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In this paper, a multi-objective multi-echelon supply-distribution model is proposed to optimize interactions of entities located within an Industrial Cluster (IC) including small- and medium-sized enterprises (SMEs), using a third-party logistics provider (3PL)-managed supply-demand hub in industrial cluster (SDHIC) as a specific public provider of warehousing and logistics services.

The three considered objectives are minimizing the total logistics costs, maximizing the rate of demand satisfaction and maximizing the quality of delivery. Because some parameters such as “demand of customers” are naturally fuzzy because of incompleteness and/or inaccessibility of the needed information, the triangular fuzzy number is applied for all fuzzy parameters to handle this difficulty. The proposed model is primarily changed into a correspondent supplementary crisp model. To solve such a model, a revised multi-choice goal programming (RMCGP) approach is then used with the purpose of finding a compromise solution.

Experimental results demonstrate that all enterprises involved in such a supply chain benefit with several advantages using SDHIC by consolidating shipments and merging the storage space of inventories. The applicability of the presented model is shown by conducting these experiments over an applied industrial case study.

The main contributions of this research are proposing a practical mathematical approach to the supply chain of ICs using a specific public warehouse managed by a 3PL, called SDHIC, bridging the existing gaps with respect to the already published researches in this area by applying real-world assumptions such as uncertainty; optimizing the interactions of involved entities in the supply chain of an IC, comprising suppliers, SMEs as manufacturers and customers; minimizing the total incurred logistics costs to such a system through optimum usage of lands, facilities, labors, etc. and boosting the satisfaction of customers through maximizing the service level criteria, illustrating the positive consequences of cooperation of 3PL with the SMEs/manufacturers in an IC, showing the applicability of the adopted approach by implementing it on an applied industrial instance.

The purpose of this paper is to review and analyze the perishable inventory models along various dimensions such as its evolution, scope, demand, shelf life, replenishment policy, modeling techniques and research gaps.

In total, 418 relevant and scholarly articles of various researchers and practitioners during 1990-2016 were reviewed. They were critically analyzed along author profile, nature of perishability, research contributions of different countries, publication along time, research methodologies adopted, etc. to draw fruitful conclusions. The future research for perishable inventory modeling was also discussed and suggested.

There are plethora of perishable inventory studies with divergent objectives and scope. Besides demand and perishable rate in perishable inventory models, other factors such as price discount, allow shortage or not, inflation, time value of money and so on were found to be combined to make it more realistic. The modeling of inventory systems with two or more perishable items is limited. The multi-echelon inventory with centralized decision and information sharing is acquiring lot of importance because of supply chain integration in the competitive market.

Only peer-reviewed journals and conference papers were analyzed, whereas the manuals, reports, white papers and blood-related articles were excluded. Clustering of literature revealed that future studies should focus on stochastic modeling.

Stress had been laid to identify future research gaps that will help in developing realistic models. The present work will form a guideline to choose the appropriate methodology(s) and mathematical technique(s) in different situations with perishable inventory.

The current review analyzed 419 research papers available in the literature on perishable inventory modeling to summarize its current status and identify its potential future directions. Also the future research gaps were uncovered. This systemic review is strongly felt to fill the gap in the perishable inventory literature and help in formulating effective strategies to design of an effective and efficient inventory management system for perishable items.

The motivation behind this research refers to the significant role of integration of production-distribution plans in effective performance of supply chain networks under fierce competition of today’s global marketplace. In this regard, this paper aims to deal with an integrated production-distribution planning problem in deterministic, multi-product and multi-echelon supply chain network. The bi-objective mixed-integer linear programming model is constructed to minimize not only the total transportation costs but also the total delivery time of supply chain, subject to satisfying retailer demands and capacity constraints where quantity discount on transportation costs, fixed cost associated with transportation vehicles usage and routing decisions have been included in the model.

As the proposed mathematical model is NP-hard and that finding an optimum solution in polynomial time is not reasonable, two multi-objective meta-heuristic algorithms, namely, non-dominated sorting genetic algorithm II (NSGAII) and multi-objective imperialist competitive algorithm (MOICA) are designed to obtain near optimal solutions for real-sized problems in reasonable computational times. The Taguchi method is then used to adjust the parameters of the developed algorithms. Finally, the applicability of the proposed model and the performance of the solution methodologies in comparison with each other are demonstrated for a set of randomly generated problem instances.

The practicality and applicability of the proposed model and the efficiency and efficacy of the developed solution methodologies were illustrated through a set of randomly generated real-sized problem instances. Result. In terms of two measures, the objective function value and the computational time were required to get solutions.

The main contribution of the present work was addressing an integrated production-distribution planning problem in a broader view, by proposing a closer to reality mathematical formulation which considers some real-world constraints simultaneously and accompanied by efficient multi-objective meta-heuristic algorithms to provide effective solutions for practical problem sizes.

Due to the importance of quality to customers, this study considers criteria of quality and profit and optimizes both in a multi-echelon cold chain of perishable agricultural products whose quality immediately begins to deteriorate after harvest. The two objectives of the proposed cold chain are to maximize profit and quality. Since postharvest quality loss in the supply chain depends on various decisions and factors, in addition to strategic decisions, the authors consider the temperature setting in refrigerated facilities and transportation vehicles due to the unfixed shelf life of the products which is related to the temperature found by Arrhenius formula.

The authors use bi-objective mixed-integer nonlinear programming to design a four-echelon supply chain. The authors integrate the supply chain echelons to detect the sources and factors of quality loss. The four echelons include supply, processing, storage and customer. The decisions, including facility location, assigning nodes of each echelon to corresponding nodes from the adjacent echelon, allocation of vehicles to transport the products from farms to wholesalers, processing selection, and temperature setting in refrigerated facilities, are made in an integrated way. Model verification and validation in the case study are done based on three perishable herbal plants.

The model obtains a 29% profit against a total cost of 71 and 93% of original quality of the crops is maintained, indicating a 7% quality loss. The final quality of 93% is the result of making a US$6m investment in the supply chain, including the procurement of high-quality raw materials; facility establishment; high-speed, high-capacity vehicles; location assignment; processing selection and refrigeration equipment in the storage and transportation systems, helping to maximize both the final quality of the products and the total profit.

The proposed supply chain model should help managers with modeling decisions, especially when it comes to cold chains for agricultural products. The model yields these results – optimal location-allocation decisions for the facilities to minimize distances between the network nodes, which save time and maintain the majority of the products’ original quality; choosing the most appropriate processing method, which reduces the perishability rate; providing high-capacity, high-speed vehicles in the logistics system, which minimizes transportation costs and maximizes the quality; and setting the right temperature in the refrigerated facilities, which mitigates the postharvest decay reaction rate of the products.

Comparison of the results of the present research with those of the traditional chain (obtained through experts) shows that since the designed chain increases the profit as well as the final quality, it has benefits for the main chain stakeholders, which are customers of agricultural products. This study model is expected to have a positive impact on the environment by placing strong emphasis on quality and preventing excessive waste generation and air pollution by imposing a financial penalty on extra demand production.

Since profit and quality of the final product are two important factors in all cultures and communities, the proposed supply chain model can be used in any food industry around the world. Applying the proposed model induces growth in local industries and promotes the culture of prioritizing quality in societies.

To the best of the authors’ knowledge, this is the first research on a bi-objective four-echelon (supply, processing, storage and customer) postharvest supply chain for agricultural products including that integrates transportation logistics and considers the deterioration rate of products as a time-dependent variable at different levels of decision-making.

This research seeks to identify and apply techniques that can be used in a supply chain context to diagnose the causes of variability in delivery lead time.

A literature review was conducted and a number of quality management (QM), techniques were selected as candidates for diagnosing delivery time variability. A case study of the application of these techniques is provided on the UK‐based defence supply chain that supported UK operations in the Iraq war of 2003.

Candidate QM techniques for diagnosing delivery time variability were identified, namely: Process Chart; Histogram; Failure Mode and Effect Analysis; and Cause and Effect Analysis. These techniques were successful in enabling the diagnosis of the causes of delivery time variability in the context of the case study investigated.

The work illustrates how QM techniques can be employed to address issues with supply chains, not least with regard to the important problem of variability in delivery leadtime. In practice, this highlights benefits that result to practitioners in order to improve the performance of operations in a dynamic setting, such as the defence supply chain studied here.

This work has value in presenting the findings of an in‐depth case study on the application of QM techniques in a multi‐echelon supply chain setting. It is also original in employing the FMEA technique together with an end‐customer perspective to assess the effect of failure modes in operations across a supply chain. FMEA also provided the means to examine supply chain risk, thus providing a research instrument for deploying risk as a lens. The application of QM techniques in this novel setting provides support for their application beyond the conventional setting of internal operations.

The purpose of this paper and its companion (Part I: single and two‐component supply chains) is to investigate methods to tackle complexities, constraints (including time‐varying constraints) and other challenges. In this part, attention is devoted to multi‐silo supply chain and the relationships between the components. The first part of the paper aims to consider two types of experimental supply chains: with one‐to‐many and many‐to‐one relationships. The second half of the paper aims to present two approaches on optimising the material flow in the real‐world supply chain network.

Cooperative coevolutionary and classical sequential approaches are taken to address the experimental multi‐silo supply chains. Due to the nature and the complexity of the supply chain presented in the second half of the paper, evolutionary algorithm was not sufficient to tackle the problem. A fuzzy‐evolutionary algorithm is proposed to address the problem.

The proposed systems produce solutions better than solutions proposed by human experts and in much shorter time.

The paper discusses various algorithms to provide the decision support for the real‐world problems. The system proposed for the real‐world supply chain is in the process of integration to the production environment.

To develop an innovative methodology to apply lean value chain improvement techniques to a complete supply chain for a food product from farm to consumer.

Action research based on a UK case study involving farmers, a food processor and a major retailer.

Value stream analysis (VCA) highlights significant opportunities to improve supply chain performance, profitability and relationships.

Lean/VCA methodologies can be readily applied to the retail and processor elements of food chains. However, further research is required to apply the concepts to farm operations.

Subsequent to this research, VCA techniques have been increasingly adopted in UK agri‐food sectors including meat, dairy, cereals and horticulture.

Application of lean concepts and VCA in the agri‐food sector. Development of a multi‐echelon supply chain improvement methodology.

The purpose of this paper is to propose a Generic decision support system which is based on multi-Objective Optimisation for Green supply chain network design (GOOG). It aims to support decision makers to design their supply chain networks using three key objectives: the lowest cost and environmental impact and the shortest lead time by incorporating the decision maker’s inputs.

GOOG aims to suggest the best-fitted parameters for supply chain partners and manufacturing plant locations, their order allocations, and appropriate transportation modes and lot-sizes for cradle-to-gate. It integrates Fuzzy Goal Programming and weighted max-min operator for trade-off conflicting objectives and overcome fuzziness in specifying target values of individual objectives. It is solved using exact algorithm and validated using an industrial case study.

The comparative analysis between actual, three single-objective, and multi-objective decisions showed that GOOG is capable to optimising three objectives namely cost, lead time, and environmental impact.

Further, GOOG requires validation for different supply chain scenarios and manufacturing strategic decisions. It can improve by including multi-echelon supply chain networks, entire life cycle and relevant environmental legislations.

GOOG helps the decision makers to configuring those supply chain parameters whilst minimising those three objectives.

Companies can use GOOG as a tool to strategically select their supply chain that reduces their footprint and stop rebound effect which imposes significant impact to the society.

GOOG includes overlooked in the previous study in order to achieve the objectives set. It is flexible for the decision makers to change the relative weightings of the inputs for those contradicting objectives.

The learning outcomes are to remember the overall context of global supply chain management from a stakeholder perspective, to understand the context of material handling movement in a mining industry, to apply the overall knowledge of linear programming in a supply chain context, to analyze the different constraints with flow of goods at different nodes in various location hubs and convert the same into the optimization problem and to evaluate carefully the different costs associated at different levels and then finding the optimal solution that minimizes the total cost.

This case proposes a mixed integer multi-echelon analytical model integrated with the scenario tree analysis. The integrated model is used to optimize the allocation of volumes at various stages of the supply chain of exporters of bulk materials like iron ore from Goa, India, to various countries in Asia. The scenario tree analysis is then used to evaluate decisions under certainty with demand as the stochastic parameter. The proposed integrated model has potential for collaboration in the supply chain and facilitating network design, inventory and transportation planning and policy analysis.

This course is suitable at the MBA level for the following courses: Operations Research (Focus/Session: Applications on Supply Chain Management), Supply Chain Management (Focus/Session: Global Supply Chain Management, Logistics Planning, Distribution Network), Logistics Management (Focus/Session: Transportation Planning) nd Operations Strategy (Focus/Session: Location Node Strategy).

Teaching Notes are available for educators only.

CSS 9: Operations and Logistics.

The purpose of this study is proposing a novel neutrosophical stakeholders' analysis approach for sustainable fashion supply chain (SFSC), presenting a supply chain members and objectives in order to conduct a sustainable business, investigating the roles and positions of these stakeholders, determining the contribution levels of these stakeholders to the sustainability objectives, and accordingly identifying the convergence and divergence among the stakeholders in terms of realization of the objectives.

A novel neutrosophic set-based stakeholders' analysis Method of ACTors, Objectives, strength Reports (MACTOR) approach is proposed considering the uncertain and indeterminate opinions of decision-makers. In order to obtain the mutual opinions of decision-makers, Delphi technique is employed.

The analysis results of this research emphasizes that although the manufacturers can be thought as the foremost actor is SFSC by producing the main product, they have no superior power on conducting the business. Besides, the government, customer and fashion firms are the key players shaping the fashion industry. Retailers and distribution centers can be interpreted as an intermediary in between the other stakeholders. Moreover, the eco-friendly packaging providers have not gained an important role that they were supposed to in terms of the sustainability objectives.

The application phase of the research includes the possibility of subjective judgments of the participants as a limitation. Therefore, Delphi technique is applied to overcome this challenge by multiple rounds of interviews for panel of participants in order to combine the benefits with elements of the wisdom of people.

Examining a multi-echelon supply chain is a practical implication providing the mutual opinions of experts such as designers, stylists, journalists, consultants, procurement managers, entrepreneurs, activists etc. for sustainability in the fashion industry. One can derive from the findings to determine which sub-echelon requires more attention, or which business is more important to focus on most, or which branch of activity influences others most.

This is one of the few articles that focuses on the sustainability objective and highlights the active roles of all members of the supply chain. Besides, this is the first study deploying neutrosophic sets for MACTOR analysis.