An optimisation model for purchase,production and distribution in fish supply chain – a case study

This paper presents an optimisation model for spawn purchase, fish culturing production process and harvested fish distribution in a fish supply chain. Due to the complexity and variety of real-world fish supply chains, the model is built based on a case study for a real trout fish farm to illustrate the methodology on how to incorporate influential factors from both warm chain and cold chain. Warm chain mainly considers the biological factors while fish is alive and cold chain mainly considers the economic factors after fish is ready for harvest, harvested, and processed. The model seeks to improve the trout farm production planning to help decision-making on spawn purchase quantity, the best time to harvest fish, and the farming periods. In addition, the model adopts a customer classification method in distribution planning that is able to prioritise the delivery of fresh fish to the most profitable customers. A mixed integer linear programming (MILP) model was developed to maximise the total profit. The experimental results demonstrate that farmers’ total profit can be increased after applying the proposed optimisation strategy, compared to the traditional farming strategy.     

Analysis of the bullwhip effect in multi-product,multi-stage supply chain systems–a simulation approach

This research aims to develop a simulation approach based on system dynamics modelling (SDM) and adaptive network based fuzzy inference system (ANFIS) for quantifying and reducing the bullwhip effect in a multi-product, multi-stage supply chain. The proposed model is comprised of three groups of variables influencing the bullwhip effect, namely the structure of a supply chain network, supply chain contributions (ordering process in regular situation or when a supplier has a promotion or shortage gaming) and supply chain performances (the number of defects and ordering lead time). As a result, a two layer simulation model is developed with three generic models. The flexibility of this proposed approach is its ability to model various types of ordering policies which are basic inventory policies, material requirement planning (MRP) system and just in time (JIT) approach. The supply chain of a beverage company was selected to validate and demonstrate the flexibility of the proposed model. The findings of the proposed simulation model are consistent with the results obtained from the case study. The error magnitude of the bullwhip effect level varied between 0 and 9% resulting in bullwhip effect reductions of up to 92%. Accordingly, the bullwhip effect levels are significantly decreased by using the proposed simulation model.     

Integrated production–distribution planning in two-echelon systems: a resilience view

Global supply chains are increasingly exposed to operational and disruption risks that threaten their business continuity. This paper presents a novel two-stage scenario-based mixed stochastic-possibilistic programming model for integrated production and distribution planning problem in a two-echelon supply chain over a midterm horizon under risk. Operational risks are handled by introducing imprecise (i.e. possibilistic) parameters while disruption risks are accounted for through stochastic disruption scenarios. The proposed model accounts for the risk mitigation options and recovery of lost capacities in an integrated manner. In the first stage, the structure of the chain and proactive risk mitigation decisions are determined, while the second stage specifies the recovery plan of lost capacities in addition to production and distribution plans. Considering extra capacities in the production facilities, backup routes for transportation links and pre-positioning of emergency inventory in distribution centres are considered as feasible options to improve the resilience level of the supply chain. We propose a new indicator for optimising the resilience level of the chain based on restoration of lost capacities. For the sake of robustness, the expected worst case of the second stage’s objective function is considered by utilising the conditional value at risk (CVaR) measure. The validation and applicability of the proposed model are examined through several numerical experiments.     

Tactical production planning in a hybrid Make-to-Stock–Make-to-Order environment under supply,process and demand uncertainties: a robust optimisation model

In this paper, we consider a hybrid ‘Make-to-Stock–Make-to-Order’ environment to develop a novel optimisation model for medium-term production planning of a typical multi-product firm based on the competencies of the robust optimisation methodology. Three types of uncertainties: suppliers, processes and customers, are incorporated into the model to construct a robust practical model in an uncertain business environment. The modelling procedure is started with applying deterministic linear programming to develop a new multi-objective approach for the combination of multi-product multi-period production planning and aggregate production planning problems. Then, the proposed deterministic model is transformed into a robust optimisation framework and the solution procedure is designed according to the Lp-Metric methodology. Next, using the IBM ILOG CPLEX optimisation software, the proposed model is evaluated by applying the data collected from an industrial case study. Final results illustrate the applicability of the proposed model.     

Biomass feedstock supply chain design – a taxonomic review and a decomposition-based methodology

In this paper, we present a taxonomic review of the literature devoted to the use of operations research approaches for the design and operation of biomass feedstock supply chains (BFSCs). A total of 185 publications that have appeared from 1989 to 2017 are classified based on (1) the embedded optimisation subproblems; (2) the modelling methodologies used for their formulations; and (3) the methods employed for their solution. Our objective of using this classification scheme is to highlight the presence of some useful substructures in a BFSC problem that can, then, be exploited in developing its solution procedure. We illustrate this idea on some generic BFSC problems and present a Dantzig–Wolfe decomposition-based methodology for developing customised approaches to effectively tackle these difficult and large-sized BFSC problems. Finally, we suggest several promising future research directions.     

Modeling the planning and scheduling across the outsourcing supply chain: a Chaos-based fast Tabu–SA approach

Planning and Scheduling are the interrelated manufacturing functions and should be solved simultaneously to achieve the real motives of integration in manufacturing. In this paper, we have addressed the advanced integrated planning and scheduling problem in a rapidly changing environment, where the selection of outsourcing machine/operation, meeting the customers (single or multiple) due date, minimizing the makespan are the main objectives while satisfying several technological constraints. We developed a mixed integer programming model for integrated planning and scheduling across the outsourcing supply chain and showed how such models can be used to make strategic decisions. It is a computationally complex and mathematically intractable problem to solve. In this paper, a Chaos-based fast Tabu-simulated annealing (CFTSA) incorporating the features of SA, Tabu and Chaos theory is proposed and applied to solve a large number of problems with increased complexity. In CFTSA algorithm, five types of perturbation schemes are developed and Cauchy probability function is used to escape from local minima and achieve the optimal/near optimal solution in a lesser number of iterations. An intensive comparative study shows the robustness of proposed algorithm. Percentage Heuristic gap is used to show the effectiveness and two ANOVA analyses are carried out to show the consistency and accuracy of the proposed approach.     

Genetic algorithm optimisation of an integrated aggregate production–distribution plan in supply chains

A production plan concerns the allocation of resources of the company to meet the demand forecast over a certain planning horizon and a distribution plan involves the management of warehouse storage assignments, transport routings and inventory management issues. A production–distribution plan integrates the decisions in production, transport and warehousing as well as inventory management. The overall performance of a supply-chain is influenced significantly by the decisions taken in its production–distribution plan and hence one key issue in the performance evaluation of a supply chain is the modelling and optimisation of the production–distribution plan considering its actual complexity. Based on the integration of Aggregate Production Plan and Distribution Plan, this article develops a mixed integer non-linear formulation for a two-echelon supply network (i.e. a production-distribution network) considering the real-world variables and constraints. Genetic Algorithm (GA), known as a robust technique for solving complex problems, is employed for the optimisation of the developed mathematical model due to its ability to effectively deal with a large number of parameters. To demonstrate the applicability of the methodology, a real-life case study will be finally studied incorporating the production of different types of products in several manufacturing plants and the distribution of finished products from plants to a number of end-users via multiple direct/indirect transport routes.     

A primal decomposition method for the integrated design of multi-period production–distribution systems

We study the integrated design of strategic supply chain networks and the determination of tactical production-distribution allocations in the case of customer demands with seasonal variations. Given a set of potential suppliers, potential manufacturing facilities and distribution centers with multiple possible configurations, and customers with seasonal demands, the goal is to determine the configuration of the production-distribution system with the lowest sum of supply, production, transportation, inventory, and facility costs such that seasonal customer demands are met. We develop a mixed integer programming formulation and an integrated design methodology based on primal (Benders) decomposition. For a case study in the packaging industry, specialized acceleration techniques reduced the running times by a factor of 480. The company projects savings of 2% or $8.3 million by using the integrated rather than the optimal hierarchical configuration.     

A new spanning tree-based genetic algorithm for the design of multi-stage supply chain networks with nonlinear transportation costs

The design of configuration and the transportation planning are crucial issues to the effectiveness of multi-stage supply chain networks. The decision makers are interested in the determination the optimal locations of the hubs and the optimal transportation routes to minimize the total costs incurred in the whole system. One may formulate this problem as a 0-1 mixed integer non-linear program though commercial packages are not able to efficiently solve this problem due to its complexity. This study proposes a new spanning tree-based Genetic Algorithm (GA) using determinant encoding for solving this problem. Also, we employ an efficient heuristic that fixes illegal spanning trees existing in the chromosomes obtained from the evolutionary process of the proposed GA. Our numerical experiments demonstrate that the proposed GA outperforms the other previously published GA in the solution quality and convergence rate.     

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.     

Integrated aggregate supply chain planning using memetic algorithm – A performance analysis case study

This paper aims to examine how a complex supply chain yields cost reduction benefits through the global integration of production and distribution decisions. The research is motivated by a complex real world supply chain planning problem facing a large automotive company. A mixed-integer nonlinear production-distribution planning model is solved using a customised memetic algorithm. The performance and effectiveness of the developed model and solution approach in achieving the global optimisation is investigated through experiments comparing the numerical results from the proposed integrated approach with those of a typical non-integrated (hierarchical) production–distribution optimisation.     

Set-up and production planning in hybrid manufacturing–remanufacturing systems with large returns

Hybrid systems that use both raw materials and returned products in the production process are considered. The system contains one facility, and undergoes set-ups each time it switches between two production modes. In particular, we address systems engaged mainly in remanufacturing and having a large percentage of return. This situation is encountered in companies with mature remanufacturing channels. The targeted application area is comprised of hybrid systems that uses leasing as a business model with manufacturing serving to attenuate return uncertainty. To evaluate the system performance, we take into account manufacturing and remanufacturing costs, holding costs in serviceable and return inventories, backlog and set-up costs. Our analysis of hybrid systems with high return levels reveals features that are peculiar to such systems and that differentiate them from systems with lower return rates. We first present analytical solutions for optimal production and set-up schedule, and determine the possible cycle shapes for reliable systems. Optimal policies contain intervals of manufacturing and remanufacturing at maximal rate, and intervals of on-demand and on-return remanufacturing. Failure-prone systems are studied next, using the formalism of stochastic dynamic programming. Optimal policies give rise to the trajectories converging to the patterns similar to the analytically calculated cycles.     

Optimal control of a stochastic production–inventory system under deteriorating items and environmental constraints

In this paper, we present an optimal control model of a stochastic production–inventory with deteriorating items, emission tax and pollution abatement investment. In our model, the emission tax is levied on the firm’s environmental obsolescence rate of technology rather than the total amount of the environmental externality. Our objective is to apply Hamilton–Jacobi–Bellman (HJB) equation to solve the stochastic production–inventory system with deteriorating items, emission tax and pollution abatement investment; and derive the optimal production rate and pollution abatement investment rate that maximise the objective function value. The results are discussed with some illustrative examples for different demand rate functions, and sensitivity analysis is conducted to study the effect of changing the parameters and coefficients on the objective function value.     

Control theory applications to the production–inventory problem: a review

Mathematical models based on average or steady-state conditions are insufficient when dealing with dynamic situations faced by production–inventory systems in current business environments. Therefore, the use of mathematical tools based on control theory to handle time-varying phenomena has been reinvigorated in order to accommodate these new needs. Given the variety of research approaches in the field over several decades, there is a need to provide a review of this work. This review identifies some major research efforts for applying control theoretic methods to production–inventory systems. It is shown that in general, control theory is applied to reduce inventory variation, reduce demand amplification and optimize ordering rules. Some control theory tools applied are block diagram algebra, Mason's gain formula, Bode plots, Laplace transform, Z transform and optimal control. Basic approaches are classified within stochastic control theory and deterministic control theory. Two important issues are then identified within the deterministic models. First, separate efforts to integrate systems horizontally (i.e. supply chain) or vertically (i.e. hierarchical approach) are identified. Second, none of the reviewed models implemented a systematic way to calculate all the required model parameters. Some authors presented suggestions to optimize some parameters, but no reference was found that tried to obtain these parameter values from a real system.     

A review of integrated analysis of production–distribution systems

This paper reviews recent work on integrated analysis of production–distribution systems, and identifies important areas where further research is needed. By integrated analysis we understand analysis performed on models that integrate decisions of different production and distribution functions for a simultaneous optimization. We review work that explicitly considers the transportation system in the analysis, since we are interested in the following questions: (i) how have logistics aspects been included in the integrated analysis? and (ii) what competitive advantages, if any, have been obtained from the integration of the distribution function to other production functions within a company and among different companies? In our review we also mention whether the work has been done at the strategic level, i.e., if it concerns the design of the distribution system, or at the tactical level, i.e., if it concerns optimization problems for which the characteristics of the distribution system are provided.     

Development of a multidisciplinary–multiresponse robust design optimization model

Robust design is an efficient process improvement methodology that combines experimentation with optimization to create systems that are tolerant to uncontrollable variation. Most traditional robust design models, however, consider only a single quality characteristic, yet customers judge products simultaneously on a variety of scales. Additionally, it is often the case that these quality characteristics are not of the same type. To addresses these issues, a new robust design optimization model is proposed to solve design problems involving multiple responses of several different types. In this new approach, noise factors are incorporated into the robust design model using a combined array design, and the results of the experiment are optimized using a new approach that is formulated as a nonlinear goal programming problem. The results obtained from the proposed methodology are compared with those of other robust design methods in order to examine the trade-offs between meeting the objectives associated with different optimization approaches.     

Improving the push–pull strategy in a serial supply chain by a hybrid push–pull control with multiple pulling points

Because of its benefits – from lowered inventory costs to greater flexibility in adapting to shifting market forces – the push–pull strategy is being widely used in today's competitive supply-chain designs. The push–pull strategy also brings potential supply-chain risks related to order fulfilment capability and robustness against external variability. More specifically, the use of this strategy often results in an inability to minimise the impact of lead-time variability. We present a new, hybrid push–pull strategy that incorporates additional stock points after the push–pull boundary as the pulling points in a serial supply chain, which can mitigate the risks and improve the robustness of the push–pull strategy without sacrificing its benefits in inventory cost reduction. For the evaluation and comparison of different supply-chain strategies, a nonlinear, mixed-integer programming model with a cost-minimisation objective function is developed and implemented in the numerical experimentation, with simulated annealing as the search algorithm. Results from the experiments demonstrate the potential improvement by our proposed strategy in terms of the robustness and cost-effectiveness against external variability. The results also verify the risks and limitations of the conventional push–pull strategy and provide some managerial implications regarding the use of push–pull supply chains.     

Integration of manufacturing and distribution networks in a global car company – network models and numerical simulation

Global supply chain practices and their effects have received considerable attention over the last two decades. In the recent past, the need for integration across supply chains has been identified as a key for effective and efficient operations of supply chains. This is observed with the increasing trend of collaborative partnerships among supply chain partners. This paper presents an integrated approach for manufacturing and distribution networks within the supply chain system of a global car company. The paper shows that the integration of manufacturing and distribution networks creates the environment for effective planning of many components and execution/follow-up of those plans. These components include materials, resources, operations/activities, suppliers and customers. The main features of the integration include component integration at individual networks via use of a central warehouse. This integration reduces various interfacing steps between partners and enables representations of relationships (component precedence, parent-component and component-component). The proposed integrated model is numerically tested using past data from one of Japan's auto-makers, based in the emerging economy of Thailand. The paper concludes that the integrated supply network eliminates the need for interfacing of individual networks and enables simultaneous planning of many components as well as forward planning of supply components in global supply chain operations. It also shows that the integrated approach is capable of providing visibility, flexibility, and maintainability for further improvement in the supply network environment.     

Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995–2004) for research and applications

Performance measures and metrics are essential for effectively managing logistics operations, particularly in a competitive global economy. The global economy is featured with global operations, outsourcing and supply chain and e-commerce. The real challenge for managers of this new enterprise environment is to develop suitable performance measures and metrics to make right decisions that would contribute to an improved organizational competitiveness. Now the question is whether traditional performance measures can be used and out of them which ones should be given priority for measuring the performance in a new enterprise environment. Some of the traditional measures and metrics may not be suitable for the new environment wherein many activities are not easily identifiable. Measuring intangibles and nonfinancial performance measures pose the greater challenge in the so-called knowledge economy. Nevertheless, measuring them is so critical for the successful operations of companies in this environment. Considering the importance of nonfinancial measures and intangibles, an attempt has been made in this paper to determine the key performance measures and metrics in supply chain and logistics operations. This is based on a literature survey and some of the reported case experiences. Suggestions for future research directions are also indicated.