Large-sized Instances Research Articles

Reinforcement learning-based alpha-list iterated greedy for production scheduling

Metaheuristics can benefit from analyzing patterns and regularities in data to perform more effective searches in the solution space. In line with the emerging trend in the optimization literature, this study introduces the Reinforcement-learning-based Alpha-List Iterated Greedy (RAIG) algorithm to contribute to the advances in machine learning-based optimization, notably for solving combinatorial problems. RAIG uses an N-List mechanism for solution initialization and its solution improvement procedure is enhanced by Reinforcement Learning and an Alpha-List mechanism for more effective searches. A classic engineering optimization problem, the Permutation Flowshop Scheduling Problem (PFSP), is considered for numerical experiments to evaluate RAIG's performance. Highly competitive solutions to the classic scheduling problem are identified, with up to 9% improvement compared to the baseline, when solving large-size instances. Experimental results also show that the RAIG algorithm performs more robustly than the baseline algorithm. Statistical tests confirm that RAIG is superior and hence can be introduced as a strong benchmark for future studies.

Solving the yard crane scheduling problem with dynamic assignment of input/output points

This paper introduces an automatic yard crane scheduling problem with additional assignments of input/output (I/O) points at the block. I/O points are the buffer between the block and the rest of the terminal and containers are relocated from the block to the I/O points or vice-versa. The crane schedule therefore not only considers movements, storage and retrieval of containers, but must also be coordinated with the release and due times of containers at the I/O points, which are also limited and need to be assigned during the scheduling process. This results in a complex, but at the same time, more realistic problem. Two GRASP (Greedy Randomized Adaptive Search Procedure) heuristic approaches are proposed along with improvements to the solution construction and local search phases that are specifically tailored for this problem. The proposed algorithms are statistically calibrated and comprehensive experiments are designed to assess the performance of the method, including validation across small, medium, and large size instances. The experimental results show that the proposed methods are effective and competitive when compared to existing approaches. The GRASP manages to obtain an optimality average rate of 71.25% for small instances and finds 461 new best solutions in the 480 medium and large instances, with up to 200 containers, when compared to existing approaches for this problem.

Enhancing Concurrent Emergency Response: Joint Scheduling of Emergency Vehicles on Freeways with Tailored Heuristic

Scheduling decisions for concurrent emergency response (CER) across multiple disaster sites presents numerous difficulties. The main challenge is to minimize human casualties while taking into account the rationality of resource allocation across different disaster sites. This paper establishes a joint scheduling model for emergency vehicles on freeways in the context of CER. The model aims to minimize the transportation time, dispatch cost, and casualty risk, by using the resource site scheduling scheme as the decision variable, addressing multiple disaster and resource sites. Specifically, a casualty risk function based on the rescue waiting time was designed to balance the competing needs among various disaster sites, enhance equitable resource allocation, and reduce the probability of casualties. To achieve global convergence in a high-dimensional solution space, a tailored heuristic algorithm called adaptive dual evolutionary particle swarm optimization (ADEPSO) is proposed. The numerical results show that the scheduling scheme proposed by the ADEPSO algorithm satisfies all constraints and demonstrates significant advantages in large-sized instances. Compared to the two basic algorithms, ADEPSO provides a more cost-effective scheme and reduces the average rescue waiting time. Moreover, integrating the casualty risk function significantly decreases the average rescue waiting time at both high- and low-priority disaster sites, thereby directly lowering the casualty risk.

Robust optimization algorithm for integrated crane assignment and scheduling in slab yard with uncertain arrival time

A slab yard served as a key component of the iron and steel plant. Effective slab yard crane assignment and scheduling directly affect the overall efficiency of the steel production process. This work addresses an uncertain slab yard crane assignment and scheduling problem (SYCAS) with the fluctuating arrival time. A stochastic programming model is established to obtain a robust solution that minimises the completion time of slab groups under uncertainty. Due to its NP-hardness, we developed an improved column and cut generation method (IC&CG), which divides the problem into a relaxed master problem (RMP) and a slave problem (SP). A new initialisation strategy for obtaining high-quality solutions and an acceleration strategy for discarding irrelevant scenarios are proposed. We use small-size instances to compare the performance of IC&CG with CPLEX and a generalised column and cut generation method in both deterministic and uncertain environments to validate the effectiveness of the method. For large-size instances, a lower bound (LB) of the optimal objective function is proposed to show the effectiveness of IC&CG. Furthermore, we prove the robustness of the method by sensitive analysis.

Optimal allocation and route design for station-based drone inspection of large-scale facilities

The utilization of drones to conduct inspections on industrial electricity facilities, including large-sized wind turbines and power transmission towers, has recently received significant attention, mainly due to its potential to enhance inspection efficiency and save maintenance costs. Motivated by the advantages of drones for facility inspection, we present a novel station-based drone inspection problem (SDIP) for large-scale facilities. The objective of SDIP is to determine the locations of multiple homogeneous automatic battery swap stations (ABSSs) equipped with drones, assign facility inspection tasks to the ABSSs with operation duration constraints, and design drone inspection routes with battery capacity constraints, such that minimize the sum of fixed ABSS costs and drone travel costs. The SDIP can be regarded as a variant of the location-routing problem, which is NP-hard and difficult to solve optimally. To obtain the optimal solution of SDIP efficiently, we firstly formulate this problem into an arc based formulation and a route based formulation, and then develop a logic-based Benders decomposition (LBBD) algorithm to solve it. The SDIP is decomposed into a master problem (MP) and a set of subproblems (SPs). The MP is solved by a branch-and-cut (BC) procedure. Once a feasible integer solution is found, the linear relaxation of SPs are solved by a stabilized column generation to generate Benders cuts. If the cost of all the SPs’ optimal LP solutions plus the cost of the MP’s solution is less that current best cost, the SPs are exactly solved by a Branch-and-Price (BP) algorithm to generate the logic cuts. The numerical results on five scales of randomly generated instances validate the effectiveness of the LBBD algorithm. Specifically, the LBBD can solve all small- and middle-sized instances, and seven out of ten large-sized instances in 1000 s. Furthermore, we conduct a sensitivity analysis by varying the attributes of ABSSs and drones, and provide valuable managerial insights for large-scale facility inspection.

A Swarm Intelligence Solution for the Multi-Vehicle Profitable Pickup and Delivery Problem

Delivery apps are experiencing significant growth, requiring efficient algorithms to coordinate transportation and generate profits. One problem that considers the goals of delivery apps is the multi-vehicle profitable pickup and delivery problem (MVPPDP). In this paper, we propose eight new metaheuristics to improve the initial solutions for the MVPPDP based on the well-known swarm intelligence algorithm, Artificial Bee Colony (ABC): K-means-GRASP-ABC(C)S1, K-means-GRASP-ABC(C)S2, Modified K-means-GRASP-ABC(C)S1, Modified K-means-GRASP-ABC(C)S2, ACO-GRASP-ABC(C)S1, ACO-GRASP-ABC(C)S2, ABC(S1), and ABC(S2). All methods achieved superior performance in most instances in terms of processing time. For example, for 250 customers, the average times of the algorithms was 75.9, 72.86, 79.17, 73.85, 76.60, 66.29, 177.07, and 196.09, which were faster than those of the state-of-the-art methods that took 300 s. Moreover, all proposed algorithms performed well on small-size instances in terms of profit by achieving thirteen new best solutions and five equal solutions to the best-known solutions. However, the algorithms slightly lag behind in medium- and large-sized instances due to the greedy randomised strategy and GRASP that have been used in the scout bee phase. Moreover, our algorithms prioritise minimal solutions and iterations for rapid processing time in daily m-commerce apps, while reducing iteration counts and population sizes reduces the likelihood of obtaining good solution quality.

Formulations and heuristic for the long-term preventive maintenance order scheduling problem

This paper studies the long-term preventive maintenance order scheduling problem (LTPMOSP). The problem consists of deciding which maintenance should be scheduled, assigning each maintenance simultaneously to a team and a machine over the planning horizon, and determining when each maintenance should be performed. The goal of the problem is to minimize the number of teams activated and the sum of penalties for maintenance not scheduled. This paper presents two new formulations for the problem, where the first formulation is based on time-indexed variables, and the second formulation combines time-indexed variables with precedence variables. Furthermore, to solve large-size instances, an algorithm based on the GRASP metaheuristic is proposed. The results of the computational experiments show that the new formulations significantly outperform the literature formulation by finding new optimal solutions for some instances with up to 600 maintenance orders. The second formulation stands out by finding a more significant number of optimal solutions. The results also show a superiority of the GRASP algorithm over heuristic methods in the literature, generating better upper bounds in expressively less time, resulting in a schedule with a greater percentage of maintenance planned to be performed.

Advanced constraint programming formulations for additive manufacturing machine scheduling problems

In comparison with traditional subtractive manufacturing techniques, additive manufacturing (AM) enables fabricating complex parts through a layer-by-layer process. AM makes it possible to produce one-piece and lightweight functional products, which are traditionally made from several parts. This paper introduces constraint programming (CP) models to minimise makespan in single, parallel identical and parallel unrelated AM machine scheduling environments for selective laser melting. Alternative CP formulations are explored to improve efficiency. The proposed CP model significantly benefits from the introduction of interval variables to replace binary assignment variables, and pre-definitions to narrow the search space, resulting in increased search performance. A computational study has been conducted to compare the performance of our proposed CP model with both a mixed-integer programming and a genetic algorithm from existing literature, evaluating improvements made to its search capability. Computational results indicate that the proposed CP model can obtain high-quality solutions in a timely manner even for several large-size instances.

An ALNS metaheuristic for the family multiple traveling salesman problem

This paper presents a novel variant of the Family Traveling Salesman Problem (FTSP), a well-known NP-hard problem introduced by Morán-Mirabal et al. (2014). In the FTSP, the set of nodes is partitioned into several subsets called families, and the aim is to determine the minimum-cost cycle that begins and ends at the depot and visits a predefined number of nodes in each family. We extend the FTSP by requiring that m tours be generated, each having a number of nodes between a minimum and a maximum quantity, thus yielding the family multiple traveling salesman problem (FmTSP). We present a two-index MIP formulation and develop an ALNS metaheuristic as a solution method for large-sized instances. Our proposed ALNS was initially employed to solve the FTSP benchmark instances and allowed us to improve some of the best-known solutions. The results for new derived instances requiring multiple tours show that our ALNS also achieves optimality for all instances with proven-optimal solutions.

Exact and heuristic approaches for the Truck–Drone Team Logistics Problem

Truck-and-drone routing problems are a topic of increasing interest due to advancements in drone technology. Initially, drones were thought to serve only one customer per flight; however, recent improvements in drone technology have led to the definition of multi-visit truck-and-drone routing problems, where a drone can serve multiple customers in each flight. These routing problems, which require a careful synchronization between the truck and the drone, are challenging to solve, even with a small number of customers. In this work, we address a multi-visit, single-truck, single-drone routing problem also known in the literature as the Truck–Drone Team Logistics Problem (TDTLP). In particular, we study two versions of the TDTLP arising from the possibility for the drone to land or hover at the recollection points. Our study proposes a novel mixed-integer linear programming formulation for the TDTLP, solved by a Branch-and-Cut (B&C) algorithm. Then, we introduce a matheuristic approach that incorporates our B&C algorithm to solve medium- and large-size instances. Computational results on benchmark instances demonstrate the robustness and efficacy of the proposed methods.

Mixed-model sequencing with stochastic failures: A case study for automobile industry

In the automotive industry, the sequence of vehicles to be produced is determined ahead of the production day. However, there are some vehicles, failed vehicles, that cannot be produced due to some reasons such as material shortage or paint failure. These vehicles are pulled out of the sequence, and the vehicles in the succeeding positions are moved forward, potentially resulting in challenges for logistics or other scheduling concerns. This paper proposes a two-stage stochastic program for the mixed-model sequencing (MMS) problem with stochastic product failures, and provides improvements to the second-stage problem. To tackle the exponential number of scenarios, we employ the sample average approximation approach and two solution methodologies. On one hand, we develop an L-shaped decomposition-based algorithm, where the computational experiments show its superiority over solving the extensive equivalent formulation with an off-the-shelf solver. Moreover, we provide a tabu search algorithm in addition to a greedy heuristic to tackle case study instances inspired by our car manufacturer partner. Numerical experiments show that the proposed solution methodologies generate high-quality solutions by utilizing a sample of scenarios. Particularly, a robust sequence that is generated by considering car failures can decrease the expected work overload by more than 20% for both small- and large-sized instances.

Multi-objective stochastic scheduling of inpatient and outpatient surgeries

AbstractWith the advancement of surgery and anesthesiology in recent years, surgical clinical pathways have changed significantly, with an increase in outpatient surgeries. However, the surgical scheduling problem is particularly challenging when inpatients and outpatients share the same operating room blocks, due to their different characteristics in terms of variability and preferences. In this paper, we present a two-phase stochastic optimization approach that takes into account such characteristics, considering multiple objectives and dealing with uncertainty in surgery duration, arrival of emergency patients, and no-shows. Chance Constrained Integer Programming and Stochastic Mixed Integer Programming are used to deal with the advance scheduling and the allocation scheduling, respectively. Since Monte Carlo sampling is inefficient for solving the allocation scheduling problem for large size instances, a genetic algorithm is proposed for sequencing and timing procedures. Finally, a quantitative analysis is performed to analyze the trade-off between schedule robustness and average performance under the selection of different patient mixes, providing general insights for operating room scheduling when dealing with inpatients, outpatient, and emergencies.

An adaptive evolutionary search-based method for efficiently tackling the set-union knapsack problem

This paper tackles the NP-hard set-union knapsack problem using a novel adaptive evolutionary algorithm. Such a problem has applications in database query optimization, network design, and project portfolio management. The method starts by creating an archive set with elite and diversified solutions, employing a customized procedure based on item scores and Hamming max-min distance for diversity. Various operators are added to enhance solution quality in the elite set and refine the search process. To counteract premature convergence, combination and subset reference update methods are used. The fusion strategy serves as an exploration mechanism, generating new solutions and preventing premature convergence. Finally, the effectiveness and performance of the proposed method is evaluated on a set of benchmark instances taken from existing literature and by conducting a thorough statistical analysis of all achieved results. All obtained results are systematically compared against those achieved by state-of-the-art methods, indicating a substantial improvement of 50% for the large-sized instances, outperforming those published in the literature.

Optimisation of process plan generation for reconfigurable manufacturing systems: efficient heuristics and lower bounds

ABSTRACT We address the process plan generation problem in a reconfigurable manufacturing environment. The objective is to minimise the total production time of a given part which includes the processing times of operations, as well as the changeover times for machines, configurations, and tools. A new efficient 0–1 mathematical programming formulation is proposed, together with a heuristic and two lower bounds. The formulation is based on one main decision variable (1MDV). It outperforms a well known two-main decision variable formulation (2MDV) from the literature. Using a commercial solver, the 1MDV formulation is on average almost five times faster than the 2MDV formulation across all instances where optimality was achieved. The proposed heuristic is a decomposition mathematical programming-based heuristic. For small benchmark instances solved to optimality using 1MDV formulation, the heuristic obtains solutions at less than 1% distance on average from optimum in few seconds. The best of the two proposed lower bounds, LB2, is obtained in less than 0.56 seconds, across all tested instances, while the 1MDV model needs about 30 seconds on average to reach the same result. These lower bounds are used to measure the performance of the heuristics on large size instances

Simultaneous Pickup-and-Delivery Production-Routing Problem in closed-loop supply chain with remanufacturing and disassembly consideration

In a context of Closed-Loop Supply Chain, Production-Routing Problem with Pickup and Delivery is presented. In its basic form, Production-Routing Problem (PRP) attempts to solve jointly Dynamical Lot-Sizing and Inventory-Routing Problems. In this paper, a novel PRP model seeking to minimize the total cost of manufacturing, remanufacturing, disassembly, inventory, and routing by taking into account the remanufacturing and disassembly processes of End-of-Life returned products is provided. Novel hybrid heuristics based on Two-Phase Iterative and Relax-and-Fix heuristics were developed and outperform Branch-and-Cut algorithm for large size instances with a small vehicle capacity. Main insights derived from sensitivity analyses are (i) managers can use the model to trade off the cost saving resulting from the integration of forward and reverse flows, on the one hand, and any additional cost incurred by the organizational changes related to the integration, on the other hand, (ii) a high level of remanufacturing rate for returned products is not necessarily profitable, and managers should develop a return policy that leads to the appropriate level of remanufacturing rate, and (iii) investing in expanding the remanufacturing or the disassembly capacities can significantly reduce the total cost until a threshold level, beyond which any further investment is not beneficial.

Route optimization of battery electric vehicles using dynamic charging on electrified roads

The Electric Road System (ERS) is an innovative technology enabling electric vehicles (EVs) to charge while in motion, offering increased EV range and cost-effective battery solutions. This advancement is particularly valuable for logistics companies seeking to enhance their EV adoption. To provide long-distance delivery routes for heavy-duty EVs through ERSs, we present a first-time attempt to solve the Electric Vehicle Travelling Salesman Problem with Dynamic Charging, where the EV can be charged while driving on the electrified road segments. We formulate this problem by considering path choices between consecutive customers and propose a network reduction heuristic (NRH) to solve large-size instances. We test our model and NRH on grid-like synthetic instances and real-world data developed from the UK road network. Our results demonstrate the effectiveness of the NRH and highlight the potential of ERSs in improving logistics activities and decarbonising road transport. Our experiments revealed that although the typical driving range of most EVs falls short of 400 kilometres, with the widespread deployment of ERS, EVs can traverse distances exceeding 2000 kilometres without requiring recharging stops. Our experiments also revealed that establishing infrastructure on approximately 30 % to 40 % of all roadways is adequate to ensure reliable access for EVs.

A matheuristic for a multi-period three-echelon network design problem with temporary capacity acquisition

This study addresses a three-echelon network design problem that determines the location and size of new warehouses, the removal of company-owned warehouses, the inventory levels of multiple products at the warehouses, and the assignment of suppliers as well as customers to warehouses over a multi-period planning horizon. A distinctive feature of our problem is that new warehouses operate with modular capacities that can be expanded or reduced over several periods, the latter not necessarily having to be consecutive. Moreover, in every period, the demand of a customer for a given product has to be satisfied by a single warehouse. The problem arises in the context of warehousing-as-a-service, a business scheme that offers flexible conditions for temporary capacity leasing. The associated fixed warehouse lease cost reflects economies of scale in the capacity size and the length of the lease contract. We develop a mixed-integer linear programming formulation and propose a matheuristic to solve this problem, which exploits the structure of the optimal solution of the linear relaxation to successively assign customers to open warehouses and fix other binary variables related to warehouse operation. Additional variable fixing rules are also developed, based on a scheme for managing inventories at warehouses and using the product quantities provided by suppliers. Numerical experiments with randomly generated large-sized instances reveal that the proposed matheuristic outperforms a general-purpose solver in 74% of the instances by identifying higher quality solutions in a substantially shorter computing time.

Efficiently routing a fleet of autonomous vehicles in a real-time ride-sharing system

The advent of new communication technologies (e.g., smartphone) and autonomous vehicles (AVs) is enabling real-time ride-sharing systems where the travel requests arrives in the system on very short notice or even en-route, i.e., when AVs are already serving other users. Each request specifies an origin, a destination and a time window of pick-up, and must be immediately either accepted or rejected. Each request accepted must be assigned to an AV and both scheduled and inserted in its route considering the other possible requests already assigned to the same AV. In a lexicographic way, first we want to maximize the total number of new requests accepted, then we want to minimize the total traveled distance and finally, the total time of serving the requests. The problem is formulated as a Mixed Integer Linear Program and solved by a rolling horizon approach (MILP-RH). To efficiently address medium/large-sized instances, a rolling horizon Local Search (RHLS) is also designed, with moves properly tailored for the problem Numerical comparisons show that, on both the small-sized and some medium-sized instances, the RHLS outperforms the MILP-RH concerning the total computational time. Instead, on some medium-sized and on the large-sized instances, the RHLS is the only viable method since the MILP-RH is not able to even find a feasible solution in the given time limit. A sensitivity analysis on possible variation of some parameters is also performed deriving some useful managerial insights.

Modeling and optimization algorithm for energy-efficient distributed assembly hybrid flowshop scheduling problem considering worker resources

Considering increasingly serious environmental issues, sustainable development and green manufacturing have received much attention. Meanwhile, with the development of economic globalization and requirement of customization production, distributed hybrid flowshop scheduling problem (DHFSP) and assembly shop problem (ASP) have widely existed in realistic manufacturing systems. In addition to machine resources, worker resources are a key element affecting production efficiency. However, previous studies have not considered the integration mode of DHFSP, ASP, and worker resources in green manufacturing systems. Therefore, this paper focuses on an energy-efficient distributed assembly hybrid flowshop scheduling problem considering worker resources (EDAHFSPW) for the first time. To solve this problem, a mixed-integer linear programming (MILP) model and a multi-objective memetic algorithm (MOMA) are proposed with minimization the total tardiness (TTD) and total energy consumption (TEC) objectives. In MOMA, a speed-related decoding method is developed to improve the quality of solutions. To generate excellent initial solutions, an initialization strategy is proposed based on problem characteristics. A local search strategy is presented to improve the exploitation capability. An energy-saving strategy is designed to further optimize TEC. Additionally, to validate the proposed MILP model, we implement CPLEX to solve it on 12 small-sized instances. To verify the effectiveness of the proposed MOMA, extensive experiments are conducted to compare with other 5 comparison algorithms on 90 large-sized instances. Experimental results illustrate that MOMA is superior to its competitors.

Many-to-many locomotive routing problem for the steel industry

The locomotive routing problem (LRP) considers the transportation of non-motorised containers called torpedo ladle cars (TPCs) within a steelworks. Locomotives are responsible for picking up and delivering TPCs to fulfil requests for various facilities, including steelmaking, ironmaking, heating, and repair facilities. This study introduces the many-to-many locomotive routing problem (M2MLRP), specifically tailored for the steel industry. M2MLRP was formulated using pattern- and routing-based models, and a logic-based Benders-decomposition-based matheuristic algorithm was developed. The pattern-based model successfully solved 20 small randomly generated instances, while the proposed matheuristic algorithm demonstrated the rapid generation of near-optimal solutions. Notably, unlike the pattern- and routing-based models, the proposed algorithm extended its capability to generate good feasible solutions for medium- and large-sized instances. Across all instances, the matheuristic algorithm outperformed both the routing-based model and an adaptive large neighbourhood search algorithm, showcasing its superior solution quality. This study provides a significant contribution to the field by being the first to address M2MLRP in the context of the steel industry.