand for water. Utility operators in such situations resort to rationing the supply as a partial solution to this problem; this, in turn, may lead to disproportionate allocation of water or inequity in supply. In this study, we propose a mixed integer non-linear program formulation and an efficient solution approach to minimize the inequity in supply, subject to hydraulic constraints and additional constraints on hours of supply and valve operation. Further, we show that the schedule can be obtained using a data-driven approach based on flow and level measurements, which eliminates the modelling effort and uncertainty associated with the use of hydraulic models. We demonstrate the proposed approaches through simulations of a real WDN, and experiments conducted on a topologically similar laboratory-scale network. HIGHLIGHTS A technique is proposed for scheduling intermittent WDNs that can be implemented in the field.; The operational objective is equity in supply and constraints on implementation are incorporated.; The technique is demonstrated on a simulated and laboratory scale of a real network.;

To meet the production demand of workshop, this paper proposes an efficient discrete artificial bee colony (DABC) algorithm to solve a new automatic guided vehicle (AGV) scheduling problem with delivery and pickup in a matrix manufacturing workshop. The goal is to produce a AGV transportation solution that minimizes the total cost, including travel cost, time cost, and AGV cost. Therefore, a mixed integer linear programming model is established. To improve the transportation efficiency, a dynamic calculation method is developed. In the DABC algorithm, a heuristic algorithm and a median based probability selection method are used. For improving the quality of the solutions, four effective neighborhood operators are introduced. In the local search, a rule is given to save the operation time and a problem-based search operator is proposed to improve the quality of the best individual. Finally, a series of comparison experiments were implemented with the iterative greedy algorithm, artificial bee colony algorithm, hybrid fruit fly optimization algorithm, discrete artificial bee colony algorithm, improved harmony search, and hybrid genetic-sweep algorithm. The results show that the proposed DABC algorithm has high performance on solving the delivery and pickup problem. [ABSTRACT FROM AUTHOR]

Additive manufacturing is a rapidly growing technology shaping the future of manufacturing. In an increasingly competitive economy, additive manufacturing can help businesses to remain agile, innovative, and sustainable. This paper introduces the multi-site additive manufacturing (AM) machine scheduling problem considering carbon emissions caused by production and transportation. A mixed-integer linear programming model is developed aiming to optimise two separate objectives addressing economic and environmental sustainability in a multiple unrelated AM machine environment. The former is the total cost caused by production, transportation, set-up and tardiness penalty and the latter is the total amount of carbon emissions caused by production and transportation. The model is coded in Python and solved by Gurobi Optimizer. A numerical example is provided to represent the basic characteristics of the problem and show the necessity of the proposed framework. A comprehensive computational study is conducted under 600s and 1800s time limits for two main scenarios and the results have been elaborated. This article introduces the concept of considering both economic and environmental sustainability caused by production and transportation, proposing the first mathematical model and measuring its performance through a comprehensive experimental study. [ABSTRACT FROM AUTHOR]

This paper presents a mathematical programming-based solution approach for the scheduling problem of batch-processing parallel machines with eligibility constraints. A case study has been presented in the semiconductor industry, where the ovens are scheduled for the underfill cure operation of products. The case includes constraints, such as oven-product eligibility restrictions, loading constraints for the batching of products for ovens, daily production requirements, and oven capacity constraints. In this study, we also assess the difference between creating batches of a single product type or different product types to be allocated to the ovens. The case study results have shown that the proposed models, in comparison to the current situation, increase the occupancy rate of ovens. The execution of the models aids the company in gaining visibility on the scheduling of ovens and successfully managing the production plan and order commitment. The proposed models have been effective and supportive of the semiconductor company. [ABSTRACT FROM AUTHOR]

This paper addresses the multi‐objective maritime cargo routing and scheduling problem, in which the delivery of bulk products from pickup to delivery ports is served by a heterogeneous fleet of vessels. A mixed integer linear programming (MILP) model is formulated to simultaneously minimize total operation costs, the scheduling makespan, and delays in selected deliveries. The model accounts for several real features, such as time windows, capacity of the vessel's compartments, and ports requirements. A fuzzy weighted max–min method was applied to solve the problem. Two heuristics were developed to effectively handle the complex generated MILP models during the solution process. Experiments were conducted to evaluate the optimization approach using real‐life instances provided by a fertilizer company. Finally, a case study shows that the developed model and algorithmic framework are flexible and effective in coping with real problems, incorporating specific business rules from different companies. [ABSTRACT FROM AUTHOR]

The Flexible Job shop Scheduling Problem (FJSP), as a Production Scheduling Problem (PSP), is generally an extension of the Job shop Scheduling Problem (JSP). In this paper, the FJSP with reverse flow consisting of two flows of jobs (direct and reverse) at each stage is studied; the first flow initiates in Stage 1 and goes to Stage C (the last stage), and the second flow starts with Stage c and ends up in Stage 1. The aim is to minimize the makespan of the jobs (the maximum completion time). A Mixed Integer Programming (MIP) is presented to model the problem and the Branch and Bound (B&B) method is used to solve the problem. A numerical small-size problem is presented to demonstrate the applicability, for which the Lingo16 software is employed for a solution. Due to the NPhardness of the problem, a meta-heuristic, namely the Vibration Damping Optimization (VDO) algorithm with tuned parameters using the Taguchi method, is utilized to solve large-scale problems. To validate the results obtained using the proposed solution algorithm in terms of the solution quality and the required computational time, they are compared with those obtained by the Lingo 16 software for small-size problems. Finally, the performance of the proposed algorithm is compared with a Genetic Algorithm (GA) by solving some randomly generated larger-size test problems, based on which the results are analyzed statistically. Computational results confirm the efficiency and effectiveness of the proposed algorithm and show that the VDO algorithm performs well. [ABSTRACT FROM AUTHOR]

To provide accurate capacity and functionality needed for each demand period (DP), a reconfigurable manufacturing system (RMS) is able to change its configuration with time. For the RMS with multi-part flow line configuration that concurrently produces multiple parts within the same family, the cost and delivery time are dependent on its configuration and relating scheduling for any DP. So far, the study on solution method for the integrated optimisation problem of configuration design and scheduling for RMS is scarce. To efficiently find solutions with tradeoffs between total cost and tardiness, a multi-objective particle swarm optimisation (MoPSO) based on crowding distance and external Pareto solution archive is presented to solve practical-sized problems. The devised encoding and decoding methods along with the particle updating mechanism of MoPSO ensure any particle a feasible solution. The comparison between MoPSO and ε-constraint method versus small-sized cases illustrates the effectiveness of MoPSO. The comparative results between MoPSO and nondominated sorting genetic algorithm II (NSGA-II) against eight problems show that the MoPSO outperforms the NSGA-II in both solution quality and computation efficiency for the integrated optimisation problem. [ABSTRACT FROM AUTHOR]

This paper investigates an integrated batching and scheduling problem of electrolysis and caster in aluminium industry. The problem is to determine the assignment and scheduling of orders considering sequence-dependent setup times caused by technological and operational constraints of electrolysis cells, and determine the batching and scheduling of orders in the following casters. A novel unit-specific event-based continuous-time mixed integer linear programming model (MILP) is proposed to describe the problem. In this model, the event point is stage specific, and lower bounds are specified to tighten the model. A hybrid pointer-based differential evolution algorithm with new individual representation scheme is designed to solve the problem of industrial scale. An improved hybrid pointer-based mutation operator and a new point-cross crossover operator are proposed to enhance the performance of the algorithm. Computational experiments show that the proposed algorithm is more efficient when compared with CPLEX for medium and large size instances. Comparisons with the lower bound demonstrate that the algorithm is effective. [ABSTRACT FROM AUTHOR]

This paper presents the results of a research project aiming to optimise the scheduling of activities within a research laboratory of the 'Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA)'. To tackle this problem, we decompose every activity into a set of elementary tasks to apply standard scheduling methods. We model the problem as an extended version of the Multi-Skill Project Scheduling Problem (MSPSP). As a first approach, we propose a Multi-Skill Project Scheduling Problem with penalty for preemption, along with its mixed-integer/linear programming (MILP) formulation, where the preemption is allowed applying a penalty every time an activity is interrupted. However, the previous approach does not take into account all safety constraints at the facility, and a more accurate variant of the problem is needed. We propose then to integrate the concept of partial preemption to the MSPSP. This concept, that has not been yet studied in the scientific literature, implies that only a subset of resources is released during preemption periods. The resulting MSPSP with partial preemption (MSPSP-PP) is modelled using two methodologies: MILP and constraint programming. Regarding the industrial need of having good solutions in a short time, we also present a greedy algorithm for the MSPSP-PP. [ABSTRACT FROM AUTHOR]

Physical Internet (PI) was introduced as a global standardised and interconnected logistics system based on PI-nodes, PI-movers and PI-containers as a mean toward global logistics sustainability. One important issue regarding PI-nodes concerns the planning and scheduling of operations and the management of PI-containers, both in a deterministic and a perturbed environment. This research considers the Road-Rail PI-hub sustainable truck scheduling and PI-containers grouping problem. In our research we consider the weighted sum of the number of used wagons, the internal distance travelled by PI-containers from PI-docks to wagons as well as the trucks' tardiness, which translate the search for sustainable logistics. In this paper, an effective and reactive multi-agent system based model (MAS) is developed for the resolution of the trucks scheduling and PI-containers grouping. To ensure the efficiency of the MAS and improve the quality of each of its solutions, three concurrent hybrid meta-heuristics are embedded within three parallel scheduling agents. Then, a mixed integer linear programming model (MILP) is proposed to evaluate the performance of the MAS. Finally, the MAS is also evaluated under internal perturbations. The obtained results show the ability of the MAS to provide alternative sustainable solutions by rescheduling trucks in case of disruptions. [ABSTRACT FROM AUTHOR]

Scientific communities are motivated to schedule their large-scale data analysis workflows in heterogeneous cluster environments because of privacy and financial issues. In such environments containing considerably diverse resources, efficient resource allocation approaches are essential for reaching high performance. Accordingly, this research addresses the scheduling problem of workflows with bag-of-task form to minimize total runtime (makespan). To this aim, we develop a mixed-integer linear programming model (MILP). The proposed model contains binary decision variables determining which tasks should be assigned to which nodes. Also, it contains linear constraints to fulfill the tasks requirements such as memory and scheduling policy. Comparative results show that our approach outperforms related approaches in most cases. As part of the post-optimality analysis, some secondary preferences are imposed on the proposed model to obtain the most preferred optimal solution. We analyze the relaxation of the makespan in the hope of significantly reducing the number of consumed nodes. [ABSTRACT FROM AUTHOR]

• We studied the advanced air mobility aircraft scheduling problem at vertiports. • We considered separation rules at touchdown and lift-off pads and blocking constraints. • Two mixed integer linear programming formulations are presented for optimally solving small instances. • We propose two heuristic algorithms for solving real-life sized instances. • The computational results provide insights into vertiport operations. Recent progress in electric vertical take-off and landing (eVTOL) vehicles suggests that soon these vehicles could safely and efficiently transport people and cargo in urban areas. Therefore, advanced air mobility vehicles could become an alternative means of transport to overcome traffic congestion in cities in the upcoming years. There has been enormous interest from companies and governments in recent years in developing such technologies and enabling markets for new air transportation services. Despite the interest in the topic, little research has been done to address the aircraft scheduling problem in advanced air mobility take-off and landing areas (vertiports). The vertiports serve as the airports of eVTOL vehicles and could experience congestion problems similar to those of airports. This work proposes two optimization models for scheduling departing and landing aircraft at the vertiports' common ground taxi routes (taxiways), gates, and touchdown and lift-off (TLOF) pads. The mathematical models include advanced air mobility features such as separation rules and blocking constraints. As scheduling objectives, the first model maximizes the vertiport throughput, and the second model minimizes the deviation from the expected take-off/landing time. In addition, as a solution methodology, we developed two heuristic algorithms that use scheduling rules to assign and sequence the aircraft to the vertiport components. Computational results show that the optimization models find optimal schedules for small-sized instances of up to 10 aircraft, while the heuristic algorithms provide good results in terms of solution quality and computational time for large instances. [ABSTRACT FROM AUTHOR]

The service restoration to sound sections within a short period as much as possible, performed after the permanent fault occurs in a distribution system, is significant for maintaining power supply reliability. This paper proposes an efficient method to find service restoration procedures based on fault searching and the optimal work scheduling of crews, considering the power supply priorities of loads and the probability of occurrence of faults. The proposed method is composed of two‐stage problems to reduce the outage sections efficiently: (i) the problem to find a series of searching procedures for unknown fault locations in the first stage and (ii) the optimization problem of work schedules of crews in the second stage. In the first stage, the order of operation of switches opened for searching for the fault direction is determined, constituting the searching tree. After that, the proposed optimization method can efficiently solve the work scheduling problem by formulating it as a mixed‐integer linear programming problem in the second stage. The computational experiments using a large‐scale distribution system model with many remote and manual switches show that the proposed method can provide efficient service restoration procedures within a reasonable computational time. [ABSTRACT FROM AUTHOR]

The paper addresses the discrete characteristics of the processing crowdsourcing task scheduling problem in the context of social manufacturing, divides it into two subproblems of social manufacturing unit selecting and subtask sorting, establishes its mixed-integer programming with the objective of minimizing the maximum completion time, and proposes an improved artificial hummingbird algorithm (IAHA) for solving it. The IAHA uses initialization rules of global selection, local selection, and random selection to improve the quality of the initial population, the Levy flight to improve guided foraging and territorial foraging, the simplex search strategy to improve migration foraging to enhance the merit-seeking ability, and the greedy decoding method to improve the quality of the solution and reduce solution time. For the IAHA, orthogonal tests are designed to obtain the optimal combination of parameters, and comparative tests are made with variants of the AHA and other algorithms on the benchmark case and a simulated crowdsourcing case. The experimental results show that the IAHA can obtain superior solutions in many cases with economy and effectiveness. [ABSTRACT FROM AUTHOR]

Under support of industry 4.0, researchers have shown an increased interest in MapReduce scheduling problems to process big data. However, very few studies investigate MapReduce scheduling problems under parallel batch machine environment, which is also common in practice. Motivated by this, we study a parallel batch machine scheduling problem in which all the jobs are belonging to MapReduce type. The objective of the considered problem is of minimizing the total weighted tardiness. For solving this problem, we first establish a mixed integer linear programming model, and then a rule-based genetic algorithm is developed to solve it. Numerical experiments are also conducted to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

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