Chain Scheduling Research Articles

Evolutionary computation algorithms to coordinating order acceptance and batch delivery for an integrated supply chain scheduling

This paper proposes a novel approach of coordinating decisions in an integrated supply chain (ISC): coordinating order acceptance (OA) and batch delivery (BD) due to round trip transportation (RTT) and using third-party logistics (3PL) vehicles. The paper aims at trading off among accepted orders revenue, delivery costs as well as any penalties incurred in the ISC to maximize the total benefit. A novel mixed-integer programming is proposed for the problem. In addition, the paper provides a heuristic to form batches and develops a hybrid evolutionary computation algorithms based on particle swarm optimization (PSO) and genetic algorithm (GA) to solve the problem. An information sharing mechanism is improved and applied. To explore and to locate the proposed PSO in a better neighborhood, a local search is proposed. Taguchi experimental design is utilized to set the appropriate values of the algorithms’ parameters and random instances are generated to evaluate the performance of the algorithms. The paper investigates the profitability sensitivity of the problem to parameters and analyzes the effect of the changes in the parameters on the performance of our proposed algorithms. The attained results show the appropriate performance of our algorithms.

Mathematical programming and grey wolf optimiser for minimising sum of the total due date assignment, maximum tardiness and delivery costs for a supply chain scheduling problem

This paper focuses on the integration of the three most important and practical decisions in a supply chain, including due date assignment, production scheduling and outbound distribution. The issue under consideration is to minimise the summation of maximum tardiness, and due date assignment and batch delivery costs. To provide solution methods, first, two mathematical programming models including one mixed nonlinear model and one mixed linear model to solve it are presented. After these developments, since the problem is NP-hard and solving real problems on a large scale is virtually impossible, the combinational meta-heuristic algorithm based on Grey Wolf optimiser (GWO) and heuristic algorithm for solving the problem are presented. The GWO is a fast and robust algorithm in which the number of parameters that should be controlled is few. In this study, various computational tests have also been used to evaluate the effectiveness of the development methods - Taguchi method to set the parameters, design of experiments to generate the experiments, and analysis of variance to analyse the results. The computational results show that the combinational meta-heuristic method introduced in this paper puts in a high performance.

Stochastic versus Deterministic Approach to Coordinated Supply Chain Scheduling

The purpose of this paper is to consider coordinated selection of supply portfolio and scheduling of production and distribution in supply chains under regional and local disruption risks. Unlike many papers that assume the all-or-nothing supply disruption pattern, in this paper, only the regional disruptions belong to the all-or-nothing disruption category, while for the local disruptions all disruption levels can be considered. Two biobjective decision-making models, stochastic, based on the wait-and-see approach, and deterministic, based on the expected value approach, are proposed and compared to optimize the trade-off between expected cost and expected service. The main findings indicate that the stochastic programming wait-and-see approach with its ability to handle uncertainty by probabilistic scenarios of disruption events and the much simpler expected value problem, in which the random parameters are replaced by their expected values, lead to similar expected performance of a supply chain under multilevel disruptions. However, the stochastic approach, which accounts for all potential disruption scenarios, leads to a more diversified supply portfolio that will hedge against a variety of scenarios.

Mathematical programming and grey wolf optimiser for minimising sum of the total due date assignment, maximum tardiness and delivery costs for a supply chain scheduling problem

This paper focuses on the integration of the three most important and practical decisions in a supply chain, including due date assignment, production scheduling and outbound distribution. The issue under consideration is to minimise the summation of maximum tardiness, and due date assignment and batch delivery costs. To provide solution methods, first, two mathematical programming models including one mixed nonlinear model and one mixed linear model to solve it are presented. After these developments, since the problem is NP-hard and solving real problems on a large scale is virtually impossible, the combinational meta-heuristic algorithm based on Grey Wolf optimiser (GWO) and heuristic algorithm for solving the problem are presented. The GWO is a fast and robust algorithm in which the number of parameters that should be controlled is few. In this study, various computational tests have also been used to evaluate the effectiveness of the development methods - Taguchi method to set the parameters, design of experiments to generate the experiments, and analysis of variance to analyse the results. The computational results show that the combinational meta-heuristic method introduced in this paper puts in a high performance.

Coordination of inbound and outbound transportation schedules with the production schedule

This paper studies the coordination of production and shipment schedules for a single stage in the supply chain. The production scheduling problem at the facility is modeled as belonging to a single process. Jobs that are located at a distant origin are carried to this facility making use of a finite number of capacitated vehicles. These vehicles, which are initially stationed close to the origin, are also used for the return of the jobs upon completion of their processing. In the paper, a model is developed to find the schedules of the facility and the vehicles jointly, allowing for effective utilization of the vehicles both in the inbound and the outbound. The objective of the proposed model is to minimize the sum of transportation costs and inventory holding costs. Issues related to transportation such as travel times, vehicle capacities, and waiting limits are explicitly accounted for. Inventories of the unprocessed and processed jobs at the facility are penalized.The paper contributes to the literature on supply chain scheduling under transportation considerations by modeling a practically motivated problem, proving that it is strongly NP-Hard, and developing an analytical and a numerical investigation for its solution. In particular, properties of the solution space are explored, lower bounds are developed on the optimal costs of the general and the special cases, and a computationally-efficient heuristic is proposed for solving large-size instances. The qualities of the heuristic and the lower bounds are demonstrated over an extensive numerical analysis.

A Faster FPTAS for a Supply Chain Scheduling Problem to Minimize Holding Costs with Outsourcing

This paper addresses a scheduling problem in a flexible supply chain where the jobs can be either processed in house, or outsourced to a third-party supplier with the goal of minimizing the sum of holding and delivery costs subject to an upper bound on the outsourcing cost. The problem with identical job processing times has been proved as binary [Formula: see text]-hard one and a fully polynomial time approximation scheme (FPTAS) that runs in [Formula: see text] time has also been given. The aim of this paper is to derive a more effective FPTAS running in [Formula: see text] time for this problem.

Two new meta-heuristics for a bi-objective supply chain scheduling problem in flow-shop environment

In this study, an integrated multi-objective production-distribution flow-shop scheduling problem will be taken into consideration with respect to two objective functions. The first objective function aims to minimize total weighted tardiness and make-span and the second objective function aims to minimize the summation of total weighted earliness, total weighted number of tardy jobs, inventory costs and total delivery costs. Firstly, a mathematical model is proposed for this problem. After that, two new meta-heuristic algorithms are developed in order to solve the problem. The first algorithm (HCMOPSO), is a multi-objective particle swarm optimization combined with a heuristic mutation operator, Gaussian membership function and a chaotic sequence and the second algorithm (HBNSGA-II), is a non-dominated sorting genetic algorithm II with a heuristic criterion for generation of initial population and a heuristic crossover operator. The proposed HCMOPSO and HBNSGA-II are tested and compared with a Non-dominated Sorting Genetic Algorithm II (NSGA-II), a Multi-Objective Particle Swarm Optimization (MOPSO) and two state-of-the-art algorithms from recent researches, by means of several comparing criteria. The computational experiments demonstrate the outperformance of the proposed HCMOPSO and HBNSGA-II.

An agent-based fuzzy constraint-directed negotiation model for solving supply chain planning and scheduling problems

Supply chain planning and scheduling problems require manufacturers (project managers) to determine product configurations, select suppliers (contractors) for task allocation, and schedule project tasks while considering various production constraints among manufacturers and suppliers. Most relevant studies have focused on finding an optimal solution based on complete information provided by each enterprise in a supply chain. However, the practical implementation of complete information sharing is difficult, if not impossible, because of the fully distributed nature of the supply chain process. This work proposes an agent-based fuzzy constraint-directed negotiation (AFCN) model to solve problems associated with supply chain planning and scheduling, which are modeled as a set of fuzzy constraint satisfaction problems (FCSPs) that are interlinked via inter-agent constraints. To accommodate the perspectives and interests of each enterprise in a supply chain, conflicts among FCSPs are resolved using the AFCN protocol through the iterative exchange of offers/counter-offers with limited information sharing and without privacy breaches. A proposed offer/counter-offer represents not only a set of acceptable solutions and preferences for an operational task but also the possibility of conflict in this area. For each FCSP, the incremental process of offer/counter-offer evaluation eliminates redundant and infeasible solutions. The sharing of limited non-strategic sensitive information among agents enables them to elucidate their opponents’ intentions through iterative negotiations, such that the agents can reach an agreement while ensuring that the solution to a project planning and scheduling problem is satisfactory. The AFCN model is also sufficiently flexible to incorporate different negotiation strategies such as competitive, win-win, and collaborative strategies, for various production environments. Herein, a numerical study was conducted to examine the practical viability and effectiveness of the proposed AFCN model. The experimental results show that the proposed AFCN model not only can generate a schedule that is comparable to a near-optimal solution but also is time-efficient. This indicates that the proposed AFCN is a practical and effective method for solving supply chain planning and scheduling problems in fully distributed environments.

Minimizing sum of the due date assignment costs, maximum tardiness and distribution costs in a supply chain scheduling problem

In production systems, manufacturers face important decisions that affect system profit. In this paper, three of these decisions are modelled simultaneously: due date assignment, production scheduling, and outbound distribution scheduling. These three decisions are made in the sales, production planning and transportation departments. Recently, many researchers have devoted attention to the problem of integrating due date assignment, production scheduling and outbound distribution scheduling. In the present paper, the problems of minimizing costs associated with maximum tardiness, due date assignment and delivery for a single machine are considered. Mixed Integer Non-Linear Programming (MINLP) and a Mixed Integer Programming (MIP) are used for the solution. This problem is NP-hard, so two meta-heuristic algorithms, an Adaptive Genetic Algorithm (AGA) and a Parallel Simulated Annealing algorithm (PSA), are used for solution of large-scale instances. The present paper is the first time that crossover and mutation operators in AGA and neighbourhood generation in PSA have been used in the structure of optimal solutions. We used the Taguchi method to set the parameters, design of experiments (DOE) to generate experiments, and analysis of variance, the Friedman, Aligned Friedman, and Quade tests to analyse the results. Also, the robustness of the algorithms was addressed. The computational results showed that AGA performed better than PSA.

Applied Variable Neighborhood Search-Based Approach to Solve Two-Stage Supply Chain Scheduling Problems

Abstract When a company uses a make-to-order or direct-order business model, it needs to keep a low finished goods inventory level to be competitive. However, a low inventory level will cause the company to have little buffer to face the unforeseeable demand surge, which strengthens the close relationship between the stage of production and distribution. This paper probes an integrated production and distribution scheduling problem in which jobs are first produced by a set of unrelated parallel machines and then directly distributed to the corresponding customers by vehicles with limited capacity, with no inventory kept at the production stage. The objective is to find a joint production and distribution schedule such that the total cost, considering both customer service level and total distribution cost, is minimized. This paper integrated the earliest available machine first (EAMF) and variable neighborhood search (VNS) to solve the two-stage scheduling problem and used EAMF, which considers the load of the unrelated parallel machine to generate a set of production schedules to be systematically optimized by VNS, along with optimized routing, and applied a shaking mechanism of VNS to escape from the local optimal solution. The results of the computational experiments showed that the proposed method is better than ant colony optimization (ACO) heuristic methods in the computation time and solution quality.

Improved critical chain buffer management framework considering resource costs and schedule stability

The critical chain scheduling and buffer management (CC/BM) methodology has proven to be a favorable approach to schedule resource-constrained projects and to offer a valuable control tool for monitoring projects under uncertainty. The previous studies on CC/BM seem to have neglected the cost performance, which might render its wider applications to the modern economic activities that are mostly performed in a project way. This paper presents an improved CC/BM framework that allows additional resource allocation/reallocation to bring forward activity starting times based on cost and schedule stability criteria. In the planning phase, the decision is made concerning the regular resource availability period in order to minimize the expected resource costs. In the execution phase, a scheduled order repair method for rescheduling along with two reactive resource allocation procedures as the corrective action whenever delays are beyond a certain buffer threshold are presented and examined in order to exhibit a comprehensive project schedule/cost control system that is adaptive to the CC/BM management logic. Finally, our computational experiment demonstrates the benefits of the proposed reactive methods under different cost or availability parameters.

A Novel Rescheduling Model for Agricultural Food Products Management in Cross-Border Electronic Commerce

This study aims to investigate the rescheduling model for agricultural food products management in cross-border electronic commerce using logistic chain theory. Agricultural products chain scheduling in cross-border electronic commerce manages and controls the project progress through critical chain identification and buffer insertion but suffers from left-extension problem. To solve this issue, a new rescheduling model for agricultural food products management in cross-border electronic commerce using logistic chain theory is proposed in this study. In the new rescheduling model, the critical logistic chain was adopted to reschedule the plan with left-extension. Then the influence factors in the chain were analyzed to evaluate their effects on the left-extension. By doings so, a reasonable rescheduling model was established for managing the agricultural products critical chain. Numerical validation has been carried out in this study to examine the performance of the proposed rescheduling model. The analysis results indicate that the proposed rescheduling model could solve the left-extension problem in the agricultural products critical chain and hence is useful for practical application in cross-border electronic commerce.

An activity-based approach for optimisation of land use and transportation network development

This paper tentatively makes use of an activity-based approach to investigate the optimisation problem of land use allocation and transportation network enhancement, in which the budget for investment and some other constraints are given for the purpose of sustainable urban development. To make investigation on residential and employment development as well as road link capacity expansions for short-term strategic planning purposes, a new bi-level programming model is proposed to capture the interactions between land use and transportation network development together with their impacts on activity-travel choice behaviours. The lower level of the proposed model is used to model the choice behaviour of commuters on activity chain, departure time, path and activity scheduling duration simultaneously over the time of day, while the upper level is to maximise the population allocation and network enhancement subject to a set of given constraints. A heuristic solution method is developed to solve the proposed bi-level model. Finally, two numerical examples are presented to demonstrate the application of the proposed model and solution algorithm together with some insightful findings.

Simulating operations process to achieve a hybrid optimal operational performance in supply chain scheduling: a case study

One of the most challenging aspects of supply chain management is synchronising job-shop production and transportation in a network to guarantee on-time delivery to distributed customers. In multiple job-shop problems, there are j jobs that need to be processed by m machines with a certain objective function to be minimised and it has been classified as a combination problem. This study uses genetic algorithm (GA) with some modifications to deal with the problem of multiple job-shop scheduling. At the end, the most suitable machine arrangement would be presented from the program due to achieve sustainable supply chain management model.

Critical chain scheduling: a new approach for feeding buffer sizing

Critical chain techniques for overcoming uncertainty which exist in a project, offers that buffers are added at the end of critical chain and feed chains. The key point is amount of the buffer size, because even bad buffer size causes the loss of critical chain technique in philosophy. Although with a large buffer size consideration project is protected from uncertainties, this causes longer lead time and inefficient use of this technique. Small size selection of the project buffer does not protect it against uncertainty. In this research, an effective method for determining the buffer size based on post density factor with regard to limit resources, location of task in a network, environmental risks and risk of each task is proposed. Effectiveness of the proposed method compared with the C&P method and RSEM and APRT and APD. Simulation results show that it could be used as suitable method for specifying size of buffer.

A Method to Measure The Resilience of Algorithm for Operation Management

This paper proposes a new concept called resilience of algorithm in the context of operation management of a system. Resilience is a behavioral property of the system and it refers to the persistence of the system performance when the system is subject to damages. Operation management of a system can be modeled as a mathematical problem. Algorithms are to generate a solution to the mathematical problem. When a system is subject to damage, the corresponding mathematical problem is changed. The resilience of a system thus reduces to the issue of how and whether the existing algorithm can still generate a solution to the changed problem. This paper further proposes a measure of the resilience of algorithm and takes a supply chain scheduling algorithm as an example to validate the proposed measure. A simulated experiment is provided.

Lot Sizing in Supply Chain Scheduling with Two Items Production System and Rework

Lot Sizing in Supply Chain Scheduling with Two Items Production System and Rework

Assessing the efficiency of supply chain scheduling algorithms using data envelopment analysis

Where different supply chain planning algorithms are used, generally similar results may pose some challenges on the differentiating powers of evaluating different production schedules because of the increasing complexity of a supply chain network structure. For the comparison purpose, performance evaluation of different supply chain planning algorithms aims to use different supply chains models with different demands, capacities, and commonality through efficiency perspective by using a modified network rational data envelopment analysis (DEA) model. The proposed DEA model has the abilities: (1) to treat only undesirable outputs that exist without normal output, and the situation where input and output are both zero by introducing two new parameters to denote the maximum inventory and amount of delayed demands of a given node in a given time period; and (2) to evaluate the effect of the undesirable outputs/inputs on efficiency with assumption that they leave the system at the end of the current time period and re-enter the system at the beginning of the next time period. To prove the effectiveness of this DEA model, eighteen scenarios with different demands, capacities, and multiple periods are compared. In addition, this study tests the DEA model on a wafer testing/probing operation of a leading global semiconductor manufacturing and testing company in Taiwan by internal supply chain perspective. Results show that the DEA model proposed in this study can be used to assess the efficiency of a real-world operation with undesirable outputs/inputs, such as inventory and delayed demands.

Incorporation of activity sensitivity measures into buffer management to manage project schedule risk

Critical Chain Scheduling and Buffer Management (CC/BM) has shown to provide an effective approach for building robust project schedules and to offer a valuable control tool for coping with schedule variability. Yet, the current buffer monitoring mechanism faces a problem of neglecting the dynamic feature of the project execution and related activity information when taking corrective actions. The schedule risk analysis (SRA) method in a traditional PERT framework, on the other hand, provides important information about the relative activity criticality in relation to the project duration which can highlight management focus. It is implied, however, that control actions are independent from the current project schedule performance. This paper attempts to research these defects of both tracking methods and proposes a new project schedule monitoring framework by introducing the activity cruciality index as a trigger for effective expediting to be integrated into the buffer monitoring process. Furthermore, dynamic action threshold settings that depend on the project progress as well as the buffer penetration are presented and examined in order to exhibit a more accurate control system. Our computational experiment demonstrates the relative dominance of the integrated schedule monitoring methods compared to the predominant buffer management approach in generating better control actions with less effort and an increased tracking efficiency, especially when the increasing buffer trigger point is combined with decreasing sensitivity action threshold values.

A multi-mode critical chain scheduling method based on priority rules

Although considerable research effort has been directed at the development of the critical chain method (CCM), the existing research is based on the assumption that each project activity is performed with only one execution mode, and thus, its decision-making ability and application scopes are limited. This article aims to evaluate the performances of the priority rules for the critical chain project scheduling problem with multiple execution modes. Firstly, a multi-mode critical chain project scheduling problem (MCCPSP) is formulated, in which precedence relationships, resource constraints, multiple modes and the characteristics of the CCM are all considered. Subsequently, the heuristic solution of MCCPSP is designed, and the priority rules used in resource-constrained project scheduling problems are built into the heuristic of MCCPSP. Finally, a large number of existing priority rules are evaluated on the PSPLIB data sets by computation experiments. The computational results show that the activity priority rule GRD and mode priority rule least total resource usage have the performances for MCCPSP.