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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]

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. [ABSTRACT FROM AUTHOR]

Dynamic facility layout planning (DFLP) involves determining an appropriate arrangement scheme of the elements making up the production system for each time period into which the planning horizon is divided. When formulating the problem as an optimisation model, using the traditional top-down approach is usual, which firstly determines the block layout (BL) and then the detailed layout (DL) of each work cell. However by this approach, the BL generates area constraints in the detailed phase, which sometimes limit its implementation. In this context, the present paper presents a multi-objective mixed integer non-linear programming (MOMINLP) model that allows the problem to be addressed by considering an alternative approach, known in the literature as the bottom-up approach. The proposed model, called bottom-up mDFLP, considers three objective functions: (1) minimise the total material handling cost (TMHC) and the total rearrangement cost (TRAC); (2) maximise the total closeness rating (TCR) between departments; (3) maximise the area utilisation ratio (AUR). The original MOMINLP is transformed into a more computationally efficient multi-objective mixed integer linear programming (MOMILP) model. The proposed model is applied and validated in a case study of a company in the metal-mechanic sector with 12 departments for three 4-month periods. [ABSTRACT FROM AUTHOR]

Considering ergonomics at the design phase can lead to assembly lines that protect and improve workers' health and well-being. However, this may reduce efficiency and productivity. Evaluating the potential benefits of incorporating ergonomics into line design versus the additional costs associated with the number of extra stations required or increased idle time, as well as exploring other ergonomic design alternatives, is critical for decision-makers. An assembly line balancing problem considering time, space and ergonomics, is used to evaluate the impact of ergonomic considerations. A model for maximising line efficiency is proposed and linearised for solution by the solver CPLEX. The mixed-integer linear model is compared to an alternative linearisation approach in which a decision variable is parameterised and then iteratively solved. An example and a case study are used to observe the competitiveness of both solution methods, where the iterative approach is shown to be superior for real size problems. The results show that ergonomic evaluation in line design leads to safer but less productive lines, which in turn increases the number of workstations and costs. Specifically, limiting ergonomic risk to a moderate level for the engine assembly line examined in this study, means maximum daily drop of 27 engines. [ABSTRACT FROM AUTHOR]

Facility location, supply inventory and distribution, and evacuation planning are key operational functions in a humanitarian relief network, it is critical to integrate these three functions and schedule their activities jointly in a coordinated manner. Considering uncertain demands and evacuation rates of injured people, we develop a distributionally robust model for the multi-period humanitarian relief network design with multiple types of relief supplies. To solve the problem, we reformulate the proposed model into a mixed integer linear programme, and develop an enhanced branch-and-Benders-cut algorithm that incorporates some algorithm enhancements to solve the resulting model. Extensive numerical experiments show that: (i) the distributionally robust model provides more reliable and flexible solutions that perform the best when faced uncertainty over the deterministic and stochastic models; (ii) the algorithm enhancements are very effective to enhance the performance of the proposed algorithm, which can reduce the CPU time by up to 9.75%∼41.64% on average; (iii) the integrated solution approach is more beneficial to solve the problem when comparing with a sequential solution approach; and (iv) some model parameters have significant impact on the solution structure, which can help decision maker set proper parameters to achieve the desired trade-off among the considered metrics. [ABSTRACT FROM AUTHOR]

To tackle the balancing and sequencing problems of flexible mixed model assembly lines with alternative precedence relations, If--then rules and AND/OR graphs are adopted as modelling tools to replace precedence graphs that have limitations in representing alternative precedence relations. Mixed integer linear programming (MILP) and constraint programming (CP) models are established respectively. Moreover, an iterative decomposition method is developed to deal with large-sized problems. Computational experiments on different scales are carried out to test those methods. The computational results reveal that the performance of CP is better than MILP especially when more OR relations exist. And the iterative decomposition method outperforms MILP and CP in terms of solution quality and time. [ABSTRACT FROM AUTHOR]

In this article, we study a shelf-stock allocation problem in a data-driven setting where the demand distribution is unknown. The retailer needs to decide which commodities to place on each level of the shelf (location decision), and determine the stock level for each selected commodity at the shelf (stock decision) that can be adjusted based on updated demand prediction, under constraints on allocation and capacity, so as to maximize total expected profit. Key issues of the problem include demand endogeneity on vertical-location effect and the demand ambiguity that may arise from the ever shorter life cycles of commodities and other complicated determinants. We address the problem in a rolling-horizon fashion and develop a two-phase data-driven robust optimization model leveraging a decision-dependent Wasserstein metric that incorporates time-series demand forecasts and captures vertical-location effect under demand ambiguity. We derive tractable solution schemes for the model, in which the phase-I model determines the optimal location decision by solving one instance of mixed-integer linear program, while the phase-II model admits an analytical solution for updating the optimal stock. Furthermore, we discuss the sensitivity implications on how the ambiguity aversion impacts the optimal stock, and prove the consistency of the model solution under regularity condition on the underlying demand process that well justifies the asymptotic performance of the proposed data-driven approach. Finally, we extend our model to incorporate substitution effect, and conduct sufficient numerical experiments with real-life data that demonstrate the performance of our model. [ABSTRACT FROM AUTHOR]

This research proposes a Mixed Integer Linear Programming model to assist structural engineers in the reinforcement detailing phase of circular foundations, with a specific focus on wind tower foundations, aiming to optimise the design process and, consequently, reduce the costs associated with constructing these elements. After analysing the internal forces in circular foundations, the designer must define the calculation sections to determine the required area of steel and select the diameter of the steel bars and their positioning in the base. This determination is often based on the designer's experience, which can lead to choices that unnecessarily increase costs, thus justifying the creation of a model to optimise this process. The proposed model consists of three phases: the first involves a non-linear regression to obtain an equation that represents the bending moments at any point in the foundation; the second applies a linear quadratic model to choose the ideal layout of the calculation sections; and the third uses a binary integer linear model to determine the ideal diameter of the bars and their consequent positioning. This search for optimality must comply with the restrictions imposed by normative criteria, including maximum bar lengths and rebar lap splices, maintaining good constructability based on standardisation. Validation tests were conducted with confirmed cases using the proposed model, resulting in an average reduction of 10.72 % in the value of the rebar weight due to the model's second stage and a decrease of 12.28 % in the best case during the final step. It can, therefore, be concluded that the proposed model is viable for application in circular foundation projects, leading to savings in the total weight of steel used in the foundations. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of finding the optimal position and sizing of battery energy storage (BES) devices using a two-stage optimization technique. The primary stage uses mixed integer linear programming (MILP) to find the optimal positions along with their sizes. In the secondary stage, a relatively new algorithm called mountain gazelle optimizer (MGO) is implemented to find the technical feasibility of the solution, such as voltage regulation, energy loss reduction, etc., provided by the primary stage. The main objective of the proposed bi-level optimization technique is to improve the voltage profile and minimize the power loss. During the daily operation of the distribution grid, the charging and discharging behaviour is controlled by minimizing the voltage at each bus. The energy storage dispatch curve along with the locations and sizes are given as inputs to MGO to improve the voltage profile and reduce the line loss. Simulations are carried out in the MATLAB programming environment using an Australian radial distribution feeder, with results showing a reduction in system losses by 8.473%, which outperforms Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and Cuckoo Search Algorithm (CSA) by 1.059%, 1.144%, and 1.056%, respectively. During the peak solar generation period, MGO manages to contain the voltages within the upper boundary, effectively reducing reverse power flow and enhancing voltage regulation. The voltage profile is also improved, with MGO achieving a 0.348% improvement in voltage during peak load periods, compared to improvements of 0.221%, 0.105%, and 0.253% by GWO, WOA, and CSA, respectively. Furthermore, MGO's optimization achieves a reduction in the fitness value to 47.260 after 47 iterations, demonstrating faster and more consistent convergence compared to GWO (47.302 after 60 iterations), WOA (47.322 after 20 iterations), and CSA (47.352 after 79 iterations). This comparative analysis highlights the effectiveness of the proposed two-stage optimization approach in enhancing voltage stability, reducing power loss, and ensuring better performance over existing methods. [ABSTRACT FROM AUTHOR]

The article discusses the transition to digital manufacturing and supply chain management in the context of Industry 4.0, emphasizing the integration of operations research and artificial intelligence. It explores various technologies such as additive manufacturing, robotics, digital twins, and blockchain in creating seamless end-to-end visibility in supply chains. The special issue features papers that demonstrate how automation, collaboration, identification, and data processing technologies can generate benefits for manufacturing and supply chains. The research presented in the article provides insights into optimizing supply chain operations through innovative approaches and interdisciplinary methods. [Extracted from the article]

This study investigates the application of Reinforcement Learning (RL) to optimize intraday operations of hydropower reservoirs. Unlike previous approaches that focus on long-term planning with coarse temporal resolutions and discretized state-action spaces, we propose an RL framework tailored to the Hydropower Reservoirs Intraday Economic Optimization problem. This framework manages continuous state-action spaces while accounting for fine-grained temporal dynamics, including dam-to-turbine delays, gate movement constraints, and power group operations. Our methodology evaluates three distinct action space formulations (continuous, discrete, and adjustments) implemented using modern RL algorithms (A2C, PPO, and SAC). We compare them against both a greedy baseline and Mixed-Integer Linear Programming (MILP) solutions. Experiments on real-world data from a two-reservoir system and a simulated six-reservoir system demonstrate that while MILP achieves superior performance in the smaller system, its performance degrades significantly when scaled to six reservoirs. In contrast, RL agents, particularly those using discrete action spaces and trained with PPO, maintain consistent performance across both configurations, achieving considerable improvements with less than one second of execution time. These results suggest that RL offers a scalable alternative to traditional optimization methods for hydropower operations, particularly in scenarios requiring real-time decision making or involving larger systems. [ABSTRACT FROM AUTHOR]

In the context of new power systems, the rapid development of distributed renewable energy and the drive of dual carbon targets have prompted community-level clean energy and energy storage configuration to become the key to improving energy efficiency and reducing carbon emissions. Based on the regional autonomy balance and sharing mechanism, this paper establishes a community distributed energy and energy storage optimization configuration model. With the goal of minimizing the total operating cost of the community, the established model is linearized by using the Big-M method and the McCormick method and transformed into a mixed integer linear programming model that is easy to solve. In order to comprehensively evaluate the comprehensive benefits of the established optimization scheme, this paper introduces the indicators of clean energy self-consumption rate, load self-supply rate, static investment payback period, and static CO2 investment payback period from the aspects of energy utilization, the economy, and the environment. Finally, a calculation example analysis is conducted, and the results show that, compared with the scenario where energy storage is configured separately and distributed energy resources are not shared, the configuration strategy proposed in the article can reduce the energy storage configuration capacity by 46.6% and the distributed energy configuration capacity by 21.1%. Investment costs can be reduced by 15.6%. At the same time, 91.75% of distributed energy self-consumption and 96.80% of load self-supply are achieved, reducing grid interaction and promoting regional autonomy and balance. The static CO2 investment payback period is also significantly shortened, and the carbon emission reduction effect is significant, providing an important reference for community energy system optimization planning and green and low-carbon development. [ABSTRACT FROM AUTHOR]

Water utility operators prioritize timely repairs of leakages, an activity that refers to a challenging task by the spatial localization of multiple existing leakages within a water distribution network (WDN). In the literature, genetic algorithms (GAs) are applied to find the best combinations of leakages, typically in combination with a reduced number of candidate leakage nodes to reduce computational effort. To address this limitation, a graph-based GA is proposed in this work by considering the topological relationship between the leakage candidate nodes and sizes for the creation of the offspring. In greater detail, each gene in the graph-based GA consists of a leakage place and leakage size, and two random genes are selected based on the roulette wheel selection for the creation of the offspring. Afterward, possible offspring nodes are selected within the shortest path between these two leakage places or in spatial proximity connected to the shortest path, where the leakage size is set to a random value between the leakage sizes of the two genes. The developed approach was tested on part of a WDN with 100 different leakage scenarios and varying number of leakages, sizes, and locations. As the results showed, the graph-based GA significantly improved leakage localization compared with a classic GA and linear programming solver, with a median distance of 44 m between suspected and actual leakage locations given perfect conditions while also being computationally efficient. However, the achievable performance was strongly affected by measurement errors, model uncertainties, and partially unknown nodal demands and was more accurate for localizing leakage places near exactly measured locations and with larger leakage sizes. [ABSTRACT FROM AUTHOR]

The university course timetabling is a tactical level problem that almost all academic departments encounter before each semester. Nowadays, in order to meet some standards, set by educational accreditation agencies within the scope of higher education quality assurance, it is required to generate efficient course timetables, which ensure optimal distribution of basic capabilities. Actually, these capabilities should be first specified based on the learning outcomes defined by lecturers for each course that are also associated with program outcomes and are aimed to be acquired by all of the students. Based on this motivation, a multi-objective mixed-integer non-linear programming model is developed for a novel capability-based course timetabling problem. Its validity and practicality are tested on a real-life application in an Industrial Engineering Department. When the balanced solutions provided by compromise and fuzzy goal programming techniques were compared with the existing schedules of the previous years, it was revealed that significant improvements could be achieved in terms of several conflicting objectives (i.e., optimal capability distribution over the timetable, acquisition of maximum number/variety of different capabilities by the students, meeting expectations of lecturers by minimizing total temporal difference between the periods his/her courses are assigned, total penalty cost related to soft constraints). [ABSTRACT FROM AUTHOR]

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Purpose: A circular economy (CE) is an economic system that tries to eliminate waste and continually use resources. Due to growing environmental concerns, supply chain (SC) design should be based on the CE considerations. In addition, responding and satisfying customers are the challenges managers constantly encounter. This study aims to improve the design of an agile closed-loop supply chain (CLSC) from the CE point of view. Design/methodology/approach: In this research, a new multi-stage, multi-product and multi-period design of a CLSC network under uncertainty is proposed that aligns with the goals of CE and SC participants. Recycling of goods is an important part of the CLSC. Therefore, a multi-objective mixed-integer linear programming model (MILP) is proposed to formulate the problem. Besides, a robust counterpart of multi-objective MILP is offered based on robust optimization to cope with the uncertainty of parameters. Finally, the proposed model is solved using the e-constraint method. Findings: The proposed model aims to provide the strategic choice of economic order to the suppliers and third-party logistic companies. The present study, which is carried out using a numerical example and sensitivity analysis, provides a robust model and solution methodology that are effective and applicable in CE-related problems. Practical implications: This study shows how all upstream and downstream units of the SC network must work integrated to meet customer needs considering the CE context. Originality/value: The main goal of the CE is to optimize resources, reduce the use of raw materials, and revitalize waste by recycling. In this study, a comprehensive model that can consider both SC design and CE necessities is developed that considers all SC participants. [ABSTRACT FROM AUTHOR]

Purpose: To maximize the network total profit by calculating the difference between costs and revenue (first objective function). To maximize the positive impact on the environment by integrating GSCM factors in RL (second objective function). To calculate the efficiency of disassembly centers by SDEA method, which are selected as suppliers and maximize the total efficiency (third objective function). To evaluate the resources and total efficiency of the proposed model to facilitate the allocation resource process, to increase resource efficiency and to improve the efficiency of disassembly centers by Inverse DEA. Design/methodology/approach: The design of a closed-loop logistics network for after-sales service for mobile phones and digital cameras has been developed by the mixed-integer linear programming method (MILP). Development of MILP method has been performed by simultaneously considering three main objectives including: total network profit, green supply chain factors (environmental sustainability) and maximizing the efficiency of disassembly centers. The proposed model of study is a six-level, multi-objective, single-period and multi-product that focuses on electrical waste. The efficiency of product return centers is calculated by SDEA method and the most efficient centers are selected. Findings: The results of using the model in a case mining showed that, due to the use of green factors in network design, environmental pollution and undesirable disposal of some electronic waste were reduced. Also, with the reduction of waste disposal, valuable materials entered the market cycle and the network profit increased. Originality/value: (1) Design a closed-loop reverse logistics network for after-sales services; (2) Introduce a multi-objective multi-echelon mixed integer linear programming model; (3) Sensitivity analysis use Inverse-DEA method to increase the efficiency of inefficient units; (4) Use the GSC factors and DEA method in reverse logistics network. [ABSTRACT FROM AUTHOR]

Abstract: Inventory supply chain planning involves determining the quantity of products to be transported among entities within a specified planning horizon. Often, inventory levels are reviewed at set intervals: (1) Background: In this paper, periodic review (s,S) policy is used to optimize inventories from an integrated perspective of inventory management across the supply chain. The decision to place an order and the order quantity are based on the inventory level at the review time. If the inventory falls below a certain level (s), an order is placed to replenish it to a target level (S); (2) Methods: The planning model is implemented using a mixed integer linear programming model. It determines the inventory levels, supply levels and the (s) and (S) levels for each entity, as well as the flow of products between them. To test the model, a case study is conducted to demonstrate its applicability; (3) Results: The experimental data confirm the model's validity, as its behavior aligns with the expectations for a periodic review (s,S) policy; (4) Conclusions: Since a fixed replenishment frequency is mandatory, with no continuous inventory review required, this policy offers simplicity and ease of implementation, making it a practical choice for certain inventory management. [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]

Ports are essential nodes in global trade, linking maritime and land transport. As maritime logistics increasingly drive global supply chains, optimizing port operations has become vital for enhancing economic efficiency and environmental sustainability. This study presents a Mixed Integer Linear Programming (MILP) model to address inefficiencies in berth allocation and stevedoring processes at the Port of Leixões, Portugal. By integrating real operational data, the model reduces vessel waiting times by 47.56% (from 8.1 to 4.2 h) and operational delays by 37.39% (from 11.5 to 7.2 h). These optimizations also result in a 41.85% reduction in greenhouse gas emissions per ship, aligning with global emissions regulations and promoting sustainable port management. The model's innovations include real-time data integration and a holistic resource allocation approach to mitigate congestion and inefficiencies. Key findings demonstrate the model's potential to streamline operations and minimize environmental impacts. These advancements align economic efficiency with environmental sustainability, addressing global emissions regulations. However, the study acknowledges limitations, such as excluding unpredictable factors like weather conditions and equipment failures. Future research should explore dynamic variables, such as weather conditions and mechanical failures, and expand the model's applicability to other seaports worldwide. [ABSTRACT FROM AUTHOR]

A scheduling problem for a two‐stage flexible flow shop with s‐batching machines motivated by processes in additive manufacturing is considered. A batch is a group of jobs that are processed together on a single machine. Each job belongs to an incompatible family. Only jobs of the same family can be batched together. A maximum batch size is given. A setup time occurs between different batches. The jobs of a batch are processed in a serial manner, that is, the processing time of a batch is the sum of the processing times of the jobs forming the batch. Batch availability is assumed. A job has a weight, a due date, and a release date. The total weighted tardiness is considered as performance measure. We establish a mixed integer linear programming formulation for this scheduling problem. For large‐sized problem instances, an iterative decomposition approach (IDA) is proposed that uses a grouping genetic algorithm or an iterated local search (ILS) scheme to solve the single‐stage subproblems. Moreover, an alternative ILS scheme based on a disjunctive graph representation of the problem at hand is designed. Results of computational experiments based on randomly generated problem instances demonstrate that the IDA hybridized with ILS outperforms the two other schemes. [ABSTRACT FROM AUTHOR]

Background: Discontinuous transcription allows coronaviruses to efficiently replicate and transmit within host cells, enhancing their adaptability and survival. Assembling viral transcripts is crucial for virology research and the development of antiviral strategies. However, traditional transcript assembly methods primarily designed for variable alternative splicing events in eukaryotes are not suitable for the viral transcript assembly problem. The current algorithms designed for assembling viral transcripts often struggle with low accuracy in determining the transcript boundaries. There is an urgent need to develop a highly accurate viral transcript assembly algorithm. Results: In this work, we propose Cov-trans, a reference-based transcript assembler specifically tailored for the discontinuous transcription of coronaviruses. Cov-trans first identifies canonical transcripts based on discontinuous transcription mechanisms, start and stop codons, as well as reads alignment information. Subsequently, it formulates the assembly of non-canonical transcripts as a path extraction problem, and introduces a mixed integer linear programming to recover these non-canonical transcripts. Conclusion: Experimental results show that Cov-trans outperforms other assemblers in both accuracy and recall, with a notable strength in accurately identifying the boundaries of transcripts. Cov-trans is freely available at https://github.com/computer-Bioinfo/Cov-trans.git. [ABSTRACT FROM AUTHOR]

Modelling with fuzzy relations in approximate reasoning is obstructed sometimes by the inconsistency of obtained fuzzy relational equations. This paper tackles the inconsistency resolving problem for a finite system of max–min equations by modifying only the right-hand side vector as slightly as possible with respect to the sum of absolute deviations. It is demonstrated that this problem may be reformulated equivalently as a polynomial-sized mixed integer linear programming problem. Although such a reformulation results in a problem of much larger size than its original compact form, it may be solved to optimality on instances of moderate size or even large size by an off-the-shelf solver for mixed integer linear programming and in some sense does not require a tailored solving method. [ABSTRACT FROM AUTHOR]

The rapid development of intelligent warehouse systems is resulting in the realization of automation in warehouse activities and raising awareness of decarbonization, particularly the need to reduce carbon emissions from electricity consumption. Driven by the decarbonization trend, microgrid systems with rooftop photovoltaic panels are becoming more popular in warehouses and are providing zero-carbon electricity for warehouse operations. How to make better use of microgrid systems and reduce the consumption of electricity generated from traditional energy sources is becoming increasingly important in warehouse systems. This paper investigates an operational problem in a warehouse system equipped with a shuttle-based storage and retrieval system, in which a microgrid system acts as the main electricity source. Power-load management is applied to avoid peaks of energy consumption, and a mixed linear programming model is developed to optimize task sequencing and scheduling with decarbonization awareness. To solve the proposed problem, a data-driven variable neighbourhood search algorithm is built. Numerical experiments are conducted to validate the model and algorithm. Sensitivity analysis shows the effectiveness of power-load management and the influence of system configuration on energy consumption. [ABSTRACT FROM AUTHOR]

The rapid growth of cities and their populations in recent years has resulted in significant tidal passenger flow characteristics, primarily manifested in the imbalance of passenger numbers in both directions. This imbalance often leads to a shortage of train capacity in one direction and an inefficient use of capacity in the other. To accommodate the tidal passenger flow demand of urban rail transit, this paper proposes a timetable optimization method that combines multiple strategies, aimed at reducing operating costs and enhancing the quality of passenger service. The multi-strategy optimization method primarily involves two key strategies: the unpaired operation strategy and the express/local train operation strategy, both of which can flexibly adapt to time-varying passenger demand. Based on the decision variables of headway, running time between stations, and dwell time, a mixed integer linear programming model (MILP) is established. Taking the Shanghai Suburban Railway airport link line as an example, simulations under different passenger demands are realized to illustrate the effectiveness and correctness of the proposed multi-strategy method and model. The results demonstrate that the multi-strategy optimization method achieves a 38.59% reduction in total costs for both the operator and the passengers, and effectively alleviates train congestion. [ABSTRACT FROM AUTHOR]

This paper proposes a multi‐level coordinated scheduling strategy for shared energy storage systems (SESS) under electricity spot and ancillary service markets to maximize the overall operational profit. At the upper level, an optimal day‐ahead bidding model is formulated to allocate optimal capacities of SESS for engaging in electricity transactions and offering peak shaving reserve over the scheduling horizon. Then, a joint market clearing model is presented to simultaneously optimize awarded capacities of SESS and marginal prices of electricity spot and ancillary service markets at the middle level. Furthermore, the lower level aims to correct the coordinated scheduling strategy for minimizing the intra‐day scheduling cost under the uncertainty of renewable energy sources and load demand. Finally, a linear relaxation method is applied to transform the proposed strategy into a mixed‐integer linear programming problem for enhancing the computation efficiency. Comparative case studies have validated the superior performance of the proposed strategy for better utilization of available storage capacity and arbitrage maximization. [ABSTRACT FROM AUTHOR]

Hydrogen direct reduction iron coupled with electronic arc furnace (H2DRI‐EAF) technology, as an important technology for decarbonisation in the iron and steel industry, has the advantages of high electrification and low carbon emissions. However, the large demand for hydrogen in this technology relies significantly on the production of electrolytic hydrogen, leading to a substantial increase in power consumption in the steel production process. Moreover, the use of an unclean power source in electrolytic hydrogen production leads to increases in indirect carbon emissions, reducing the low‐carbon attributes of the technology. This study investigates the integrated flexible operation mode of a steel plant. An illustrating method is utilised for modelling the entire steel production process and power to hydrogen (PtH2) process in detail for the H2DRI‐EAF steel plant, which includes natural gas, photovoltaic, wind power self‐provided power plants, and carbon capture and storage (CCS) systems. A mixed integer linear programming (MILP) model is developed for the comprehensive scheduling of the steel mill. The results of the case studies indicate that by reliably integrating the production of renewable energy and natural gas power plants, the PtH2 system can fully consume the renewable energy output while ensuring the smooth progress of steel production and maximising the reduction of carbon emissions from hydrogen production and the total cost of steel production. [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]

This study develops a framework for designing hydrogen supply chains (HSC) in island territories using Mixed Integer Linear Programming (MILP) with a multi-period approach. The framework minimizes system costs, greenhouse gas emissions, and a risk-based index. Corsica is used as a case study, with a Geographic Information System (GIS) identifying hydrogen demand regions and potential sites for production, storage, and distribution. The results provide an optimal HSC configuration for 2050, specifying the size, location, and technology while accounting for techno-economic factors. This work integrates the unique geographical characteristics of islands using a GIS-based approach, incorporates technology readiness levels, and utilizes renewable electricity from neighboring regions. The model proposes decentralized configurations that avoid hydrogen transport between grids, achieving a levelized cost of hydrogen (LCOH) of €8.54/kg. This approach offers insight into future options and incentive mechanisms to support the development of hydrogen economies in isolated territories. [Display omitted] • A framework for optimal hydrogen supply chain design in island territories is defined. • The multi-period problem is modelled using Mixed Integer Linear Programming. • The framework aims to minimize cost, emissions, and a risk-based index. • Island-specific geography is integrated using a Geographic Information System. • The hydrogen produced meets the transportation demand of Corsica from 2025 to 2050. [ABSTRACT FROM AUTHOR]

Intelligent operation and green distribution have become a mainstream task to enable fast development of urban distribution applications. However, how to improve the distribution efficiency with low operating costs, and mitigate environmental pollution with high service quality is still a significant challenge in the practical industry applications. To address the above challenge, in this paper, we take into account both the economic cost and environmental cost, and propose a joint distribution path planning model based on neural architecture search (NAS) for electric vehicles with double-decked drones. More specifically, in our design, the factors such as energy consumption and carbon emissions of vehicles and drones during different distribution stages are considered. Then, a mixed integer linear programming model is established under the constraints of customer time window, vehicle capacity and vehicle battery capacity. Based on this model, a hybrid genetic algorithm is proposed to solve the optimization problem, where the carbon emission cost is estimated by the convolution neural network model, which is optimized by the neural architecture search technique. We conduct extensive experiments to validate the effectiveness of the proposed method. The experimental results show that, compared with CPLEX, the proposed method excelled in both solution quality and speed, which verify the effectives of our hybrid algorithm in dealing with the 2E-VRPD problem, where delivery routes can be well optimized, the efficiency of vehicle-machine collaborative delivery can be improved, and delivery costs are reduced. [ABSTRACT FROM AUTHOR]

For industries like the cement industry, switching to a carbon-neutral production process is impossible. They must rely on carbon capture, utilization and storage (CCUS) technologies to reduce their production processes' inevitable carbon dioxide ( CO 2 ) emissions. For transporting continuously large amounts of CO 2 , utilizing a pipeline network is the most effective solution; however, building such a network is expensive. Therefore minimizing the cost of the pipelines to be built is extremely important to make the operation financially feasible. In this context, we investigate the problem of finding optimal pipeline diameters from a discrete set of diameters for a tree-shaped network transporting captured CO 2 from multiple sources to a single sink. The general problem of optimizing arc capacities in potential-based fluid networks is already a challenging mixed-integer nonlinear optimization problem. The problem becomes even more complex when adding the highly sensitive nonlinear behavior of CO 2 regarding temperature and pressure changes. We propose an iterative algorithm splitting the problem into two parts: (a) the pipe-sizing problem under a fixed supply scenario and temperature distribution and (b) the thermophysical modeling, including mixing effects, the Joule–Thomson effect, and heat exchange with the surrounding environment. We demonstrate the effectiveness of our approach by applying our algorithm to a real-world network planning problem for a CO 2 network in Western Germany. Further, we show the robustness of the algorithm by solving a large artificially created set of network instances. [ABSTRACT FROM AUTHOR]

With advanced connected automated vehicles (CAVs) technologies, joint optimisation of trajectories and signal control can significantly improve traffic mobility and safety. This study presents a joint optimisation method of trajectory and signal control at intersections mixed with human-driven vehicles (HVs) and CAVs based on vehicle departure sequence estimation. First, a signal control optimisation method based on the trajectories detected by CAVs was proposed. By estimating the virtual arrival time of HVs and CAVs, the optimal departure sequence with minimal delay was determined. Then, a joint optimisation of trajectories and signal control was developed depending on the position of CAVs in the mixed vehicles platoon, i.e. leader and follower. Simulation experiments under different scenarios were conducted to analyse the performance of the proposed method. Comparison results show that the method decreases the average delay by 19.66% when the CAV's penetration rate ranges from 10% to 20%. [ABSTRACT FROM AUTHOR]

The high penetration of renewable energy sources (RESs) in modern power systems poses two conflicting issues. First one is the reduction in the operation costs resulted from RESs utilization instead of the expensive fossil fuel thermal generating units. However, the RESs are characterized by its uncertain and intermittent behavior that their output power is not dispatchable and not known exactly due to forecast errors. Therefore, reserves are scheduled in the day-ahead market to meet the uncertain supply from RESs which add some costs to the system. The decision maker should be aware of those two conflicting objectives in order to operate the system in the optimal way with minimum operation costs. The problem of the market clearing is formulated as Mixed Integer Linear Programming (MILP) problem using GAMS software. It is considered a two stage stochastic programming with the objective of minimizing the expected total operation energy and reserve costs while satisfying the various operational constraints. The results show the effectiveness of the RESs integration in different cases with the consideration of load shedding, RESs curtailment and transmission congestion. As shown from the results, when the network is congested, the operation costs are increased due to the load shedding and RESs curtailment events occur. Moreover, scheduled reserves are increased to face the uncertainty of RESs. As the RESs power penetration increases with no network congestion, a decreasing trend in the operation cost can be seen. However, this trend is less marked as the uncertainty of RESs generation increases. For the network congested case, the reduction rate of the operation cost is decreased for increasing RESs power penetration levels. [ABSTRACT FROM AUTHOR]

We investigate a new family of distributionally robust optimization problem under marginal and copula ambiguity with applications to portfolio optimization problems. The proposed model considers the ambiguity set of portfolio returns in which the marginal distributions and their copula are close—in terms of the Wasserstein distance—to their nominal counterparts. We develop a cutting-surface method to solve the proposed problem, in which the distribution separation subproblem is nonconvex and includes bilinear terms. We propose three approaches to solve the bilinear formulation, namely (1) linear relaxation via McCormick inequalities, (2) exact mixed-integer linear program reformulation via disjunctive inequalities, and (3) inner approximation method via a novel iterative procedure that exploits the structural properties of the bilinear optimization problem. We further carry out a comprehensive set of computational experiments with distributionally robust portfolios featuring Conditional Value-at-Risk (CVaR) measures. These tests aim to compare the accuracy of the proposed algorithms, analyze the impact of the radius of the Wasserstein ambiguity ball on the portfolio, and assess portfolio performance. We use a rolling-horizon approach to conduct the out-of-sample tests, which show the superior performance of the portfolios under marginal and copula ambiguity over the equally weighted and ambiguity-free Mean-CVaR benchmark portfolios. [ABSTRACT FROM AUTHOR]

In this article, real-time pricing (RTP) for a smart grid with complicated non-convex and/or non-smooth constraints is explored. First, piecewise linear functions are adopted to approximate any kinds of utility function and RTP is formulated into bilevel non-convex programming. Then, a 0–1 relaxation method is used to linearize sparse constraints and piecewise linear utility functions, to obtain mixed integer linear programming. Finally, a distributed simultaneous perturbation stochastic approximation (DSPSA) method is designed to solve the bilevel non-convex programming. The RTP strategy is generalizable because sparse constraints and non-differentiable and/or non-concave utility functions are widespread in practice, and the piecewise linear functions approximation method is applicable to all kinds of utility function, not just to non-smooth and/or non-concave utility functions. In addition, the 0–1 linear relaxation and stochastic approximation method make the bilevel non-convex programming easily solvable by DSPSA, which converges rapidly while maintaining the high accuracy of solutions. [ABSTRACT FROM AUTHOR]

Food supply chain management is a critical global issue due to its impact on food costs and, consequently, everyone's life. This study aims to optimize the location of central markets for fruits and vegetables in Jordan, which has significant cost implications for various stakeholders including farmers, investors, infrastructure providers, and society at large. Notably, this research is pioneering as it is the first to address central market locations in Jordan. The study focuses on optimizing total costs while satisfying constraints that reflect stakeholders' needs, employment opportunities, accessibility, congestion reduction, and minimizing long-distance travel. The problem is formulated as a mixed integer linear program (MILP) and solved using MATLAB. We conducted a comprehensive process that included problem formulation, data collection, MATLAB programming, and solution determination. Key findings include specifying the optimal locations for the central markets, as well as the quantities to be shipped from farms to each central market and from each central market to retailers. Additionally, a roadmap for improving central market status based on budget and priorities was developed. The study proposes a balanced trade-off between cost reduction and implementation feasibility. It suggests limiting the number of potential central markets to six, resulting in a total cost of 79.52 million JD and achieving a 26.50% savings compared to the current situation. This research provides a practical framework for policymakers to enhance the efficiency of food supply chains in Jordan. [ABSTRACT FROM AUTHOR]

The aviation industry has rapidly developed in recent years. Due to the increased number of flight operations, managing air traffic has become essential. The air traffic management system aims to reduce the air traffic control workload and use existing resources more efficiently. This study proposed a new mixed integer linear programming model to minimise the total fuel consumption during taxi operations for the runway assignment problem, comparing the actual Istanbul Airport runway assignment data. The average taxi times are calculated using the 30,000-flight operations data for each arrival and departure taxi route. Also, 47 different aircraft types are obtained using the data for the fuel consumption calculation. The International Civil. Aviation Organisation (IACO) aircraft engine emissions databank provides the fuel consumption values for each aircraft according to engine type. This approach allows our model to calculate more realistic fuel consumption for taxi operations, as each aircraft engine type has a different fuel consumption value. The proposed model is implemented at Istanbul Airport, the busiest airport in Turkey, where multiple parallel runway operations are applied. The results showed that the proposed model reduced total fuel consumption for taxi operations between 6.6% and 14.4% compared to the actual Istanbul Airport runway assignment data. [ABSTRACT FROM AUTHOR]

With ageing societies and increasing number of patients, there is a growing need for quality services that help transporting non-urgent patients to hospitals. In logistics, patient transportation problems are usually modeled as a dial-a-ride problem. In a Dial-a-Ride problem, a fleet of vehicles is providing the delivery services between the loading points and the delivery destinations. The demands are known in advance. In most cases the total travel distance of the vehicles is to be minimized. In this paper, we consider a specific dial-a-ride problem, where a single vehicle is used to transport patients to the same hospital. In determining the optimal route, the multiple and different travel needs of patients, such as their maximum travel time, are also taken into consideration. We introduce 4 different mixed integer linear programming models of the routing problem. Finally, the efficiency of the four models was compared using some real-life problems by solving them with a commercial solver. [ABSTRACT FROM AUTHOR]

The objective of Assembly Line Balancing (ALB) is to find the proper assignment of tasks to workstations, taking into consideration various types of constraints and defined management goals. Early research in the field focused on solving the Simple Assembly Line Balancing problem, a basic simplified version of the general problem. As the production environment became more complex, several new ALB problem types appeared, and almost all ALB problems are NP-hard, meaning that finding a solution requires a lot of time, resources, and computational power. Methods with custom-made algorithms and generic approaches have been developed for solving these problems. While custom-made algorithms are generally more efficient, generic approaches can be more easily extended to cover other variations of the problem. Over the past few decades, automation has played an increasingly important role in various operations, although complete automation is often not possible. As a result, there is a growing need for partially automated assembly line balancing models. In these circumstances, the flexibility of a generic approach is essential. This paper compares two generic approaches: mixed integer linear programming (MILP) and constraint programming (CP), for two types of partially automated assembly line balancing problems. While CP is relatively slower in solving the simpler allocation problems, it is more efficient than MILP when an increased number of constraints is applied to the ALB and an allocation and scheduling problem needs to be solved. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims at developing a multi-objective mathematical model of circular economy that integrates key concept of leasing as a strategy in addition to reuse, refurbishing, primary recycling, secondary recycling and disposal. Design/methodology/approach: This paper proposes multi-objective fuzzy mixed integer linear programming mathematical model considering multi-product, multi-echelon and multi-capacitated concepts of the circular economy. The three objectives of the proposed model, namely, economic, environmental and social are solved simultaneously using constraint approach to obtain balanced trade-off between the objective functions. The model is validated by solving a case study from the literature. The proposed model is made pragmatic for industrial application by considering multi-external suppliers multi-customer zones, multi-disassembly centers, multi-collection centers and multi-refurbishing centers and accounting for purchasing, processing, transportation, set-up costs and capacity constraints at the same time. Findings: The results show that the leasing of the products improves the economic function in addition to the known environmental improvements. The proposed model also shows that the circular economy can generate the jobs for the unskilled people at different locations. Research limitations/implications: The proposed model can be further improved by considering the non-linearity due to economy of scale at various centers and in transportation. The model can be further extended to make it multi-period model. Practical implications: The proposed model of circular economy can be used by the organizations as a policy tool to decide the optimum number of collection centers, disassembly centers, refurbishing centers, recycling centers and disposal centers and their optimum locations and allocations. The organizations can also trade-off among economic, environmental and social benefits of their proposed decisions in circular economy. Originality/value: The originality of the proposed mathematical model is consideration of leasing as a strategy to have better control over the supply chain for circularity; considering the training of unskilled people for backward supply chain jobs and accounting for primary recycling and secondary recycling separately for economical computation. [ABSTRACT FROM AUTHOR]

The order scheduling and rack sequencing problem deals with the order picking process in robotic mobile fulfillment systems: automated guided vehicles lift and transport movable storage racks to picking stations to supply items requested by customer orders which are put together in cardboard boxes on a workbench of limited capacity. To efficiently operate the station, the sequence in which racks visit the station one after another and the intervals at which customer orders are scheduled must be coordinated. We present a dynamic programming algorithm for the order scheduling and rack sequencing problem at a single picking station minimizing the number of rack visits. Despite monolithic mixed integer linear programming formulations, our approach appears to be the first combinatorial solution method for the problem in literature. A computational study demonstrates the effectiveness of the approach. [ABSTRACT FROM AUTHOR]