A portfolio approach to supply chain disruption management

A new, computationally efficient portfolio approach to supplier selection in the presence of supply chain disruption risks is proposed, where the selection of supply portfolios for parts is combined with production scheduling of finished products. Unlike most of reported research on the supply chain risk management which focuses on the risk mitigation decisions taken prior to a disruption, the proposed portfolio approach combines decisions made before, during and after the disruption. The two decision-making approaches are considered: an integrated approach with the perfect information about the future disruption scenarios, and a hierarchical approach with no such information available. In the integrated approach, which accounts for all potential disruption scenarios, the primary supply portfolio that will hedge against all scenarios is determined along with the recovery supply portfolio and production schedule for each scenario. In the hierarchical approach, first the primary supply portfolio is determined, and then, when a primary supplier is hit by a disruption, the recovery supply portfolio is selected. For the integrated and the hierarchical decision-making, mixed integer programming models are developed with the two risk-neutral conflicting objectives that account for both time and cost of recovery: minimising expected cost or maximising expected service level. The findings indicate that for both objectives, the integrated decision-making selects a more diversified primary supply portfolio than the hierarchical approach and when all primary suppliers are shutdown by disruption, a single sourcing recovery portfolio is usually selected.     

An integrated weighted fuzzy multi-objective model for supplier selection and order scheduling in a supply chain

This paper presents a new weighted fuzzy multi-objective model to integrated supplier selection, order quantity allocation and customer order scheduling problem to prepare a responsive and order-oriented supply chain in a make-to-order manufacturing system. Total cost and quality of purchased parts as well as the reliability of on-time delivery of customer orders are regarded as the objectives of the model. On the other hand, flexible suppliers can contribute to the responsiveness and flexibility of entire supply chain in the face of uncertain customer orders. Therefore, a mathematical measure is developed for evaluating the volume flexibility of suppliers and is considered as the other objective of the model. Furthermore, by considering the effect of interdependencies between the selection criteria and to handle inconsistent and uncertain judgments, a fuzzy analytic network process method is used to identify top suppliers and consider as the last objective. In order to optimise these objectives, the decision-maker needs to decide from which supplier to purchase parts needed to assemble the customer orders, how to allocate the demand for parts between the selected suppliers, and how to schedule the customer orders for assembled products over the planning time horizon. Numerical examples are presented and computational analysis is reported.     

Coordinating supplier selection and project scheduling in resource-constrained construction supply chains

In this paper, we study the problem of coordinating supplier selection and project scheduling, motivated by a real-life operational challenge encountered in the construction industry. In particular, we consider a project network consisting of multiple concurrent projects, with the objective of minimising the total tardiness of all projects. These projects are independent in operation but are subject to shared suppliers and the final quality inspection by the same committee, which then leads to the need for project review sequencing. The earliest starting time of each activity in a project depends on the availability of required resources (both renewable and non-renewable), as well as the activity precedence constraints. We formulate this problem as a mixed integer linear programming model, and propose a mathematical programming-based heuristic to solve the model. The heuristic decomposes the model into subproblems, and solves the subproblems through an iterative process. Each subproblem has a much smaller size and can be solved quickly and independently. The information obtained in solving subproblems is used to guide the search process. Numerical examples show the computational effectiveness of the proposed heuristic, and the benefits of coordination.     

On the risk-averse optimization of service level in a supply chain under disruption risks

The worst-case optimization of service level in the presence of supply chain disruption risks is considered for the two different service levels measures: the expected worst-case demand fulfillment rate and the expected worst-case order fulfillment rate. The optimization problem is formulated as a joint selection of suppliers and stochastic scheduling of customer orders under random disruptions of supplies. The suppliers are located in different geographic regions and the supplies are subject to random local and regional disruptions. The obtained combinatorial stochastic optimization problem is formulated as a mixed integer program with conditional service-at-risk as a worst-case service level measure. The risk-averse solutions that optimize the worst-case performance of a supply chain are compared for the two service level measures. In addition, to demonstrate the impact on the cost in the process of optimizing the worst-case service level, a joint optimization of expected cost and conditional service-at-risk using a weighted-sum approach is considered and illustrated with numerical examples. The findings indicate that the worst-case order fulfillment rate shows a higher service performance than the worst-case demand fulfillment rate. Maximization of the expected worst-case fraction of fulfilled customer orders better mitigates the impact of disruption risks. The supply portfolio is more diversified and the expected worst-case fraction of fulfilled orders is greater for most confidence levels. Finally, the results clearly show that worst-case service level is in opposition to cost.     

Applied column generation-based approach to solve supply chain scheduling problems

More and more enterprises have chosen to adopt a made-to-order business model in order to satisfy diverse and rapidly changing customer demand. In such a business model, enterprises are devoted to reducing inventory levels in order to upgrade the competitiveness of the products. However, reductions in inventory levels and short lead times force the operation between production and distribution to cooperate closely, thus increasing the practicability of integrating the production and distribution stages. The complexity of supply chain scheduling problems (integrated production and distribution scheduling) is known to be NP-hard. To address the issues above, an efficient algorithm to solve the supply chain scheduling problem is needed. This paper studies a supply chain scheduling problem in which the production stage is modelled by an identical parallel machine scheduling problem and the distribution stage is modelled by a capacitated vehicle routing problem. Given a set of customer orders (jobs), the problem is to find a supply chain schedule such that the weighted summation of total job weighted completion time and total job delivering cost are minimised. The studied problem was first formulated as an integer programme and then solved by using column generation techniques in conjunction with a branch-and-bound approach to optimality. The results of the computational experiments indicate that the proposed approach can solve the test problems to optimality. Moreover, the average gap between the optimal solutions and the lower bounds is no more than 1.32% for these test problems.     

Fuzzy scheduling of a build-to-order supply chain

The overwhelming majority of the literature in the area of supply chain planning and scheduling considers the traditional make-to-stock (MTS) environment. However, manufacturers of assembled products such as cars, computers, furniture, etc. adopt the build-to-order supply chain (BOSC) to become agile in a mass customization process in order to meet diversified customer requirements. In this paper we propose an integrated production–distribution planning model for a multi-echelon, multi-plant and multi-product supply chain operating in a build-to-order (BTO) environment. The uncertainties associated with estimation of the various operational cost parameters are represented by fuzzy numbers. The BOSC scheduling model is thus constructed as a mixed-integer fuzzy programming (MIFP) problem with the goal of reducing the overall operating costs related to component fabrication, procurement, assembling, inspection, logistics and inventory, while improving customer satisfaction by allowing product customization and meeting delivery promise dates at each market outlet. An efficient compromise solution approach by transforming the problem into an auxiliary multi-objective linear programming model is also suggested.     

An approach to integrate production-transportation planning and scheduling in an aluminium supply chain network

This article is based on a real-life problem of a global aluminium supply chain network driven by an aluminium smelter. At each echelon of the aluminium supply chain network, several members are involved which are scattered around the world. Producing aluminium begins with bauxite mining. Next, aluminium oxide is made from bauxite and finally aluminium is produced from aluminium oxide. A novel type of mixed-integer decision-making model, including a time-continuous representation of the planning period, is presented. The model enables coordination of production quantities and times of all supply chain members in order to minimise production and transportation costs of the whole supply chain minus bonus payments for early deliveries which are stipulated between the supply chain network and its customers. Material flows can take place with or without temporary storage of intermediate products at supplying and/or receiving sites. Furthermore, relax-and-fix heuristics are presented. A number of randomly generated scenarios are presented to demonstrate that the heuristics can find nearly optimal solutions along with drastically reduced computation times. The relax-and-fix heuristic enables iterative planning between centralised and decentralised decision makers.     

Supplier selection and order allocation in closed-loop supply chain systems using hybrid Monte Carlo simulation and goal programming

Supplier selection is an important strategic design decision in closed-loop supply chain systems. In addition, and after identifying the candidate suppliers, optimal order allocations are also considered as crucial tactical decisions. This research presents a multi-objective optimisation model to select the best suppliers and configure manufacturing and refurbishing facilities with the optimal number of parts and products in a closed-loop supply chain network. The objective functions in this research are formulated as total profit, total defective parts, total late delivered parts and economic risk factors of the candidate suppliers. The proposed multi-objective model is solved by hybrid Monte Carlo simulation integrated with three different variants of goal programming method. The effectiveness of the mathematical model and the proposed solution algorithms in obtaining Pareto-optimal solutions is demonstrated in a numerical example adopted from a real case study.     

A hybrid optimisation approach to configure a supply chain for new product diffusion: a case study of multiple-sourcing strategy

Configuring a supply chain for a new product is a challenging task due to the lack of historical demand data and the dynamic/uncertain nature of the new product diffusion process. An integrated supply chain configuration (SCC) and new product diffusion (NPD) model is developed to explicitly account for the impact of demand dynamics during a new product's diffusion on an optimal supply chain configuration. Our hybrid NPD-SCC model allows a manufacturer to source from multiple suppliers, vendors or modes for its supply chain entities. Such a multiple-sourcing approach not only helps the manufacturer to diversify its pool of suppliers and maintain bargaining power, but also builds redundancy into the supply chain to hedge against potential demand surge and supply disruption during the new product life cycle. Through a case study and a comprehensive computational study, we find that although the single-sourcing solution is able to achieve lower unit-manufacturing cost (UMC), the multiple-sourcing approach is superior to single-sourcing on the overall supply chain performance in the environment with random supply disruptions. By building-in redundancy as multiple suppliers and modes, the resultant supply chain has less chance of being disrupted and achieves higher overall profit on average. We also draw several other managerial insights closing the gap between some supply chain operations and marketing strategies.     

Optimisation of the broiler production supply chain

In this paper, we propose a mixed integer programming (MIP) model for the Chicken Flock Sizing, Allocation and Scheduling Problem (CFSASP), which is an important planning problem in the broiler production supply chain. To solve the CFSASP efficiently, two variants of rolling horizon heuristics (RHHs) have been developed and applied on the case of a Norwegian broiler production company. Computational results show that the RHHs successfully obtain high-quality solutions within a reasonable time. The value of optimisation is verified through comparison with the case company's plans, where the solutions from optimisation outperforms the current solutions. Sensitivity analyses are also conducted to provide managerial insights regarding certain strategic decisions, such as how many and which days to use for hatching of chickens. Due to the promising results, the case company is now implementing an optimisation-based decision support system based on the MIP model and solution methods shown in this paper.     

Multi-objective mixed integer programming and an application in a pharmaceutical supply chain

Multi-objective integer linear and/or mixed integer linear programming (MOILP/MOMILP) are very useful for many areas of application as any model that incorporates discrete phenomena requires the consideration of integer variables. However, the research on the methods for the general multi-objective integer/mixed integer model has been scant when compared to multi-objective linear programming with continuous variables. In this paper, an MOMILP is proposed, which integrates various conflicting objectives. We give importance to the imprecise nature of some of the critical factors used in the modelling that can influence the effectiveness of the model. The uncertainty and the hesitation arising from estimating such imprecise parameters are represented by intuitionistic fuzzy numbers. The MOMILP model with intuitionistic fuzzy parameters is first converted into a crisp MOMILP model, using appropriate defuzzification strategies. Thereafter, the MOMILP is transformed into a single objective problem to yield a compromise solution with an acceptable degree of satisfaction, using suitable scalarisation techniques such as the gamma-connective technique and the minimum bounded sum operator technique. The proposed solution method is applied to several test problems and a multi-objective pharmaceutical supply chain management model with self generated random data.     

On the robust decision-making in a supply chain under disruption risks

This paper considers a robust decision-making problem associated with supplies of parts and deliveries of finished products in a customer driven supply chain under disruption risks. The robustness refers to an equitably efficient performance of a supply chain in average-case as well as in the worst-case, which reflects the decision-makers common requirement to maintain an equally good performance of a supply chain under different conditions. Given a set of customer orders for products, the decision-maker needs to select suppliers of parts required to complete the orders, allocate the demand for parts among the selected suppliers and schedule the orders over the planning horizon, to equitably optimise average and worst-case performance of the supply chain. The supplies are subject to independent random local and regional disruptions. The obtained combinatorial stochastic optimisation problem is formulated as a mixed-integer program with conditional value-at-risk as a risk measure. The ordered weighted averaging aggregation of the expected value and the conditional value-at-risk of the selected optimality criterion is applied to obtain a robust solution. The risk-neutral, risk-averse and robust solutions that optimise, respectively average, worst-case and equitable average and worst-case performance of a supply chain are determined and compared for cost and customer service level objective functions. Numerical examples and computational results, in particular comparison with the mean-risk approach, are presented and some managerial insights are reported.     

Reverse supply chain optimisation with disassembly line balancing

Due to responding environmental issues, conforming governmental legislations and providing economic benefits, there has been a growing interest in recycling activities through the supply chains. Reverse supply chain (RSC) optimisation problem has a great potential as an efficient tactic to achieve this goal. While disassembly, one of the main activities in RSC, enables reuse and recycling of products and prevents the overuse, disassembly line balancing problem involves determination of a line design in which used products are partially/completely disassembled to obtain available components. The aim of this study is to optimise a RSC, involving customers, collection/disassembly centres and plants, that minimises the transportation costs while balancing the disassembly lines, which minimises the total fixed costs of opened workstations, simultaneously. A non-linear mixed-integer programming model, which simultaneously determines: (i) optimal distribution between the facilities with minimum cost, (ii) the number of disassembly workstations that will be opened with minimum cost, (iii) the cycle time in each disassembly centre and (iv) optimal assignment of tasks to workstations, is developed. A numerical example is given to illustrate the applicability of the proposed model. Different scenarios have been conducted to show the effects of sensitivity analyses on the performance measures of the problem.     

An improved ant colony optimisation algorithm for three-tier supply chain scheduling based on networked manufacturing

This paper presents a study on supply chain scheduling from the perspective of networked manufacturing (NM). According to feature analysis of supply chain scheduling based on NM, we comprehensively consider the combined benefits of cost, time, and satisfaction level for customised services. In order to derive a scheduling strategy among supply chain members based on NM, we formulate a three-tier supply chain scheduling model composed of manufacturer, collaborative design enterprise and customer. Three objective functions – time function, cost function and delay punishment function – are employed for model development. We also take into account multi-objective optimisation under the constraint of product capacity. By using an improved ant colony optimisation algorithm, we add different pheromone concentrations to selected nodes that are obtained from feasible solutions and we confine pheromone concentrations τ within the minimum value τ _min and the maximum value τ _max, thus obtaining optimal results. The results obtained by applying the proposed algorithm to a real-life example show that the presented scheduling optimisation algorithm has better convergence, efficiency, and stability than conventional ant colony optimisation. In addition, by comparing with other methods, the output results indicate that the proposed algorithm also has better solutions.     

A blockchain-based approach for a multi-echelon sustainable supply chain

Blockchain technology is destined to revolutionise supply chain processes. At the same time, governmental and regulatory policies are forcing firms to adjust their supply chains in response to environmental concerns. The objective of this study is therefore to develop a distributed ledger-based blockchain approach for monitoring supply chain performance and optimising both emission levels and operational costs in a synchronised fashion, producing a better outcome for the supply chain. We propose the blockchain approach for different production allocation problems within a multi-echelon supply chain (MESC) under a carbon taxation policy. As such, we couple recent advances in digitalisation of operations with increasingly stringent regulatory environmental policies. Specifically, with lead time considerations under emission rate constraints (imposed by a carbon taxation policy), we simultaneously consider the production, distribution and inventory control decisions in a production allocation-based MESC problem. The problem is then formulated as a Mixed Integer Non-Linear Programming (MINLP) model. We show that the distributed ledger-based blockchain approach minimises both total cost and carbon emissions. We then validate the feasibility of the proposed approach by comparing the results with a non-dominated sorting genetic algorithm (NSGA-II). The findings provide support for policymakers and supply chain executives alike.     

DSS approach for heterogeneous parallel machines scheduling considering proximate supply chain constraints

This paper describes the basis of a Decision Support System (DSS) designed to schedule fertiliser production orders to be delivered within time windows, in plants made up of multiple heterogeneous parallel processors (production lines), considering that fertiliser production rates and nomenclatures depend on lines, that setup times depend on sequence and lines, and taking into account downtime constraints (preventive maintenance?…). A mixed linear programming model is encapsulated in the DSS which considers the schedule’s impacts, immediately upstream and downstream of plants in the supply chain. These side-effects may make the proposed solution unfeasible and the DSS helps redefining the problem to avoid them.     

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.     

Stochastic optimisation model for integrated decisions on relief supply chains: preparedness for disaster response

This paper proposes a stochastic linear mixed-integer programming model for integrated decisions in the preparedness and response stages in pre- and post-disaster operations, respectively. We develop a model for integrated decisions that considers three key areas of emergency logistics: facility and stock prepositioning, evacuation planning and relief vehicle planning. To develop a framework for effective relief operations, we consider not only a cost-based but also an equity-based solution approach in our multiple objectives model. Then a normalised weighted sum method is used to parameterise our multiple objective programming model. This paper suggests a compromise between the cost, and the equity of relief victims. The experiments also demonstrate how time restrictions and the availability of relief vehicles impact the two objective functions.     

Multi-objective analysis of an integrated supply chain scheduling problem

We study the problem of minimising the total weighted tardiness and total distribution costs in an integrated production and distribution environment. Orders are received by a manufacturer, processed on a single production line, and delivered to customers by capacitated vehicles. Each order (job) is associated with a customer, weight (priority), processing time, due time, and size (volume or storage space required in the transportation unit). A mathematical model is presented in which a number of weighted linear combinations of the objectives are used to aggregate both objectives into a single objective. Because even the single objective problem is NP-hard, different heuristics based on a genetic algorithm (GA) are developed to further approximate a Pareto-optimal set of solutions for our multi-objective problem.     

An integrated approach to evaluating the production system in closed-loop supply chains

The increasing demands for environmental resource protection and sustainable development have been forcing enterprises to put sustainable supply chain management on their agendas in recent years. At the same time, intense global competition requires organisations to adopt practices that enable them to provide high-quality products and services. In this paper, we consider the problem of comprehensively evaluating the production system in closed-loop supply chains. We first propose an evaluation framework that consists of economic evaluation, product quality evaluation and ecological evaluation modules. Based on mathematical probability theory and the dynamic characteristics of reverse supply chain logistics, we then focus on the evolution dynamics in the quality evaluation dimension, where the concept of product quality, which builds on the reliability and the time-utility value of a product, is proposed. The basic production evaluation model is then extended to incorporate different sustainable procurement strategies, which take into consideration the trade-offs among cost, environment and quality. An outline and corresponding flow chart of corporate procurement strategy optimisation are provided which allow the proposed evaluation model to be implemented in computer-aided decision-making, further providing decision support for production system and supply chain management. Simulation and case studies are presented to promote a better understanding of the model approach and its managerial implications. Results also suggest that quality characteristics of components and sustainable procurement strategies are two important factors that determine the final production performance and should be paid special attention in closed-loop supply chain practice.