Mathematical programming model for centralised master planning in ceramic tile supply chains

The object of this article is to develop a centralised replenishment, production, and distribution model for ceramic tile supply chains. These supply chains are assumed to be multi-item, multi-supplier, multi-facility, multi-type and multi-level distribution centres. The model deals with multi-period master planning where sourcing considerations for replenishments, production facilities, and distribution centres are important to maximise an objective function involving the total net profit. This model is deterministic and has been formulated as a mixed-integer linear programming (MILP) model. An example based on modifications of real-world industrial problems is presented.     

Closed loop supply chain network design and optimisation using fuzzy mixed integer linear programming model

Owing to the revolution in sustainable and green manufacturing the production planning and network design of closed loop supply chain concept has got the attention of researchers and managers. In this paper, a multi-product, multi-facility capacitated closed-loop supply chain framework is proposed in an uncertain environment including reuse, refurbish, recycle and disposal of parts. The uncertainty related to demand, fraction of parts recovered for different product recovery processes, product acquisition cost, purchasing cost, transportation cost, processing, and set-up cost is handled with fuzzy numbers. A fuzzy mixed integer linear programming model is proposed to decide optimally the location and allocation of parts at each facility and number of parts to be purchased from external suppliers in order to maximise the profit of organisation. The proposed solution methodology is able to generate a balanced solution between the feasibility degree and degree of satisfaction of the decision maker. The proposed model has been tested with an illustrative example.     

A two-period model for selection of resilient multi-tier supply portfolio

A two-period decision-making model is developed for selection of resilient supply portfolio in a multi-tier supply chain under disruption risks. The planning horizon is divided into two aggregate periods: before and after the disruption. The resilience of the supply chain is achieved by selection ahead of time primary supply portfolio and by pre-positioning of risk mitigation inventory of parts at different tiers that will hedge against all disruption scenarios. Simultaneously, recovery and transshipment portfolios are determined for each disruption scenario and decisions on usage the pre-positioned inventory are made to minimise expected cost or maximise expected service level. Some properties of optimal solutions, derived from the proposed model provide additional managerial insights. The findings also indicate that the developed portfolio approach with an embedded network flow structure leads to computationally efficient stochastic mixed integer program with a strong LP relaxation.     

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.     

Robust material requirement planning with cumulative demand under uncertainty

In this paper, we deal with the problem of tactical capacitated production planning with the demand under uncertainty modelled by closed intervals. We propose a single-item with backordering model under small uncertainty in the cumulative demand for the Master Production Scheduling (MPS) problem with different rules, namely the Lot For Lot rule and the Periodic Order Quantity rule. Then we study a general multilevel, multi-item, multi-resource model with backordering and the external demand on components for the Material Requirement Planning (MRP) problem under uncertainty in the cumulative demand. In order to choose robust production plans for the above problems that hedge against uncertainty, we adopt the well-known minmax criterion. We propose polynomial methods for evaluating the impact of uncertainty on a given production plan in terms of its cost and for computing optimal robust production plans for both problems (MPS/MRP) under the assumed interval uncertainty representation. We show in this way that the robust problems (MPS/MRP) under this uncertainty representation are not much computationally harder than their deterministic counterparts.     

An optimisation model for the design of global multi-echelon supply chains under lead time constraints

This article presents a mixed integer programming model for the design of global multi-echelon supply chains while considering lead time constraints. Indeed, we impose that the delivery lead time that can be promised by the company must be smaller than the lead time required by the customer. The delivery lead time is calculated based on the lead times of purchasing, manufacturing and transportation that are triggered by the customer order while considering the stock levels of purchased, intermediate and final products that must be kept at the different facilities. Computational studies are conducted in order to analyse the impacts of including lead times on the supply chain design decisions and to prove the solvability of the model.     

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.     

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.     

A strategic model for exact supply chain network design and its application to a global manufacturer

This paper presents a comprehensive model that captures significant strategic decisions involved in designing or redesigning high-performance supply chains from the perspective of the manufacturer. The problem considers deterministic demand by multiple clients, for multiple products, over the periods of a long-term horizon. The design decisions involve selection of suppliers, establishment or resizing of production facilities and distribution centres, possible subcontracting of related activities, and selection of transportation modes and routes. The problem is formulated by a Mixed Integer Linear Programming model. Its objective is to minimise the overall costs associated with procurement, production, inventory, warehousing, and transportation over the design horizon. Appropriate constraints model the complex relationships among the links of the supply chain. The proposed model has been applied to a large case study of a global manufacturing firm, providing valuable insights into the transformation of the firm’s current supply chain network, as well as into the potential of the proposed approach.     

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.     

Novel model and kernel search heuristic for multi-period closed-loop food supply chain planning with returnable transport items

Closed-loop supply chain (CLSC) is of utmost importance to sustainable development and has received increasing attention in recent decades. However, food CLSC with returnable transport items (RTIs) has been rarely studied although its growing applications in practice. This paper aims to investigate a multi-period CLSC planning problem that coordinates the flows of perishable food products and RTIs considering food quality. The objective is to maximise the total profit of the holistic supply chain over a finite planning horizon. To this end, a novel mixed integer linear programming model is first formulated. As the problem is proven NP-hard, an improved kernel search-based heuristic is then developed. A real case study deriving from a food manufacturer in China shows the applicability of the proposed model and method. The results indicate that the manufacturer’s profit can be improved by more than 10% with our method. Numerical experiments on randomly generated instances demonstrate that the proposed heuristic can yield high-quality solutions with much less computation time compared with the commercial solver CPLEX and an existing heuristic.     

An empirical investigation of antecedents to information exchange in supply chains

In this investigation, following the principal-agent theory, possibilities to reduce existing information asymmetries are derived and their effects on the information exchange are examined. Results of an empirical investigation based on a structural equation model confirm that monitoring measures and frequent meetings positively influence the exchange of information while explicit contracts have rather negative effects. Premiums and specific investments, however, seem to play insignificant roles. Measures, such as building a reputation mechanism, will probably prove to be effective only in the long run.     

A mixed-integer linear programming model for integrated production and preventive maintenance scheduling in the capital goods industry

The scheduling literature is extensive, but much of this work is theoretical and does not capture the complexity of real world systems. Capital goods companies produce products with deep and complex product structures, each of which requires the coordination of jobbing, batch, flow and assembly processes. Many components require numerous operations on multiple machines. Integrated scheduling problems simultaneously consider two or more simultaneous decisions. Previous production scheduling research in the capital goods industry has neglected maintenance scheduling and used metaheuristics with stochastic search that cannot guarantee an optimal solution. This paper presents a novel mixed integer linear programming model for simultaneously solving the integrated production and preventive maintenance scheduling problem in the capital goods industry, which was tested using data from a collaborating company. The objective was to minimise total costs including: tardiness and earliness penalty costs; component and assembly holding costs; preventive maintenance costs; and set-up, production, transfer and production idle time costs. Thus, the objective function and problem formulation were more extensive than previous research. The tool was successfully tested using data obtained from a collaborating company. It was found that the company’s total cost could be reduced by up to 63.5%.     

An integrated decision support system for strategic supply chain optimisation in process industries: the case of a zinc company

We introduce a menu-driven user-friendly decision support system (DSS) for supply chain planning based on optimisation. The DSS is based on a multi-source (supplier), multi-destination (warehouse) network having multiple manufacturing facilities, with multiple materials and multiple storage areas. This integrated supply chain model performs multiple period planning. The use of this DSS requires little knowledge of management sciences tools. We discuss the need for an integrated approach towards supply chain modelling for the process industry. We present the integrated model in the form of a database structure. We validate the model with the real data of a zinc company and demonstrate the impact of optimisation in terms of percentage improvement. The result shows that it is possible to improve unit contribution to profit from 1.89 to 4.66%.     

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.     

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.     

Monolithic versus hierarchical approach to integrated scheduling in a supply chain

The two approaches, monolithic and hierarchical, with a set of mixed integer programming formulations are proposed and compared for multi-objective integrated scheduling in a customer driven supply chain. The supply chain consists of multiple manufacturers (suppliers) of parts, a single producer of finished products and a set of customers who generate final demand for the products. Each supplier has a number of identical production lines in parallel for the manufacture of parts, and the producer has a flexible assembly line for assembly of products. Given a set of orders, the problem objective is to determine which orders are to be provided with parts by each supplier, find a schedule for the manufacture of parts by each supplier and for the delivery parts from each supplier to the producer, and find a schedule for the assembly of products for each order by the producer, such that a certain performance measure of the supply chain is optimised. The selection of the parts supplier for each order is combined with due date setting for some orders, subject to the suppliers and the producer available capacity. Different objective functions are considered that take into account both customer service level and total manufacturing, delivery and production cost. Numerical examples are presented that are modelled by real-world integrated scheduling in a customer driven supply chain of high-tech products, and some computational results are reported to compare the two approaches.     

Intangible capital,knowledge and new product development competence in supply chains: process,interaction and contingency effects among SMEs

Leveraging the strengths of a firm’s supply chain partners for new product development (NPD) has become essential to satisfy rapidly changing customer demands and to remain competitive. Firms are, therefore, aiming to further their NPD competence, which we define as the ability of the supply chain to improve and generate new products and services, based on the processes and relationships established with suppliers and customers. This study examines how intangible capital and knowledge further the development of NPD competence within the context of a supply chain. A theoretical model, based upon resource-advantage theory, is tested via structural equation modelling utilising survey data collected from 195 small- and medium-sized enterprises in the manufacturing industry reporting on their primary supply chain. Our findings indicate that more easily transferable capital manifests itself in explicit knowledge and less easily transferable capital manifests itself in tacit knowledge. We further identify complementarities of the two types of intangible capital as influencing knowledge type development. More importantly, we find that the two types of knowledge differ in their ability to influence NPD competence in the supply chain, and that these links are moderated by relationship length. Supply chain management implications for academics and practitioners are presented.