Distributed Scheduling Research Articles

Bi-objective carbon-efficient distributed flow-shop scheduling with multistep electricity pricing

A bi-objective distributed flow-shop scheduling problem with multistep electricity pricing and carbon emissions is studied. One objective is to minimise the completion time of production, and the other is to minimise the total cost of multistep electricity pricing and carbon emissions. A mixed integer programming model and a two-stage knowledge based cooperative algorithm with a local reinforcement strategy are proposed for the problem. The extensive numerical experiments show that the two-stage algorithm was effective statistical significantly in generating non-dominated solution sets and Pareto frontiers. Simulations on Electricity Prices are applied to examine different multistep electricity pricing schemes, and management implications were drawn for both government and companies.

Intelligent learning-based cooperative and competitive multi-objective optimization for energy-aware distributed heterogeneous welding shop scheduling

This research is focused on addressing the energy-aware distributed heterogeneous welding shop scheduling (EADHWS) problem. Our primary objectives are to minimize the maximum finish time and total energy consumption. To accomplish this, we introduce a learning-based cooperative and competitive multi-objective optimization method, which we refer to as LCCMO. We begin by presenting a multi-rule cooperative initialization approach to create a population that combines strong convergence and diversity. This diverse population forms the foundation for our optimization process. Next, we develop a multi-level cooperative global search strategy that explores effective genes within solutions from different angles and sub-problems. This approach enhances our search for optimal solutions. Moreover, we design a competition and cooperation strategy for different populations to expedite convergence. This strategy encourages the exchange of information and ideas among diverse populations, thereby accelerating our progress. We also introduce a multi-operator cooperative local search technique, which investigates elite solutions from various directions, leading to improved convergence and diversity. In addition, we integrate Q-learning into our competitive swarm optimizer to explore different regions of the objective space, enhancing the diversity of the elite archive. Q-learning guides the selection of operators within the small-size population, contributing to more efficient optimization. To evaluate the effectiveness of LCCMO, we conduct numerical experiments on 20 instances. The experimental results unequivocally demonstrate that LCCMO outperforms six state-of-the-art algorithms. This underscores the potential of our learning and knowledge-driven evolutionary framework in enhancing performance and autonomy when it comes to solving EADHWS.

Shared manufacturing-based distributed flexible job shop scheduling with supply-demand matching

Shared Manufacturing (sharedMfg) is a peer-to-peer (P2P) paradigm for sharing manufacturing resources, derived from the sharing economy. In sharedMfg, manufacturing service resources are integrated into a large set with defined service types. Based on its resource organization structure, we build a model for the shared manufacturing-based distributed flexible job shop scheduling problem (SM-DFJSP) with supply-demand matching. The goal is to minimize both the total cost and the makespan. The SM-DFJSP model enables the scheduling of jobs requiring different manufacturing services across distributed, heterogeneous, and flexible service resource units (SRUs) with diverse manufacturing functions. To solve the SM-DFJSP, we propose a hybrid estimation of distribution algorithm and Tabu search (EDA-TS), including EDA and TS components. Additionally, a multi-populations strategy and non-dominated solutions memory mechanism are designed to improve the exploration ability of the algorithm. Within the EDA component, three probability distribution models and some dispatching rules are designed to generate a new population. In the TS component, three neighborhood search structures are built that adopt hybrid short and long memory tabu strategy. Finally, comparison and ablation experiments on 25 instances demonstrate the superior performance of the EDA-TS algorithm in solving the SM-DFJSP, highlighting the effectiveness of the multi-population strategy and non-dominated solutions memory mechanism.

A Q-learning memetic algorithm for energy-efficient heterogeneous distributed assembly permutation flowshop scheduling considering priorities

Most studies on distributed assembly permutation flowshop scheduling do not consider product priorities and factory heterogeneity. This causes delays in critical products and cannot reflect the real-world production situation. This paper focuses on the energy-efficient heterogeneous distributed assembly permutation flowshop scheduling considering priorities (EHDAPFS-P) to minimize total tardiness and total energy consumption simultaneously. Unlike traditional models, factory heterogeneity and product priorities are considered to better reflect the production environment and customer satisfaction in real-world situations. Then, a Q-learning memetic algorithm (QLMA) is proposed to solve this problem: (i) a high-quality initial population is obtained using a hybrid initialization strategy that combines four problem-specific heuristics; (ii) six efficient neighborhood structures are tailored to guide the population to converge to the promising areas; (iii) the most useful neighborhood structure is selected among the six structures using the Q-learning algorithm to accelerate the convergence, thus maximizing the cumulative and future improvements according to the population state; and (iv) an energy-saving strategy is developed to optimize the total energy consumption without deteriorating the total tardiness. The proposed QLMA is compared with seven state-of-the-art algorithms on 261 benchmark instances to demonstrate its superiority or at least competitiveness.

Effect of modified income assistance payment schedules on substance use service access: Evidence from an experimental study

BackgroundDespite being critical to reducing the impacts of poverty internationally, synchronized monthly government income assistance payments are linked to intensified drug use and associated harms, including disrupted access to substance use-related services. This study evaluates whether alternative income assistance distribution schedules improve harm reduction (HR), pharmacotherapy and substance use service utilization. MethodsThis exploratory, parallel group, unblinded, randomized controlled trial analyzed data from adults (n = 192) in Vancouver, Canada receiving income assistance, and reporting active, regular illicit drug use. Participants were randomly assigned on a 1:2:2 basis for six income assistance payment cycles to: (1) existing government schedules (control); (2) a “staggered” single monthly payment; or (3) “split & staggered” twice-monthly payments. Generalized linear mixed models analyzed secondary outcomes of HR, pharmacotherapy and substance use service utilization as well as barriers accessing these services. ResultsForty-five control, 71 staggered, and 76 split & staggered volunteers participated between 2015 and 2019. Multivariable modified per-protocol analyses demonstrate increased access to substance use services (Adjusted Odds Ratio [AOR] 1.64, 95% Confidence Interval [CI] 1.02–2.64) for split & staggered arm participants, and, conversely, increased barriers to HR for participants in the staggered (AOR 2.34, 95% CI 1.24–4.41) and split & staggered (AOR 2.16, 95% CI 1.08–4.35) arms. Results also showed decreased barriers to pharmacotherapy around government payments (AOR 0.23, 95% CI 0.06–0.90), pharmacotherapy around individual payments (AOR 0.12, 95% CI 0.02–0.58), and HR around individual payments (AOR 0.11, 95% CI 0.02–0.63) for staggered arm participants. ConclusionModifying payments schedules demonstrate improved access to overall substance use services, and reduced barriers to HR and pharmacotherapy around income assistance payments. However, increased overall barriers to HR access were also shown. These complex, predominantly beneficial findings support the exploration of offering alternative payment schedules to support service access.

Traffic rate agnostic end-to-end delay optimization using receiver-based adaptive link scheduling in 6TiSCH networks

Link scheduling is a key component in 6TiSCH networks. It mainly specifies a node is allowed to transmit packets through which link at what time and in which channel. There exist many link scheduling schemes in the literature for 6TiSCH networks. Among the distributed schemes, the Self-healing distributed scheduling for end-to-end delay optimization (Stratum) was specifically devised to minimize end-to-end latency. However, it performs worse in case of higher traffic rates. On the other hand, the de-facto link scheduling standard Minimal Scheduling Function (MSF) allocates cells (i.e. timeslot and channel) randomly in a slotframe for scheduling transmissions, and thus its end-to-end delay performance is not good under all traffic rates. Therefore, this paper proposes a traffic rate agnostic distributed link scheduling function (RTDS) for 6TiSCH networks. The RTDS follows a receiver-based cell selection and allocation strategy to reduce the waiting time of a packet in the buffer of receiving node before forwarding the packet towards final destination. Unlike MSF, the RTDS uses lesser number of control packet exchanges to adapt the schedule of a link based on the present network condition and traffic rate. The proposed RTDS scheme minimizes end-to-end latency under any traffic rate. RTDS is implemented in the 6TiSCH simulator. Simulation results show that RTDS can significantly improve the end-to-end latency compared to the benchmark schemes. For example, in an 80-node network, the RTDS reduces end-to-end latency by 58% and 43% in low and high traffic rates, respectively, compared to that in MSF. Further, it outperforms Stratum by 77% and 75% under the low and high traffic rates, respectively.

Event-triggered privacy-preserving distributed charging scheduling for plug-in electric vehicle with resilience against Denial-of-Service attack

Event-triggered privacy-preserving distributed charging scheduling for plug-in electric vehicle with resilience against Denial-of-Service attack

New computational results for integrated production and outbound distribution scheduling problems for a product with a short lifespan

In this paper, we consider an integrated production and outbound distribution scheduling problem with a single production site, and its extension to multiple plants. A set of orders must be satisfied such that the required pieces from a single product must be first processed on a single machine in a plant, then must be delivered to the customers before their lifespan expire using a single vehicle. The goal is to minimize the makespan of the solution, which is the return time of the vehicle after its last trip. We propose an elementary variable neighborhood search to solve the problem, using two new local search operators. Our computational results show that this simple procedure outperforms the existing, sometimes complex approaches on the widely used benchmark dataset. We also review the existing computational results, and demonstrate that in some cases the comparisons in the literature are invalid due to the use of different rounding of the data. By re-evaluating the accessible solutions we provide a fair comparison for each rounding method. We also consider the extension of the problem to multiple plants, and adapt our solution approach for this extension. Our experiments show that our method is competitive in terms of solution quality with the existing solution approach for the problem.

Elaboration of water distribution schedules in periods of scarcity

Elaboration of water distribution schedules in periods of scarcity

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.

Model Komunikasi Polresta dalam Penyaluran Bantuan Tunai Pedagang Kaki Lima dan Warung (BTPKLW) di Pekanbaru

The distribution of cash assistance to street vendors and small shop owners (BTPKLW) is one of the efforts made by the Government to provide support to the informal sector in facing the economic impacts of the COVID-19 pandemic. One of the institutions involved in the distribution of Cash Assistance for Street Vendors and Small Shop Owners (BTPKLW) in Pekanbaru is the City Police Resort (Polresta). This study aims to analyze the communication model used by Polresta Pekanbaru in the distribution of cash assistance to BTPKLW, focusing on the Laswell communication model and the simple linear communication model with feedback. This research used a qualitative approach with data collection techniques such as observation, interviews, and documentation. The research respondents consisted of members of the Community Development Unit (Binmas) of Polresta Pekanbaru, street vendors, and small shop owners. The data analysis techniques used in this study are data reduction, presentation, and conclusions. The results of the study showed that Polresta Pekanbaru utilized various communication channels, including official websites and social media, as well as direct meetings with street vendors and small shop owners. The communicators, represented by the Binmas Unit of Polresta Pekanbaru, provided information on the requirements, schedule, and mechanism of cash assistance distribution to the vendors and shop owners. The messages conveyed by the communicators were clear and detailed. Feedback from the recipients was demonstrated through the completion of registration forms for prospective BTPKLW recipients. However, the study also identified several obstacles in the implementation of cash assistance distribution, particularly related to the verification of recipient data. These obstacles included inaccuracies in the data provided by the recipients, document authenticity, and data duplication. Based on the findings of this research, it is recommended that cash assistance recipients carefully pay attention to the information provided by the Binmas Unit of Polresta Pekanbaru, comply with the established procedures, complete the required documents accurately, ask for clarification when needed, maintain effective communication, and utilize the assistance wisely.

Enhancing resilience by outage management strategy based on semidefinite programming relaxation method in unbalanced microgrids

During electrical power outages caused by extreme events, microgrid operators (MGOs) attempt to restore as much electrical load as possible. This work suggests a strategy for outage management (OM) to improve microgrid resilience by using two optimal actions: distribution feeder reconfiguration (DFR) and scheduling of distributed energy resources (DERs). After a line fault, the radial network topology is determined by the proposed model using the rank of the incidence matrix. The proposed model is formulated by an analytical optimization method created by semidefinite programming (SDP) relaxation. The SDP changes the non-convex and nonlinear model suggested for OM into an approximated convex model, which commercial software applies. The proposed model considers the uncertain behavior of non-dispatchable DERs and the electrical demands that deal with an information gap decision theory (IGDT) based on a risk-averse strategy. To improve the proposed model's flexibility, the shortened electrical demand as a demand response program (DRP) is considered. The aim of optimization is the minimization of the accumulative cost for dispatchable DER operation and load reduction. The suggested SDP model is figured out using the MOSEK solver in GAMS software. Using the offered model in the 69-bus unbalanced test system displays that the SDP model averagely decreases total operation cost and execution time by 1.04% and 61.29% on all scenarios in comparison with the conventional GAMS model.

Order assignment and two-stage integrated scheduling in fruit and vegetable supply chains

Fruit and vegetable supply chains require tight integration due to freshness-keeping requirements during the post-harvest, production, and distribution stages. This paper addresses the scheduling problem of a two-stage flow shop in the fruit and vegetable supply chain involving procurement, production, distribution, and order assignment. The supply chain consists of contract growers, processors, and demand points. Growers and processors maintain limited capacity. Growers have processing lines to sort and grade fruits and vegetables, which are then shipped to processors for further processing. The first stage of the problem involves determining the production and distribution schedules of growers to processors. The second stage concerns assigning demand points to processors, scheduling production at processors, and distributing from processors to demand points. This paper considers its two sub-problems and proposes a mixed-integer linear programming problem: growers’ and processors’ production and distribution scheduling with order assignment. Also, we present two respective strongly polynomial algorithms for solving the two sub-problems. Besides, we develop a two-phase heuristic algorithm for the integrated problem by solving sub-problems together. We conduct computational experiments to justify the proposed algorithms and integration value. Computational results reveal that the proposed algorithms outperform the CPLEX on large-scale problem instances. Finally, we analyze the impacts of waste reduction, unit processing time and cost, and inbound and outbound shipping rates on supply chain performance, along with managerial insights.

Optimization Model and Strategy for Dynamic Material Distribution Scheduling Based on Digital Twin: A Step towards Sustainable Manufacturing

In the quest for sustainable production, manufacturers are increasingly adopting mixed-flow production modes to meet diverse product demands, enabling small-batch production and ensuring swift delivery. A key aspect in this shift is optimizing material distribution scheduling to maintain smooth operations. However, traditional methods frequently encounter challenges due to outdated information tools, irrational task allocation, and suboptimal route planning. Such limitations often result in distribution disarray, unnecessary resource wastage, and general inefficiency, thereby hindering the economic and environmental sustainability of the manufacturing sector. Addressing these challenges, this study introduces a novel dynamic material distribution scheduling optimization model and strategy, leveraging digital twin (DT) technology. This proposed strategy aims to bolster cost-effectiveness while simultaneously supporting environmental sustainability. Our methodology includes developing a route optimization model that minimizes distribution costs, maximizes workstation satisfaction, and reduces carbon emissions. Additionally, we present a cloud–edge computing-based decision framework and explain the DT-based material distribution system’s components and operation. Furthermore, we designed a DT-based dynamic scheduling optimization mechanism, incorporating an improved ant colony optimization algorithm. Numerical experiments based on real data from a partner company revealed that the proposed material distribution scheduling model, strategy, and algorithm can reduce the manufacturer’s distribution operation costs, improve resource utilization, and reduce carbon emissions, thereby enhancing the manufacturer’s economic and environmental sustainability. This research offers innovative insights and perspectives that are crucial for advancing sustainable logistics management and intelligent algorithm design in analogous manufacturing scenarios.

A Q-learning-based multi-objective evolutionary algorithm for integrated green production and distribution scheduling problems

In recent years, integrated production and distribution scheduling (IPDS) has received growing attention from academia and industry. With increasingly global economic activities, it is vital to extend IPDS to distributed environments. Besides, the production and distribution systems are the main contributors to carbon emissions due to their high energy consumption. Driven by this, this work introduces an integrated green distributed production and distribution scheduling problem with the objectives of total tardiness and total carbon emissions minimization. To provide a clear definition, a mixed-integer mathematical programming model is created. Considering the problem's NP-hard nature, a Q-learning-based multi-objective evolutionary algorithm (Q-MEA) is designed to handle it. To evaluate the performance of Q-MEA, a comparison pool including four variants, five meta-heuristics, and an exact optimizer named CPLEX is constructed to implement numerical experiments. The results demonstrate that Q-MEA has clear advantages over its rivals in tackling our problems. Furthermore, comparing the integrated model with a separate one shows that the former can achieve an average decrease of about 13.10% in total tardiness and 21.02% in total carbon emissions.

A biogeography-based optimization algorithm with modified migration operator for large-scale distributed scheduling with transportation time

To expand production scale and enhance enterprise competitiveness, modern supply chains and manufacturing systems have shifted from the single factory production to the multi-factory production network with the characteristics of large scale, diversification of varieties, redundant production factories, flexible production processes, and high-value products. This production mode is called distributed scheduling. The transportation time caused by geographically distributed factories in Distributed Scheduling Problem (DSP) with a series-parallel network plays an important role in the scheduling scheme. To minimize the global makespan over all factories, a two-segment representation has been taken as an encoding scheme while the decoding procedure applies the First Come First Served (FCFS) heuristic rule. A Biogeography-Based Optimization with Modified Migration Operator (BBO-MMO) is then proposed to address the heterogeneous DSP with transportation time between factories at adjacent processing stages, which improves the migration operator by modifying the immigration rate according to processing time and transportation time. A cosine migration model has been adopted to enhance the ability of BBO-MMO to break through local optimum. BBO-MMO is compared with the Improved Memetic Algorithm (IMA), and several groups of examples with various parameters have been generated to test the performance of BBO-MMO. The results show that the proposed BBO-MMO can effectively solve the large-scale heterogeneous DSP with transportation time.

A decentralized production–distribution scheduling problem: Solution and analysis

In modern production–distribution supply chains, decentralization has increased significantly, due to increasing production network efficiency. This study investigates a production scheduling and vehicle routing problem in a make-to-order context under a decentralized decision-making structure. Specifically, two different decision makers hierarchically decide the production and distribution schedules to minimize their incurred costs and we formulate the problem as a bi-level mixed-integer optimization model as a static Stackelberg game between manufacturer and distributor. At the upper level, the manufacturer decides its best scheduling under a flexible job-shop manufacturing system, and at the lower level, the distributor decides its distribution scheduling (routing) which influences the upper-level decisions. The model derives the best production–distribution scheduling scheme, with the objective of minimizing the cost of the manufacturer (leader) at the lowest possible cost for the distributor (follower). As the lower level represents a mixed-integer programming problem, it is challenging to solve the resulting bi-level model. Therefore, we extend an efficient decomposition algorithm based on Duplication Method and Column Generation. Finally, to discuss the decentralization value, the results of the presented bi-level model are compared with those of the centralized approach.

Integrated scheduling of production and distribution with two competing agents

This paper studies a two-agent scheduling problem on a single batch machine. Each job j has a processing time pj and a size sj . Jobs should be delivered to the agent as soon as possible. The objective is to minimize the service span of the first agent subject to an upper bound on the makespan of the other agent. We propose two approximation algorithms when jobs have identical processing times and in the general case. Then we conduct theoretical analyses to provide the provable guarantees on the performances of the algorithms. Finally, we conduct simulation analyses based on randomly generated instances to evaluate the average performances of the algorithm in the general case.

Inverse model and adaptive neighborhood search based cooperative optimizer for energy-efficient distributed flexible job shop scheduling

Solving the energy-efficient distributed flexible job shop scheduling problem (EEDFJSP) obtains increased attention. However, most previous studies barely considered the large-scale nature of the decision variables of EEDFJSP. In this paper, the large-scale EEDFJSP with two minimized objectives of makespan and total energy consumption (TEC) is studied. To efficiently deal with this problem, an inverse model and adaptive neighborhood search based cooperative optimizer is proposed. First, the inverse model is applied to the job shop scheduling problem. Then, the inverse model and adaptive local search operators cooperate search is designed to obtain offspring. Furthermore, an adaptive strategy for local search operators is developed. Finally, it is compared with other multi-objective optimization algorithms to confirm the effectiveness of the proposed algorithm, including MOEA/D, NSGA-II, IM-MOEA/D, HMMA, HSLFA, and SPAMA. Experimental results demonstrate the superior performance in solving EEDFJSP compared to these six state-of-the-art multi-objective optimization algorithms.

Q-learning-based multi-objective particle swarm optimization with local search within factories for energy-efficient distributed flow-shop scheduling problem

Q-learning-based multi-objective particle swarm optimization with local search within factories for energy-efficient distributed flow-shop scheduling problem