Integrated Production Scheduling Research Articles

A double-layer Q-learning driven memetic algorithm for integrated scheduling of procurement, production and maintenance with distributed resources

The existing research on integrated production scheduling typically focuses on activities related to both production and post-production (e.g., operation and maintenance), with limited consideration for simultaneously integrating pre-production activities (e.g., raw material procurement). However, achieving the equilibrium between the manufacturer and the demander for intelligent manufacturing systems requires the optimal scheduling solution that integrates both pre-production and post-production activities. Inspired by this, we investigate a novel integrated scheduling problem that concurrently considers raw material procurement, production scheduling and equipment maintenance (abbreviated as SIPPM). A mixed-integer linear programming model is developed to simultaneously minimize the total costs for the manufacturer and the demander. Furthermore, a double-layer Q-learning driven memetic algorithm (DQMA) is proposed to solve the SIPPM. In DQMA, a well-tailored three-layer hybrid encoding method is presented for chromosome representation. The global search of DQMA employs three crossover and three mutation operators. Moreover, a knowledge-based local search operator with six methods, guided by an effective double-layer Q-learning structure, is devised to enhance local exploitation capabilities. The superiority of DQMA is verified through comparison with three popular multi-objective optimization algorithms on 108 newly established benchmark instances. The proposed integrated scheduling mode is proven to be more effective than two separated scheduling modes without considering raw material procurement.

Integrated Optimization of Production Scheduling and Haulage Route Planning in Open-Pit Mines

In mining, deposits are divided into blocks, forming the basis for open-pit mine planning, covering production and haulage route planning. Current studies often stage optimization and lack the consideration of road capacity, leading to suboptimal solutions. A novel approach integrates production scheduling and haulage route planning through a bilevel optimization model. The upper-level model integrates ore mining constraints to establish a mixed-integer production scheduling model, minimizing haulage costs. Spatiotemporal correlation constraints for block mining are determined using a two-stage algorithm. The lower-level model incorporates road capacity, forming a haulage route optimization model based on multicommodity network flow. A solution algorithm with a distance penalty strategy facilitates feedback between the upper and lower levels, achieving optimal solutions. Tested on a real open-pit coal mine with over 5 million blocks, this approach reduces haulage costs by 10.06% compared to stage optimization. Additionally, this approach allows for adjusting haulage demand in both temporal and spatial dimensions, effectively preventing road congestion. This study advances rational mining processes and enhances the efficiency of open-pit mining haulage systems.

Metaheuristic Algorithms for the Optimization of Integrated Production Scheduling and Vehicle Routing Problems in Supply Chains

Metaheuristic Algorithms for the Optimization of Integrated Production Scheduling and Vehicle Routing Problems in Supply Chains

A self-adaptive Gaussian mutation-based arithmetic optimiser algorithm for integrated production scheduling and vehicle routing problem in the distributed manufacturing environment

ABSTRACT In response to the challenges posed by economic globalisation and increasing customer demands, enterprises are compelled to adapt and refine their production models and operational objectives, which motivates this paper to address the integrated production scheduling and vehicle routing problem in the distributed manufacturing environment (IPSVRP-DME). The objective is to simultaneously minimise both total energy consumption and total earliness/tardiness. Initially, a mixed integer programming model is proposed to address small-scale problems using Gurobi. Considering the NP-hardness of the problem, a novel Self-adapted Gaussian Mutation-based Arithmetic Optimiser Algorithm (SGMAOA) is developed to handle medium-scale and large-scale instances. To address the complexity of decision-making, an innovative two-level encoding method that encompasses four decision dimensions is introduced. Additionally, an incremental repair strategy is devised to rectify infeasible solutions caused by unreasonable delivery batching, while a time relaxation strategy is proposed to further enhance service levels without compromising energy consumption. Comparative experiments are conducted to demonstrate the effectiveness of SGMAOA, which is benchmarked against five prominent metaheuristics. In addition, a specific example is applied for the discussion of managerial applications and to illustrate the practicality of the proposed model and solution method.

The influence of shelf life on the integrated production scheduling and vehicle routing optimization for perishable products

The influence of shelf life on the integrated production scheduling and vehicle routing optimization for perishable products

Fuzzy Superposition Operation and Knowledge-driven Co-evolutionary Algorithm for Integrated Production Scheduling and Vehicle Routing Problem with Soft Time Windows and Fuzzy Travel Times

Fuzzy Superposition Operation and Knowledge-driven Co-evolutionary Algorithm for Integrated Production Scheduling and Vehicle Routing Problem with Soft Time Windows and Fuzzy Travel Times

Integrating Production Scheduling and Vehicle Routing Decisions at the Operational Decision Level: A Review and Discussion Extension

As a method of reducing costs and enhancing service quality, companies integrate production and distribution scheduling to improve performance. Thirty-two articles published between 2017 and 2022 are classified based on a review methodology as a continuation of a literature review of the integrated production and distribution scheduling problems at the operational level that was published in 2016. The characteristics of mathematical models and the solution algorithms used are discussed in order to identify gaps and determine future research directions. Through the review, it was found that some real-life characteristics still need further study. Future research may need to consider uncertainty, stochastic elements and evaluating different heuristics and metaheuristics in different production environments. Finally, additional research is necessary to determine under what conditions integration is most effective.

Integrated production scheduling and vehicle routing problem with energy efficient strategies: Mathematical formulation and metaheuristic algorithms

This paper addresses integrated production and distribution scheduling problem in which orders/jobs are undergone a single operation on any one of the identical machines in parallel and upon the completion of the production they are distributed to destined customers by limited number of vehicles. Customers located in dispersed regions place their orders with predetermined demand size and time windows. On production side, machines operate under discrete speed modes, low of which requires less energy cost or vice versa. On distribution side, energy consumed by a vehicle varies depending on the size of load on it. Therefore, objective is minimizing the sum of the weighted cost emanating from early and tardy deliveries plus production and distribution costs. Operational decisions for (i) production are to determine the allocation of jobs to the machines and sequence of jobs on any machine as well as speed mode of each machine for a particular job. As for (ii) distribution: We need to decide vehicle assignment to specific subset of consolidated jobs and the sequence of customer visitation for each vehicle. We develop a formulation for the problem at hand involving parallel machine scheduling and vehicle routing to obtain solutions to optimality. Not surprisingly, however, CPLEX only provides optimum solution for all instances with customer number up to and 6, for which reason we present two metaheuristics, namely Memetic Algorithm (MA) and Iterated Local Search (ILS) for practical sized instances. Computational results indicate that ILS yields better solutions in shorter times as compared to its counterpart.

A gamified teaching–learning based optimization algorithm for a three-echelon supply chain scheduling problem in a two-stage assembly flow shop environment

The supply chain scheduling problem, which is an integrated production scheduling and distribution through the batch delivery system, has been studied by researchers for more than two decades. So far, all researches in this regard have studied the two-echelon supply chain network. The first novelty of this research is that, for the first time, the three-echelon supply chain scheduling problem, including supplier, manufacturer and customer, is investigated considering two separated batch delivery systems for transportation, simultaneously. To do so, a two-stage assembly flow shop environment is used. In the first stage, there are a number of decentralized suppliers who are responsible for manufacturing components. The components are dispatched to the second stage (assembly stage) with the first batch delivery system and after assembly, the orders are dispatched to the customer through secondary batch delivery system. The purpose of this problem is to minimize the cost of total completion time plus batch delivery costs. First, a mixed integer linear programming model (MILP) is presented that is able to solve small-size instances in a logical time. The second novelty of this research is that, to solve large-size instances, a novel gamified teaching–learning based optimization (GTLBO) algorithm is developed with applying a number of dominance rules and gamification tools. The computational results indicate the superiority of the new TLBO variant over its standard form and two well-known methods, i.e. GA and PSO, especially by applying the dominance rules.

A hybrid collaborative framework for integrated production scheduling and vehicle routing problem with batch manufacturing and soft time windows

This paper studies a new integrated production scheduling and vehicle routing problem where the production of customer orders is performed under a batch manufacturing environment and order deliveries are made by multi-trip heterogeneous vehicles in soft time windows. A bi-objective mixed-integer programming model with maximizing total profits and minimizing total weighted earliness and tardiness has been established. We develop a hybrid collaborative framework to solve this problem, which nests the collaborative mechanism in an optimization mode based on the hybrid algorithm. In the collaborative mechanism, a property on the ideal optimal departure time of the tour is first proposed, based on which an exact strategy is developed to simultaneously coordinate batch manufacturing and tour departure schedules. High-quality integrated solutions are provided by simultaneously making both production scheduling and vehicle routing decisions. Then, in order to get the best integrated solution, we adopt a multi-objective evolutionary algorithm improved by an adaptive large neighborhood search strategy based on the specific problem and coding form to realize the optimization mode. Computational experiments are performed on a dataset containing 30 instances of various scales. The results show that the proposed hybrid collaborative framework performs well in cardinality, convergence, distribution and spread, which is a very competitive method to solve this problem.

Evaluation of the Robustness for Integrated Production Scheduling and Maintenance Planning Problem

In this paper, we consider the surrogate measures of robustness for the integration of production scheduling and preventive maintenance planning problem with stochastic machine failures. First, the influence of two categories of maintenance on operation completion time is analyzed. Since it does not come with an exact solution, a novel measure algorithm for evaluating two types of robustness simultaneously is proposed, based on the internal relationships among the scheduling structure, the number and sequence of preventive maintenance activities, the probability and downtime of failures, and the expected completion time of operation. Extensive experiments are conducted on 19 benchmark problems with random machine breakdowns. Experimental results first show that the correlation between our algorithm and Monte Carlo simulation in two robustness indicators is above 99%, and the former computes much faster than the latter. A thorough comparison is made with the other three surrogate robustness measures, which further proves the accuracy of our algorithm. Additional experiments also confirm the benefits by integrating preventive maintenance.

Production scheduling in a reconfigurable manufacturing system benefiting from human-robot collaboration

Nowadays, the manufacturing sector needs higher levels of flexibility to confront the extremely volatile market. Accordingly, exploiting both machine and workforce reconfigurability as two critical sources of flexibility is advantageous. In this regard, for the first time, this paper explores an integrated production scheduling and workforce planning problem in a Reconfigurable Manufacturing System (RMS) benefiting from reconfigurable machines and human-robot collaboration. A new Mixed-Integer Linear Programming (MILP) model and an efficient Constraint Programming (CP) model are developed to formulate the problem, minimising the makespan as the performance metric. Due to the high complexity of the problem, the MILP model cannot handle large-sized instances. Hence, to evaluate the performance of the CP model in large-sized instances, a lower bound is derived based on the relaxation of the problem. Finally, extensive computational experiments are carried out to assess the performance of the devised MILP and CP models and provide general recommendations for managers dealing with such a complex problem. The results reveal the superiority of the CP model over the MILP model in small- and medium-sized instances. Moreover, the CP model can find high-quality solutions for large-sized instances within a reasonable computational time.

Exact and metaheuristic approaches to solve the integrated production scheduling, berth allocation and storage yard allocation problem

This work studies an industrial complex specialized in producing and exporting fertilizers. The integration of the production scheduling, berth allocation, and storage yard allocation decisions is necessary to improve the overall performance of the complex as these problems are interrelated. Exact and metaheuristic approaches are proposed to solve the problem. First, a mixed-integer linear formulation along with a branch-and-cut procedure are proposed. The numerical experiments show that these exact methods solve small-sized instances optimally but fail to provide good quality solutions for industrial-sized instances. Therefore, a multi-start GRASP-ILS is proposed where initial solutions are constructed using a greedy randomized adaptive search procedure (GRASP) and improved using an iterated local search algorithm (ILS). The metaheuristic approach is based on a disjunctive graph representation and additional conditions to respect the storage yard constraints and other operational constraints. The results show that the multi-start GRASP-ILS can be effectively used to solve larger instances of the problem.

An improved memetic algorithm for integrated production scheduling and vehicle routing decisions

Motivated by traditional Chinese medicine decoction and delivery problems in our collaborative Chinese medicine decoction company, this study addresses integrated production scheduling and vehicle routing decisions. To deal with this problem, we first propose a mixed integer linear programming model considering hybrid flow shop production and multi-trip multi-vehicle delivery, which is challenging to solve for medium- and large-size instances. We then propose an improved memetic algorithm (MA) that combines a genetic algorithm (GA) with education operators, including local search procedures. To improve the performance of the MA, we consider the contribution of diversity in the fitness function to enhance the exploratory ability, propose a parent selection operator by considering the softmax function to balance exploitation and exploration, and design three customized crossover operators and five education operators with local search procedures. Numerical experiments show that the integrated scheduling method shortens the total makespan by 11.19% compared with separated production scheduling and vehicle routing methods. We compare the proposed MA with Gurobi for small-size instances, and with the GA for special cases (single-stage production) for medium- or large-size instances. The results of numerical experiments for 360 instances show that the MA can find solutions with a gap of no more than 2% from the optimal solution within 3 s for small-size instances, and can improve the solutions of the GA and the adaptive large neighborhood search (ALNS) algorithm by more than 30% and 10%.

Incremental LNS framework for integrated production, inventory, and vessel scheduling: Application to a global supply chain

This paper presents a multiobjective, mixed-integer linear programming (MILP) model that integrates production scheduling, inventory management, and vessel assignment for a global supply chain. Given that such large-scale problems are NP-hard and usually suffer from symmetry, we conduct an exploratory analysis to identify complexity sources. Following this, we design a novel variant of the large neighborhood search metaheuristic to tackle the problem efficiently. While symmetry is considered an issue in the literature, the implemented algorithm provides a practical way of profiting from instead of breaking it. Computationally, we reach near-optimal solutions in real-world instances. Compared to the default CPLEX and a reference algorithm that mimics real life, we gain significantly in terms of time, quality, and the number of feasible integer solutions found during the solving process. In addition to efficiency, integrated optimization enhances operations management capabilities and supply chain resilience.

Optimization of integrated production scheduling and vehicle routing problem with batch delivery to multiple customers in supply chain

In supply chain literature, production coordination and vehicle routing have received a lot of attention. Even though all functions in the supply chain are interrelated, they are normally handled independently. This disconnected approach might lead to less-than-ideal outcomes. Increasing total efficiency by integrating manufacturing and delivery scheduling processes is popular. This study focuses on synchronic production–distribution scheduling difficulties, particularly permutation flow shop scheduling in production and sequence-dependent setup time (SDST) and vehicle routing alternatives in distribution. To create a cost-effective distribution among the placement of geographically separated clients and hence to minimize delivery costs, batch delivery to customers employing a succession of homogenized capacity limitation vehicles is examined here. However, this might result in the failure to complete multiple client orders before their deadlines, raising the cost of lateness. As a result, the goal of this study is to lower the overall cost of tardiness and batch distribution in the supply chain. To accomplish so, a mixed-integer nonlinear programming model is developed, and the model is solved using a suggested genetic algorithm (GA). Because there is no established benchmark for this issue, a set of genuine problem scenarios is created in order to assess the proposed GA in a viable and difficult environment. Ruiz's benchmark data, which is derived from Taillard's benchmark cases of permutation flow shops, was supplemented with SDSTs in the production of test examples. In comparison to an exact method, the results show that the proposed GA can rapidly seek solutions to optimality for most small-sized instances. Furthermore, for medium and large-scale cases, the proposed GA continues to work well and produces solutions in a fair amount of time in comparison to GA without the local search.

The Fish Feed Production Routing Problem

This paper introduces the fish feed production routing problem (FFPRP) faced by Norwegian salmon feed producers. The FFPRP is comprised of a production scheduling problem and a rich vehicle routing problem (VRP) and thus denotes a variant of the integrated production scheduling and vehicle routing problem. We present a discrete time mixed integer programming (MIP) model of the FFPRP. Specifically, the model incorporates a multi-product, multi-trip, and multi-depot setting, where orders are produced at production lines at factories, and delivered within their respective time windows to customers by a heterogeneous fleet of vessels. The main objective is to minimize total costs, including production costs, routing costs, and costs of not delivering orders. We propose a heuristic, combining decomposition and the adaptive large neighborhood search (ALNS) heuristic, to solve the FFPRP. The decomposition-based ALNS heuristic is tested on a number of test instances which are generated based on vessel, factory, and order data from two of Norway’s largest fish feed producers. For small problem instances, for which the commercial MIP solver often is able to prove optimality, the average optimality gaps of the proposed heuristic are relatively small, while for the larger problem instances, the heuristic significantly outperforms the commercial MIP solver both in terms of solution quality and run time.

Bi-objective coordinated production and transportation scheduling problem with sustainability: formulation and solution approaches

ABSTRACT This paper studies a new variant of integrated production scheduling and vehicle routing problem where production of customer orders are performed under job-shop environment and order deliveries are made by a heterogeneous fleet of vehicles, each of which is allowed to take multiple trips. Two conflicting objectives are considered, namely minimisation of the total amount of CO2 emitted by the vehicles and minimisation of maximum tardiness resulting from late deliveries. To this end, we present a bi-objective mixed-integer programming formulation. Augmented ε-Constraint (Augmecon) method is implemented to find Pareto optimal solutions. Due to problem complexity, Augmecon cannot provide solutions even with small-sized problems. Thus, we adopt Pareto Local Search (PLS) and non-dominated sorting genetic algorithm-II (NSGA-II) for practical sized instances. For small-sized instances involving 5, 6, and 7 customers, experimental results indicate that CPU time of Augmecon are 11, 84, and 524 sec, respectively with an average number of Pareto efficient solution of 3.5. In terms of hypervolume index, Augmecon shows the best performance, followed by NSGA-II with 11.32% and PLS with 20.75% degradation for small-sized instances. For medium and large-sized instances, PLS shows worse performance than NSGA-II by 16.87% and 40.48%.

Minimizing maximum tardiness subject to collect the EOL products in a single machine scheduling problem with capacitated batch delivery and pickup systems

This paper introduces capacitated batch delivery and pickup systems (here it is named closed-loop batch delivery systems) in an integrated production scheduling and distribution operation problem for the first time. A comprehensive and integrated model is developed for production scheduling, delivering products to the multiple customers using batch delivery system, and picking-up End of Life (EOL) products in order to recycle and reuse in the manufacturer plant. The aim of this model is to minimize the maximum tardiness subject to collect the EOL product in order to improve sustainability. First, a Mixed Integer Linear Programming (MILP) model is developed to solve small-size instances. Due to the fact that this problem is NP-hard, a branch and bound algorithm is developed by introducing effective lower bounds in order to obtain optimal solutions for the large-size instances. In order to show the application of the problem, a real case study in a motor oil supply chain is presented. The results illustrate that paying attention to the problem of collecting used motor oils through simultaneous pick-up and delivery can reduce their disposal in nature or reduce non-standard recycling of motor oil, which in turn improves sustainability. Finally, experimental results on the random generation problems show that the proposed branch and bound algorithm is especially useful to solve large-size instances.

Predictive maintenance integrated production scheduling by applying deep generative prognostics models: approach, formulation and solution

To harness the full potential of predictive maintenance (PdM), PdM information has to be used to optimally plan production and maintenance actions. Hence, operation-specific modelling of degradation, i.e. predictions of the health condition under time-varying operational conditions, has to be realized. By utilizing operation-specific degradation information, maintenance and production can be planned with regard to each other and thus, predictive maintenance integrated production scheduling (PdM-IPS) is enabled. This publication proposes a novel PdM-IPS approach consisting of two interacting modules: an operation-specific Prognostics and Health Management (PHM) module and an integrated production scheduling and maintenance planning (IPSMP) module. Specifically, the mathematical problem of the IPSMP module based on an extended version of the maintenance integrated flexible job shop problem is formulated. A two-stage genetic algorithm to efficiently solve this problem is designed and subsequently applied to simulated condition monitoring, as well as real industrial data. Results indicate that the approach is able to find feasible high quality PdM integrated production schedules.