Hybrid approach for the integrated scheduling of production and transport processes along supply chains

The rise of new information and communication technologies leads to enhanced information transparency in supply chains. In order to utilise the resulting potentials, novel scheduling approaches that are capable of processing large amounts of data and coping with dynamic disturbances of manufacturing and transport stages have to be developed. For this purpose, the paper at hand proposes a hybrid approach for the integrated scheduling of production and transport processes along supply chains. The procedure combines mixed integer linear programming, discrete event simulation and a genetic algorithm. Obtained results show a significant reduction in the number of late orders, substantiating that proper scheduling approaches combined with information visibility allow for operational improvements in manufacturing supply chains.     

Robust mixed strategies in fuzzy non-cooperative Nash games

Game theory has traditionally used real-valued utility functions in decision-making problems. However, the real information available to assess these utility functions is normally uncertain, suggesting the use of uncertainty distributions for a more realistic modelling. In this sense, utilities results or pay-offs have been normally modelled with probability distributions, assuming random uncertainty. However, when statistical information is unavailable, probability may not be the most adequate paradigm, and can lead to very large execution times when some real complex problems are addressed. In this article possibility distributions are used to model the uncertainty of utility functions when the strategies are probability distributions (mixed strategies) over a set of original and discrete strategies (pure strategies). Two dual approaches to solve the resulting non-cooperative fuzzy games are proposed: modelling players’ risk aversion, and thus providing realistic conservative strategies. Two examples show the robustness of the strategies obtained with the proposed approaches.     

Flexible Mixed-Integer Programming Formulations for Production Scheduling Problems

We present an approach to modeling practical dynamic production planning problems with product sequencing considerations that yields a significant reduction in the number of required binary variables over traditional approaches and improves the structure of special ordered sets which can be exploited in state-of-the-art mixed integer programming codes. In addition, the variable definition enables the modeler to develop a wide variety of objective functions, thus facilitating model development and interpretation.     

A stochastic programming approach to determine robust delivery profiles in area forwarding inbound logistics networks

One technique to coordinate the suppliers’ and the producers’ production plans in a supply chain is the use of delivery profiles, which provide fixed delivery frequencies for all suppliers. The selection of a delivery profile assignment has major effects on the cost efficiency and the robustness of a supply chain and thus should be performed carefully. In this work, we consider planning approaches to select delivery profiles for the case of area forwarding-based inbound logistics networks, which are commonly used in several industries to consolidate supplies in an early stage of transport. We present a two-stage stochastic mixed integer linear programming model to determine robust delivery profile assignments under uncertain and infrequent demands and complex tariff systems. The model is embedded into a solution framework consisting of scenario generation and reduction techniques, a decomposition approach, a genetic algorithm, and a standard MILP solver. On the basis of an industrial case study, we show that our approach is computationally feasible and that the planning solutions obtained by our model outperform both a deterministic approach and the planning methodology prevailing in industrial practice.     

Configuration design of scalable reconfigurable manufacturing systems for part family

Intense global competition, dynamic product variations, and rapid technological developments force manufacturing systems to adapt and respond quickly to various changes in the market. Such responsiveness could be achieved through new paradigms such as Reconfigurable manufacturing systems (RMS). In this paper, the problem of configuration design for a scalable reconfigurable RMS that produces different products of a part family is addressed. In order to handle demand fluctuations of products throughout their lifecycles with minimum cost, RMS configurations must change as well. Two different approaches are developed for addressing the system configuration design in different periods. Both approaches make use of modular reconfigurable machine tools (RMTs), and adjust the production capacity of the system, with minimum cost, by adding/removing modules to/from specific RMTs. In the first approach, each production period is designed separately, while in the second approach, future information of products’ demands in all production periods is available in the beginning of system configuration design. Two new mixed integer linear programming (MILP) and integer linear programming (ILP) formulations are presented in the first and the second approaches respectively. The results of these approaches are compared with respect to many different aspects, such as total system design costs, unused capacity, and total number of reconfigurations. Analyses of the results show the superiority of both approaches in terms of exploitation and reconfiguration cost.     

The impact of information transparency on the dynamic behaviour of a hybrid manufacturing/remanufacturing system

A combined manufacturing/remanufacturing system is modelled and its dynamic behaviour is investigated using a control engineering approach. The model is an extension of the automated pipeline, inventory and order based production control system (APIOBPCS), which is well understood from a dynamic perspective. It is assumed that the remanufacturing process is based on a PUSH policy. Utilising different levels of information transparency from the remanufacturing process, three system types are developed and compared. Mathematical and simulation analyses of the system types were undertaken and their robustness to remanufacturing process uncertainties were tested. Our analysis indicates that the greater the degree of information transparency, the greater the robustness of the hybrid system.     

A kriging metamodel-assisted robust optimization method based on a reverse model

The goal of robust optimization methods is to obtain a solution that is both optimum and relatively insensitive to uncertainty factors. Most existing robust optimization approaches use outer–inner nested optimization structures where a large amount of computational effort is required because the robustness of each candidate solution delivered from the outer level should be evaluated in the inner level. In this article, a kriging metamodel-assisted robust optimization method based on a reverse model (K-RMRO) is first proposed, in which the nested optimization structure is reduced into a single-loop optimization structure to ease the computational burden. Ignoring the interpolation uncertainties from kriging, K-RMRO may yield non-robust optima. Hence, an improved kriging-assisted robust optimization method based on a reverse model (IK-RMRO) is presented to take the interpolation uncertainty of kriging metamodel into consideration. In IK-RMRO, an objective switching criterion is introduced to determine whether the inner level robust optimization or the kriging metamodel replacement should be used to evaluate the robustness of design alternatives. The proposed criterion is developed according to whether or not the robust status of the individual can be changed because of the interpolation uncertainties from the kriging metamodel. Numerical and engineering cases are used to demonstrate the applicability and efficiency of the proposed approach.     

Intelligent control schemes for automated storage and retrieval systems

A new information system approach to the operational controls of automated storage and retrieval systems (AS/RS) is developed and examined. This approach is based on artificial intelligence, state-operator framework for problem solving. Gradually increasing the information level, several operational goal functions are identified for an industrial unit-load food produce AS/RS. These functions use real-time statistical interpolations to select the desired storage and retrieval bins. As a result the AS/RS response adapts itself to stochastic perturbations in the system conditions. Experimental evaluations using multiple variance analysis technique and detailed simulations have shown that the proposed dynamic approach is superior to the common industrial control method currently used in those industrial systems characterized by batch arrivals (and retrievals) of the UL's and non-stationary demand patterns, These evaluations further suggest that improved performance is realized with the increase in the information level. The operational control scheme developed in this paper appears to be an excellent control alternative for unit-load AS/RSs. This is due to its limited computational requirements and the augmented productivity as demonstrated here for a real case study.     

中国冷库现状及节能途径

随着人们生活水平的不断提高,对食品冷冻冷藏的要求,冷库行业的发展势在必行。本文从冷库的容积、利用率、制冷系统维修措施以及自动化控制程度等方面,论述了目前中国冷库行业发展中存在的问题,提出了一些有关冷库节能的途径。     

Continuous-time algorithms for scheduling a single machine with sequence-dependent setup times and time window constraints in coordinated chains

In this paper we address the problem of selecting and scheduling several jobs on a single machine with sequence-dependent setup times and strictly enforced time window constraints on the start time of each job. We use short-term production targets to coordinate decentralised local schedulers and to make the objectives of specific areas in line with the chain objectives by maintaining a desired work in process profile in manufacturing environments. The existing literature in this domain is based on discrete-time approaches. We depart from prior approaches by considering continuous time. We introduce a two-step mathematical programming model based on disjunctive constraints to solve small problems to optimality, and propose an insertion-based heuristic to solve large-scale instances. We provide several variations of the insertion heuristic based on different score functions. The primary objective of these approaches is to maximise the total defined score for jobs while satisfying production targets for families of jobs in each shift. Further, our models minimise the maximum completion time of all selected jobs. The effectiveness, efficiency, and robustness of the proposed algorithms are analysed and compared with the existing literature.     

混装线投产序列和工位任务的协同调度机理

柔性汽车混装线需根据订单和生产需求调整当月工位任务规划和车型投产序列.通过分析汽车行业装配生产现状,提出高效而精准的调度目标,指出面向车型投产序列规划和工位任务规划的协同调度思想;说明针对不同目标采用的投产序列和工位规划方法,陈述了协同调度的策略框架,并用实际事例递进演示了协同调度机理.实验 证明协同调度能达到混装生产作业的高效精准过程控制的目的.     

Determining sequence and ready times in a remanufacturing system

A fundamental scheduling problem is to determine a production start (ready) time for jobs based on customer-specified due dates. Typically, the objective is to delay the ready time in an attempt to minimize work-in-process inventory and maximize production system utilization. In many practical situations, notably remanufacturing operations, highly variable operation times and intricate process plans complicate this problem. In such a case, a specific ready time implies a confidence of on-time completion. Prior analytical results imply the optimal solution is a function of: (i) customer due dates; (ii) desired confidence levels; and (iii) stochastic makespan minimization. This paper proposes the use of a network flow model to represent a remanufacturing flowshop and then presents a structured heuristic approach that is able to develop sequences and ready times for remanufacturing systems by balancing the three factors. A broad experimental design is used to demonstrate that the proposed scheduling method outperforms previous list rules in terms of the calculated mean and robustness values.     

Quantitative prediction of microbial behaviour during food processing using an integrated modelling approach: a review

Microbiological safety of food relies on microbial examination of raw materials and final products, coupled with monitoring process parameters and hygiene standards. The concept of predictive microbiology was developed to evaluate the effect of processing, distribution and storage operations on food safety. The objective of this paper is to review the approaches proposed by researchers to quantify the effect of competitiveness or fluctuating conditions on bacterial behaviour. The main microbial models that quantify the effects of various hurdles on microbial kinetics are presented. To provide complementary information for microbial models, three areas have to be considered: process engineering that characterises and models mass and heat transfer; microbiology that characterises and models bacterial behaviour and metabolite production, and; applied thermodynamics that characterises and models the physico-chemical properties of a food product. Global modelling approaches, developed by integrating the previous models, are illustrated with recent results.     

A Bayesian method for robust tolerance control and parameter design

This paper proposes a Bayesian method to set tolerance or specification limits on one or more responses and obtain optimal values for a set of controllable factors. The existence of such controllable factors (or parameters) that can be manipulated by the process engineer and that affect the responses is assumed. The dependence between the controllable factors and the responses is assumed to be captured by a regression model fit from experimental data, where the data are assumed to be available. The proposed method finds the optimal setting of the control factors (parameter design) and the corresponding specification limits for the responses (tolerance control) in order to achieve a desired posterior probability of conformance of the responses to their specifications. Contrary to standard approaches in this area, the proposed Bayesian approach uses the complete posterior predictive distribution of the responses, thus the tolerances and settings obtained consider implicitly both the mean and variance of the responses and the uncertainty in the regression model parameters.     

A fuzzy multi-criteria decision-making approach for managing performance and risk in integrated procurement–production planning

Nowadays in Supply Chain (SC) networks, a high level of risk comes from SC partners. An effective risk management process becomes as a consequence mandatory, especially at the tactical planning level. The aim of this article is to present a risk-oriented integrated procurement–production approach for tactical planning in a multi-echelon SC network involving multiple suppliers, multiple parallel manufacturing plants, multiple subcontractors and several customers. An originality of the work is to combine an analytical model allowing to build feasible scenarios and a multi-criteria approach for assessing these scenarios. The literature has mainly addressed the problem through cost or profit-based optimisation and seldom considers more qualitative yet important criteria linked to risk, like trust in the supplier, flexibility or resilience. Unlike the traditional approaches, we present a method evaluating each possible supply scenario through performance-based and risk-based decision criteria, involving both qualitative and quantitative factors, in order to clearly separate the performance of a scenario and the risk taken if it is adopted. Since the decision-maker often cannot provide crisp values for some critical data, fuzzy sets theory is suggested in order to model vague information based on subjective expertise. Fuzzy Technique for Order of Preference by Similarity to Ideal Solution is used to determine both the performance and risk measures correlated to each possible tactical plan. The applicability and tractability of the proposed approach is shown on an illustrative example and a sensitivity analysis is performed to investigate the influence of criteria weights on the selection of the procurement–production plan.     

Line balancing and model sequencing to reduce work overload in mixed-model U-line production environments

Mixed-model U-lines (MMULs) are important elements of just-in-time production systems. For successful implementation of MMULs, a smoothed workload distribution among workstations is important. This requires that line balancing and model sequencing problems are solved simultaneously. This article presents a mixed, zero–one, nonlinear mathematical programming formulation for balancing and sequencing MMULs simultaneously with the objective of reducing work overload. Since the problem is NP-hard, an effective simulated annealing approach is also presented and its performance compared with existing approaches. The results show that the proposed simulated annealing algorithm outperforms existing approaches.     

Order acceptance strategies in a production-to-order environment with setup times and due-dates

A production situation is considered in which different items are produced on one machine. Setup times are incurred between the production of orders of different items. Production is driven by customer orders; each order concerns a batch of one product type and is furthermore completely characterized by its batchsize and (customer determined) due-date. Acceptance of orders may be refused if these orders are likely to cause late deliveries. The problem is to determine good acceptance strategies which naturally raises the question on what information such acceptance decisions have to be based. Three basic approaches are explored in this paper. In the monolithic approach, the acceptance decision is based on detailed information on a current production schedule for all formerly accepted orders. In the hierarchic approach, the acceptance strategy is based on global capacity load profiles only, while detailed scheduling of accepted orders takes place at a lower level (possibly later in time). In the myopic approach the acceptance decision is similar to the one in the hierarchic approach but scheduling is myopic, i.e. once the machine becomes idle only the next order to be produced is actually scheduled. The performances of these three approaches are compared by means of simulation experiments. The results indicate that the differences in performance are small. Insofar as the monolithic approach performs better, this is mainly due to the selective acceptance mechanism implicitely present in case of a heavy workload. An adaptation of the myopic approach to incorporate such a selective acceptance mechanism leads to a comparable performance     

Integration of computation and testing for reliability estimation

This paper develops a methodology to integrate reliability testing and computational reliability analysis for product development. The presence of information uncertainty such as statistical uncertainty and modeling error is incorporated. The integration of testing and computation leads to a more cost-efficient estimation of failure probability and life distribution than the tests-only approach currently followed by the industry. A Bayesian procedure is proposed to quantify the modeling uncertainty using random parameters, including the uncertainty in mechanical and statistical model selection and the uncertainty in distribution parameters. An adaptive method is developed to determine the number of tests needed to achieve a desired confidence level in the reliability estimates, by combining prior computational prediction and test data. Two kinds of tests — failure probability estimation and life estimation — are considered. The prior distribution and confidence interval of failure probability in both cases are estimated using computational reliability methods, and are updated using the results of tests performed during the product development phase.     

Two-period vs. multi-period model for supply chain disruption management

A novel two-period modelling approach is developed for supply chain disruption mitigation and recovery and compared with a multi-period approach. For the two-period model, planning horizon is divided into two aggregate periods: before disruption and after disruption. The corresponding mitigation and recovery decisions are: (1) primary supply and demand portfolios and production before a disruption, and (2) recovery supply, transshipment and demand portfolios and production after the disruption. In the multi-period model, a multi-period planning horizon is applied to account for a detailed timing of supplies and production. The primary and recovery portfolios are determined simultaneously and for both approaches the integrated decision-making, stochastic mixed integer programming models are developed. While the simplified two-period setting may overestimate (for best-case capacity constraints) or underestimate (for worst-case capacity constraints) the available production capacity, it can be easily applied in practice for a fast, rough-cut evaluation of disruption mitigation and recovery policy. The findings indicate that for both two- and multi-period setting, the developed multi-portfolio approach leads to computationally efficient mixed integer programming models with an embedded network flow structure resulting in a very strong linear programming relaxation.